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Investigating the Conformation of S100β Protein Under Physiological Parameters Using Computational Modeling: A Clue for Rational Drug Design

Background: Physiochemical factors such as temperature, pH and cofactors are well known parameters that confer conformational changes in a protein structure. With S100β protein being a metal binding brain-specific receptor for both extracellular and intracellular functions, a change in conformation due to the above-mentioned factors, can compromise their cellular functions and therefore result in several pathological conditions such as Alzheimer’s disease, Ischemic stroke, as well as Myocardial Infarction. Objective: The studies conducted sought to elucidate the effect of these physiological factors on the conformational dynamics of S100β protein using computational modeling approaches. Method: Temperature-dependent and protein-cofactor complexes molecular dynamics simulations were conducted by varying the temperature from 100 to 400K using GROMACS 5.0.3. Additionally, the conformational dynamics of the protein was studied by varying the pH at 5.0, 7.4 and 9.0 using Ambertools17. This was done by preparing the protein molecule, solvating and minimizing its energy level as well as heating it to the required temperature, equilibrating and simulating under desired conditions (NVT and NPT ensembles). Results: The results show that the protein misfolds as a function of increasing temperature with alpha helical content at 100K and 400K being 57.8% and 43.3%, respectively. However, the binding sites of the protein was not appreciably affected by temperature variations. The protein displayed high conformational instability in acidic medium (pH ~5.0). The binding sites of Ca2+, Mg2+ and Zn2+ were identified and each exhibited different groupings of the secondary structural elements (binding motifs). The secondary structure analysis revealed different conformational changes with the characteristic appearance of two beta hairpins in the presence of Zn2+and Mg2+. Conclusion: High temperatures, different cofactors and acidic pH confer conformational changes to the S100β structure and these results may inform the design of novel drugs against the protein.

Background
S100β protein is a metal-binding receptor located in the metal ion-rich synaptic regions of the human brain. At the onset of any neurodegenerative disorders such as Alzheimer's, Down's syndrome, trauma, brain injury, myocardial infarction and stroke, there is an increase in the concentration of S100β proteins which serves as a biomarker for clinical purposes [1]. The presence of metal ions in physiological environment makes metal-binding a necessary proviso for triggering conformational changes which ultimately can result in biological activity [2,3]. Conformational changes in proteins refer to an alteration in the tertiary structure of the protein influenced by physiochemical conditions such as temperature, pH and ionic binding [4,5]. The failure of S100β protein to maintain its native functional threedimensional (3D) conformation has been linked to pathological conditions known as neurodegenerative disorders [6] ( Fig. 1). Although native S100β has two helix-loop-helix calcium binding structural motifs, under unfavourable physiochemical conditions (i.e. temperature, pH and cofactors), these proteins experience a change in their native 3D structure [7]. The protein is a member of the family of calcium-binding proteins known as the S100 family [8,9]. The biological function of these proteins is regulated when bound to calcium ions. The family consists of 25 members which are functionally categorized into three groups: those that perform intracellular regulatory effects, extracellular regulatory effects as well as the combination of the two [10]. The members of this family are considered to be low molecular weight proteins with an average monomer molecular weight of around 12-14 kDa but 21-23 kDa in its homodimer structure. They are specifically found in vertebrates, suggesting that they are evolutionarily young [11]. S100β proteins are basically characterized by two EF-hand (pseudo and typical EF-hand) calcium active binding motifs and so undergoes a large conformational change upon binding with calcium. However, it has been shown that the pseudo-EF-hand has minor structural changes when Ca 2 + binds whereas the typical has a large repositioning of several side chain oxygen ligand during Ca 2+ coordination (EF-2: D61-E72). Several target structures have been exploited including Zn 2+ site in S100β inhibition which is relevant in drug design, hence can mimic target binding which can enhance Ca 2+ binding affinity. This exposes the hydrophobic residues and results in protein-protein interaction affecting biological activities in the cellular environment.
A typical dimeric S100-target interaction is Ca 2+ -dependent and involves at least 11 possible states, 13 dissociation constants, 4 conformational changes and the corresponding rate constants, per symmetric subunit. Furthermore, Ca 2+ binding to S100β causes conformational changes and the binding events for a single target are symmetric and occur independently of those on the other subunit, thereby making it interesting to consider one S100β subunit in Calcium binding site Calcium binding site thermodynamic scheme [12].
These proteins also show a high degree of specialization by their cell-specific expression patterns. For instance, S100β is brain-specific while S100α1 is restricted to cardiomyocytes and cells of the kidney only. S100β proteins are characterized by two calcium-binding active sites and the name S100 is coined because they are soluble in 100% saturated ammonium sulphate at neutral pH [10,11]. Exogenous S100β excite the survival of neurons together with neuritis extension which can affect learning and memory in a chemical regulatory (paracrine, autocrine, endocrine) manner [13]. S100β also plays a role as a mediator of glial-neuronal interactions to enhance brain development and synaptic transmission possibly through G-protein coupled receptor (GPCR). Furthermore, they play a role in raising intracellular calcium concentrations either by dependent Ca 2+ -channels or by depletion of the calcium stores [14]. At the molecular level, S100β proteins modulate several biological activities such as calcium homeostasis, protein phosphorylation, motility, energy metabolism, maintenance of cytoskeleton dynamics, cell growth, as well as regulation of cell proliferation, inflammation, conduction and transmission of nerve impulses [15,16].
Temperature variations disrupt hydrogen bonds and non-polar hydrophobic interactions of a protein due to increase in its molecular kinetic energy. This may lead to denaturing of the protein. Thus, temperature variations confer unique conformational changes and alter the biological activities of the protein. Additionally, the pH dependent property through the pKa values of the protonated residues of the protein confers different conformational dynamics to the S100β protein. Much more, biological activities of proteins are mostly modulated by small molecules (cofactors or ligands). Upon ligand or cofactor binding, a receptor undergoes a structural change which triggers a specific biological response that may be critical to the functions of an organism at the cellular and molecular level [17]. For example, the process of phosphorylation activates or deactivates a receptor thereby altering its biological activity. The binding affinity between a cofactor and a protein as well as its specific residues at the binding motifs play a pivotal role in the conformational dynamics of the protein [18]. In this study, computational modeling approaches were used to predict the conformational changes of S100β protein at varying physiological conditions such as temperature, pH and cofactors. Since the structure and function of a protein are intrinsically interrelated, their comprehension is indispensable for drug design.

Ischemic Stroke
Ischemic Stroke (IS) is the leading cause of disability and death associated with old age. Health statistics revealed that with over 15 million cases of strokes recorded annually worldwide, methods to effectively diagnose and monitor therapy are needed [19]. The use of blood biomarkers is one of the methods that hold great potential in predicting the risk of stroke, diagnosing stroke as well as in monitoring treatment. Studies also reveal that among the two types of stroke (Ischemic and Haemorrhagic stroke), 87% of diagnosed stroke cases are of Ischemic nature [1,2]. IS occurs when brain cells die due to inadequate blood flow as a result of blockage of blood vessels leading to the brain [20]. At the onset of this neural disorder, S100β proteins are released continuously from the glial and Schwann cells into the blood and Cerebrospinal Fluid (CSF). This is due to astrocytic activation caused by deprivation of oxygen and nutrients [7]. Thus, the concentration of S100β proteins increases significantly at the onset of IS and correlates with infarct volume, stroke severity and functional outcome [8]. Hence, clinically, increased concentrations of S100β proteins in the blood and CSF serve as a useful neuro-biomarker in the diagnosis and long-term prediction of clinical outcomes pertaining to IS. The stressful physiochemical conditions in brain Ischemia results in protein misfolding and aggregation of abnormal proteins [21]. Due to the fact that the native three-dimensional conformation accounts for proper cellular function of a protein; any alteration in this tertiary structure either leads to a gain of other toxic functions or loss of key functions (e.g. the loss of motor functions which is prevalent in stroke patients). Hence, conformational changes due to unfavourable cellular conditions such as ion, pH, temperature and ligands are important in the progression of protein misfolding pathologies as well as altering of biological activity.

Alzheimer's Diseases
Alzheimer's Disease (AD) is a neuropathological ailment which is characterized by a gradual increase in dementia. This kind of dementia impairs memory, thinking and behaviour. Symptoms usually progress slowly and worsen with time, becoming profound enough to inhibit the performance of daily tasks [22]. The characteristic neuro-pathological markers of AD are the formation of amyloid plaques and tangling of the neuro-fibrils. Patients suffering from AD experience a significant increase of S100β protein levels due to conformational changes as well as a seeming interplay between ß-amyloids and expression of S100β protein. The ß-amyloid stimulates the synthesis of both S100β mRNA and S100β protein in astrocyte cultures. Extracellular S100β might contribute to inflammations in the brain regions by astrocytes and microglial neurons activations. The inflammation affects the expression of S100β proteins, making it pathologically relevant in the degeneration of the Central Nervous System (CNS) in AD [13].

