extfieldsofstudy: Medicine Chemistry

text:
Somatostatin Receptors as Molecular Targets in Human Uveal Melanoma

Uveal melanoma (UM) is the most common primary intraocular malignancy in adults, with an incidence of 4–5 cases per million. The prognosis of UM is very poor. In the present study, our aim was to investigate the expression of mRNA and protein for somatostatin receptor types-1, -2, -3, -4, -5 (SSTR-1–5) in human UM tissue samples and in OCM-1 and OCM-3 human UM cell lines by qRT-PCR, western blot and ligand competition assay. The mRNA for SSTR-2 showed markedly higher expression in UM tissues than SSTR-5. The presence of SSTRs was demonstrated in 70% of UM specimens using ligand competition assay and both human UM models displayed specific high affinity SSTRs. Among the five SSTRs, the mRNA investigated for SSTR-2 and SSTR-5 receptors was strongly expressed in both human UM cell lines, SSTR-5 showing the highest expression. The presence of the SSTR-2 and SSTR-5 receptor proteins was confirmed in both cell lines by western blot. In summary, the expression of somatostatin receptors in human UM specimens and in OCM-1 and OCM-3 human UM cell lines suggests that they could serve as a potential molecular target for therapy of UM using modern powerful cytotoxic SST analogs targeting SSTR-2 and SSTR-5 receptors.

Introduction
Uveal melanoma is the most common primary intraocular malignancy in adults [1], with a yearly incidence of 4-5 cases per million [2]. The intraocular uveal tract is comprised of the iris, choroid and ciliary body. The reason for malignant UM formation is unknown, but various predisposition factors have been associated with the development of this quite aggressive disease. A few personal features, such as fair complexion [3], light irides [4,5], uveal naevi [6], dysplastic naevus syndrome [7,8], oculodermal and ocular melanocytosis [9] and neurofibromatosis type 1 (NF1) [10,11], have been connected with an increased chance of UM [12]. The mortality due to UM has remained relatively unchanged, in spite of earlier detection and consequently smaller primary tumor burdens [13,14]. Approximately half of all patients with UM will eventually develop metastatic disease. It is generally supposed that the primary reason for treating UM is to avoid metastatic spreading of the tumor from the eye [13,15]. The liver is the most common site of metastases and once liver metastases are clinically apparent the prognosis becomes poor. One of the most significant predictors for UM-related death is loss of chromosome 3 [16][17][18]. The incidence of UM in whites is eight times higher than in blacks [19]. The leading predictors of survival for UM are histologic cell type, largest tumor diameter, age, gender and tumor location [20]. More unbiased classification parameters have appeared from comprehensive cytomorphometrical studies. Life expectancy of UM patients with metastatic disease depends on the rapidity of the metastatic process. In order to reduce the mortality caused by melanoma, it is essential to prevent or eliminate metastatic disease. This requires early detection and the improvement of prognostic factors [20,21]. It is crucial to increase our knowledge of the mechanism of metastasis and describe reliable progression parameters as prognostic markers in primary UM [20]. These facts inspire a constant search for new concepts to improve quality of life and extend survival of patients. For patients with UM there is no effective therapy if metastases have developed. Despite several therapeutic strategies and successful eradication of the ocular tumor, metastatic disease is almost always fatal [21].
Uveal melanoma consists of a well-defined population of melanocytes, from which uveal malignant melanoma originates. As melanocytes originate in the neuroectodermal-neural crest, it is theorized that melanoma shows characteristics similar to neuroendocrine tumors. Somatostatin was initially discovered as a hypothalamic neurohormone. It is widely distributed in the central and peripherial nervous system and has also been found in the endocrine pancreas, gut, thyroid, adrenal glands, and kidneys [22][23][24].
Somatostatin receptors (SSTRs) have been shown to be expressed in several different types of cancer, including uveal melanoma [22,[24][25][26]. There are two different forms of bioactive peptides which are produced in mammals. Somatostatin-14 is a cyclic peptide including 14 amino acids, and somatostatin-28 consists of 28 amino acids. Somatostatin (SST) acts mainly as an inhibitory factor in cell proliferation and hormone secretion with endocrine, paracrine and autocrine activities [23]. At least five subtypes of SSTRs have been characterized in humans (1, 2A, 2B, 3, 4, and 5), and SSTR-2 and SSTR-5 are the ones mostly studied in human cancers including primary UM. The genes for these subtypes are located on different chromosomes [22,27]. SSTR-2 otherwise splices to generate two isoforms named SSTR-2A and SSTR-2B, which deviate in their C-terminal sequences. They all bind to SST-14 and -28, but they have a slightly higher affinity to SST-14 [28].
Somatostatin could be involved in tumor growth suppression, as confirmed by the use of SST analogs to treat neuroendocrine tumors [24,29,30]. Earlier in vitro and in vivo experimental studies have already demonstrated the inhibitory effect of various SST analogs and cytotoxic SST analog AN-162 in breast cancer, lung cancer, glioblastoma, and colon carcinoma, suggesting the potential application of modern powerful cytotoxic SST analogs in patients suffering from malignant tumors [23,24,26,[30][31][32][33][34][35]. Even if each tumor expresses more than one subtype of SSTRs, SSTR-2 is the most regularly observed. The biological effects of SST are mediated by specific G protein-coupled plasma membrane receptors, which are placed in specific target cells of the gastrointestinal [36] tract, the peripheral nervous system and various blood vessels.
As a result of the similar origin of SST and UM cells, a possible correlation or interaction can be found. A connection between eye tissue and neurohormones has already been discovered in several studies [37][38][39]. However, there is still limited information about SSTR expression and characterization in human UM. Therefore, we aimed to widen our research regarding the expression of SSTR types in human UM, in order to recognize specific membrane receptors as molecular targets for diagnostic and therapeutic purposes.
In the present study, our goal in the experimental work was to examine the expression of mRNA for somatostatin receptor types-1, -2, -3, -4 and -5 (SSTR-1-5) in human UM cell lines and tissue samples.
We also aimed to study the presence and binding characteristics of SSTR protein by Western blot and ligand competition assays. An additional goal was to compare our results with clinicopathological data in order to better understand their diagnostic and therapeutic significance.

Expression of SSTRs in Human UM Tissue Samples
In our study 46 human UM tissue samples were investigated. The clinicopathological data of patients are shown in Table 1. Twenty nine samples were obtained from men and 17 samples from women. More than half of the patients (24, 52%) were over 66 years of age, while the remaining samples were equally distributed among the different age groups. Histopathological classification of these samples revealed that 10 of them belonged to spindle type. Seven samples were determined to be epithelioid type and 2 samples were mixed of spindle and epithelioid types. Two samples were not classified histologically. Due to the limited amount of good quality RNA isolated from UM tissue samples. 46 specimens were studied for the expression of mRNA for SSTR-2 and nine specimens for SSTR-5.
The expression of mRNA for SSTR-2 was detected in 30 of the 46 human UM specimens (65.2%) (Table 1, Figure 1). Nine human UM tissue samples were tested for SSTR-5 expression and we found that 66.6% (6 specimens) of these samples were positive for SSTR-5 (Table 1, Figure 2). Among the positive cases for SSTR-2, 60.0% were men and 40.0% women ( Table 1). The distribution of SSTR-2-positive samples by age group, showed the following findings: age 30-45 years: 62.5%; age 46-55: 75.0%; age 56-65: 66.7%; age above 66 years: 66.7%. There was no correlation between the expression of different SSTR-types and age groups. 40% of the samples positive for SSTR-2 were spindle, 36.7% were epithelioid and 10% were mixed histological type. Four SSTR-5-positive samples were spindle (66.7%) and two specimens were epithelioid (33.3%).  The presence of SSTRs, characteristics of these SST-binding sites and specific binding of radioiodinated RC-160 to membrane receptors on human UM samples were determined using ligand competition assays. Of the 20 specimens examined, 14 (70%) showed SSTR binding (Table 1). Receptor binding affinity and concentration of SSTRs in tumor membranes were measured in displacement experiments. Analyses of the typical displacement curves of [ 125 I]RC-160 by the same unlabeled peptide (RC-160) showed that the one-site model could provide the best fit. Based on these results the presence of one class of high affinity SSTR in crude membranes derived from human UM samples was indicated. The computerized non-linear curve-fitting program and the Scatchard plot analyses of the SST receptor binding data in 14 receptor positive tumor samples showed that the single class of binding sites had a mean dissociation constant (K d ) of 7.14 nM (range 3.18-11.8 nM), with a mean maximal binding capacity (B max ) of 604.0 fmol/mg membrane protein (range 260-1052 fmol/mg protein). Biochemical specifications and parameters crucial for characterizing specific binding sites were also defined. Thus, the binding of [ 125 I]RC-160 was detected to be reversible, temperatureand time-dependent, and linear with protein concentration in the human UM specimens examined (data not shown). Competitive binding studies also demonstrated the specificity of SST binding sites using numerous peptides structurally related or unrelated to SST. The binding of radiolabeled RC-160 was displaced completely by increasing concentrations (10 −12 -10 −6 M) of SST-14, whereas none of the structurally and functionally different and unrelated peptides analyzed, such as luteinizing hormone-releasing hormone (LHRH), growth hormone-releasing hormone (GHRH), epidermal growth factor (EGF), [Tyr 4 ]bombesin, and insulin-like growth factor I inhibited the binding of radioiodinated RC-160 at concentrations as high as 1 µM (data not shown).

