extfieldsofstudy:
Computer Science
Medicine

text:
A novel scoring schema for peptide identification by searching protein sequence databases using tandem mass spectrometry data

Background Tandem mass spectrometry (MS/MS) is a powerful tool for protein identification. Although great efforts have been made in scoring the correlation between tandem mass spectra and an amino acid sequence database, improvements could be made in three aspects, including characterization ofpeaks in spectra, adoption of effective scoring functions and access to thereliability of matching between peptides and spectra. Results A novel scoring function is presented, along with criteria to estimate the performance confidence of the function. Through learning the typesof product ions and the probability of generating them, a hypothetic spectrum was generated for each candidate peptide. Then relative entropy was introduced to measure the similarity between the hypothetic and the observed spectra. Based on the extreme value distribution (EVD) theory, a threshold was chosen to distinguish a true peptide assignment from a random one. Tests on a public MS/MS dataset demonstrated that this method performs better than the well-known SEQUEST. Conclusion A reliable identification of proteins from the spectra promises a more efficient application of tandem mass spectrometry to proteomes with high complexity.

Background
A major goal of proteomics is to study biological processes comprehensivelythrough the identification, characterization, and quantification of expressed protein in a cell or a tissue. Recently, tandem mass spectrometry has been shown to be a powerful tool for sensitive high-throughput identification of proteins [1,2]. Following enzymatic digestion of proteins, the resulting peptides are separated in the mass analyzer according to their mass to charge ratio (m/z-value). Peptides with the same m/z value are mostly brokeninto two fragments at a single peptide bond, forming N-terminal ions, i.e. a, b or c-ion and C-terminal ions, i.e., x, y or z-ion. All ions are then detected to generate a MS/MS spectrum (See Figure 1). In a mass spectrum, the intensity of a peak is generally proportional to the frequency of ions with the corresponding m/z-value [3].
A number of factors complicate the interpretation of MS/ MS data. Neither the peaks corresponding to the a/b/c ver-sus the x/y/z ions, nor the charge states of the ions are known. Some product fragments may be absent because either the fragments are in a neutral state or the precursorion hardly breaks down into these products; and unexplainable peaks may appear as the result of contamination or rare fragmentation styles. In addition, some peaks deviate from their expected positions because the corresponding ions contain isotopic atoms or have lost a chemical group. Consequently, effective identification of proteins remains a challenge [4].
Existing methods for interpreting mass spectrum data can becategorized into two types: database searching methods and 'de novo' approaches independent of databases. Most database searching methods start with construction of a hypothetic spectrum for each peptide derived from a protein database, followed by comparing hypothetic and experimental spectra. The peptides with the highest score are reported as potential solutions. Employing this strategy, many systems have proven fairly successful [3,5,6].
An effective scoring function for evaluating matches between experimental spectrum and candidate peptide is a key issue in the interpretation of a mass spectrum. Most scoring functions, such as SEQUEST [5] and Sonar http:// 65.219.84.5/ProteinId.html are based on shared peaks. pvalue is an alternative way to evaluate the probability of recognizing a set of fragments in a protein database, as implemented in MOWSE http://www.hgmp.mrc.ac.uk/ Bioinformatics/Webapp/mowse/, Mascot [7] and ProbID [8]. Characterizing ion types and their probabilities, Dancik et al [9] proposed a likelihood-based approach, which was generalized in SCOPE [10] to involve more prior knowledge. An extension of Dancik's scoring approach into an intensity-based statistical scorer incorporated a variety of experimental observations and prior knowledge on peptide fragmentation [11]. ProbID [8], a method based on a probabilistic model, adopted a Bayesian approach to interpret mass spectra data.
Random matching between experimental and theoretical masses may bring aboutfalse-positive results, giving rise to another key problem of peptide identification---the criteria for evaluation of the reliability of the matching. The difference between the highest and second highest scores [5] and p-value [7] estimate are used to filter false positives. Jan Eriksson [12] and Keller [13] built a model to work out the distribution ofscores from random matches, which allowed significance testing undergeneral database searching constraints. Therefore, filtering criteria to distinguish a valid match from all matches should be developed towards being dependent on quantitative estimates rather than on experience.
The present paper aims to tackle the two problems mentioned above.
1. We introduce a new, effective probabilistic scoring function. Adopting astatistical model similar to Dancik et al [8], we have employedrelative entropy (i.e., K-L distance) to measure the similarity between hypothetic and experimentally observed spectra. We present a brief proof toshow that relative entropy is indeed the simplified form of the conditionalprobability that the spectrum is generated from the peptide.
2. We present an EVD-based criterion to distinguish valid matches from random ones. Each spectrum will acquire the best score from correlation with all candidate peptides. Such best scores conform to the extreme value distribution, which underlies the quantitative threshold of the significance test.
A system written in C, the Protein Identifier (PI), was implemented adopting the relative entropy scoring function and tested on real data. Tests showed that it performed better than a widely used cross-correlation scoring approach [5]. For the sake of convenience, we have made the program available on our website PI http://www.bio info.org.cn/MSMS/papers/supp1/.