Myocardial Infarction
Myocardial Infarction (MI), usually known as heart attack occurs when there is a decreased perfusion to the heart tissues as a result of blockage of the left coronary artery. This results in an increased myocardial metabolic demand causing a decreased supply of nutrients and oxygen to the myocardial cells via the coronary circulation [14]. S100β is a well-known protein marker of brain damage. At the onset of MI, there is an increased concentration of S100β protein in the serum. This shows that, a careful assessment of the function and the structural alterations of these proteins in heart attack progression might contribute to a better understanding of the regulatory and counter regulatory mechanisms underlying the progressive decline in cardiac function in patients with myocardial infarction [15].

Computational Modelling via Molecular Dynamics (MD) Simulations
Computational modelling is basically the use of computational techniques to simulate and study the behaviour of complex systems using mathematics, physics, informatics, control theories and computer science. This platform uses various methods to desegregate tentative and scientific data into models used to demonstrate biomedical phenomena and exploit system responses, predictions and develop hypothesis [23]. From the above stems molecular dynamics simulation that simulates the trajectory of a system of particles. This is prominent for the generations of nonequilibrium ensembles as well as dynamic events analysis. Molecular dynamics simulation is based on Newton's equation of motion represented as: where, F i is the force on any atom, r i is the positions of atoms in the system and m i is the atom mass.
This equation of molecular dynamic simulations is prominent in classical mechanics and from it stems varied algorithms to simulate trajectories of a system of particles [24]. Thus, the movement of the atoms is simulated by numerically solving the Newton's equations of motion based on the summation of the forces between non-bonded and bonded atom interactions as well as restrains and/or external forces. This allows the determination of the potential and kinetic energies of the atoms as well as its pressure tensor [25]. These simulations help in determining the structural alterations of complex systems and are employed in studying the conformational dynamics of S100β protein at varying physiochemical parameters to unravel new knowledge in supporting clinical decisions.

MATERIALS AND METHOD
The X-ray crystal structure of the S100β protein (PDB ID: 5D7F) with a resolution of 1.3Å was acquired from Protein Data Bank (PDB) [26]. PDB files contain standard atomistic coordinates, experimental method, resolution and secondary structure of a protein [26]. Chain A of the 5D7F structure was utilized in the computational modeling since it binds to more glycerol ligands as opposed to the chain B from the solved structure. The equilibrium structure of S100β was obtained by a long-time-scale molecular dynamics (MD) simulation using GROningen MAchine for Chemical Simulations (GROMACS) 5.0.3 [27]. Chiron [28] and YASARA [29] servers were used to predict the steric clashes, void volume and optimization of the protein. Single Point Charge (SPC) water model was used to solvate the protein without considering hydration shells. Furthermore, equivalent number of Na + ions were added to make the simulation system electrically neutral to determine the molecular conformation of the protein. Energy minimization was applied to reduce and stabilize the large forces and structural distortions due to the number of hydrogens added as well as the broken hydrogen bond networks. The pressure, temperature and volume equilibration of the system was performed using the total number of Particles-Pressure-Temperature (NPT) and Particles-Volume-Temperature (NVT) ensembles for 1000 ns, and the coordinates were saved at 1 ps frame intervals.
All the simulations were conducted utilizing the Optimized Potential for Liquid Simulations All Atom (OPLS AA) force fields and Assisted Model Building with Energy Refinement (AMBER) force fields [30,31]. GROMACS 5.0.3, a platform used to perform molecular dynamic simulations and energy minimization utilities such as sasa, hbonds, rms and rmsf were used to analyse the simulation data [27]. The same simulation protocol was used to assess the conformational dynamics of the protein regarding cofactor-bound S100β protein as well as temperature variations. The pressure equilibration algorithm employed was the Berendsen coupling thermostat scheme. The long range electrostatic interactions of the protein were calculated using the Linear Constraint Solver (LINCS) algorithm defined in GROMACS with a cut-off length of 1A o for the Van der Waals interactions. The trajectories were saved every 100 ps frame and the total NPT and MD run simulation time was 10000 ps for each case of temperature simulated. The resulting structures from the simulations were uploaded to Protein Data Bank Summary (PDBsum) Server for secondary structure contents analysis [32]. Ambertools17 [30] was used to protonate the amino acid residues of the protein to mimic the various pH values using the constant pH molecular dynamics (CpHMD) techniques. Metal Ion Binding (MIB) server was also utilized to assess the binding sites of the protein-cofactor complex [33]. Open Babel software [34] was also used to convert PQR file to PDB file format. Xmgrace plotting tool was used to plot the data obtained from the MD simulations. Statistical analysis was performed using IBM SPSS Statistics version 20 [35]. Statistical computations were undertaken using 95% confident intervals to determine the significant differences among the results obtained from the simulations. The error bars in the graphs also depicts statistically significant differences among the quantitative results obtained from the simulations.

pH-Induced Conformational Changes
A statistical measure of the root-mean-square deviation (RMSD) between corresponding atoms in protein structures can aid in obtaining the protein conformational changes at different physiological parameters [36]. The structure of S100β was monitored as a function of pH and the RMSD values depicts the average distance between the backbone atoms (Fig. 2). For the different pH 5.0, 7.4, and 9.0, considerable conformational changes were observed during the initial few picoseconds of the RMSD values (0.8824, 0.8431, and 0.8288mm), respectively. The size of the backbone fluctuation is maximum at pH~5.0 and decreases with basic pH values. Statistically, there was no significant difference in the RMSD between the physiological and basic pH values. Fig. (2). Root-mean-square deviation (RMSD) versus time graph. The graph determines the backbone fluctuations of S100β protein at varied pH values.

Temperature-Induced Conformational Changes
In order to understand the behaviour of S100β protein at varying temperatures (i.e.100 to 400K), MD simulations using the Berendsen coupling scheme in GROMACS were conducted. Graphs of RMSD, the Root-Mean-Square Fluctuation (RMSF), hydrogen bonds as well as solvent accessible surface area (SASA) versus time at the various temperatures (100K, 200K, 300K, 310K and 400K) are displayed in Fig. (3). As shown in Fig. (3a), the RMSD graph pH Variation Of S100β RMSD with pH for each temperature changes significantly with 400K having the highest deviation. Simulation at 400K was basically performed to explore how higher temperatures could compromise the structural alterations of a protein.
The results indicate a positive correlation between temperature and conformational stability. Further comparison of the crystallization (200K) and physiological (310K) temperatures showed a relatively low deviation in the former, depicting small deviations of the residues from the backbone as a function of time. In order to evaluate the individual residue fluctuations of the protein at the different temperatures, RMSFs were generated from trajectory analysis obtained by the molecular dynamic simulation and are shown in Fig. (3b). The RMSF plot at the different temperatures showed distinct fluctuations with 400K having the highest fluctuation at the protein termini. The RMSF calculation shows that, for the entire simulation period of 1000 ps, native residues fluctuate within the range of ~0.05-0.6 nm. It can therefore be predicted that higher temperatures confer much fluctuations and flexibility in the S100β structure and that the termini are the most flexible portions of the protein. hydrogen bonds and bond length affect the stability of a protein [37]. As indicated in Fig. (3c), the hydrogen bond graph showed significant variations at different temperatures (i.e. 100 -400K) with 400K having the lowest number of hydrogen bonds. This could be due to the formation of more unstructured regions of S100β at higher temperatures. It can be suggested that higher temperatures result in the destruction of the hydrogen bonds holding the secondary and tertiary structures together. The SASA graph shown in Fig. (3d) depicts significant variations at different temperatures (i.e. 100K -400K) with 400K having the highest area of approximately 76 nm 2 . This portrays that more hydrophobic residues get exposed at high temperatures which indicates denaturation marked by an increase in the exposed surface area as water solvates the unfolding proteins.  Fig. (4). The mean RMSD for the crystallization (200K) and physiological (310K) temperatures are compared in Fig. (4a). The mean RMSF values for the crystallization (200K) and physiological (310K) temperatures are also compared in Fig. (4b). The RMSF graphs show significant variations between the crystallization and physiological temperatures. This is an indication of distinct fluctuations within the protein at the higher temperature. Also, in Fig. (4c), it can be observed that the individual amino acids are held by a relatively larger number of hydrogen bond networks at 200K compared to the number of hydrogen bonds at 310K. Fig. (4d) shows the solvent accessible surface area of the protein at 200K and 310K. At 310K, the protein becomes less compact and accordingly more regions which were initially buried at the crystallization temperature are exposed to interact with the solvent.

Temperature-Induced Secondary Structure Analysis
The secondary structure of a protein is the geometric structure caused by intermolecular and intramolecular hydrogen bonding of the amide groups [38]. Fig. (7a) shows the comparison between the secondary structure of the protein at 200K and 310K. Uniform formations of alpha helices at 200K were compared to non-uniform formation of alpha helices at 310K. The alpha helices of the protein at 200K were higher in number and more confined than that at 310K. This is due to the significant reduction and breakage of these alpha helices as a result of the destruction of the intramolecular hydrogen bonds holding the structure together. This illustrates changes in the conformational stability due to the varied metal-ion binding sites of the protein. Hence temperature appears to affect the conformational changes of S100β proteins.

