Molecular modeling of protein function regions

Current advances in screening for bioactive components from medicinal plants by affinity ultrafiltration mass spectrometry

INTRODUCTION

Medicinal plants have played an important role in maintaining human health for thousands of years. However, the interactions between the active components in medicinal plants and some certain biological targets during a disease are still unclear in most cases.

OBJECTIVE

To conduct the high-throughput screening for small active molecules that can interact with biological targets, which is of great theoretical significance and practical value.

METHODOLOGY

The ultrafiltration mass spectrometry (UF-LC/MS) is a powerful bio-analytical method by combining affinity ultrafiltration and liquid chromatography-mass spectrometry (LC/MS), which could rapidly screen and identify small active molecules that bind to biological targets of interest at the same time. Compared with other analytical methods, affinity UF-LC/MS has the characteristics of fast, sensitive and high throughput, and is especially suitable for the complicated extracts of medicinal plants.

RESULTS

In this review, the basic principle, characteristics and some most recent challenges in UF-LC/MS have been demonstrated. Meanwhile, the progress and applications of affinity UF-LC/MS in the discovery of the active components from natural medicinal plants and the interactions between small molecules and biological target proteins are also briefly summarised. In addition, the future directions for UF-LC/MS are also prospected.

CONCLUSION

Affinity UF-LC/MS is a powerful tool in studies on the interactions between small active molecules and biological protein targets, especially in the high-throughput screening of active components from the natural medicinal plants.

Computational analysis revealing that K634 and T681 mutations modulate the 3D-structure of PDGFR-β and lead to sunitinib resistance

Platelet-derived growth factor receptor-beta (PDGFR-β) is expressed by endothelial cells (ECs) of tumor-associated blood vessels and regulates primarily early hematopoiesis.

Alpha shape and Delaunay triangulation in studies of protein-related interactions

In recent years, more 3D protein structures have become available, which has made the analysis of large molecular structures much easier. There is a strong demand for geometric models for the study of protein-related interactions. Alpha shape and Delaunay triangulation are powerful tools to represent protein structures and have advantages in characterizing the surface curvature and atom contacts. This review presents state-of-the-art applications of alpha shape and Delaunay triangulation in the studies on protein-DNA, protein-protein, protein-ligand interactions and protein structure analysis.

Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes

We developed a new protocol for in silico drug screening for G-protein-coupled receptors (GPCRs) using a set of "universal active probes" (UAPs) with an ensemble docking procedure. UAPs are drug-like compounds, which are actual active compounds of a variety of known proteins. The current targets were nine human GPCRs whose three-dimensional (3D) structures are unknown, plus three GPCRs, namely β(2)-adrenergic receptor (ADRB2), A(2A) adenosine receptor (A(2A)), and dopamine D3 receptor (D(3)), whose 3D structures are known. Homology-based models of the GPCRs were constructed based on the crystal structures with careful sequence inspection. After subsequent molecular dynamics (MD) simulation taking into account the explicit lipid membrane molecules with periodic boundary conditions, we obtained multiple model structures of the GPCRs. For each target structure, docking-screening calculations were carried out via the ensemble docking procedure, using both true active compounds of the target proteins and the UAPs with the multiple target screening (MTS) method. Consequently, the multiple model structures showed various screening results with both poor and high hit ratios, the latter of which could be identified as promising for use in in silico screening to find candidate compounds to interact with the proteins. We found that the hit ratio of true active compounds showed a positive correlation to that of the UAPs. Thus, we could retrieve appropriate target structures from the GPCR models by applying the UAPs, even if no active compound is known for the GPCRs. Namely, the screening result that showed a high hit ratio for the UAPs could be used to identify actual hit compounds for the target GPCRs.

Structure-based discovery of antibacterial drugs

The modern era of antibacterial chemotherapy began in the 1930s, and the next four decades saw the discovery of almost all the major classes of antibacterial agents that are currently in use. However, bacterial resistance to many of these drugs is becoming an increasing problem. As such, the discovery of drugs with novel modes of action will be vital to meet the threats created by the emergence of resistance. Success in discovering inhibitors using high-throughput screening of chemical libraries is rare. In this Review we explore the exciting opportunities for antibacterial-drug discovery arising from structure-based drug design.