Data set
A publicly available spectrum set from Keller et al [14] was used to test our algorithm. It contains spectra generated by digesting two 18-proteins mixtures with trypsin. To assign a candidate peptide to each spectrum, Keller et al [14] ran SEQUEST against a human peptide database, obtaining 37,044 pairs of peptide and spectrum [14]. Among these assignments, 125 assignments of peptides with a single charge, 1,656 with double charges and 984 with triple Schematic figure of peptide fragmentation Figure 1 Schematic figure of peptide fragmentation. A peptide breaks into two parts, induced by the collisional gas. Breakage favors the peptide backbone, resulting in N-terminal a/b/ c ions and C-terminal x/y/z/ions; small chemical groups such as water and amino molecules often dissociate from specific residues.
charges were confirmed manually. For thefollowing two reasons, the original dataset above was subjected to a filtering procedure for refinement. First, because the enzyme had been slightly contaminated (private communication with A. Keller who published the data), peptides with abnormal termini were screened out. Second, the spectra from triply charged ions were filtered out because their fragmentation pattern is not yet fully understood. As for partial proteolysis, peptides with up to 5 trypsin cleavage sites were kept for database searching. The filtering procedure produced a refined query set containing 19,000 spectra, and 1,247 correct peptide assignments. Thereafter the 1,247 correct assignments was divided into a training set derived from 2 proteins and a test set derived from the other proteins for cross validation. The training set was used to study the characteristics of the mass spectrometry while the testing set was to test the performance of our algorithm. (Available at the website PI http://www.bio info.org.cn/MSMS/papers/supp1/).

Description of algorithm
In order to describe our statistical model, a set of terms similar to Dancik et al [9] and Vineet Bafna et al [10] were defined as follows: Let A be the set of amino acids, each amino acid having a molecular mass m(a), a ∈ A. A peptide is denoted as a sequence of amino acids P = p 1 p 2 ...p n with mass , p i ∈ A. Dissociation at the i-th peptide bond forms two partial peptides, the N-terminal peptide Fragmentation in mass spectrometry is a stochastic process governed by the collision dynamics and the physicochemical properties of a peptide [10]. The fragmentation of a set of the peptide in spectrometry was modeled by a random "ball tossing" trial, each trial tossing an N-terminal ion and/or a C-terminal ion in some bins. Thus, after a series of trials, the number of ions in a bin will be proportional to the intensity of the corresponding peak. The fragmentation of the different peptides, i.e., tossing of dif-ferent balls, is assumed to be mutually independent. Let random variable C be the cleavage site, and Pr(C = i,P) be the probability of cleavage at the i-th bond of peptide P. Let Pr(δ i ) be the probability that a breakage generates an i-type ion in a trial. Since fragmentation is residue dependent [15], we can adopt a more accurate definition, the conditional probability Pr(δ i |p j ), where p j is the amino acid adjacent to the cleavage site. As mass spectrometry may also generate peaksdue to merely "random noise", Pr(R) is introduced to denote the probability of such event. For the sake of simplicity, only N-terminal ions are studied here; the considerations would have been similar for C-terminal ions.

Parameter learning and hypothetic spectrum generating
To estimate the above peptide and instrument specific parameters, a learning procedure was modeled according to the offset frequency function [9], with improvements added to take account of intensity. For a set of experimental spectra with known peptide sequences thereof, let S = {I 1 ,I 2 ,...I N } be a spectrum corresponding to the peptide P, ε be the m/z precision level of the instrument, N(S,P i ,δ j ) be the sum of the peak intensities within a distance ε from Fragmentation tends to occur more frequently in the middle than at ends of a peptide. J. Simpson [16] represented the relationship between peak's intensity and cleavage sites as a function of the relative position of a breakpoint, ranging from 0 to 1, whereas David L. Tabb [17] adopted a function based on relative mass of the partial peptide.
Here, we used Tabb's approach to work out Pr(C = i,P).
Utilizing the above probability, we produced a hypothetic spectrum for a given peptide through simulating its dissociation in thespectrometry. A set of the peptide fragments at the i-th peptide bond with the probability Pr(C = i,P), and generates the j-type ion with probability Pr(δ j ).
Hence, peaks with mass m(P i ) -δ j were assigned with relative intensity Pr(C = i,P)*Pr(δ j ), and other peaks were assigned with Pr(R) to simulate random noises.