Metal Ions Induced Conformational Changes
The conformational changes of the S100β protein was investigated at pH ~ 7.4 in the presence of Ca 2+ since the metal ion is preferred for physiological functioning of S100β protein. The outcome of the binding was compared with other metal ions such as Zn 2+ and Mg 2+. The metal ions were selected for the prediction due to the fact that they are rich in the synaptic regions of the brain where S100β protein is found. In Table 1, unlike Zn 2+ , it is evident that Ca 2+ and Mg 2+ bind to the same protein residues. The residues that are in contact with Ca 2+ and Mg 2+ include 62D, 64D, 66D, 68E and 73E, however, the binding affinity score for Ca 2+ is higher. The RMSD was calculated for hydrogen bond interactions in the S100β protein after one least square fit for the different protein-metal ion trajectories. For the different metal ions, considerable conformational changes were observed during the early few picoseconds with RMSD value of 0.1 nm as shown in Fig. (5a). The RMSD graph for each of the protein-metal ion complex varied significantly with Ca 2+ -S100β complex having the highest deviation followed by Mg 2+ and Zn 2+ . Even though, Mg 2+ has more residue contacts compared to Zn 2+ , there is no significant deviation in terms of the RMSD between the two metal ions.
Accordingly, it can be deduced that Mg 2+ and Zn 2+ ions confer conformational dynamics to the S100β which could compromise its cellular functions.  In order to evaluate the individual residue fluctuations of the protein-cofactor complex, RMSF graphs were ( generated from trajectory analysis obtained from MD simulations and the results are displayed in Fig. (5b). The RMSF calculations show that in the whole simulation period of 1000 ps, residues showed fluctuation values within 0.25 -0.1 nm. Stable regions of a protein depict lesser fluctuations as compared with flexible regions. After 10 ns of simulation using GROMACS with different metal ions, it can be deduced that Zn 2+ and Mg 2+ -S100β complexes show higher stability as compared with Ca 2+ -S100β complex. Bonds made in Zn 2+ and Mg 2+ -S100β complexes binding sites stabilize the protein-metal ion complex and thereby reduce its flexibility depicting a unique conformational change. This flexibility appears to be accounted for by motions in the loops and hairpins around the active site of the protein.
Structural changes in the flexibility of the S100β protein were further analysed by determining the number of hydrogen bonds formed and the results are shown in Fig. (5c). The mean number of hydrogen bonds and distribution at varied protein-cofactor complexes were analysed. The average number of hydrogen bonds per frame is higher in Mg 2+ -S100β complex as compared with Ca 2+ and Zn 2+ -S100β complexes. This is probably due to the high electronegativity of magnesium which favours the attraction of hydrogen atoms in the formation of hydrogen bonds. The average number of hydrogen bonds ranges from 225 to 242 at different protein-cofactor complexes. This plays relevant roles in the maintenance of the secondary and tertiary structure of a protein. Moreover, hydrogen bonds are important in predicting the specific molecular interactions in biological recognition processes of a protein. Changes in the hydrogen bond pairs within 0.35 nm distance and angle, may affect the conformational stability of a protein [37]. The hydrogen bond graph exhibited significant variations at different protein-cofactor complex, with Zn 2+ -S100β complex having the lowest number of hydrogen bonds depicting protein instability as shown in Fig. (5c). This is possibly due to the formation of more unstructured regions of S100β at different protein-cofactor complexes.
The Solvent Accessible Surface Area (SASA) is a measure of the accessibility of a solvent to the hydrophobic core of a protein [39]. Variations in the solvent accessible surface area indicates the change in the exposed amino acid residues and could affect the secondary and tertiary structure of proteins. The SASA graph (Fig. 5d) showed significant variations with Ca 2+ -S100β complex and Zn 2+ -S100β complex as having the highest exposed surface area of approximately 50.2 nm 2 . The average SASA values revealed significant difference between calcium and magnesium. This could be as a result of the burial of the hydrophobic residues into the core, signifying a conformational change in tertiary structure of the S100β protein.
From Fig. (6a), the binding of a cofactor to the native S100β at 310K causes a conformational change which is manifested as a disappearance of an appreciable number of hydrogen bonds. This further confirms that S100β is activated by metal-ion binding for physiological functioning. It can also be observed that Mg 2+ -bound S100β has more hydrogen bond networks present than that of Ca 2+ and Zn 2+ -bound states.
In Fig. (6b), the apo-S100β has more residues interacting with the solvent than the cofactor-bound S100β protein at 37°C (310K). The cofactor-bound protein has more regions buried in the hydrophobic core and hence less surface area accessible to the solvent. This shift in the unfolding of buried residues from the hydrophobic core signifies conformational alterations triggered by cofactors at the physiological temperature. Moreover, Fig. (6c) shows the changes in the secondary structure of the alpha-helices as a function of temperature. From the same bar graph, the 310K had lower percentage of alpha helical structures as compared to 200K. As helical content of a protein determines its stability, the more the alpha-helix present in a protein structure, the more stable it is [4]. Accordingly, the protein misfolds as temperature increases thereby causing a conformational change in the S100β protein. Also, Fig. (6d) indicates the changes in the secondary structure alpha-helices with different cofactors of the protein. From the bar graph (Fig. 6d), Zn 2+ -S100β also had the lowest percentage of helical content. Hence, the different cofactors-bound S100β protein affects the conformational changes of S100β proteins. Fig. (7b) shows the number of residues and the corresponding secondary structures in each frame of the MD simulations for different cofactors bound to S100β. The secondary structure analyses were conducted to elucidate the regions that are essential in the structural changes of a protein caused by intermolecular and intramolecular hydrogen bonding of the amide group [22]. Highly fluctuated residues show a higher inclination for the formation of unstructured protein regions than regular α-helical and β-strands secondary structural elements as depicted in the RMSF graph at different protein-cofactor complexes. Additionally, it shows the comparison between the secondary structures of the respective protein-cofactor complexes. Uniform formation of alpha helices at residue 3 to 16 for Ca 2+ -bound protein was identified and compared to a non-uniform formation of alpha helices at residue 15 to 19 for Zn 2+ -bound S100β protein secondary structure. The alpha helices of the Mg 2+ -S100β was higher in number and confined relative to Zn 2+ -S100β with the characteristic appearance of two beta hairpins. Mean number of hydrogen bonds: Apo-S100β VS Cofactor-bound S100β

SUMMARY OF FINDINGS AND ITS IMPLICATIONS
As part of the model verification, the different binding domains of the cofactors identified were closely related to experimental binding positions of typical EF-hand (EF-2: D61-E72) [12]. The molecular dynamics simulations of the different protein-cofactor complex appear to have minor structural changes when Ca 2+ is bound to the pseudo-EF-hand as opposed to the typical EF-hand. Different S100β structural alterations which could be biologically relevant were identified as a result of the weak binding affinity of the cofactors. The low binding affinity of the cofactors appear to trigger the higher concentrations of S100β proteins in cells without depleting the cofactors levels [40]. The molecular dynamics simulations also corroborate that Ca 2+ , Mg 2+ and Zn 2+ S100β complexes are relevant secondary messengers in all living cells, since these cofactors helps to maintain the integrity of ion signalling and transmission in a spatially coordinated manner.
Moreover, the different binding domains of the cofactors divulges the varied affinities of the divalent metal ions. This may also help in understanding oligomerization properties as well as posttranslational modification enhancement (relevant in drug resistance) in the characterization of S100β proteins. Also, Ca 2+ -bound S100β proteins appears not to only cause structural transitions and alterations but also results in a tighter dissemination of helices within the EF-2 binding motif relevant for target protein interactions. Additionally, acidic pH (~5.0) appears to confer conformational instability to S100β proteins. Protein misfolds at higher temperatures by the disappearance of helical regions which are responsible for proper folding. It was observed that at 100K and 400K, alpha helical content was 57.8% and 43.3%, respectively.

CONCLUSION
The study aimed to investigate the impact of physicochemical factors such as temperature, pH and selected cofactors on the conformation dynamics of S100β protein. There were significant changes that were observed in the conformation and folding patterns of S100β protein and how they relate to the type of bound co-factor (Ca 2+ , Mg 2+ and Zn 2+ ). The backbone fluctuations of the S100β protein were shown to be higher in acidic medium. The two binding motifs of the protein were not affected appreciably by varying temperatures. The studies provide great potential for experimental and structural characterisation of the S100β protein. Even though, these findings are primarily based on computational predictions; experimental validation could enrich existing efforts geared towards exploiting S100β as a potent diagnostic biomarker as well as the design of novel drugs.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE
Not applicable.

HUMAN AND ANIMAL RIGHTS
No animals/humans were used for studies that are the basis of this research.

CONSENT FOR PUBLICATION
Not applicable.