Expression of SSTRs in Human UM Cell Lines
Our RT-PCR and qRT-PCR results clearly show that SSTR-1-5 were expressed in both human UM cell lines examined. CACO-2 cell line as a positive control also showed the expression of all five SSTRs examined. The highest expression of all the investigated SSTRs was observed in OCM-3 cell line. However, in both cell lines, the expression of SSTR-2 and -5 was stronger than those of SSTR-1, -3 and -4. All five SSTRs examined displayed significantly (p < 0.0001) higher expression in OCM-3 UM cell line than in OCM-1 ( Figure 3). Western blot analysis also confirmed the presence of SSTR-2 and SSTR-5 receptor protein in OCM-1 and OCM-3 human UM cell lines. Similarly to our findings on receptor mRNA, higher expression levels of SSTR-2 and SSTR-5 receptor protein were found in OCM-3 tumor cell line, than in OCM-1 cell line detected by western blot (Figure 4).  In cell membranes of OCM-1 and OCM-3 human UM cell lines, ligand competition studies also revealed a single class of high affinity binding sites for SST with a mean dissociation constant (K d ) of 5.34 and 6.72 nM, respectively. The concentration of SSTRs was 433 fmol/mg membrane protein in OCM-1 cells, while OCM-3 cells showed a markedly higher receptor level (981 fmol/mg membrane protein).
Western blot analysis confirmed the presence of SSTR-2 and SSTR-5 receptor proteins in OCM-1 and OCM-3 human UM cell lines. CACO-2 human Caucasian colon adenocarcinoma cell line was used as a positive control. Anti-HPRT (1:1000 dilution) was used as a housekeeping gene. A description of experimental conditions is found in the Materials and Methods section.

Discussion
Uveal melanoma is the most frequently diagnosed primary malignant intraocular tumor in adults, and the second most common form of melanoma after cutaneous melanoma [20,40]. Over the past 50 years, despite advances in diagnosis and effective local therapies, stage-specific UM mortality rates have remained essentially unchanged and continue to be associated with significant mortality. The liver is the most common and important target of metastasis, and liver failure due to metastasis is the immediate cause of death in most patients [15,36].
Patients with UM face a gloomy prognosis, as about 45% die due to metastasis, irrespective of the fact that the tumor is most frequently diagnosed and locally cured before any signs of clinical distributed disease appear [41]. This led to the theory that micrometastases are already present early in the disease process, but they remain inactive for many years before a clinically noticeable macrometastasis develops [1].
Tumors of the ciliary body and choroid pose a severe threat to life. Treatment of primary uveal melanoma is still disputed. It is still mostly agreed that enucleation is unavoidable for many large ciliary body and choroidal melanomas. It seems justified to subject clinically relatively small, "active" melanomas to some type of treatment. Many large melanomas and most medium-sized melanomas are being treated nowadays by irradiation with or without thermotherapy, or by enucleation. It has been suggested that modifications of local treatment will not result in any remarkable improvement in survival, and that research must be directed towards treatment of metastatic disease [36].
The systemic treatment of UM is very limited. Adjuvant systemic therapy is primarily used in patients at high risk of metastasis or developed metastatic patients, but the response rates for classic chemotherapeutic agents remain between 7% and 25% [42]. Systemic chemotherapy for the treatment of UM is ineffective. In spite of new insights into the genetic and molecular background of metastatic UM, satisfactory systemic treatment approaches are currently lacking [43]. There are currently no effective systemic therapies available that could be used for the primary tumor. Despite the improvement in the diagnosis and therapy of primary UM, the number of metastatic deaths has not been significantly reduced over the past 20 to 30 years [44,45]. Therefore, innovative therapeutic methods are urgently awaited [24,25,[46][47][48].
Both forms of the hypothalamic neuropeptide somatostatin (somatostatin-14 and -28), also exist in the gastrointestinal tract and their function is to inhibit the secretion of many hormones including growth hormone, insulin and gastrin, glucagon, secretin and cholecystokinin. In addition to its general endocrine effects, SST can function as an autocrine/paracrine regulator and is also present in the gastric mucosa, pancreas, and duodenum, where it suppresses exocrine secretions including gastric acid and pancreatic enzymes, respectively [49].
Because of the short plasma half-life of SST-14 (3 min), more stable and more potent synthetic SST analogs have been developed, including octreotide (Sandostatin ® ) [50], vapreotide (RC-160, Octastatin ® ), and lanreotide (Somatulin ® ) [26,51]. The plasma half-life of SST analogs is 120 min, and these peptide analogs are about 50 times more potent than SST in inhibiting growth hormone release from the pituitary. It has been well published that SST and its octapeptide analogs exert their effects through specific G-protein coupled membrane receptors [26,52]. While native SST shows similar high affinity to SSTR-1-5, the synthetic octapeptides such as octreotide, RC-160, and RC-121 bind especially to SSTR-2 and SSTR-5, present a moderate affinity to SSTR-3, and a low affinity to SSTR-1 and SSTR4 [49]. Somatostatin analogs, including RC-160, have been shown to function as effective tumor growth suppressors in experimental models of various cancers. To increase therapeutic efficacy, new analogs have been prepared which contain the cyclohexapeptide, designated SOM230 (pasireotide), which binds with high affinity to SSTR-1, -2, -3 and SSTR-5 [53]. The presence of somatostatin receptors, mainly SSTR-2 on tumors, allows the localization of certain primary tumors and their metastases using scintigraphic techniques. Radiolabeled analogs of SST, such as [ 111 In-DTPA-D-Phe1]-octreotide (OctreoScan ® ) are used clinically for the localization of tumors expressing receptors for somatostatin. Targeted radiotherapy, in which somatostatin analogs are linked to numerous radionuclides such as 68 Gallium or 90 Yttrium, is also advancing clinically [30,[54][55][56].
In this study, the expression of mRNA for SSTR-2 was studied in 46 UM specimens, and SSTR-5 in 9 UM tumor samples. Primary tumors were also tested at the protein level. In addition to detecting SSTRs, OCM-1 and OCM-3 UM cell lines were also analyzed for receptor mRNA with quantitative real-time RT-PCR. Thirty-one (65.2%) samples showed SSTR-2 positivity and 6 of nine samples (66.6%) were SSTR-5 positive. Conversely, Ardjomand et al. [22] identified SSTR-2 expression in almost all of the 25 tested UM samples. Furthermore, using ligand competition assay we investigated the binding of radioiodinated RC-160 to membrane fractions of 20 UM specimens. We found that 70% of the human UM samples studied displayed specific SSTRs with a mean K d of 7.14 nM and with a mean B max of 604 fmol/mg membrane protein. It is also important to note that all receptor-positive tumor samples expressed a well-detectable amount of the SSTR-2 or SSTR-5 receptor gene.
Histological distribution of the analyzed samples showed that more than half of the samples (56.1%) were spindle cells, 36.6% were epithelioid, and the remaining 7.3% were mixed histologically. Our findings suggest that the patient's gender and age are not predicting factors. Our results demonstrate that SSTR-2 and SSTR-5 are expressed in human UM specimens. qRT-PCR results show that SSTR-2 and SSTR-5 are also highly expressed in both UM cell lines investigated. In this study, we provide evidence for the existence of SSTRs in two human UM cell lines and demonstrate that OCM-3 cells express SSTRs at a significantly higher level than OCM-1 cells (p < 0.0001). The receptor protein encoded by mRNA for SSTR-2 or SSTR-5 was also demonstrated by western blot in both UM cell lines.
Targeted therapy of cytotoxic peptide analogs consisting of a cytotoxic group, such as a doxorubicin, conjugated to peptide carriers should be more effective and less toxic than conventional systemic chemotherapy. The only major side effect of these analogs is myelosuppression caused by the infrequent chemical cleavage of the cytotoxic radical doxorubicin [57,58]. The high incidence of SST receptors in UM suggests that this type of tumor might be a good candidate for therapy with SST analogs including the targeted cytotoxic peptide AN-162 [24,26]. Since therapy for UM is not effective, our work helps to identify specific molecular targets for the prevention of metastasis or further proliferation of disseminated metastases.

Cell Lines and Culture Conditions
OCM-1 and OCM-3 human primary UM cell lines were kindly provided by the Department of Biophysics and Cell Biology, University of Debrecen, Hungary. In all our in vitro experiments as a positive control CACO-2 (human Caucasian colon adenocarcinoma) cell line was used, which was kindly provided by the Department of Pharmaceutical Technology, University of Debrecen, Hungary. OCM-1 and OCM-3 cell lines were cultured in RPMI 1640 medium supplemented with L-glutamine, 10% fetal bovine serum (FBS), and 1% penicillin/streptomycin in a humidified chamber in 5% CO 2 at 37 • C. CACO-2 cell line was grown in Dulbecco's Modified Eagle's Medium, supplemented with 3.7 g/L NaHCO 3 , 10% (v/v) heat-inactivated fetal bovine serum (FBS), 1% (v/v) non-essential amino acids solution, 1% (v/v) L-glutamine, and 100 IU/mL penicillin/streptomycin in a saturated humidified chamber in an atmosphere of 5% CO 2 at 37 • C. Cells were subcultured every 3 days using a standard trypsinization procedure. This research received approval from the Institutional Ethics Committee of the University of Debrecen.