Relative entropy based scoring function
Before comparison to the hypothetic spectrum, an experimental spectrum was pre-processed by dividing the intensity of each peak by the total ion intensity and filling blank bins with the value Pr(R) to simulate random noise. Hence, both the experimental and hypothetic spectra were transformed into distributions of ions over N bins, and relative entropy was employed to measure the similarity between them.
Relative entropy is a pertinent statistical tool to measure distance between two distributions [18]. The relative entropy measure is essentially a simplified form of the likelihood Pr(S|P). Provided that q i is the probability that an ion will be tossed into the i-th bin, and I i the number of ions tossed into this bin after tosses, the count vector I = <I 1 ,I 2 ,...I N > conforms to a multi-nominal distribution. Hence, Pr(S|P) can be computed as follows: . For a spectrum, I i corresponds to the observed intensity of a peak with m/z-value i, where I is the vector form of a spectrum. Using Stirling's Formula, we can rewrite the above expression as follows: Hence ln[Pr(S|P)] ≅ lnK + I total lnI total -H(I,Q), where C denotes combination and embodies a characteristic of the spectrum. The above formula means that fora specific peptide, the smaller the relative entropy, the more likely that the spectrum was generated by that particular peptide.

Threshold settings based on extreme value distribution
A score matrix was produced after comparing a set of spectra S = {S 1 ,S 2 ...S m } with a set of candidate peptides C = {C 1 ,C 2 ...C n } (Table. 1).
For a specific spectrum S i , the peptide with the minimal score was thought to be the one most likely to have generated it. The minimal score of a row, recorded in the "min" column, conforms to the extreme value distribution (EVD). In fact, the distribution of items in the min column is the superposition of two EVD distributions because of the following reason. Dividing the spectra in S into two classes, i.e. S = D ∪ E, where D includes those that were generated from a peptidein C, and E the rest. Thus, the match of a spectrum with a peptide in E can occur only by chance, and therefore results in a minimal score higher than the matches with peptides in D. The gap between matches of the twopeptide sets, along with the robustness of EVD, forms a foundation to distinguish valid matches from random ones. More important, EVD allows us to quantitatively estimate the confidence for a given criterion. It should be noted that since the score of the peptides depends on the mass of the precursor ions, so does the threshold.

Result and discussion
We performed the parameter learning, the hypothetic spectrum generation andthe search procedure on the chosen dataset as described above (See Methods section)

Parameter learning
For each round of test, the frequency of different ions were learnt from a training assignment comprising peptides from two to four proteins(generally less than 300 peptides assignments), including non-peptide bond backbone cleavage, isotope forms, and neutral loss ( Figure 2). Here, the precision of an ion-trap mass spectrometry was set to 0.5 Dalton. It could beobserved that b, a, b -H 2 O, b -NH 3 , appear more frequently than other product ions, and isotope effect was also dominant. In Figure 3, the relationship between cleavage probability and relative position is shown as a bell-like curve. A conclusion could be made that cleavage tends to occur more frequently at middle than in the ends. The above observation coincides with the characteristic of fragmentation [11]. When the learning procedure was performed on different training sets, no obvious differences were observed for the learnt parameters.

Hypothetic spectrum generation
To visualize the similarity and difference between hypothetic and experimental spectra, the hypothetic spectrum for a peptide VGDANPALQK was generated using the learnt parameters (shown in Figure 4 along with the experimental spectrum). As can be seen from the comparison, the hypothetic spectrum matches the experimental at important ions with respect to relative abundance, including serial ions b, y, andtheir isotope and group-loss forms (for the clarity of the figure, only b and y ion had been showed).
It should be pointed out that for the positions without hypothetic peaks, random noise learnt from training set was assigned to it. In fact, noise was an intrinsic feature for ion-trap mass spectrometry [19].
However, for some ions, there exists an obvious difference in the intensities between the two spectra. The main reasons for these discrepancies are the residues dependent cleavage and the complicated mobility patterns of the proton. To improve the accuracy of the learning procedure advanced learning techniques, such as EM methods, should be introduced in future work.