Carbon Monoxide Hydrogenation on Activated Carbon Supported Co-Ni Bimetallic Catalysts Via Fischer-Tropsch Reaction to Produce Gasoline

Fischer-Tropsch Synthesis (FTS) is a process which converts synthesized gas (a mixture of H2 and CO) to synthetic liquid fuels and valuable chemicals with the existence of metal catalysts and suitable operational conditions. Less costly and plentiful biomass from agricultural waste can be converted into synthesized gas by thermal gasification. FTS derived Biofuel is a high quality, clean fuel and have a very low sulfur content in comparison to conventional fuel. In this study, FTS reaction was investigated in a tubular fixed bed reactor on prepared Co-Ni bimetallic catalysts supported by walnut shells derived activated carbon (AC) to study the synergistic effect of the active metals on the catalyst’s physical properties as well as hydrocarbon liquid product distribution. Employed catalysts were synthesized by wet impregnation method and were characterized afterwards by XRD, TPR-H2, BET surface area and FESEM-EDX techniques to identify the morphology and physical properties of the catalysts. Maximum gasoline selectivity of 69% was achieved on the 7Co7Ni/AC bimetallic catalyst, which was considered as the best bimetallic catalyst among others. Temperature increase from 220°C to 300°C enhanced gasoline selectivity from 69% to 92%. In addition, carbon monoxide (CO) conversion increased as well from 43% to 65% on the 7Co7Ni/AC bimetallic catalyst. On the contrary, increased reaction pressure from 1 bar to 9 bar decreased gasoline selectivity from 92% to 36% but increased CO conversion is from 65% to 84% on the 7Co7Ni/AC bimetallic catalyst. The optimum reaction conditions were considered based on the maximum selectivity of gasoline which was 300°C reaction temperature and 1 bar reaction pressure. In conclusion, the employing of bimetallic Co-Ni catalysts supported by AC in Fischer-Tropsch reaction has significantly enhanced the catalytic activity and improved gasoline selectivity due to the achieved high metal dispersion, better reduction degree and large surface area. Higher reaction temperatures increased gasoline selectivity whereas, higher reaction pressures decreased gasoline selectivity.

Introduction
Hydrogenation of the carbon monoxide takes place through a highly exothermic, heterogeneous catalysed polymerisation reaction that converts syngas into a wide range of organic products (hydrocarbons, oxygenates and some functional groups). The obtained variety of products depends on several factors such as the type of the catalyst and support, conditions of the process represented by the temperature, pressure and the flowrate, but the main products are always n-paraffin and 1-olefin. FTS also forms some minor products such as branched hydrocarbons, 2-olefin and various oxygenate species (alcohols, aldehydes, ketones etc.).
FTS derived liquid fuel is considered as an important source for clean energy as well as significant alternative for crude oil derived fuel due to the low sulfur and low lifecycle CO2 emissions. Many studies have investigated the effect of the employed catalysts and the reaction conditions on the achieved selectivity of the hydrocarbon liquid product fractions. Bechara and colleagues have mentioned that their catalyst 15Co/Al2O3 which was tested in a fixed bed reactor, achieved selectivity of 37% towards light liquid hydrocarbons (gasoline fraction) at a reaction conditions of 180°C temperature, 1 bar pressure, H2/CO = 2 and 500°C reduction temperature. Increase of the reaction temperature to 190°C under the same reaction pressure, resulted in enhanced gasoline selectivity to 45%. The ratio of light liquid hydrocarbons (C5₋C11) to heavy hydrocarbons (C12₋C20) was improved from 0.58 to 0.8 after 10°C increase in reaction temperature [1,2]. In different study, it was reported that promoting Fe/Mn catalyst with different potassium ratios in the range of 0 -3.0 wt.% resulted in increased crystallite size of the catalyst and a decrease in BET surface area. The increasing of potassium content retarded the reduction of the catalyst due to the strong interaction of iron oxide with potassium oxide and affected active metal sites [3]. Gasoline content increased from 51 to 59 wt.% when potassium promotion of 0.2 wt.% took place, whereas further increase of potassium content 0.7, 1.5 and 3 wt.%, decreased gasoline selectivity to 50, 33 and 25 wt.% respectively [4]. Reaction was carried out in a fixed bed reactor at operating conditions of 280°C, 1 bar, H2/CO = 2 and 250°C reduction temperature [5]. CO hydrogenation was also performed on 5 wt.% cobalt supported on different types of microporous and mesoporous zeolites such as sodium-type zeolites (Na₋ZSM-5, Na₋beta and faujasite NaY) and hydrogen-type zeolite (H₋mordenite). Catalytic performance revealed that achieved gasoline fraction selectivity over the mentioned catalysts decreased in the order Co/Na₋ZSM-5 (74%) > Co/Na₋beta (69%) > Co/Hmordenite (66%) > Co/NaY (61%). FT reaction in this study, was carried out in a fixed bed reactor operated at 20 bar, 250°C, H2/CO = 2 and 400°C reduction temperature [6]. Klaigaew and colleagues have studied the effect of catalyst preparation methods on the activation of Co/SF for FTS. They prepared their cobalt supported on silica fibre (Co/SF) catalysts by five different methods which are the wet impregnation (IM), strong electrostatic adsorption (SEA), deposition precipitation (DP) with sodium hydroxide (DPNa) or ammonia (DPNH) solution and hydrothermal method (HT). Reaction was carried out by a fixed bed reactor under reaction conditions of 240°C, 10 bar and 400°C. They found that the activity and selectivity of the catalysts were strongly affected by the catalyst preparation method. Catalysts with different particle size of Co3O4 showed different catalytic activity. Co/SF catalysts prepared by SEA, DPNH and DPNa had the largest Co3O4 particle size of 9, 12 and 14 nm respectively and were more selective for higher molecular weight hydrocarbons fraction (C12+) with values of 22%, 24% and 25% respectively; due to the dissociative adsorption of CO, which leads to the formation of the ₋CH2₋ fragments that required for chain growth. Furthermore, the effective participation of olefins in the carbon-carbon chain propagation leading α-olefins to re-adsorbed on the catalyst surface and undergo further chain initiation to produce larger hydrocarbons and promote chain growth. Although cobalt particle size in Co/SF-IM is the largest among the four catalysts of 30 nm, it presented the lowest activity and selectivity towards (C12+) of only 16% and highest selectivity to gasoline (C5-C11) of 71%. On the other hand, the catalyst Co/SF-HT with the smallest crystallite particle size of 4 nm, provided high selectivity to light hydrocarbon products in the range (C5-C11) of 57% and very low selectivity to heavy products (C12+) of only 3%. The smaller crystallite size particles facilitate the desorption of lighter hydrocarbons than heavier ones [7]. The present work was conducted to study the performance of prepared mono and bimetallic catalyst of Co and Ni active metals supported by activated carbon on the Fischer-Tropsch reaction, and to investigate the influence of the corresponding metal loading ratios and operating conditions on the gasoline selectivity and carbon monoxide conversion.  3 ) with 65% purity was purchased from USA (J.T. Baker). The liquid products standard for both alkane and alkene standard n-(C 8 -C 20 ) and internal standard 1-bromohexane for gas chromatograph (GC) analysis were purchased from Sigma Aldrich. n-Hexane (GC grade) with purity > 98% was obtained from Merck (USA). Gas mixture cylinders with 99.99% purity with a composition of (CO: H 2 :N 2 3:6:1, respectively) and 5% hydrogen gas in argon mixture cylinder 99.99% purity were supplied by Linde Sdn. Bhd. Malaysia.

Catalyst Preparation
Activated carbon support was prepared using a two-step process: physical and chemical activation. Initially, walnut shells were cleaned and washed several times with water to remove dust and impurities. The washed shells were then dried overnight in an oven at 100°C. Dry walnut shells were crushed and grinded afterwards into fine powder particles. The powder was sieved through a 200 µm metal sieve and then carbonized under N2 stream up to 700°C for 4 h at a rate of 5°C min -1 . Prior to the metal precursors introduction, the carbon support is treated with 65% HNO3 purity reflux at 120°C for 12 h in order to purify it by removing soluble impurities and activate it, followed by thoroughly washing with deionized water through a vacuum filtration process until the solution was neutral. The moist filtrate was finally left to dry in an oven at 100°C for 12 h. The catalyst was prepared by wet impregnation of the support with appropriate solutions of metal precursors using cobalt nitrate (Co (NO3)2.6H2O) and nickel nitrate (Ni(NO3)2.6H2O) 99.0% purity, dissolved in deionized water with a maximum metal loading of 15% weight percent in both monometallic and bimetallic catalysts. After impregnation step, catalyst aqueous solution was left on magnetic stirring overnight with 600-650 rpm agitation. Water removal of the solution was achieved using rotary evaporator at 55-60°C. Further drying of the catalyst powder was also achieved by means of an oven at 120°C overnight and then calcined at 730°C for 4 h under stream of N2 at a rate of 5°C/min. The AC supported metal catalysts were denoted as 15Co/AC, 15Ni/AC, 10Co5Ni/AC, 7Co7Ni/AC, 10Ni5Co/AC.