Preparation of Uveal Melanoma Tissue Samples from Patients
Specimens of primary human UM were obtained from 46 patients at the time of initial surgical treatment at the Department of Ophthalmology, University of Debrecen, Debrecen, Hungary. The local Institutional Ethics Committee approved the collection and use of these specimens for the current study and informed consent was obtained from these patients. Tumor tissues were immediately frozen in liquid nitrogen and stored at −80 • C until further processing. Histopathological examination of each specimen was undertaken to confirm the presence of cancer with minimal mixed non-malignant tissue. All cancer samples were primary tumors and without metastases. Clinicopathological data of the UM specimens are shown in Table 1.

RNA Isolation and Reverse Transcription PCR
Homogenization of the UM tissue samples were performed with Tissue Ruptor (IKA ® -WERKE GmbH, Staufen im Breisgau, Germany). Total RNA from tumor tissues and OCM-1 and OCM-3 cells was isolated with NucleoSpin DNA/RNA/Protein Kit (Macherey-Nagel, Düren, Germany) according to manufacturer's protocol. Quantitative and qualitative assays for RNA were performed using a NanoDrop spectrophotometer (ND-1000, Bioscience, Budapest, Hungary).
Two hundred fifty nanograms of RNA from each sample were reverse-transcribed into cDNA using Tetro cDNA Synthesis Kit (Bioline Reagents, London, UK). Reaction was performed according to the manufacturer's instructions. RT-PCR reaction was performed in 25 µL reaction volume with gene specific primers. The primer sequences are shown in Table 2. The reaction consisted of 35 cycles (95 • C for 15 s, 60 • C for 30 s, 72 • C for 10 s) and lasted for 2 min extension at 72 • C. PCR was performed with gene-specific primers for SSTR-1, SSTR-2, SSTR-3, SSTR-4 and SSTR-5 using PCR MyTaq Master Mix (Bioline Reagents). PCR reaction was performed in a C1000 TM Thermal Cycler RT-PCR system (Bio-Rad, Hercules, CA, USA). For SSTR-1, -2, -3, -4, -5 and β-actin an initial denaturing step at 94 • C for 30 s was followed by 30 PCR cycles consisting of denaturation at 94 • C for 15 s, annealing at 60 • C for 30 s and extension at 72 • C for 15 s. β-actin was used as a positive internal control. PCR product was separated in a 1.5% agarose gel containing GelRed and detected in UV light, digitalized with AlphaDigiDoc™ RT (Alpha Innotech, Santa Clara, CA, USA). To determine the size of DNA, 50 bp DNA marker (Bioline Reagents) was used.

Quantitative Real-Time PCR
Total RNA (1000 ng) was reverse transcribed into cDNA using Tetro cDNA Synthesis Kit (Bioline Reagents) according to the manufacturer's instruction. To quantity mRNA for SSTR-1, -2, -3, -4 and SSTR-5 real-time RT-PCR method was performed with iTaq™ Universal SYBR ® Green Supermix (Bio-Rad) in CFX96 Touch TM Real-Time PCR detection System (Bio-Rad) with a 20 µL final reaction mixture. The reaction was carried out at 95 • C for 10 min, followed by 45 cycles at 95 • C for 15 s and 60 • C for 60 s. β-actin was used as an endogenous reference gene. All real-time amplifications were measured in triplicates. Relative mRNA expression for SSTRs was measured by ∆∆Ct method with threshold cycle times of each target SSTR and β-actin. Template-free and reverse transcription-free controls excluded non-specific amplification and DNA contamination.

Radioligand Binding Studies
Radioiodinated derivatives of SST analog RC-160 were prepared using the chloramine-T method and purified using reverse phase high performance liquid chromatography (RP-HPLC) as described earlier [59]. Somatostatin receptor binding studies were performed as previously reported [59] with some minor modifications using in vitro ligand competition assays based on binding of [ 125 I]RC-160 as radioligand to uveal melanoma membrane fractions. This radioiodinated ligand was well characterized as reported earlier and demonstrated high affinity binding to SSTR-2 and SSTR-5 [59]. Tumor membrane homogenates were incubated with 50,000-70,000 cpm of radioiodinated RC-160 and 10 −12 -10 −6 M of nonradioactive peptides as competitors. After 2 h of incubation the binding reactions were terminated, and the bound ligand was separated then counted in a gamma-counter. The LIGAND-PC computerized curve-fitting software of Munson and Rodbard was used to identify the type of receptor binding, dissociation constant (K d ), and the maximal binding capacity of the receptors (B max ) [59].

Statistical Analysis
Correlation analysis was carried out among the expression of mRNA for SSTR-1, -2, -3, -4, -5 receptors in OCM-1 and OCM-3 cell lines with the use of GraphPad Prism 7-one way ANOVA test to assess the significance of the expression of SSTR-1-SSTR-5 genes of OCM-1 and OCM-3 cell lines (GraphPad Software Inc., La Jolla, CA, USA).

Conclusions
Our findings, that a high percentage of human UM samples express SST receptors, support the view that targeted cytotoxic SST analogs such as AN-162 could be used to effectively treat UM. Future investigations are needed to prove the therapeutical and clinical significance of our findings and provide better understanding of the development of human UM. Our studies should help in the early diagnosis of metastasis and in the use of SST analogs in subsequent improved targeted therapies.


extfieldsofstudy: Physics Medicine

text:
Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-Grained Simulations and Elastic Network Models

Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this minireview, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions, and highly simplified structure-based elastic network models of protein flexibility. In contrast to classical all-atom molecular dynamics, the modeling strategies discussed here allow the quite accurate modeling of much larger systems and longer-time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.

Introduction
The biological activity of proteins involves adopting a specific conformation, their local fluctuations and, in many cases, structural transitions between different conformations. Structural flexibility can range from small side-chain fluctuations to large rearrangements of the entire protein backbone or its fragments, for example, disordered regions [1][2][3]. Most of the knowledge about protein structures comes from X-ray crystallography that, in most cases, offers only a static view of well-ordered protein regions. Determination of protein structure dynamics remains difficult using either experiment or computer simulations, despite significant effort in the field of drug design [4][5][6]. All-atom molecular dynamics (MD), the classical simulation technique, enables relatively inexpensive studies of small protein fluctuations (e.g., side-chain or very local backbone moves) and rather small protein systems [7]. In practice, many of protein systems are either too large to be effectively simulated using MD or require very large supercomputer resources. Some acceleration of MD can be achieved using so-called Go (or structure-based) models that provide quite valuable but limited insight into large-scale protein dynamics [8][9][10]. Significant computational speed-up is possible by simplifying the protein description to a less complex level than that of MD. This is offered by coarse-grained (CG) protein models [8,11] and elastic network models (ENM) used in conjunction with normal-mode analyses (NMA) [11][12][13][14]. Both approaches can be used as key components of multiscale modeling methods [15,16] merging computational tools of various resolutions from the low-resolution to atomistic level [17]).
In this review, we briefly discuss two representative CG simulation models of protein dynamics (Sections 2.2 and 2.3) and various ENM-based modeling techniques (Section 2.4). Next, we review their applications to modeling protein local dynamics: structural flexibility of folded proteins (Section 3.1), and large-scale structural transitions (Section 3.2). The discussed approaches ( Figure 1) differ in their levels of accuracy of protein structure representation, employed models of the interaction scheme, and their sampling of system dynamics. Interestingly, for many problems all these methods lead to surprisingly similar results, with one difference: the ENM approach is the simplest and therefore can be effectively used for studies of very large systems, obviously with all the limitations of structure-based models. Finally, we discuss the advantages and limitations of the presented approaches.

From All-Atom to Coarse-Grained Modeling
Molecular modeling of protein dynamics plays important roles in many branches of biophysics, molecular biology, and related sciences. The most popular strategies for modeling proteins and other biomolecular systems are classical MD simulation methods. MD, however, has several limitations [18]. Important biomacromolecular systems can be very large, and the timescale of interesting processes can be beyond the MD simulation range accessible to contemporary computers. Using the most powerful and dedicated computing devices, it is now possible to simulate structure assembly dynamics of small fast-folding proteins [19]. An alternative is to use CG modeling tools [8,20]. The CG modeling of proteins, initiated half a century ago by Levitt (and others) [15,16], has recently become a rapidly growing branch of molecular modeling. The general idea is to reduce the number of explicitly treated degrees of freedom in the modeled systems ( Figure 3) and simplify energy calculations to speed up computations and thereby allow simulations of larger systems and/or longer processes. Increasing range of applicability of dynamics modeling, from MD to deeply coarse-grained simulations. CABS (assuming C-Alpha, C-Beta, and side-chain representation of polypeptide chains) is a moderate resolution model of protein structure and dynamics. SURPASS is a deeply CG model, where single united residues represent entire amino acids. Both CG structure models use Monte Carlo dynamics sampling for fast simulations of long-time protein dynamics.
Many CG approaches have been proposed, using different levels of reduced representations of biomacromolecules, different force-field schemes, and different strategies for sampling the conformational space [8,[20][21][22][23]. We discuss the models that, in spite of their reduced numbers of explicitly treated atoms or pseudoatoms, allow realistic reconstruction of atomic details, and thereby open a possibility for the multiscale integrative modeling of protein systems. In addition, instead of the physics-based force field, we opted for statistical, knowledge-based models of interactions. Both approaches have their advantages and disadvantages, but since proteins (and generally biomacromolecules) can be characterized by many specific features, and owing to the rapidly growing databases of structural biology, statistical force fields can be a good choice. Consequently, instead of using various molecular dynamics schemes, we prefer properly designed Monte Carlo dynamics sampling. Such approaches prove to be very productive in the computational prediction of protein structures, modeling of protein dynamics, and molecular docking mechanisms [8].
Below, we discuss in more detail two representative models of various resolutions based on the above-outlined strategy of CG modeling: a relatively high-resolution Cα, Cβ, side-chain (CABS) [24] CG model (assuming C-Alpha, C-Beta, and side-chain representation of polypeptide chains) and a low-resolution Single United Residue per Preaveraged Secondary Structure fragment (SURPASS) [25] CG model. As illustrated in Figure 2, CG simplifications of the models can significantly increase (by orders of magnitude) both the size and timescale of tractable systems. Obviously, the deeper the coarse-graining of the modeled structures, the larger the systems that can effectively be simulated. On the other hand, coarse-graining of the studied structures always results in neglecting some subtle features. How costly it is to simplify the representation depends not only on the assumed level of coarse-graining, but also on the force-field model and sampling schemes used in simulations. CABS, SURPASS, or other related CG models can address a broad range of structure dynamics problems, including protein folding-unfolding or associations with other biomacromolecules, obviously with the above-mentioned computational limitations.  Figure 2). In both cases of CG representation, the atomistic structure can be approximately reconstructed thanks to the properly defined geometry and interaction schemes of these models.