Distribution of score and threshold setting
For each spectrum in a training set, similarity with all candidate peptidesare measured. The distribution of the best similarity gives a plot conforming to the extreme value distribution ( Figure 5). For spectraderived from peptides with no counterpart in the candidate set, the score values are all generated by chance, and one peak with higher score is observed in diagram ( Figure 5, dash line). However, for spectra that are derived from peptides with a counterpart in the candidate set, the likely correct matches between spectra and candidate peptides form a peak with lower score. Therefore, two peaks will be observed---one corresponding to the random matches, and the other to the likely correct matches, the latter with lower score values ( Figure 5, solid curve). From Figure 5, it is clear that there is a distinct distance between the two peaks, which provides a basis for setting a threshold value to effectively distinguish correct matches from random ones. In our experiment, the threshold was set to 3.8, i.e., the location of the cross-point of the two distributions, which minimizes the sum of false-positives and false-negatives. Therefore, if a match had a score less than 3.8, it was considered highly probable that the spectrum was produced Breakage preference in the position along peptides Figure 3 Breakage preference in the position along peptides. Breakage tends to occur at middle rather than terminal positions of the peptides. The Abscissa denotes the position of residues relative to the whole sequence. The relative probability is normalized to a total of 100.0. Occurrence probability of different N-terminal ions Figure 2 Occurrence probability of different N-terminal ions.
In general, serial ions bear a larger probability, however, their variants (like dehydrated form) also often appear. The m/z of the peptide bond is set to 0, and the relative probability is normalized to a total probability of 100.0 (including C-terminal ions, which are not shown).
as result of the matched peptide. Threshold for three different training sets were 3.81, 3.66 and 4.09, respectively, that suggested the relative robustness of the threshold setting.

Performance of the score function
Searching the query spectra against the same human peptide database as Keller et al [14] had been performed three times by selecting different proteins as testing sets. Take the first round as an example (Thefirst two proteins in the mixture were composed of the training set and therest test set). Our program PI reported 1,053 potential assignments, having 801 assignments overlap with the test set. For the 252 assignments inconsistent with the test set, manual inspection showed that 171 of them were correct. The difference in performance arose from SEQUEST neglecting some correct assignment, as reported by Andrew. Finally, PI achieved an average sensitivity of 0.87 and an average error rate of 0.07. Table 2 shows the comparison between the performances of SEQUEST and PI. As for the stability of our program, when PI was tested on a different dataset from OPD [20] it still displayed a sound performance (sensitivity 0.89 and error rate 0.06). It is interesting that performance of PI on OPD dataset was better than that on Keller's mixture dataset although the parameter training was done on the latter. This may be attributed to the different quality of these datasets. All the assignments are presented on our website at PI http:// www.bioinfo.org.cn/MSMS/papers/supp1/.

Comparison of score functions
Roughly speaking, factors influencing the dissociation of peptides come in two categories. One is the chemical property of a peptide, and the other isthe collision energy. In recent years, much effort has been made to unravelthe fragmenting mechanism [15,[21][22][23][24][25][26]. Based on of theoretical computation, experimental studies, and statistical analyses, models for prediction of product ions have become increasingly sophisticated and accurate, taking into consideration the chemistry of the ion rearrangements, gas-phase basicity of the individual residues, and the location effect. However, there is apparently still a way to go before the fragmentation mechanism is completely understood. Therefore, due to the incomplete understandingof the fragmentation chemistry, analyzers still have to rely on statisticalapproaches. A probabilistic formulation was originally brought forth by Dancik [9] and provoked several subsequent studies [10,27]. However, the principle of "a premium for present ions" and "a penalty for non-presentions" [9] aims only at the hypothetic but not at the experimentalspectrum. In other words, that formulation concentrates on what should appear in a hypothetic spectrum and neglects what emerges in the observed spectrum. Hence the formulation has the substantial defect that there is no penalty for emergence of unexplainable ions in an experimental spectrum. Our sys-Distribution of minimal score values of MS/MS spectra Figure 5 Distribution of minimal score values of MS/MS spectra. The score of a spectrum is the best match to the peptide library. The solidcurve results from correlating a set of MS/MS spectra with a peptide library containing the query peptide, while the dashed curve results from matcheswith a library not containing the query at all (i.e. all matches are random). Threshold is set to be the location of the cross-point of the two distributions, which minimizes the sum of falsepositives and false-negatives. Therefore, if a match had a score less than 3.8, it was considered highly probable that the spectrum was produced as result of the matched peptide.
The hypothetic and experimental spectra of the sequence VGDANPALQK Figure 4 The hypothetic and experimental spectra of the sequence VGDANPALQK. The serial ions are marked with the observed intensity.
tem describes the correlation between theoretical and experimental spectra in a bi-directional manner, taking account of non-coherence in a homogeneous way.

Conclusion
In this paper we present a novel method for correlating peptides with tandem mass spectra. The collision-induced dissociation is analyzed as a random event and occurrence probabilities for characteristic ions are calculated. By using these parameters, a statistic model to predict the theoretical spectra for peptides is built. Based on the hypothetical spectra, the relative entropy is presented to correlate the experimental spectra with the hypothetical ones. Then it is pointed out that the score of spectra obeysthe superposition of two extreme value distributions, which allows the quantitative estimate for the confidence of peptide assignments. Computational experiment was done on two public databases and it showed that the performance of the present method was is superior in comparison with SEQUEST.