Catalyst Characterization
X-ray diffraction (XRD) analysis was employed to provide Information about the average metal sizes, phase compositions, crystalline nature and structure of the supported catalysts. XRD analysis was performed using a Shimadzu diffractometer (model XRD-6000). The specific surface area and pore volume of the catalysts were determined by the Brunauer -Emmett -Teller (BET) method using an N2 adsorption/desorption analyser (Thermo-Finnigan Sorpmatic 1990 series). The catalysts were degassed overnight at 150°C to remove moisture and foreign gases on the surface. Adsorption and desorption process of N2 on the catalyst surfaces were analysed in a vacuum chamber at −196°C. Furthermore, the required reduction temperature to reduce the metal from oxide phase to active phase prior to FTS reaction was determined. This confirmation is required since the metallic phase is the active component for a successful FTS reaction. The reducibility of the catalyst was examined by H2 temperature programmed reduction (H2-TPR) technique. For this analysis, the Thermo Finnegan TPD/R/O 1100 instrument was used. Basically, the TPR measurements were carried out in a conventional flow system condition with a moisture trap connected to a thermal conductivity detector (TCD). Approximately 20 mg of the sample was placed in a quartz tube reactor with internal diameter of (6mm) and then pretreated. Pretreatment step was carried out under the flow of N2 gas (20ml min -1 ) at 150°C for 10 min and the left to cool down to ambient temperature. TPR analysis was performed using mixture of 5% H2/Ar with flow of (25ml min -1 ) from 60-950°C with ramping of 10°C min -1 . Generally, by using this analysis, the reducibility can be monitored by the hydrogen consumption versus temperature increment. Field emission scanning electron microscopy (FESEM) images were recorded on a LEO 1455 VP electron microscope. The FESEM device was fitted with EDX analysis performed by Rayny EDX-720 spectrometer for determination of O, C, N2, Ni and Co. FTS catalytic activity was carried out in a stainless steel fixed bed reactor as shown in Figure 1 (a) and (b). The catalyst was weighed and placed in the middle of the reactor surrounded by glass wool to hold the catalyst and prevent it from moving. After setting up the reaction system, connecting the cold trap and placing the thermocouple in the furnace, the catalyst bed of 1 g was subjected to a flow of 5% of Hydrogen in Argon for 5 h as a pre-treatment stage. The flow rate was maintained at 10 cc/min under a temperature of 630°C to reduce the catalyst from oxide phase to active metal phase prior to FT reaction. Temperature of the reactor was decreased afterwards by reducing the temperature of the furnace to the desired reaction temperature under the flow of hydrogen in order to start the reaction. The flow of hydrogen was then stopped to end the pre-treatment stage and a flow of mixture of reactant gases consists of CO, H2 and N2 was established through the reactor with a flowrate of 10 cc/min and left for 6 h. The mixture gas with a composition of CO: H2:N2 3:6:1, respectively with N2 being employed as internal standard gas to maintain the desirable ratio 2/1 between H2 and CO. The produced hydrocarbon gas was condensed through cool ice traps and collected afterwards by small GC vials using hexane (GC grade) with purity > 98%. The unreacted CO, H2 and N2 gases were collected in sampling gas bags. The collected liquid hydrocarbons and the unreacted tail gases were sent later for offline analysis using GC-FID Shimadzu QP5050A model and GC-TCD Hewlett-Packard 6980A model.

Product Analysis
The total product stream was split into two phases; a gaseous phase and liquid phase. The gaseous phase was analysed with a Hewlett-Packard 6980A model gas chromatograph (GC). The gaseous components were CO, H 2 , N 2 , and CO 2 . The unreacted gaseous components were separated on a Carboxen packed column (support: Carboxen 1000; support size: 80/100 mesh; length/OD: 1.5m×1/8") and analysed by means of a thermal conductivity detector (TCD). The initial temperature of the oven (35°C) was maintained for 5 minutes, after which it was increased to 200°C at the rate of 3°C/min. Once it had reached 200°C, that temperature was maintained for 60 minutes. The total analysis time was 60 minutes. During this period, all the components of interest were eluted. The synthesized hydrocarbon liquid products were identified using alkane and alkene standards n-(C 8 -C 20 ), which was obtained from Sigma Aldrich (USA). The liquid products were quantitatively analysed on a gas chromatography (Shimadzu GC-14B) equipped with HP-5 capillary column (length: 30 m× inner diameter: 0.32 mm × film thickness: 0.25µm) with a flame ionisation detector (FID) operating at 300°C. The 1-bromo hexane was used as internal standard for quantitative analysis. An aliquot of 1µL of sample was injected into the GC column. The injection temperature was 250°C and nitrogen gas was served as the carrier gas. The initial temperature of the oven was set at 40°C and held for 6 min, then ramped to 270°C, at heating rate of 7°C/ min. The performances of the catalysts were evaluated by GC-FID in terms of hydrocarbon selectivity [8].

Mass Balance Calculation
The calculations used to determine the mass balance are similar to those used by research thesis [8][9][10][11][12]. The configuration of the experimental set-up used in this study ( Figure 1) allows setting the inlet volumetric flowrate, from which the outlet flow rate can be calculated. N 2 was used in the reactor feed to serve as an internal standard. As it is an inert gas during the FT reaction, N 2 is present only in the feed stream and in the reactor outlet gas stream. The N 2 balance across the reactor is therefore expressed as: Here, F in = total molar flowrate [mol/min] of the reactor feed; F out = total molar flowrate [mol/min] of the reactor outlet gas stream; X N2,in = molar fraction of nitrogen in the reactor feed; X N2,out = molar fraction of nitrogen in the reactor outlet gas.
The rate of CO conversion can be calculated as follows: Where X CO,in and X CO,out are the CO molar fractions in the reactor feed and outlet gas, respectively.
After introducing expression (3) and (4) in expression (2) and after expressing F in as a function of F out using equation (1), the rate of CO consumption rate was expressed as: In present study, Equation (6), given below, was used to calculate the rate of CO conversion directly, as X CO,in and X N2, in were known from the premixed gas cylinder and X CO, out and X N2, out were derived from the reactor outlet gas analysis. F out was also calculated from the total gas volumetric flow rate at the reactor exit by assuming the ideal gas law.
The CO conversion was calculated as follows: The product selectivity for hydrocarbons S i was calculated for component x i as follows:

Catalyst Characterization
X₋ray diffraction patterns of the synthesized carbon, doped cobalt and nickel mono and bimetallic catalysts (15Ni/AC, 15Co/AC, 10Ni5Co/AC, 10Co5Ni/AC and 7Co7Ni/AC) after calcination at 730°C for 4h are shown in Figures 2 and 3 respectively. The XRD diffractogram of raw carbonized walnut shells and acid treated carbon samples showed an amorphous structure at around 2θ of 25°, which is a typical characteristic of activated carbon [13,14]. The amorphous structure of carbonized walnut shells was drastically increased after acid refluxed and thermal activation treatment, which proves the great effect that has been given on the structure of the carbon after those two steps. Furthermore, it was also found that applying thermal activation after acid treatment has influenced positively the crystallinity of the carbon where, diffraction peaks of carbon at 2θ of 25°, 44°, 64.5°, and 77.5° became narrower after treatment indicating the high crystallinity of the synthesized carbon as shown in Figure 2 (b). A high level of crystallized graphitic structure for carbon material would facilitate the electron transfer between the FT active metal and CO molecule and hence be conducive for the CO activation [15]. Crystallite size was determined by Debye-Scherrer equation and was found decreased after acid treated and thermal activation step. Crystallite size of a and b is 43.8 nm and 14.1 nm, respectively [16]. The monometallic 15Co/AC sample exhibited the pattern of (Co 3 O 4 ) spinel at 2θ values of 36.7° and 74° JCPDS # 00-009-0418 [15,17] as well as the pattern of cobalt oxide phase (CoO) at 2θ of 42.5° and 61.5° JCPDS # 00-001-1227. In addition, 2θ value of 51.5° was noticed to show the pattern of (Ni 2 O 3 ) phase on the sample 15Ni/AC JCPDS # 00-014-0481 [18,19] as well as the pattern of nickel oxide phase (NiO) at 2θ of 76° JCPDS # 01-073-1519. Regarding bimetallic samples 10Ni5Co/AC, 10Co5Ni/AC and 7Co7Ni/AC, diffraction lines of (Co 3 O 4 ) spinel phase and (CoO) phase were identified at 2θ values of 36.7°, 42.5°, 61.5° and 74° on the three bimetallic samples except for the catalyst 10Ni5Co/AC which did not show a peak at the value of 74° because of either well dispersion of the metal precursor into the support or the low loading percent of cobalt precursor in the catalyst [17,20]. Furthermore, the diffraction patterns of (Ni 2 O 3 ) and (NiO) phases were noticed on the three bimetallic catalysts at 2θ of 51.5° and 76°. Bimetallic mixed oxides (NiCo 2 O 4 ) were also detected at 2θ of 36.7°, 44°, 64.5° and 77.5 JCPDS # 00-020-0781 on the three bimetallic catalysts .  The average crystallite size for cobalt-nickel bimetallic catalysts were calculated from the line broadening XRD patterns of the most intense peak intensity using Debye-Scherrer equation as listed in Table 1. The average crystallite size decreased from the highest values of 12.9 nm and 12.6 nm on the monometallic catalyst 15Ni/AC and 15Co/AC, respectively to lower values of 10.7 nm, 9.8 nm and 9.6 nm on the bimetallic catalysts 10Co5Ni/AC, 7Co7Ni/AC and 10Ni5Co/AC, respectively. Results of crystallite sizes indicated a better dispersion of the bimetallic catalysts into the support in comparison to the monometallic catalysts as they possess the smallest crystallite sizes, reflecting the positive effect of incorporating two active metals in a single bimetallic catalyst. Increase of Ni loading from 5 to 7 and 10 wt.% has decreased crystallite size and enhanced dispersion for the bimetallic catalysts 10Co5Ni/AC, 7Co7Ni/AC and 10Ni5Co/AC, respectively. Similar results were also reported by others [21,22] over nickel promoted Fe/SiO 2 catalyst and Fe-Ni bimetallic catalyst, respectively [23]. They reported that the increase of nickel addition improved the dispersion of iron oxides, leading to an increase in BET surface area of bimetallic catalyst and formation of metal oxides with small crystallite sizes. Furthermore, the incorporation of Co and Ni in a bimetallic catalyst has also led to decrease the oxide particle size from 17.5 nm on the monometallic 6Co/SiO 2 and 6Ni/SiO 2 to 14.4 nm and 12.7 nm on the bimetallic 3Co3Ni/SiO 2 and 6Co6Ni/SiO 2 , respectively. Increasing Ni loading to maximum limit of 6 wt.% has decreased crystallite size to smaller value of 12.7 nm [24]. The reducibility of the calcined catalysts was investigated by temperature programmed reduction in hydrogen environment (TPR-H 2 ). Those experiments determine the appropriate reduction temperatures for catalysts in pretreatment step. TPR profiles for mono and bimetallic catalysts were investigated and shown in Figure 4. Catalyst 15Co/AC showed two different hydrogen consumption peaks which appeared at 490°C and 573°C. Those peaks were assigned to the reduction process of cobalt oxide phase Co 3 O 4 (tri-cobalt tetroxide) into active metal phase as follow: Co 3 O 4 → CoO → Co° [25]. Catalyst 15Ni/AC showed two hydrogen consumption peaks as well. The two hydrogen consumption peaks appeared at 296°C and 597°C and were attributed to the reduction of nickel oxide phase Ni 2 O 3 (nickel trioxide) into active metal phase as follow: Ni 2 O 3 → NiO → Ni° [26]. All bimetallic samples 7Co7Ni/AC, 10Ni5Co/AC and 10Co5Ni/AC displayed lower reduction temperatures at 516°C, 524°C and 565°C, respectively, than both cobalt and nickel metals in their monometallic form which were 573°C and 597°C for 15Co/AC and 15Ni/AC, respectively, suggested an easier reduction process [27]. Studies have showed that cobalt has the ability to minimize the reduction temperature when it is incorporated with second metal in a bimetallic system catalyst [28]. The Degree of reduction and the amount of hydrogen consumption during TPR are given in Table 2. Degree of reduction (D r ) is described in Equation (8) and defined as the ratio of consumed hydrogen for the complete reduction of metal oxides to the amount of hydrogen calculated for this complete reduction [29].
Hydrogen consumption and reduction degree for the catalysts are listed in Table 2. The hydrogen intake for monometallic 15Co/AC and 15Ni/AC was found to be 2883 µmol/g and 2483 µmol/g, respectively. The incorporation of the two active metals Co and Ni in the bimetallic catalyst has decreased the hydrogen consumption for all binary catalyst system to values of 1747, 1821 and 1446 µmol/g for the bimetallic catalysts 10Co5Ni/AC, 10Ni5Co/AC and 7Co7Ni/AC, respectively in comparison to monometallic catalysts. The catalyst 7Co7Ni/AC was found to consume the minimum amount of H 2 of 1446 µmol/g. The degree of reduction was 58% and 54% for monometallic catalysts 15Co/AC and 15Ni/AC, respectively. Whereas, the binary system catalysts revealed enhanced reducibility to values of 61%, 67% and 72% for the samples 10Co5Ni/AC, 10Ni5Co/AC and 7Co7Ni/AC, respectively. The enhanced reducibility of the bimetallic 7Co7Ni/AC was believed to be due to the high surface area it possesses (483 m 2 /g), as well as relatively big pore volume (2.7 nm) compared to other catalysts which facilitates the diffusion and adsorption process of the reactant gases on the surface and in the support pores causing a better reaction with the active metal phases. Furthermore, smaller sizes of metal crystallite suggested a well dispersion into the support and easier reduction into active metal phases [15]. In elsewhere study, reduction temperatures have also decreased from 350°C and 570°C on pure iron catalyst to lower values of 250°C and 365°C on Ni promoted iron catalyst, suggested an easier reduction process from Fe 2 O 3 oxide phase to Fe active metal phase, which was provided by the synergistic effect of Ni and Fe active metals. The study mentioned that reduction peaks of the catalysts were shifted to lower temperatures with the increase of nickel content, indicated that the incorporation of nickel improves the catalyst reduction rate in H 2 . The decrease in reduction temperatures was ascribed to the fact that the crystallite size of iron oxides is decreased with the increase of the catalyst nickel content, since iron oxide with little crystallite size can be easily reduced. Furthermore, due to the presence of metallic nickel, the hydrogen spill over from nickel metal to iron oxide might take place and promote the reduction of iron oxide [22]. Similar behaviours from Ni active metal on the reduction temperatures and crystallite size were also reported by Rytter and colleagues [30]. N 2 adsorption-desorption was carried out to characterize the textural properties of the support and the corresponding supported catalysts. As shown in Figure 5 A, the isotherm of activated carbon was found to agree with type I isotherm exhibiting a hysteresis loop type H4 which is according to the IUPAC classification, indicating the characteristic of micro and mesoporous material [15]. It is worth noting that the shape of the impregnated catalyst isotherm in Figure 5 B remained almost identical to that of AC support, suggesting that AC support structure did not suffer any significant change after metal incorporation. The structural parameters of the support and catalysts are listed in Table 3. BET surface area and pore volume measured after carbonization was 381 m 2 /g and 0.259 cm 3 /g, respectively, whereas the carbon exhibited larger surface area and pore volume of 532 m 2 /g and 0.305 cm 3 /g, respectively after acid treatment and calcination. The significant increase in surface area was attributed to the effect of acid which gave a special kind of defect on the carbon surface led to extreme change in the structure morphology and hence improved the surface area of the support [31]. A decrease was noticed in the surface area and pore volume of the support after impregnating metal precursors into the support. This can be attributed to the excess of active metals embedded into the channel of parent materials and incorporated into the pore of AC causing pores blockage [32]. All impregnated and calcined catalysts presented larger pore diameters than their corresponding support which can be attributed to the dispersion of metal oxide particles on the support surface as well as the porous structure of these oxides [15].  Morphological features of the solid catalysts were also studied to investigate the geometry structure, pattern and particles shape on catalysts' solid surface. SEM images for raw carbonized walnut shells, acid treated carbon and FESEM images of calcined 7Co7Ni/AC bimetallic catalysts were characterized and shown in Figure 6. The carbonized sample in Figure 6 (a) exhibited rough block carbon with considerably random and irregular shaped structure morphology, whereas acid treated carbon in Figure 6 (b) revealed a denser, self-assembled, aggregated and softer structure. The defection of the carbon support surface after acid treatment helps as anchoring site for metal particles [31]. FESEM images of the bimetallic catalysts 7Co7Ni/AC showed a uniform distribution of the metal oxides on the support as shown in Figure 6 (c). Particles size and shape were dramatically changed after the fresh walnut shell experienced chemical activation step. It can be observed that the preparation condition has a great influence on morphology of the carbon. Particles diameters for the most abundant ones of both raw carbonized shells and acid treated carbon were observed ranging from (20-40µm) as shown in Figure 6 (a) and (b). whereas, particles diameters have decreased on the calcined impregnated bimetallic catalysts to (1-3µm) as shown in Figure 6 (c). High performance catalysts are usually consisting of high surface area materials. It was shown that particle size, shape and porosity of catalyst are influencing the reaction activity greatly. Therefore, possessing very small particles of highly-ordered porous support would provide a high surface area and large active site for the catalysts, offering a beneficial effect towards the reaction activity [14]. The elemental composition of the prepared catalyst was investigated by EDX analysis as shown in Table 4.