Coarse-Grained CABS Model
CABS [24] is a coarse-grained model for simulations of protein structures and dynamics (see Figure 4 and recent review on its design and applications [8]). CG representation of amino acid residues is reduced to up to four pseudoatoms or united atoms. These pseudoatoms of CABS models represent main-chain alpha carbons (CA), beta carbons (B), and the center of mass of side chains (S). The fourth pseudoatom is placed in the center of the Cα-Cα pseudobond and is used for the CG definition of the main-chain hydrogen bonds.  [8,24]). Allowing small fluctuation of the expected Cα-Cα distance enables hundreds of possible orientations of these pseudobonds. Side-chain pseudoatoms (Cβ in blue and an additional center of the side chain, where applicable, in yellow) are not restricted to the lattice and their positions are defined by the geometry of the main chain and statistical properties of specific amino acids. A move of a single Cα leads to specific displacements of three side chains (new positions of the side chains are shown in gray). Thanks to the high-coordination lattice representation of the main chain geometry, all possible local moves are stored in large precalculated data tables.
The force field of CABS is knowledge-based, derived from the statistical analysis of a representative set of known protein structures, although it also works surprisingly well for partially unfolded proteins [8,24]. The force field contains local conformational biases dependent on amino acid identities and excludes volumes of pseudoatoms, a model of strongly directional main-chain hydrogen bond attractions and contact potentials for side chains [26]. The latter have a specific feature, making the CABS force field very sensitive to the amino acid content in the studied proteins. Namely, this potential (for single-domain proteins) strongly depends on the mutual orientations of the contacting side chains [24,26]. For instance, when modeling a single-domain globular protein, oppositely charged side chains are strongly attractive if their contacts are parallel, and antiparallel contacts are repulsive. Indeed, when looking into globular proteins, oppositely charged residues usually come into contact on the protein surface, and consequently the corresponding side chains are almost parallel. On the contrary, contacts of charged residues in the central regions of a globule, where parallel and antiparallel orientations of side chains in contact are possible, are extremely rare, therefore the antiparallel contacts of charged residues are treated as repulsive (unlike). This orientation-dependent definition of side-chain contact interactions is a powerful way to introduce a crude account for implicit solvent effects in CG models.
The sampling scheme of the CABS model (constant temperature simulations, cooling/heating simulations, replica-exchange simulations, and other sampling strategies) is based on the MC dynamics method. The dynamics of the entire protein structure is modeled as a very long random sequence of small local moves, including single amino acid moves to small-fragment (composed of two, three, and rarely a slightly larger number of amino acids) local moves. MC dynamics provides very reasonable dynamics trajectories for long-time processes, although the time unit of such dynamics is not a priori defined. MC dynamics can be properly scaled by fitting (using mean-square displacements, or related parameters, as global motion criteria) fragments of the MC trajectories to corresponding MD trajectories and/or to appropriate experimental data. CABS MC dynamics simulations are very fast, not only due to the reduced representation of protein structures, but also thanks to the high-co-ordination lattice representation of the model structures. The underlying lattice enables use of precalculated data tables of local geometry and interactions. This makes computing new randomly selected structures and interactions extremely fast. On the other hand, the very high co-ordination number of interacting pseudoatoms (due to the high resolution of the underlying lattice) does not impose any noticeable directional biases that is so painful for lower-resolution lattice models of protein structures [27].
The CABS model has been successfully used in numerous studies, including the folding of globular proteins [28][29][30][31][32][33], dynamics of protein-peptide binding [34,35], flexibility of globular proteins [36][37][38][39][40], chaperonin mode of action [32], protein insertion into the biological membrane [41], structure prediction of proteins [42][43][44] and protein-peptide complexes [45][46][47][48][49]. The CABS has been implemented as a simulation component of several multiscale modeling tools, publicly available as web servers and standalone packages (see http://biocomp.chem.uw.edu.pl and http://lcbio.pl). The CABS-based tools are targeted onto specific simulation tasks, including simulations of near-native (or in the vicinity of another provided structure) protein flexibility [37][38][39][40], modeling of protein folding (including de novo or template-based structure prediction) [42,50], and unrestrained molecular docking of flexible peptides to flexible protein receptors [45][46][47]49]. Very recently, the CABS model has been made available as the CABS-flex standalone package [40], which is a flexible simulation environment for modeling protein dynamics including small-scale fluctuations (based on the input structure) or large-scale moves. The CABS-flex package offers easy modification of the CABS simulation parameters (and other modeling stages like scoring, structural clustering, or all-atom reconstruction) and using various input data (in the form of user-defined distance restraints). Therefore, the CABS-flex package can be easily incorporated into other multiscale modeling methodologies of structural biology.

Coarse-Grained SURPASS Model
By implementing specific structural regularities characteristic to protein globules, it is possible to design deeply coarse-grained representations of protein chains that are much simpler than CABS (and other models of similar resolution), and which, regardless of their simplicity, maintain some of the most important features of real systems. SURPASS is an example of such approach. SURPASS [25,51] is based on the fundamental idea that three-dimensional structures of globular proteins (and also the majority of membrane proteins) are to a large extent defined by the local ordering of polypeptide chains, defined as the secondary structure. A united residue of the SURPASS chain represents a single amino acid unit, located in the center of mass of four consecutive alpha carbons. The choice of the four-residue averaging of united residue positions is not incidental. For regular elements of secondary structure, such as helices or β-strands (but also, to a lesser extent, other secondary structure fragments), such averaging leads to a very simple, almost linear, representation of polypeptide chains ( Figure 5). On this level of protein-drawing, it is possible to derive several simple statistical rules controlling three-dimensional structures [25,51]. For instance, helical fragments can be treated as linear chains composed of partially overlapping thick spheres, while β-strand fragments are thinner, with the asymmetric shape of their excluded volume profiles, representing differences in distances between neighboring strands belonging to the same β-sheets, and between strands of different sheets. Characteristic distances between helices and β-sheets, and other structural regularities, can also be easily incorporated in the statistical force field of the model. In its simplest version, the SURPASS force field is based on secondary-structure assignment (or predictions), as the only sequence-defined variable-defining interaction patterns. The solvent effects, similarly as to what was assumed in the CABS model, are treated in an implicit fashion. Reconstruction of more detailed structure representations from SURPASS models is not trivial, but, as tested on a large set of globular structures, it is possible, and does not create drastic inaccuracy of the resulting atomistic models. In this context, the structural accuracy (resolution) of the model is in the range of 2-3 angstroms. This is an acceptable range for all-atom structure refinement protocols [52]. The dynamics of SURPASS proteins is simulated by Monte Carlo dynamics schemes within single = trace or replica-exchange (Replica Exchange Monte Carlo (REMC) dynamics) runs. The simplicity of the model allows simulations of much larger systems than accessible for higher-resolution models [25,51]. As shown for representative sets of single-domain globular proteins, even for relatively large proteins, a single REMC run provides trajectories covering the entire conformational space of the model, with multiple visits of native-like structure regions. Due to its computational speed and the fact that its low-resolution results can be used as the starting frames for higher-resolution simulations, SURPASS modeling opens up a possibility for efficient multiscale modeling of the long-time dynamics of large proteins and protein systems.