3-( 4 , 4 , 5 , 5-Tetramethyl-1 , 3 , 2-dioxaborolan-2-yl ) aniline

In the course of our studies (Giles et al., 2003; Coghlan et al., 2005) into the potential catalytic utility of bifunctional compounds (Rowlands, 2001) containing both a nitrogenbased Lewis base and a boron-based Lewis acid, we turned to the title compound, (I), as a protected precursor for the synthesis of phenylguanidine-2-boronic acid derivatives, which we were interested in as bifunctional catalysts. Unfortunately, synthesis of such compounds proved unsuccessful, producing a complex mixture of products.

In the title compound, C 12 H 18 BNO 2 , the amino group is less pyramidal than in aniline, only one of its H atoms forming a strong hydrogen bond.

Comment
In the course of our studies (Giles et al., 2003;Coghlan et al., 2005) into the potential catalytic utility of bifunctional compounds (Rowlands, 2001) containing both a nitrogenbased Lewis base and a boron-based Lewis acid, we turned to the title compound, (I), as a protected precursor for the synthesis of phenylguanidine-2-boronic acid derivatives, which we were interested in as bifunctional catalysts. Unfortunately, synthesis of such compounds proved unsuccessful, producing a complex mixture of products.
Compound (I) was prepared by a modified version of the procedure reported by Vogels et al. (1999), who synthesized it en route to various platinum complexes and imines (Vogels et al., 2001;King et al., 2002).
The asymmetric unit contains one molecule. The B atom has planar-trigonal coordination; the coordination plane is inclined by 10.4 (2) to the benzene ring plane. The borolane ring adopts a twist conformation, the C7 and C8 atoms deviating from the BO 2 plane by 0.20 (2) and 0.27 (2) Å in opposite directions, with two equatorial (C9 and C11) and two axial (C10 and C12) methyl substituents. The amino group forms one strong intermolecular hydrogen bond ( Table 2). The remaining amino hydrogen atom, H2N, points towards the p orbital of the benzene C4 atom of another molecule. The H2NÁ Á ÁC4 ii distance [2.61 (2) Å , corrected for the idealized N-H bond length of 1.01 Å ; symmetry code: (ii) 1 À x, 1 2 + y, 1 2 À z], which is considerably shorter than the sum of van der Waals radii of 2.88 Å (Rowland & Taylor, 1996) and the N-HÁ Á ÁC angle of 167 (2) suggest that this contact is a weak hydrogen bond. The N atom in (I) has a less pyramidal geometry than in unsubstituted aniline. The dihedral angle between the benzene ring and the NH 2 group (so-called 'inversion angle'), which equals 37-38 in both solid (Fukuyo et al., 1982) and gaseous (Lister et al., 1974) aniline, is reduced to 16 (2) in (I). The C1-N bond in (I) [1.3790 (18) Å ] is shorter than in aniline [solid: 1.392 (6) Å ; gas: 1.402 (2) Å ]. Both differences indicate that the boryl substituent enhances the interaction of the electron lone pair of N with the aromatic ring and hence sp 2 hybridization of the N atom. It is noteworthy that, in the two complexes of Pd and Pt where molecule (I) acts as an N-ligand (Vogels et al., 1999), the C-N bond is lengthened to 1.438 (4) and 1.45 (1) Å , respectively, as the -conjugation is disrupted, the lone pair being donated to the metal atom instead.
In (I), the N atom deviates by 0.081 (2) Å from the benzene ring plane, but its H atoms are situated on the other side of this plane, 0.04 (2) Å from it. A similar, but stronger, distortion is shown by the aniline molecule in its crystal structure, where the amino group both donates and accepts a hydrogen bond.
Amino H atoms were refined in an isotropic approximation, giving N-H distances of 0.92 (2) and 0.85 (2) Å . Phenyl H atoms were treated as riding in idealized positions with C-H bond lengths of 0.95 Å and U iso (H) = 1.2U eq (C). Methyl groups were refined as rigid bodies rotating around the C-C bonds, with C-H bond lengths of 0.98 Å and a common refined U iso (H) for all three H atoms of each methyl group.