Catalytic Activity and the Effect of Bimetallic Catalyst
In order to investigate the effect of different metal loading ratios of Co-Ni group of catalysts on catalytic activity and product selectivity, five different mono and bimetallic catalysts were prepared with different metal loading ratios. The prepared catalysts were denoted as 15Co/AC, 10Co5Ni/AC, 7Co7Ni/AC, 10Ni5Co/AC and 15Ni/AC. It was revealed from XRD and TPR analysis (Tables 1 and 2, respectively) that the incorporation of Ni and Co active metals into bimetallic catalysts has enhanced the quality of the catalysts in terms of minimizing oxide crystallite size and reducibility of the catalyst. The size of crystallite particle was reduced from 12.9 nm and 12.6 nm on monometallic catalysts 15Ni/AC and 15Co/AC, respectively to 10.7 nm, 9.8 nm and 9.6 nm on the bimetallic catalysts 10Co5Ni/AC, 7Co7Ni/AC and 10Ni5Co/AC, respectively. Indicating better dispersion into the support and hence, improved reducibility of the catalyst [22,33,34]. Reduction degree has decreased in the order of 7Co7Ni/AC (72%) > 10Ni5Co/AC (67%) > 10Co5Ni/AC (61%) > 15Co/AC (58%) > 15Ni/AC (54%). Smaller oxide particles were dispersed easily into the support resulted in better reduction degree and thus enhanced catalytic activity [21,24,30]. Increasing Ni metal loading ratio from 5 wt.% to 7 and 10 wt.% in the Co-Ni bimetallic catalysts has resulted in enhancing BET surface area as follows: 7Co7Ni/AC (483 m 2 /g) > 10Ni5Co/AC (456 m 2 /g) > 10Co5Ni/AC (433 m 2 /g) due to the better dispersion of metal particles which was offered by mixing Co and Ni active metals [35]. The enhanced reducibility of the bimetallic 7Co7Ni/AC was believed to be due to the high surface area the catalyst possesses, as well as relatively big pore volume in comparison to other catalysts which facilitated the diffusion and adsorption process of the reactant gases on the surface and in the support pores causing a better reaction with the active metals phase. Furthermore, smaller sizes of metal crystallite suggested a well dispersion into the support and easier reduction into active metal phase [15]. CO conversion was the maximum on the bimetallic catalyst 7Co7Ni/AC (43%) and decreased in the order of 10Ni5Co/AC (32%) > 10Co5Ni/AC (31%) > 15Co/AC (26%) > 15Ni/AC (24%) as shown in Figure 7. The hydrocarbon liquid product distribution achieved on the mono and bimetallic catalysts Co-Ni is shown in Figure 7. All catalysts revealed hydrocarbon liquid products in the range of C 7 -C 20 . Selectivity to gasoline fraction (C 7 -C 11 ) was highly produced on the bimetallic catalysts compared to monometallic catalysts and decreased in the order of 7Co7Ni/AC (69%) > 10Ni5Co/AC (59%) > 10Co5Ni/AC (40%) > 15Ni/AC (33%) > 15Co/AC (11%).  The effect of metal loading on the production of gasoline has also investigated previously by Ishihara and colleagues on the bimetallic Co-Ni and Co-Fe catalysts supported by SiO2. Increasing Ni loading from 25 wt.% to 50 wt.% has increased gasoline selectivity from 11.5% on monometallic Co/SiO 2 catalyst to 25% on 75Co25Ni/SiO2 catalyst reaching a maximum selectivity of 31.5% on 50Co50Ni/SiO2. CO conversion was also the maximum on the later catalyst. Further increasing of Ni on 25Co75Ni has decreased selectivity to 23%. The combination of Co-Ni catalysts was found better than Co-Fe in terms of gasoline production. The author suggested that there is an optimum composition in every bimetallic system for production of desired hydrocarbons. In his case, the bimetallic catalyst 50Co50Ni/SiO 2 was the most selective for production of gasoline fraction. The author attributed the results to the tendency of the catalyst to the formation of higher hydrocarbons as well as the enhanced activity for CO conversion. Furthermore, alloying of cobalt and nickel in the mentioned catalyst increased both the amount of adsorbed activated hydrogen and carbon monoxide on the surface and thus increased selectivity towards gasoline fraction [36]. Previous studies have shown that nickel active metal has a good ability to catalyse the hydrocracking of hydrocarbons [37]. The inclusion of Ni in the bimetallic catalysts with Co has played an important role in hydrocracking the heavier hydrocarbons and resulted in enrichment of lighter ones. It was found that increasing of Ni from 0.5 wt.% to 2 and 5 wt.% in the bimetallic catalyst with Co has decreased the selectivity towards longer chain products (C21+) and increased the selectivity towards short chain products [38]. It was suggested that there is a balance between the carbonchain growth on the Co active sites and the hydrocracking of heavier hydrocarbons on the Ni active sites, that maximizes the production of lighter liquid hydrocarbons. In other words, there is a degree of Ni metal existence in the catalyst which is required for maximizing the selectivity of the catalyst towards lighter hydrocarbons. In their case, the selectivity for lighter liquid hydrocarbons (C5 -C20) was significantly improved from 39% to the highest value of 62% on the bimetallic catalyst Ni-Co/CNT when the loading of Ni was 5 wt.% [38]. The detailed hydrocarbon liquid products distribution for all prepared catalysts and the achieved CO conversion are listed in Table 5. In recent study, increasing Ni content from 5 to 7 wt.% in the bimetallic catalyst 7Co7Ni/AC resulted in increased number of active sites which implied an increased number of H atoms through a high concentration of adsorbed hydrogen on the surface of the catalyst; eliminating the desorption of olefin accordingly and promoting the hydrogenation process to paraffin via improving olefin re-adsorption, leading to enhanced selectivity for gasoline.

The Effect of Reaction Temperature on CO Conversion and Gasoline Selectivity
The influence of the reaction temperature on the best catalyst 7Co7Ni/AC at reaction temperature range of 220°C ₋ 300°C, reaction pressure of 1 bar and H 2 :CO stoichiometric ratio of 2 was studied. It was found that reaction temperature has a significant effect on the CO conversion and gasoline selectivity. Increasing the reaction temperature from 220°C to 240, 260, 280 and 300°C resulted in increasing the CO conversion from 43% to values of 45%, 49%, 53% and 65%, respectively, as shown in Figure 9. The improved conversion of CO at elevated reaction temperatures was attributed to the high dispersion of metal active phase into the support as metal dispersion has a major influence on activity and selectivity of the catalyst [39]. A decrease in CO conversion was attributed to the low metal dispersion in a previous study [40]. Increasing reaction temperature on the bimetallic catalyst 7Co7Ni/AC resulted in improved selectivity towards short hydrocarbon chain (gasoline fraction) and a decrease towards longer hydrocarbon components (diesel and wax fraction).
Gasoline fraction has increased at all elevated temperatures from 69% at 220°C to values of 73%, 75%, 86% and 92% at temperature values of 240°C, 260°C, 280°C and 300°C, respectively, as shown in the Figure 10. It was mentioned elsewhere, that since the bimetallic catalyst is found to be selective for a desired hydrocarbon product (in this case gasoline), then active sites are favouring the production of gasoline fraction increasingly at higher temperatures [39]. Increasing CO conversion and selectivity to lighter hydrocarbon products by increasing reaction temperature has also reported by other studies [41][42][43]. The chain growth probability equals the ratio of chain propagation reaction rate to the chain termination reaction rate. The reaction with higher activation energy will respond faster to the change of reaction temperature. Therefore, in the case of producing lighter hydrocarbon fraction at increased temperatures, it is believed that the chain termination reaction has the higher activation energy than the chain propagation reaction, resulted in decreasing the chain growth probability [43,44]. Other study has mentioned that high temperature favours chain termination or desorption reactions since desorption is an endothermic process, so increasing temperature shifts selectivity towards lighter hydrocarbons [45]. It was also found that higher temperatures promote CO dissociation and provide more surface C atoms leading to the release of lighter hydrocarbons [46]. Optimal reaction conditions were also investigated on Co based catalyst supported on diamond carbon. Increase of reaction temperature from (240°C -260°C) resulted in increased CO conversion as well as increased selectivity to lighter hydrocarbons. However, selectivity to heavy hydrocarbons were found to decrease with increasing reaction temperature. They attributed results to the tendency of the lower molecular mass compounds to increase with increasing reaction temperature [47].
The effect of reaction temperature on product distribution was also investigated in different study at 250, 260, and 270°C on Co-Ni/CNT. As a result, CO conversion increased with increasing temperature. Because the mobility of the hydrogen atom on the catalyst surface was promoted at higher temperatures resulted in enhanced CO conversion and light hydrocarbon production [31]. The hydrocracking of long chain hydrocarbons on Ni was improved with an increase in temperature, resulting in lighter and less heavy hydrocarbons as mentioned elsewhere. It was reported that, at a proper reaction temperature of 260°C, the carbon chain growing on Co and the cracking on Ni reached a good balance to obtain the highest selectivity of liquid hydrocarbons [38]. Table 6 displays the detailed product distribution and CO conversion on 7Co7Ni/AC bimetallic catalyst.

The Effect of Reaction Pressure on CO Conversion and Gasoline Selectivity
The effect of reaction pressure on the best catalyst 7Co7Ni/AC at the best reaction temperature of 300°C, H 2 :CO stoichiometric ratio of 2 and reaction pressure of 1 -9 bar, in terms of CO conversion and gasoline selectivity was investigated. It was found that the reaction pressure has a great impact on the distribution of hydrocarbon liquid products and CO conversion. Increasing the reaction pressure from 1 bar to elevated values of 3 bar, 5 bar, 7 bar and 9 bar resulted in increased CO conversion from 65% to 67%, 69%, 74% and 84% as shown in Figure 11. On the other hand, increasing the reaction pressure on 7Co7Ni/AC bimetallic catalyst from 1 bar to higher values of 3 bar, 5 bar, 7 bar and 9 bar, resulted in a decrease in light hydrocarbon products (gasoline fraction) from 92% to 86%, 63%, 42% and 36%, respectively and an increase in the heavier hydrocarbons (wax fraction) from 6% to 12%, 36%, 55% and 62%, respectively, as shown in Figure 12. Similar behaviour was also reported by a group of researchers, when they mentioned an increase in both CO conversion and selectivity towards heavier hydrocarbon products at 350°C, H 2 /CO = 2/1 and pressure range 1 -10 bar. CO conversion was found increasing greatly from 70% to 90% through increased reaction pressure. However, the increase in liquid hydrocarbon products selectivity was limited only from 5% to 20% [41]. In elsewhere study, it was reported that the increase of reaction pressure from 5 to 20 bar resulted in increased CO conversion from 23% to 47% as well as increased heavy hydrocarbon selectivity from 10% to 80%. The author has ascribed the increase in CO conversion to the increase in partial pressure of hydrogen, since the rate of the reaction was reported to be proportional to the partial pressure of hydrogen [47]. It was suggested that higher pressure is preferred for the chain growth because increasing the pressure improves the diffusion probabilities of reactants into active metals on catalyst surface facilitating the production of long chain hydrocarbons [46]. The detailed hydrocarbon product distribution and CO conversion for 7Co7Ni/AC bimetallic catalyst is listed in Table 7. Table 7. The product distribution and CO Conversion for 7Co7Ni/AC bimetallic catalysts at reaction pressure (1 -9 bar), reaction temperature = (300°C), H2/CO stoichiometric ratio = (2).