Elastic Network Models
In the past decades, a large number of studies have shown the usefulness of NMA and Principal Component Analysis (PCA) for the prediction and analysis of protein co-operative motions [13,14,[53][54][55][56][57][58][59][60][61][62]. Many of these studies were conducted using computationally simple ENM that use uniform harmonic potentials for interacting atom (or residue) pairs instead of more complicated potentials.
In the early 1980s, the NMA method was adapted by Go [63], Karplus [64], and others for studies of thermal fluctuations of proteins around their native structures. PCA and NMA of proteins and other biomolecules applied to their equilibrium dynamics around native states have shown that low-frequency normal modes correspond to the co-operative motions of large parts of the protein structure that are essential for protein function, while high-frequency modes corresponding to the isolated, non-co-operative vibrations of small parts of the structure are functionally meaningless. The long-range character of low-frequency modes enables allosteric interaction of various parts of proteins that are spatially distant from one another. The old paradigm that the protein sequence encodes protein structure, and the structure determines protein function has been modified in the last 20 years by a new paradigm assuming that sequence encodes the structure, the structure determines protein dynamics, and the dynamics encodes protein function. It has also been shown that, for globular proteins, their dynamics is strongly determined by protein shape [65]. Besides describing the near-native motion of globular proteins, PCA can also be applied to structure snapshots from molecular dynamics or Monte Carlo simulations, to structures from nuclear magnetic resonance ensembles (NMR) [66], or to the analysis of large sets (clusters) of protein or RNA structures determined by X-ray crystallography, such as, for example, a set of over 350 HIV-1 protease structures [67].
ENMs have become extremely successful in biology after their reformulation as the Gaussian Network Model (GNM) of proteins [54,68]. GNM assumes that each residue of a protein is represented by a single node, with its co-ordinates usually given by co-ordinates of the Cα atom, although different definitions of nodes (e.g., the center of mass of a side chain) are also frequently used. Nodes that are separated by less than a cut-off distance (usually assumed to be around 7 Å) are connected by identical harmonic springs. Nodes that are further than a cut-off distance are not connected by springs. The model is based on the assumption of Tirion [69,70] that bonded and nonbonded contact interactions in biological structures can be described by a single uniform spring constant parameter. Although such approximation seems, at first, rather unrealistic, the results of normal-mode analysis are almost undistinguishable from the results derived using sophisticated energy-function potentials, such as the CHARMM force field (Chemistry at HARvard Macromolecular Mechanics) [71]. The excellent performance of springlike potentials for normal-mode analysis is due to the fact that any complicated potential function is very well approximated around its minimum by a harmonic function. The basic difference between elastomeric polymer networks and proteins is that a protein is a densely packed collapsed heteropolymer with a unique packing of amino acids and many nonbonded interactions, whereas for polymers these nonbonded interactions are not important. The fluctuations of residues in proteins computed with GNM agree surprisingly well with experimental temperature factors in the Protein Data Bank (PDB). Excellent agreement is typically achieved by this approach when crystallographic B-factors [68] or H/D exchange data [72] are plotted against mean-square fluctuations computed with GNM as a function of the residue index. Additionally, the covariance of fluctuations of two residues i and j that provides information about correlations of equilibrium fluctuations of residues in protein structure can be simply computed with elastic network models. Pairs of residues that have large values of such correlations may communicate with each other because of functional reasons, for example, by participating in common allosteric communication paths. In the GNM, it is assumed that fluctuations of residues around their mean positions are spherically symmetric.
The Anisotropic Network Model (ANM) assumes that fluctuations of residue positions around their starting points are, in contrast to the spherical approximation of GNM, anisotropic and are represented by ellipsoids. The model can be used to calculate normal modes from a single structure [73,74]. The cut-off distance for defining the springs is usually assumed to be 13 Å that is significantly larger than the cut-off distance of around 7 Å that is usually used in GNM. For most of the proteins in PDB, crystallographic B-factors are scalars and GNM very well predicts the magnitude of fluctuations of residues. Protein structures solved with high or ultrahigh resolution (less than 0.8 Å) usually have anisotropic B-factor tensors deposited in PDB, and ANM that predicts the tensor of anisotropic B-factors is a very useful tool for modeling proteins with very high resolution reference structures ( Figure 6). Most co-operative (lowest-frequency) modes of motion are almost insensitive to the details of the model and parameters, and can be satisfactorily captured by adopting low-resolution ENMs. The idea was applied by Doruker and Jernigan [75] to low-resolution EN representation, where each node stands for a group of m residues condensed into a single interaction site. Calculations performed for different levels of coarse-graining (m = 1, 2, 10, 40) have shown that the slowest modes of motion are preserved regardless of the level of coarse-graining. Mixed coarse-graining has also been introduced for EN models, where a protein's native conformation is represented by different regions having high and low resolution [76]. The aim here was to capture the dynamics of the interesting parts in structures at higher resolution and retain the remainder of the structure at lower resolution, keeping the total number of modes sufficiently low for computational tractability.
CABS and SURPASS obviously have several limitations, characteristic for all CG models of low and moderate resolution. First, in multiscale simulations the shift from atom level to CG representations is easy (unique mapping), while the reverse projection from CG to the atomistic representation is always nonunique and less accurate, and needs to be carefully performed [8]. Knowledge-based force fields, while working very well for "typical systems", are also not easy-to-use in studies of rare and more complicated events as, for instance, structure prediction for proteins adopting different secondary structures for the same or very similar sequence fragments. It should be, however, noted that the secondary-structure biases in properly designed knowledge-based statistical potentials (as in CABS or SURPASS models) are weak, and may compete with other forces, allowing quite realistic simulations of long-time dynamics and conformational transitions [8]. In this context, the use of knowledge-based force fields in CG simulations is less restrictive than use of reference structures in elastic network models.

Modeling of the Structural Flexibility of Folded Proteins
When the range of protein structural dynamics studies can be limited to a roughly defined vicinity of a specific folded structure (determined by experiment), ENM and CG models or their modifications can be very effective in predicting protein flexibility [8,36,[81][82][83].
In the past decades, ENM-based modeling has definitely been the most common approach to modeling fluctuations near the input folded structures. There are many available methods based on ENM, some of them as easy-to-use web servers [67,[84][85][86][87][88][89][90][91][92][93][94][95]. CG methods using more sophisticated potentials that are not derived from the input structure are much less common in predictions of protein fluctuations [8,36,37,39,96,97]. These include the CABS-flex method using the CABS CG model for MC simulations of protein flexibility [37,39] near a predefined reference structure that can be taken from experiments or predicted theoretically. The CABS-flex method has been shown to provide a consistent view of protein flexibility with short timescale MD simulations [36] and NMR ensembles [38]. While qualitatively similar to ENM predictions, CABS-flex generated better correlated pictures of protein fluctuations (on average for the studied set) in comparison to all-atom MD simulations [36]. The CABS-flex method is currently available as a web server (an improved 2.0 version was recently published [39]) and a standalone package [40].
Below, in Figure 7, we present the comparison of fluctuation profiles (root mean square fluctuation (RMSF) for protein 1hpw (using the first model from the 1hpw PDB ensemble as the input) obtained from: • NMR ensemble: data calculated using 10 models deposited in the PDB code: 1hpw. • MD all-atom simulation: data obtained using a 10 nanosecond trajectory with an AMBER8.0 force field taken from the MoDEL database of MD trajectories [38]; RMSF was calculated for the entire trajectory consisting of 10,000 models. • CG simulation using the CABS model: data obtained using the CABS-flex 2.0 web server [39]; results calculated using the default server settings; RMSF was calculated for the set of 10 representative models (obtained by a cluster analysis of 10,000 snapshots) from the simulation trajectory. • CG simulation using the SURPASS model: data obtained with the following SURPASS [25] settings: isothermal MC simulation in low reduced temperature (T = 0.2), 10,000 MC steps, 1 and 3-bead motions; RMSF was calculated for the entire trajectory of 100 models. • ENM modeling: data obtained using the DynOmics web server [85,95] that integrates two ENM methods: the GNM and the ANM, calculated using default server settings; real time calculation: <1 min; RMSF was calculated for the set of 20 models based on the 10 slowest modes (2 models per mode for extreme positions during movement); the amplitude of motion along each mode was chosen so that the RMSD (root-mean-square deviation of atomic positions) between the models was less than 2Å; all models were generated using the 'Molecular Motions-Animation' option available on the results page of the DynOmics server. Figure 7 shows that ENM and CABS MC simulations provide dynamic profiles closest to experimental characteristics (such experiments have been made for many protein systems, see reference [38] for more details). The deeply CG SURPASS method is less accurate, probably mostly due to its extremely simplified interaction model, although dynamic profiles are quite realistic. Obviously, CG methods are computationally much less expensive than atomistic simulations by MD. Results presented in Figure 7, while quite illustrative, should be treated with some caution. For instance, an NMR ensemble certainly does not show the full picture of protein flexibility, and MD simulations are probably too short to sample all possible conformations.
The ENM-based exploration of protein conformational space near the given folded structure can also be practically used in the refinement of models derived from protein-structure prediction methods [98][99][100][101][102][103][104]. For example, Feig proposed and successfully tested a modeling scheme based on NMA and MD simulations with constraints and an efficient sampling scheme [105,106]. The procedure used by Feig's group was an iterative one. First, they performed molecular mechanics energy minimization, and then employed NMA computations around the local energy minimum. Subsequently, they generated and evaluated an ensemble of possible new conformations along the lowest-frequency normal modes. Normal modes provide guidance toward the effective refining of protein structures. A similar approach was used by Tama and Brooks to refine cryo-Electron Microscopy structures [107]. Similarly to ENM and related models, CG Monte Carlo simulations can also be easily combined with MD simulations [108][109][110][111].