sup-2
Acta Cryst. (2006). E62, o466-o468 Geometry. All e.s.d.'s (except the e.s.d. in the dihedral angle between two l.s. planes) are estimated using the full covariance matrix. The cell e.s.d.'s are taken into account individually in the estimation of e.s.d.'s in distances, angles and torsion angles; correlations between e.s.d.'s in cell parameters are only used when they are defined by crystal symmetry. An approximate (isotropic) treatment of cell e.s.d.'s is used for estimating e.s.d.'s involving l.s. planes. Refinement. Refinement of F 2 against ALL reflections. The weighted R-factor wR and goodness of fit S are based on F 2 , conventional R-factors R are based on F, with F set to zero for negative F 2 . The threshold expression of F 2 > σ(F 2 ) is used only for calculating R-factors(gt) etc. and is not relevant to the choice of reflections for refinement.


extfieldsofstudy: Chemistry Medicine

text:
Full Paper Microwave Assisted Synthesis of Molybdenum and Tungsten Tetracarbonyl Complexes with a Pyrazolylpyridine Ligand.

We report the one-step syntheses in good yields of the complexes cis-[M(CO)4(pzpy)] {M = Mo, W; pzpy = ethyl[3-(2-pyridyl)-1-pyrazolyl]acetate} directly from the corresponding M(CO)6 starting materials by using microwave-assisted heating and reaction times of either 30 s (M = Mo) or 15 min (M = W). The structure of the molybdenum tetracarbonyl complex was determined by single crystal X-ray diffraction. The compound is monomeric and the molybdenum atom has a highly distorted octahedral geometry. The close packing of the individual cis-[Mo(CO)4(pzpy)] species is essentially driven by the need to fill the space effectively, closely mediated by weak C-H-O and pi-pi interactions.

Introduction
Transition metal carbonyl compounds continue to be investigated for applications in various fields, particularly catalysis [1][2][3][4], non-linear optics [5][6][7] and medicine [8][9][10]. The complexes may be homoleptic, such as molybdenum hexacarbonyl [Mo(CO) 6 ], or may contain a combination of ligands, such as [Re(CO) 3 (2,2'-bipyridine)Cl]. They have been investigated as anti-cancer agents [11], biological tracer agents [12,13] and carbon monoxide-releasing molecules [14]. Several catalytic applications are also known. For example, [Mo(CO) 6 ] is used as a precursor to a Mo VI catalyst in the commercial process for the epoxidation of propylene by tert-butyl hydroperoxide [15]. Tetracarbonyl metal complexes of the type cis-[M(CO) 4 (L) n ] (M = Cr, Mo, W or Re), bearing monodentate or bidentate organic donor ligands (L), are also potentially interesting as catalyst precursors [2]. These compounds are usually prepared directly from the parent homoleptic metal carbonyls (by thermal or photochemical activation) or by treating cis-[M(CO) 4 (pip) 2 ] (pip = piperidine) with a refluxing solution of the ligand L in an organic solvent. Hogarth and co-workers showed that the important synthon, cis-[Mo(CO) 4 (pip) 2 ], could be prepared in 80-90% yield over 30-40 min by microwaveassisted reflux (in comparison with the 4 h required by conventional reflux) [16]. It was subsequently reported that the microwave-assisted synthesis (MAS) of various compounds of the type [M(CO) 4 (L) n ], using either an open reflux system or a sealed teflon-lined autoclave, results in a reduction in reaction times and an increase in yields over previously published syntheses [17,18]. The rate enhancement results from the coupling of solvents with high dielectric loss tangents with the microwave's irradiation, resulting in super-heating, thereby leading directly to reaction acceleration. Despite these promising results, the potential use of microwave-accelerated syntheses of organometallic compounds has hardly been explored, especially when compared with work in organic synthesis using this method [19]. In the present work, we report the MAS of tetracarbonyl complexes of molybdenum and tungsten with a pyrazolylpyridine ligand. The crystal structure of the Mo complex is described. We are particularly interested in the coordination chemistry of pyrazolylpyridine ligands because their complexes often possess excellent catalytic or photophysical properties [20,21].