extfieldsofstudy: Medicine Chemistry

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Synthesis of benzo[4,5]imidazo[1,2-a]pyrimidines and 2,3-dihydroquinazolin-4(1H)-ones under metal-free and solvent-free conditions for minimizing waste generation

Brønsted acidic ionic liquid was found to be an efficient and recyclable catalyst for the synthesis of benzo[4,5]imidazo[1,2-a]pyrimidines and 2,3-dihydroquinazolin-4(1H)-ones. The reactions proceeded smoothly with a broad scope of substrates providing the expected products in good to excellent yields under an atom-economical pathway. The low-cost recyclable catalyst, metal- and solvent-free conditions, and the ease of product isolation are the highlighted advantages in solving the issue of trace metal contamination in synthesized pharmaceuticals.

replacing ZnCl 2 with ethylene glycol, oxalic acid or glucose (  Table 1, metal chlorides could be employed in the model reaction. However, these metal chlorides are moisture sensitive and easily decompose in the presence of a small amount of water. Furthermore, these metal chlorides could not be recovered and reused aer the aqueous workup. The prominent feature of [(4-SO 3 H)BMIM]HSO 4 is stability in water and in almost all cases, catalytic use, recovery, and reuse of [(4-SO 3 H)BMIM]HSO 4 are possible. In our study, we tested different ratios of [(4-SO 3 H)BMIM]HSO 4 with the aim of obtaining the desired product in high yield. The best yield was obtained in the presence of 10 mol% [(4-SO 3 H)BMIM]HSO 4 . When the amount of catalyst was increased to 15 mol%, the yield slightly improved. All attempts to reduce the amount of [(4-SO 3 H)BMIM]HSO 4 loading led to diminished yields.
With the optimized conditions in hand, we focused on using a variety of aromatic aldehydes. The results are presented in Scheme 1. A series of p-substituted benzaldehyde smoothly reacted with 2-aminobenzimidazole and ethyl acetoacetate to provide benzo [4,5]imidazo[1,2-a]pyrimidines in excellent yields. Electron-donating group on benzaldehyde, such as methoxybenzaldehyde, gave the expected products in excellent yield (90%), but longer reaction time was required (150 min). Electron-withdrawing groups on benzaldehyde, such as 2nitrobenzaldehyde and 4-nitrobenzaldehyde, were all effectively reactive and provided the corresponding products in excellent yields within shorter reaction times (75 min). Benzaldehyde bearing halogen substituents could also react smoothly to attain the expected products in high yields (88-92%), and the substituent position did not signicantly affect the reaction yield. In the interest of extending the scope of reaction, we examined acetonylacetone under the current method. To our delight, the reactions proceed smoothly to provide the desired products in high yields (88-93%) and shorter reaction times (60-90 min). Moreover, the method avoids the use of volatile organic solvents, harmful conditions, and additives making it greener than the previous reports. In addition, the pure products were obtained easily by recrystallization; thus, this method shows promise for application in industry.
The comparison of the present method with previously reported literature is shown in Table 2. The multicomponent reaction of 2-aminobenzimidazole, ethyl acetoacetate, and benzaldehyde in the presence of [(4-SO 3 H)BMIM]HSO 4 afforded the expected product in good yield within 2 h under metal-free and solvent-free conditions ( Table 2, entry 6). The previous studies reported that the same reaction also achieved the target products in moderate to good yields, but some methods required longer reaction time and/or high temperature ( Table 2, entries, 1-5). More importantly, the [(4-SO 3 H)BMIM]HSO 4 could be recovered and reused without any considerable loss in catalytic activity in a test of six cycles of reuse.
To get more information about the plausible mechanism, we performed three control experiments under the optimized conditions. 2-Benzylidene-3-oxobutanoate intermediate was detected as the major product (by GC-MS) when the model reaction was performed for 20 min. Thus, the reaction mainly followed Knoevenagel condensation between benzaldehyde and ethyl acetoacetate in the rst step, where the [(4-SO 3 H)BMIM] HSO 4 acted as a Brønsted acidic catalyst. The control experiments with only two components, i.e., the reaction of benzaldehyde with ethyl acetoacetate, also provided the desired product (2-benzylidene-3-oxobutanoate) in 70% yield, which was higher than the reaction yield of 2-aminobenzimidazole and benzaldehyde. The intermediate could completely convert into the desired product when 2-aminobenzimidazole was added. Based on the above control experiments and previous literature, the plausible mechanism is proposed, as presented in Scheme 2. The reaction rst followed Knoevenagel condensation between benzaldehyde and ethyl acetoacetate to produce 2-benzylidene-3-oxobutanoate as intermediate, which could further react with 2-aminobenzimidazole through Michael reaction. Finally, the desired product was obtained through cyclization and dehydration. The [(4-SO 3 H)BMIM]HSO 4 played a vital role in four steps of the reaction.

presence of [(4-SO 3 H)BMIM]HSO 4 (Scheme 3)
. The substituent on the benzene did not affect the reaction rate. In addition, pure products were obtained easily by recrystallization. Remarkably, the condensation between anthranilamide and benzaldehyde was successfully performed on a 50 mmol scale, and the expected yield was obtained equally with 1 mmol scale. This method avoids the use of volatile organic solvents, harmful conditions, and metal catalysts, thus showing promise for application in industrial processes. The comparison of the presented method with previously reported literature is shown in Table 3. The reaction of anthranilamide and benzaldehyde in the presence of [(4-SO 3 H) Table 2 Comparison of the present method with previous literature for the one-pot multicomponent synthesis of benzo [4,5] Table 3, entry 6). The previous studies reported that the same reaction also achieved the target products in good to excellent yields, but some methods required the use of a solvent, high temperature and/or longer reaction time (Table  3,  Recyclability of the catalyst is a necessary feature for theapplication in industrial processes. The recyclability of [(4-SO 3 H)BMIM]HSO 4 in this study was investigated in the model reactions with benzaldehyde under optimal conditions (Fig. 1). Aer completion of the reaction, the reaction mixture was diluted with ethanol, and the desired product was obtained by ltration. The catalyst was recovered, dried under vacuum for 6 h and reused for next cycles. The yields of the product slightly decreased aer the sixth cycle. The FT-IR spectra conrmed that there was no change in functional groups of [(4-SO 3 H)BMIM]HSO 4 (Fig. 2).
Scheme 3 Reaction scope with aldehydes.

Conclusions
We have developed a facile, efficient, and atom-economic method for preparing benzo [4,5]imidazo[1,2-a]pyrimidines and 2,3-dihydroquinazolin-4(1H)-ones under metal-and solvent-free conditions. The presented method provides a straightforward and green approach for the preparation of biologically nitrogen-heterocyclic compounds from the readily available starting materials in good to excellent yields within short reaction time. Remarkably, [(4-SO 3 H)BMIM]HSO 4 Brønsted acidic ionic liquid showed to be particularly suitable for these transformations as highlighted by its environmental friendliness, low-cost, recyclability, and simplicity of operation.

Materials and instrumentation
All the reagents were purchased from Merck, Acros, and Sigma-Aldrich and used without further purication. Ultrasonic irradiation was performed in Elma sonic S30H. FT-IR spectra were analyzed using a Bruker Vertex 70 (KBr pellets). Thermogravimetric analysis was performed on Analyzer TGA Q5000. 1 H and 13 C NMR spectra were performed on a Bruker Advance 500.

Synthesis of [(4-SO 3 H)BMIM]HSO 4 under solvent-free sonication
[(4-SO 3 H)BMIM]HSO 4 was synthesized via a one-pot two-step procedure under solvent-free sonication according to our previous literature report. 22 General procedure for the synthesis of benzo [4,5]    Aer completion of the conversion, the reaction mixture was quenched with cold ethanol (10 mL). The crude product was ltered and washed with petroleum ether (10 mL), and then puried by recrystallization from ethanol to obtain the desired pure product.
General procedure for the synthesis 2,3-dihydroquinazolin-4(1H)-one A mixture of anthranilamide (136 mg, 1 mmol), benzaldehyde (106 mg, 1 mmol), and [(4-SO 3 H)BMIM]HSO 4 (31.6 mg, 0.1 mmol) was sonicated for 30 min at room temperature and the progress of the reaction was monitored by TLC or GC-MS. Aer completion of the conversion, the reaction mixture was quenched with ethanol (10 mL). The crude product was ltered and washed with petroleum ether (2 Â 5 mL), and then puried by recrystallization from ethanol to afford the desired pure product.

Conflicts of interest
There are no conicts to declare.