Modeling Large-Scale Structural Transitions
ENMs have been shown to be useful to study large-scale protein conformational transitions in cases when starting and ending conformational states of the transition are available [14,53,96,[112][113][114][115][116][117][118]. Protein conformational transitions frequently occur in biology, and usually relate to protein function. The most frequently occurring large-scale conformational transition involves two protein conformations: the "open" and the "closed" form, which are the starting and the ending states of the transition. Assuming that the physical mechanism of normal modes is used in nature to drive large-scale conformational transition, an ENM can be used to model such transitions using both the "open" and the "closed" structures [112]. Since such large-scale conformational transitions are frequently slow and difficult for detailed modeling with MD simulations, the above-mentioned ENM approach represents a very useful and computationally inexpensive alternative method to study them. Both approaches usually give similar results [77]. ENM-based methods have been successfully applied to structural transitions of very large molecular structures [14], such as a ribosome [119][120][121].
CG protein simulation models, such as CABS or SURPASS, offer an alternative approach to predicting large-scale transitions that do not require starting and ending conformational states. The CABS model has been used for simulations of folding mechanisms from the fully denatured to the folded state [28][29][30][31][32]43]. This way, with some successes, the CABS model can be used for de novo structure prediction. Obviously, with the growing structural database, de novo modeling of protein structures becomes a rather theoretical challenge with decreasing practical importance. For the majority of new proteins, it is now possible to identify close or distant homologs with known, experimentally determined structures. CG models, including CABS, have become powerful tools for comparative modeling, especially for the cases of distant homology, where differences between template and target structures are significant. The CABS-fold server can be easily used for de novo structure prediction (purely de novo, without any knowledge about the plausible starting structure, is reasonable only for rather small proteins) and for template/template-based comparative modeling. In a different context, using weak or limited structural restraints, and/or known starting structures, models such as CABS, UNRES, or related models [44,[122][123][124][125][126] can be powerful tools for simulations of large-scale structural rearrangements. Folding/unfolding pathways, chaperone effects, large-scale structural rearrangements during molecular docking, and other related processes can be efficiently simulated. The medium-resolution CG models, while opening the timescale about three to four orders of magnitude wider than that accessible for classical MD simulations, still remain computationally too expensive for modeling multiple large-scale structural transitions of large proteins. The SURPASS model opens up such possibility. Example results of SURPASS simulations of folding-unfolding processes in a globular protein are illustrated in Figure 8. The entire conformational space of large proteins can be effectively sampled. This allows very detailed studies of the very long-time dynamics of proteins and protein complexes, although biased by the necessary simplifications of structure representations and the model (knowledge-based, statistical) force field. Although maintaining fundamental features of protein structure, such crude models can be combined with more accurate modeling tools, for example, providing representative starting conformations for de novo simulations with higher resolution tools, or a plethora of random, partially folded and more compact folded/misfolded structures for derivation of realistic statistical potentials for other CG models.

Concluding Remarks and Perspectives
Computational exploration of protein conformational transitions has already been quite successful in many tasks of structural biology, including rational drug design [4,5,127,128], but it is often very challenging. The major difficulties arise from inexact sets of parameters (e.g., force-field parameters [4,129]), and inability to access long simulation timescales of many important biological processes. In this review, we present two approaches to extend accessible timescales and sizes of modeled systems: ENMs and CG models using MC dynamics. These can be combined with atomistic-level MD, providing efficient strategies of multiscale simulations of proteins and protein complexes. While the ENM approach is well suited for speeding up collective motions, CG MC can be applied to a broader range of functional moves.
ENM-(or NMA)-based methods are well suited only for certain problems [54]. They can describe well protein dynamics around the known experimentally solved native state, or conformational transitions between two states, such as "open"-"closed", or "bound"-"unbound", for which both structures are known. The applicability of ENM methods strongly depends on how collective the motion is [53,130]. In other words, ENM-based methods do not provide real dynamics and real transition pathways, but only an approximation based on the combination and interpolation of normal modes. The high accuracy of such a simplified approach for the description of system dynamics near the given reference structure clearly shows that the local dynamics is, to a large extent, controlled by the general structural features of proteins, and is less dependent on specific interaction patterns between amino acid residues. For example, the use of ENM in protein-ligand docking is restricted to selected cases in a few only collective motions responsible for binding a ligand [130]. In this context, CG MC simulations appear to be more realistic. In principle, simulation methods, such as MD and CG MC (or CG MD), can be used for simulations of entire folding/unfolding processes, although the cost (and limitations of practical applicability) of such simulations rapidly grows with increasing resolution of the models. Deeply CG models, such as SURPASS, discussed here, allow simulations of the largest systems, although such CG levels should be used in connection with more accurate modeling tools.
In the above context, the future will see combinations of ENM and other CG methods of various resolution with atomistic MD [14,[131][132][133]. Such hierarchical integrative approaches should be properly tailored for specific problems and utilize structural data from available experimental sources, such as X-ray crystallography, NMR, EM, and SAXS [134][135][136]. The challenges of integrative modeling call for computational tools that are available as easy-to-use software packages based on different kinds of data and merging with other tools, just as the CABS model described here [40].


s2fieldsofstudy: Biology Chemistry

text:
Molecular Mechanisms of the Methionine Sulfoxide Reductase System from Neisseria meningitidis

Neisseria meningitidis, an obligate pathogenic bacterium in humans, has acquired different defense mechanisms to detect and fight the oxidative stress generated by the host’s defense during infection. A notable example of such a mechanism is the PilB reducing system, which repairs oxidatively-damaged methionine residues. This review will focus on the catalytic mechanism of the two methionine sulfoxide reductase (MSR) domains of PilB, which represent model enzymes for catalysis of the reduction of a sulfoxide function by thiols through sulfenic acid chemistry. The mechanism of recycling of these MSR domains by various “Trx-like” disulfide oxidoreductases will also be discussed.

Introduction
Neisseria meningitidis is the infectious agent responsible for meningitis and septicemia. Its pathogenicity depends on its ability to resist activated oxygen and nitrate species produced by host macrophages in response to infection. For this, N. meningitidis has developed multiple antioxidant defense systems, including the PilB reducing system, which is capable of repairing the sulfoxide form of oxidatively-damaged methionine residues.
The pilB gene was initially identified in Neisseria gonorrhoeae in 1988, and its name derives from early studies that suggested that PilB was a transcriptional regulator inhibiting pilin synthesis [1][2][3]. However, this hypothesis was discredited in 2002 when PilB was shown not to play any role in pilin expression. Indeed, the transformation of this strain with a plasmid allowing the production of PilB under the control of an isopropylthiogalactoside (IPTG)-dependent promoter, had no effect on piliation [4].
Analysis by protein sequence identity of publicly-available translated genome data, shows that the PilB protein is encoded by only a few bacterial genomes. These include human commensal and pathogenic bacteria such as Neisseriae (N. meningitidis, N. gonorrhoeae, N. lactamica and N. cinerea), as well as the bacteria present in dental plaque, such as Fusobacterium nucleatum and Kingella oralis, and the non-pathogenic extremophile bacterium Psychrobacter cryohalolentis (Figure 1). The moderate to high sequence conservation (sequence identities between 47 and 98%) and the environmental proximity of some of these bacteria, suggest that the gene was acquired by horizontal transfer between species, conferring the capacity to fight oxidative stress.
PilB is composed of three domains: a thioredoxine (Trx)-like N-terminal domain (N-ter) with disulfide oxidoreductase activity, and two domains (named A and B) with methionine sulfoxide reductase (MSR) activity. In Neisseriae, two protein forms derived from the pilB gene have been characterized: a truncated cytoplasmic form called PilB AB containing only the two MSR domains and a periplasmic form called PilB, incorporating the three domains [4] (Figure 2). From heterologous  and Psychrobacter cryohalolentis (PilBPc) and secondary structures of N. meningitidis N-ter (2fy6), MSRA (3bqe) and MSRB (3hcg) domains. MSRAB fusion of Treponema denticola (5fa9) is also presented. The amino acid numbering is based on N. meningitidis PilB. Conserved residues at 80% are indicated in black on yellow boxes. Stars indicate residues with alternate conformations. The linker regions between N-ter and MSRA, and MSRA and MsrB are located between residues 176 and 195, and 357 and 382, respectively. The alignment was carried out using ClustalW [6], and the figure was prepared using ESPript (http://espript.ibcp.fr [7]). MSR: methionine sulfoxide reductase.
Studies by the Seifert group have shown that the PilB protein of N. gonorrhoeae, which is probably located on the outer membrane within the periplasm, is involved in the resistance to oxidative stress, as it is necessary for the survival of bacteria in the presence of H 2 O 2 [4]. This activity is in all likelihood conferred by its MSR domains, as has been demonstrated for MSR enzymes from other bacteria [8]. However, the PilB system exhibits several peculiarities relative to these discrete homologues: the location of MSRs in both the cytoplasm and the periplasm, the fusion of the MSRA and MSRB activities into a single protein, and the presence of an essential N-ter domain. The MSR-dependent repair mechanisms of N. meningitidis PilB have been extensively studied in vitro and in this review, we summarize the molecular mechanism of sulfoxide reduction by MSR domains, the role of the N-ter in the periplasm, and information concerning the periplasmic recycling partners.
Studies by the Seifert group have shown that the PilB protein of N. gonorrhoeae, which is probably located on the outer membrane within the periplasm, is involved in the resistance to oxidative stress, as it is necessary for the survival of bacteria in the presence of H2O2 [4]. This activity is in all likelihood conferred by its MSR domains, as has been demonstrated for MSR enzymes from other bacteria [8]. However, the PilB system exhibits several peculiarities relative to these discrete homologues: the location of MSRs in both the cytoplasm and the periplasm, the fusion of the MSRA and MSRB activities into a single protein, and the presence of an essential N-ter domain. The MSR-dependent repair mechanisms of N. meningitidis PilB have been extensively studied in vitro and in this review, we summarize the molecular mechanism of sulfoxide reduction by MSR domains, the role of the N-ter in the periplasm, and information concerning the periplasmic recycling partners.