Results and Discussion
The treatment of cis-[Mo(CO) 4 (pip) 2 ] with the ligand ethyl[3-(2-pyridyl)-1-pyrazolyl]acetate (pzpy) in toluene at 50 ºC for 3 h was reported to give the complex cis-[Mo(CO) 4 (pzpy)] (1) in 80% yield [22]. We found that the same compound could be obtained directly from Mo(CO) 6 in 63% yield by microwave-assisted heating (using a power of 300 W) of a mixture of the hexacarbonyl and pzpy in diglyme and toluene at 180 ºC for 30 s. The corresponding tungsten tetracarbonyl complex, cis-[W(CO) 4 (pzpy)] (2), was obtained in 85% yield by MAS at 180 ºC for 15 min, using a power of 600 W. A slightly longer reaction time was required for the formation of the tungsten complex due to the lower reactivity of W(CO) 6 [23]. The IR spectra of 1 and 2 show four active ν(C≡O) normal modes in the 1810-2015 cm -1 range, typical of cis-[M(CO) 4 L] molecules. A further band is observed at 1750 cm -1 for the CO group of the pzpy ligand. The complexes were also characterized by 1 H-and 13 C-NMR spectroscopy (see Experimental section for details).
Compound 1 crystallizes in the space group P2 1 /c and the structure is composed of a single crystallographically unique molybdenum complex with the metallic centre being coordinated to four carbonyl groups and a bidentate N,N-chelating pzpy residue (Figure 1), giving a coordination geometry that resembles a highly distorted octahedron, {MoC 4 N 2 }. The substantial deformation arises, in part, from the markedly distinct Mo-C and Mo-N bond distances [found in the 1.954(4)-2.050(4) and 2.248(3)-2.275(3) Å ranges, respectively -see Table 1]. These values lie within the known ranges for related compounds, as revealed by searches in the CSD: For Mo-C≡O (from 3194 entries), the bond length range is 1.48-2.37 Å with a median of 1.98 Å; for Mo-(N,N) chelate (from 529 entries), the bond length range is 1.92-2.64 Å with a median of 2.24 Å. A significant angular dispersion for the cis [71.63(9)-103.15 (11)º] and trans [167.24(12)-170.12 (14)º] octahedral angles is also registered. These distortions are in close agreement with those observed in related compounds as they essentially arise from the N,N-chelating nature of the pzpy residue. In fact, the most acute cis angle corresponds directly to the bite angle which is usually found in the range of 65.8-88.6º (529 entries; median 74.7º). The bite angle for 1 is in perfect agreement with the average value of 71.6º for the two structures reported by Thiel and co-workers [22,49].
The two N-donor atoms and the two carbonyl groups which make up the equatorial pseudo-plane of the {MoC 4 N 2 } octahedron are not all located on the same geometrical plane. Indeed, the carbonyl group containing C(3) is slightly above (by about 2.2º) the plane made by N(1)-N(2)-C(2). Even within the ligand pzpy, planarity is not maintained upon coordination to Mo and a dihedral angle of about 5.8º is observed between the two rings. This distortion was also reported by Thiel and co-workers [22], but only for the compound with an ethyl acetate substituent at N(3) [this compound also had a ferrocenyl substituent on the pyrazole ring]. Consequently, it is reasonable to assume that the increased flexibility of this substituent leads to a significant steric hindrance during the close packing stage, which may be partially compensated by twisting the pyridine and the pyrazole rings out of co-planarity. The angle between the two coordinated carbonyl groups forming this equatorial plane, C(3)-Mo(1)-C(2), is close to ideal and this indicates that the weak C-H···O hydrogen bonding interactions involving these groups (see below) have a radial geometry, thus not significantly compressing this section of the {MoC 4 N 2 } octahedron.
The two mutually trans-coordinated carbonyl groups containing C(1) and C(4) are bent away from the chelating pyrazolylpyridine ligand to give a C(1)-Mo(1)-C(4) bond angle of 170.12 (14)º and the long Mo-C bonds mentioned above (Table 1). This crowding feature follows the trends reported for related complexes with, for example, the corresponding inter-moiety angle usually being found in the range of 160.7-174.5º (from 52 entries in the CSD with a median of 169.2º). Moreover, the trans Mo-C≡O groups are markedly non-linear, with ∠[Mo (1) 179.6(3)º, respectively. These distortions are common for tetracarbonyl molybdenum complexes and are probably driven mainly by the need to minimise steric repulsion between the trans-coordinated carbonyl groups and the bulky chelating organic ligand.
Since the complex cis-[Mo(CO) 4 (pzpy)] does not contain any hydrogen atoms attached to either oxygen or nitrogen, which could promote the occurrence of strong hydrogen bonds, the close packing of these individual species is essentially driven by the need to effectively fill the space, closely mediated by weak C-H···O and π···π interactions. The relatively long linear conformation and high degree of flexibility (markedly evident from the thermal displacement ellipsoids depicted in Figure 1 (Table 2), thus leading to a hydrogen bonding motif best described by the R 2 2 (13) graph set ( Figure 2a) [50]. This pattern of weak hydrogen bonds is recursive along the [010] crystallographic direction and leads to what Bernstein and co-workers described as a "chain of rings" [50]. Indeed, the shortest repetitive chain motif composed of the same types of hydrogen bonds [in this case, C(13)-H(13A)···O (2)] is that highlighted in green in Figure 2a, topologically described as C 2 2 (14). According to Bernstein, the complete notation for this "chain of rings" would be      4 (pzpy)] complexes belonging to different, but adjacent, onedimensional supramolecular tapes (represented with white-and black-filled bonds for each tape). Hydrogen atoms which are not involved in C-H···O hydrogen bonding interactions and all symmetry codes related to symmetrygenerated atoms have been omitted for clarity.
The crystal packing of 1 is not mediated only by the C-H···O hydrogen bonding interactions described above. In order for the ethyl acetate groups to be structurally positioned to maximise those weak C-H···O contacts, the 2-(3-pyrazolyl)pyridine residues have to be structurally located on top of each other, slightly offset packed via a series of weak π···π contacts, as emphasised in Figure 4. Hence, the crystal structure of 1 is ultimately formed when columnar arrangements of hydrogen-bonded supramolecular tapes are packed in a parallel fashion along the [001] direction via π···π interactions, with an average interplanar distance of about 3.5 Å.  6 starting materials by using microwave-assisted heating in a closed system. The yields are comparable with those achievable by the traditional preparation routes. In addition to the short reaction times, the microwave-assisted synthesis of these compounds requires relatively small quantities of solvents and it is not necessary to use an inert atmosphere. A slightly longer reaction time was required for the formation of the tungsten complex due to the lower reactivity of W(CO) 6 . The molybdenum complex, cis-[Mo(CO) 4 (pzpy)], crystallizes in the space group P2 1 /c and the structure is composed of a single crystallographically unique complex with the metallic centre being coordinated to four carbonyl groups and a bidentate N,N-chelating pzpy residue, giving a coordination geometry that resembles a highly distorted octahedron, {MoC 4 N 2 }. The substantial deformation arises from the markedly distinct Mo-C and Mo-N bond distances, a significant angular dispersion for the cis and trans octahedral angles, and the fact that the two N-donor atoms and the two carbonyl groups which make up the equatorial pseudo-plane of the {MoC 4