MSRs of N. meningitidis
MSRs catalyze the reduction of methionine sulfoxide (Met-O) to methionine (Met) in the presence of reducing agent. To date, three structurally distinct classes of MSRs have been characterized: MSRA, MSRB and fRMSR. MSRA, MSRB and fRMSR are cysteine-containing enzymes which display different substrate specificities: MSRA and MSRB specifically reduce the Sand R-isomers of free or peptide-bound L-Met-O, respectively, whereas fRMSR (for "free-R-MSR") only reduces the R-isomer of free Met-O, with high selectivity for L-Met-R-O [9][10][11]. The oxidation of methionine residues produces both diatereoisomers of L-Met-O, referred to as R and S, due to the presence of a second asymmetric center on the sulfur atom of the sulfoxide function. Thus, the presence of both MSRA and B activities is necessary to allow for full reduction of L-Met-R, S-O back to L-Met.
In Neisseriae, PilB AB and PilB carry both MSRA and B functions, while an isolated cytoplasmic protein exhibits an fRMSR activity, the role of which is not known [12]. The chemical and catalytic mechanism of the MSR domains of PilB as well as their three-dimensional structures have been extensively studied, as they represent model enzymes for classical two cysteine MSRs, as discussed in previous reviews [13][14][15][16]. The results are thus only summarized briefly here.

Catalytic Mechanism of MSR Domains
The PilB MSRA and MSRB domains are typical two-cysteine MSRs, which act by a common three-step mechanism involving sulfenic acid chemistry ( Figure 3) [17,18]. In the absence of a reducing agent, the reduction of Met-O leads to the successive formation of a sulfenic acid intermediate on the catalytic cysteine residue and a disulfide between the catalytic and regenerating cysteine residues, concomitant to the release of a water molecule. The rate of the first step has been shown to be very fast for both enzymes (790 s −1 for MSRA, 85 s −1 for MSRB, with Ac-L-Met-R-O-NHMe as a model substrate), such that the rate-limiting step of the reaction in the absence of reducing agent is disulfide bond formation. This observation shows that although MSRs A and B have clearly distinct 3D structures [19][20][21], their actives sites are adapted to efficiently catalyze the reduction of a sulfoxide by a thiol, suggesting a convergent evolution.
In Neisseriae, PilBAB and PilB carry both MSRA and B functions, while an isolated cytoplasmic protein exhibits an fRMSR activity, the role of which is not known [12]. The chemical and catalytic mechanism of the MSR domains of PilB as well as their three-dimensional structures have been extensively studied, as they represent model enzymes for classical two cysteine MSRs, as discussed in previous reviews [13][14][15][16]. The results are thus only summarized briefly here.

Catalytic Mechanism of MSR Domains
The PilB MSRA and MSRB domains are typical two-cysteine MSRs, which act by a common three-step mechanism involving sulfenic acid chemistry ( Figure 3) [17,18]. In the absence of a reducing agent, the reduction of Met-O leads to the successive formation of a sulfenic acid intermediate on the catalytic cysteine residue and a disulfide between the catalytic and regenerating cysteine residues, concomitant to the release of a water molecule. The rate of the first step has been shown to be very fast for both enzymes (790 s −1 for MSRA, 85 s −1 for MSRB, with Ac-L-Met-R-O-NHMe as a model substrate), such that the rate-limiting step of the reaction in the absence of reducing agent is disulfide bond formation. This observation shows that although MSRs A and B have clearly distinct 3D structures [19][20][21], their actives sites are adapted to efficiently catalyze the reduction of a sulfoxide by a thiol, suggesting a convergent evolution. In terms of catalysis, the mechanism involves the deprotonation of the catalytic Cys and protonation of the oxygen atom of the sulfoxide function of the substrate, in order to facilitate the nucleophilic attack of the thiolate and the formation of a sulfurane-type transition state ( Figure 4). These proton transfers are achieved via the participation of a general acid base catalyst bearing a carboxylate or imidazole functional group for MSRA and MSRB, respectively. It is generally accepted that the formation of the sulfurane is rate-determining, and that this species evolves into a sulfonium cation which is ultimately attacked by an activated water molecule to produce the sulfenic acid intermediate, although definitive experimental data to support this hypothesis are still lacking [15,[22][23][24]. Overall, this catalytic strategy depends on the strong polarization of the sulfoxide function, which facilitates both the transfer of the oxygen atom and the reduction of the sulfoxide function.
In the presence of the reducing agent, the regeneration of the reduced form of MSRs is achieved through the reduction of the disulfide bond by a disulfide oxidoreductase exhibiting a Trx fold. Indeed, the cytoplasmic PilBAB use Trx, whereas this role is played by the N-ter in the periplasm (see Section 3). For isolated MSRA or MSRB enzymes, reduction by Trx is the rate-limiting step of the overall mechanism. For both disulfide oxidized MSRA and MSRB, this recycling process involves the initial formation of a productive oxidized MSR/reduced Trx complex, followed by the two electron- In terms of catalysis, the mechanism involves the deprotonation of the catalytic Cys and protonation of the oxygen atom of the sulfoxide function of the substrate, in order to facilitate the nucleophilic attack of the thiolate and the formation of a sulfurane-type transition state ( Figure 4). These proton transfers are achieved via the participation of a general acid base catalyst bearing a carboxylate or imidazole functional group for MSRA and MSRB, respectively. It is generally accepted that the formation of the sulfurane is rate-determining, and that this species evolves into a sulfonium cation which is ultimately attacked by an activated water molecule to produce the sulfenic acid intermediate, although definitive experimental data to support this hypothesis are still lacking [15,[22][23][24]. Overall, this catalytic strategy depends on the strong polarization of the sulfoxide function, which facilitates both the transfer of the oxygen atom and the reduction of the sulfoxide function.
In the presence of the reducing agent, the regeneration of the reduced form of MSRs is achieved through the reduction of the disulfide bond by a disulfide oxidoreductase exhibiting a Trx fold. Indeed, the cytoplasmic PilB AB use Trx, whereas this role is played by the N-ter in the periplasm (see Section 3). For isolated MSRA or MSRB enzymes, reduction by Trx is the rate-limiting step of the overall mechanism. For both disulfide oxidized MSRA and MSRB, this recycling process involves the initial formation of a productive oxidized MSR/reduced Trx complex, followed by the two electron-transfer chemical step leading to the accumulation of the reduced MSR/oxidized Trx complex, whose dissociation is overall rate-limiting [13,25,26]. This mechanism implies that specific structural recognition should occur between oxidized MSRs and reduced Trx, which is likely a prerequisite for the efficiency of the oxidoreduction process. Although the structure of a covalent PilB AB -Trx complex has not been solved to date, the inter-partner interface is likely to involve the limited but highly complementary hydrophobic surfaces typical of redox partners that exhibit short-lived interactions [27].
Antioxidants 2018, 7, x 5 of 10 transfer chemical step leading to the accumulation of the reduced MSR/oxidized Trx complex, whose dissociation is overall rate-limiting [13,25,26]. This mechanism implies that specific structural recognition should occur between oxidized MSRs and reduced Trx, which is likely a prerequisite for the efficiency of the oxidoreduction process. Although the structure of a covalent PilBAB-Trx complex has not been solved to date, the inter-partner interface is likely to involve the limited but highly complementary hydrophobic surfaces typical of redox partners that exhibit short-lived interactions [27].

Substrate Specificities of MSR Domains
The numerous kinetic and structural studies carried out on MSRs domains have not yet permitted the identification of all the molecular factors underlying their substrate specificities. Nonetheless, it is clear that the presence of the sulfoxide function is a prerequisite for binding to both MSRs. The active sites of MSRA and MSRB are both located on the surface of the enzymes and contain two mirror-image sub-sites which confer stereospecificity for the R or S stereoisomer of L-Met-O: (1) a hydrophilic pocket involving the stabilization of the protonated sulfoxide function of the substrate via hydrogen bonding; and (2) a hydrophobic pocket consisting essentially of the side chain of a Trp residue, which stabilizes the ε-methyl group of the substrate via its indole ring (Figure 4) [9,13,28,29]. The selectivity of the enzymes for protein-bound Met-O versus free Met-O probably derives from a destabilizing effect, due to repulsive interactions with the charged amino and/or carboxyl groups of the free amino acid.

Catalytic Efficiency of MSRA/MSRB Fusion
The fusion of MSRA and MSRB domains is not specific to PilB. Indeed, fusion proteins are found in more than 46 bacteria including Treponema denticola, Streptococcus pneumoniae and Shewanella oneidensis, for example, but the functional interest of such fusions remains poorly understood [30,31]. As mentioned earlier, due to their complementary stereospecificity, the presence of both MSR activities allows the reduction and thus repair of both forms of oxidized Mets in proteins. The question rather thus concerns the kinetic efficiency of the fusion protein compared to isolated enzymes.
The catalytic efficiency (k cat /K M ) of PilB AB under steady-state conditions was in fact found to be similar to that of the discrete enzymes in the presence of their preferred substrates. Thus, the fusion does not significantly alter the catalytic efficiency of the MSRs, suggesting that the two function as independent domains even within the context of PilB [18]. This contrasts with kinetic studies of other MSRA/MSRB fusion proteins, which revealed an increase in catalytic efficiency relative to the discrete enzymes (for example in S. oneidensis and T. denticola [30,31]). In the latter case, the higher catalytic efficiency of the fusion protein derives from hydrogen bond interactions between MSRA and MSRB and the linker lying between the two domains. Interestingly, this linker loop is about 13 residues longer in PilB, suggesting that the precise way in which the MSRs are fused may differ between species (Figure 1).

The N-ter Domain
In order for the catalytic activity to be regenerated, MSRs need to be reduced at the end of the catalytic cycle, a function performed by Trx in the cytoplasm and the N-ter domain in the periplasm. The N-ter domain of PilB is a disulfide oxidoreductase of the "Trx-like" family, with a TlpA fold and a WCPLC disulfide center redox potential of −0.230 V. Isolated N-ter domain was shown to reduce both isolated MSRA and MSRB domains [5], although it exhibits higher catalytic efficiency towards the MSRB domain (second-order constant k 2 approximately 10 4 M −1 .s −1 ). In this later case, the dissociation of the reduced MSRB/oxidized N-ter complex remains rate-limiting, as in the case of Trx. For MSRA (k 2 approximately 10 2 M −1 .s −1 ), the formation of the oxidized MSRA/reduced N-ter complex occurs more slowly and is in fact rate-limiting, probably reflecting the existence of a structural fit between the oxidized MSRA domain and the reduced N-ter domain (data not shown). Although the structure of the Nter/MSR complexes are not known, the interactions seem to be limited to the active site area as described for other complexes involving "Trx-like" oxidoreductases.
X-ray diffraction and NMR studies of the N-ter domain revealed the presence of a rigid 99 FLHE 102 loop covering one edge of the active site, in both its oxidized and reduced states [32,33]. This additional loop, which is not found in Trxs or in other TlpAs, plays a critical role in recognizing the PilB MSRs. Indeed, the catalytic efficiency (k 2 values) for the reduction of the MSR domains by a truncated form of the N-ter, in which the loop has been genetically removed, is 10-fold less efficient relative to the intact N-ter [34].

Recycling of MSRs Activities within PilB
In PilB, the N-ter domain efficiently reduces only the MSRB domain via an intramolecular mechanism, whereas the reduction of the MSRA domain depends on the classical Trx-like intermolecular mechanism (data not shown) ( Figure 5). The formation of an intramolecular disulfide bond between the N-ter and MSRB domains, which is kinetically favored compared to the intermolecular reaction, implies either a spatial proximity of the MSRB and N-ter domains in PilB or that the presence of long "linker" regions between the two domains allows sufficient flexibility for the formation of this transient disulfide bond. Examination of the PilB sequences reveals the existence of linkers between the three functional domains. Specifically, a linker of 20 amino acids is found between the N-ter and MSRA domains, and one of 26 amino acids between the MSRA and MSRB domains, in N. meningitidis PilB (Figure 1). The presence of these long linkers, the second of which is approximately 13 residues longer than that in the MSRA-MSRB fusion of T. denticola [31], suggests that PilB should show overall greater flexibility, which allows the N-ter and MSRB domains to approximate each other, and which additionally explains the observed independence of the MSR domains in kinetic studies. Obtaining a three-dimensional structure of PilB would, however, provide additional valuable information on the spatial organization of the three domains with respect to each other.
Antioxidants 2018, 7, x 7 of 10 X-ray diffraction and NMR studies of the N-ter domain revealed the presence of a rigid 99 FLHE 102 loop covering one edge of the active site, in both its oxidized and reduced states [32,33]. This additional loop, which is not found in Trxs or in other TlpAs, plays a critical role in recognizing the PilB MSRs. Indeed, the catalytic efficiency (k2 values) for the reduction of the MSR domains by a truncated form of the N-ter, in which the loop has been genetically removed, is 10-fold less efficient relative to the intact N-ter [34].

Recycling of MSRs Activities within PilB
In PilB, the N-ter domain efficiently reduces only the MSRB domain via an intramolecular mechanism, whereas the reduction of the MSRA domain depends on the classical Trx-like intermolecular mechanism (data not shown) ( Figure 5). The formation of an intramolecular disulfide bond between the N-ter and MSRB domains, which is kinetically favored compared to the intermolecular reaction, implies either a spatial proximity of the MSRB and N-ter domains in PilB or that the presence of long "linker" regions between the two domains allows sufficient flexibility for the formation of this transient disulfide bond. Examination of the PilB sequences reveals the existence of linkers between the three functional domains. Specifically, a linker of 20 amino acids is found between the N-ter and MSRA domains, and one of 26 amino acids between the MSRA and MSRB domains, in N. meningitidis PilB (Figure 1). The presence of these long linkers, the second of which is approximately 13 residues longer than that in the MSRA-MSRB fusion of T. denticola [31], suggests that PilB should show overall greater flexibility, which allows the N-ter and MSRB domains to approximate each other, and which additionally explains the observed independence of the MSR domains in kinetic studies. Obtaining a three-dimensional structure of PilB would, however, provide additional valuable information on the spatial organization of the three domains with respect to each other.  In conclusion, the in vitro results suggest that the mechanisms for reducing the MSRA and MSRB domains in PilB are different: the reduction of MSRB occurs intramolecularly, while MSRA is Antioxidants 2018, 7, 131 8 of 11 regenerated intermolecularly. As to the situation in vivo, PilB is a periplasmic protein that deploys its MSR activities to reduce the Met residues of oxidized periplasmic targets. In vivo, the reductive regeneration of the two MSR domains must therefore be efficient in the periplasm to allow the bacterium to cope with oxidative stress. Based on in vitro data, however, the reduction of MSRA is expected to be slower relative to MSRB. As the MSRB domain is specific for the reduction of Met-R-O, but the oxidation of Met leads to an equimolar mixture of R and S isomers, PilB should only be able to efficiently regenerate the half of the Met that is oxidized. Thus, either this activity is sufficient for the bacterium to transiently resist oxidative stress, or the reduction of the S isomer, although quite slow, is sufficient to restore the function of oxidized targets. A final possibility is that reduction of the MSRA domain involves another periplasmic "Trx-like" protein, opening up the possibility to efficiently regenerate both the Met-O stereoisomers.

Periplasmic Recycling Partners
To efficiently reduce oxidized Met, PilB has to itself be reduced in the periplasm. This role is fulfilled by the disulfide-bond formation (Dsb) D protein, the general transmembrane redox hub protein that uses the reducing power of Trx transferred to periplasmic "Trx-like" disulfide oxidoreductases via specific interactions with its N-terminal domain, called nDsbD [35] ( Figure 5). The N. meningitidis nDsbD is able to reduce periplasmic "Trx-like" such as the N-ter domain [34]. The catalytic efficiency of this reduction step is high, with k 2 values of 6 × 10 5 M −1 s −1 , suggesting the existence of specific recognition between nDsbD and N-ter. The formation of the nDsbD-N-ter complex requires the opening of the nDsbD "cap-loop" region which is positioned above the N-ter redox center. This conformational adaptation would be a prerequisite for the formation of a catalytically-competent complex leading to the formation of an intermolecular disulfide bond between nDsbD and the "Trx-like" partner. It should be noted that N-ter FLHE loop does not seem to be essential for the formation and stabilization of the complex. nDsbD efficiently reduces the N-ter domain of PilB in vitro, but since the DsbD protein is a three domain transmembrane protein localized in the inner membrane, the PilB protein must be located nearby to be able to interact with the nDsbD in vivo. However, studies by Skaar and colleagues in 2002 showed using a subcellular fractionation approach that N. gonorrhoeae PilB is anchored to the outer membrane, which is incompatible with the use of DsbD as a reducing partner [4]. However, as the outer membrane anchoring of PilB appears to be weak (it involves hydrophobic interactions between the membrane and a small amphipathic N-terminal helix (amino acids 4-22) [5]), the alternative localization of PilB, such as in the interspace where it may be associated with peptidoglycan or near the internal membrane, cannot be excluded.

Conclusions
During the past decades, the MSR system of N. meningitidis was a model study of choice in many respects. The MSR domains of PilB, which have been the subject of extensive investigation at both the kinetic and structural levels, represent model enzymes for catalysis of the reduction of a sulfoxide function by thiols through sulfenic acid chemistry. The PilB protein is able to reduce Met-O in the cytoplasm but also in the periplasm, a compartment particularly subject to oxidation due to the permeability of the outer membrane, by a unique molecular mechanism combining the catalytic advantages of modular proteins, conformational flexibility and a Trx-like recognition mode.
The contribution of PilB to the virulence of N. meningitidis suggested that it might be a good therapeutic target, in particular for the treatment of serogroup B infection for which no vaccine is available. However, the similarities between bacterial and host MSR enzymes could limit the success of this strategy. In this context, bacterial specific periplasmic Trx-like proteins, such as the N-ter of PilB or TlpA2 (involved in the essential cytochrome c maturation process [36]), may be more promising targets.
Funding: This research was funded by the Centre National de la Recherche Scientifique, the Université de Lorraine, the Institut Fédératif de Recherche 111 Bioingénierie, the Association pour la Recherche sur le Cancer (ARC-No 5436) and the French Ministry of Research (ACI BCMS047, ACI IMPBio SIRE).