General
Mo(CO) 6 , W(CO) 6 and diethylene glycol dimethyl ether (diglyme) were obtained from Aldrich and Fluka, and used as received. Ethyl[3-(2-pyridyl)-1-pyrazolyl]acetate was prepared using the published procedure [51]. The microwave-assisted syntheses were conducted with a MARS5 microwave oven (CEM Corporation, USA) at 2.45 GHz and a Teflon vessel with a volume of 100 mL. The temperature was monitored with a fibre optic probe inserted inside the Teflon vessel. The vessels were locked onto a carrousel that rotates 360º clockwise and counter clockwise. The syntheses were carried out using powers of 300 W (ramp time 30 min) and 600 W (ramp time 45 min) for the molybdenum and tungsten complexes, respectively. Elemental analyses were performed on an Exeter Analytical CE 440 Elemental Analyser (University of Cambridge). IR spectra were obtained as KBr pellets using a Mattson-7000 FTIR spectrophotometer. 1 H and 13 C-NMR spectra were measured in solution using a Bruker Avance 500 Cryo ultrashield spectrometer (University of Cambridge). Chemical shifts are quoted in parts per million from tetramethylsilane.

Single-crystal X-ray diffraction
A suitable single-crystal of cis-[Mo(CO) 4 (pzpy)] (1) was mounted on a glass fibre using FOMBLIN Y perfluoropolyether vacuum oil (LVAC 25/6) purchased from Aldrich [52]. Data were collected at 180(2) K on a Nonius Kappa charge-coupled device area-detector diffractometer (Mo K α graphite-monochromated radiation, λ = 0.7107 Å), equipped with an Oxford Cryosystems cryostream and controlled by the Collect software package [53]. Images were processed using the software packages Denzo and Scalepack [54], and data were corrected for absorption by the empirical method employed in Sortav [55,56]. The structure was solved by the direct methods of SHELXS-97 [57] and refined by full-matrix least squares on F 2 using SHELXL-97 [58]. All non-hydrogen atoms were directly located from difference Fourier maps and refined with anisotropic displacement parameters.
Hydrogen atoms attached to carbon were located at their idealised positions using appropriate HFIX instructions in SHELXL [58] (43 for the aromatic, 23 and 137 for the -CH 2 and terminal -CH 3 groups, respectively), and included in subsequent least-squares refinement cycles in riding-motion approximation with isotropic thermal displacement parameters (U iso ) fixed at 1.2 or 1.5 (for the terminal -CH 3 groups) times U eq of the carbon atom to which they were attached.
Information concerning crystallographic data collection and structure refinement details is summarised in Table 3. The last difference Fourier map synthesis showed the highest peak (0.589 eÅ -3 ) located at 1.86 Å from C (14), and the deepest hole (-0.739 eÅ -3 ) at 0.28 Å from O(5).