FINDSITE-metal: integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level

The rapid accumulation of gene sequences, many of which are hypothetical proteins with unknown function, has stimulated the development of accurate computational tools for protein function prediction with evolution/structure-based approaches showing considerable promise. In this article, we present FINDSITE-metal, a new threading-based method designed specifically to detect metal-binding sites in modeled protein structures. Comprehensive benchmarks using different quality protein structures show that weakly homologous protein models provide sufficient structural information for quite accurate annotation by FINDSITE-metal. Combining structure/evolutionary information with machine learning results in highly accurate metal-binding annotations; for protein models constructed by TASSER, whose average Cα RMSD from the native structure is 8.9 Å, 59.5% (71.9%) of the best of top five predicted metal locations are within 4 Å (8 Å) from a bound metal in the crystal structure. For most of the targets, multiple metal-binding sites are detected with the best predicted binding site at rank 1 and within the top two ranks in 65.6% and 83.1% of the cases, respectively. Furthermore, for iron, copper, zinc, calcium, and magnesium ions, the binding metal can be predicted with high, typically 70% to 90%, accuracy. FINDSITE-metal also provides a set of confidence indexes that help assess the reliability of predictions. Finally, we describe the proteome-wide application of FINDSITE-metal that quantifies the metal-binding complement of the human proteome. FINDSITE-metal is freely available to the academic community at http://cssb.biology.gatech.edu/findsite-metal/.

Cross-reactivity virtual profiling of the human kinome by X-react(KIN): a chemical systems biology approach

Many drug candidates fail in clinical development due to their insufficient selectivity that may cause undesired side effects. Therefore, modern drug discovery is routinely supported by computational techniques, which can identify alternate molecular targets with a significant potential for cross-reactivity. In particular, the development of highly selective kinase inhibitors is complicated by the strong conservation of the ATP-binding site across the kinase family. In this paper, we describe X-React(KIN), a new machine learning approach that extends the modeling and virtual screening of individual protein kinases to a system level in order to construct a cross-reactivity virtual profile for the human kinome. To maximize the coverage of the kinome, X-React(KIN) relies solely on the predicted target structures and employs state-of-the-art modeling techniques. Benchmark tests carried out against available selectivity data from high-throughput kinase profiling experiments demonstrate that, for almost 70% of the inhibitors, their alternate molecular targets can be effectively identified in the human kinome with a high (>0.5) sensitivity at the expense of a relatively low false positive rate (<0.5). Furthermore, in a case study, we demonstrate how X-React(KIN) can support the development of selective inhibitors by optimizing the selection of kinase targets for small-scale counter-screen experiments. The constructed cross-reactivity profiles for the human kinome are freely available to the academic community at http://cssb.biology.gatech.edu/kinomelhm/ .

The utility of geometrical and chemical restraint information extracted from predicted ligand-binding sites in protein structure refinement

Exhaustive exploration of molecular interactions at the level of complete proteomes requires efficient and reliable computational approaches to protein function inference. Ligand docking and ranking techniques show considerable promise in their ability to quantify the interactions between proteins and small molecules. Despite the advances in the development of docking approaches and scoring functions, the genome-wide application of many ligand docking/screening algorithms is limited by the quality of the binding sites in theoretical receptor models constructed by protein structure prediction. In this study, we describe a new template-based method for the local refinement of ligand-binding regions in protein models using remotely related templates identified by threading. We designed a Support Vector Regression (SVR) model that selects correct binding site geometries in a large ensemble of multiple receptor conformations. The SVR model employs several scoring functions that impose geometrical restraints on the Cα positions, account for the specific chemical environment within a binding site and optimize the interactions with putative ligands. The SVR score is well correlated with the RMSD from the native structure; in 47% (70%) of the cases, the Pearson's correlation coefficient is >0.5 (>0.3). When applied to weakly homologous models, the average heavy atom, local RMSD from the native structure of the top-ranked (best of top five) binding site geometries is 3.1Å (2.9Å) for roughly half of the targets; this represents a 0.1 (0.3)Å average improvement over the original predicted structure. Focusing on the subset of strongly conserved residues, the average heavy atom RMSD is 2.6Å (2.3Å). Furthermore, we estimate the upper bound of template-based binding site refinement using only weakly related proteins to be ∼2.6Å RMSD. This value also corresponds to the plasticity of the ligand-binding regions in distant homologues. The Binding Site Refinement (BSR) approach is available to the scientific community as a web server that can be accessed at http://cssb.biology.gatech.edu/bsr/.

Homology-modelling protein-ligand interactions: allowing for ligand-induced conformational change

Current homology-modelling methods do not consider small molecules in their automated processes. Therefore, the development of a reliable tool for protein-ligand homology modelling is an important next step in generating plausible models for molecular interactions. Two automated protein-ligand homology-modelling strategies, requiring no expert knowledge from the user, are investigated here. Both employ the "induced fit" concept with flexibility in side chains and ligand. The most successful strategy superimposes the new ligand over the original ligand before homology modelling, allowing the new ligand to be taken into consideration during protein modelling (rather than after), facilitating conformational change in the local backbone if necessary. We show that this approach results in successful modelling of the ligand and key binding-site residues of angiotensin-converting enzyme 2 (ACE2) from its homologue ACE, which is not possible via conventional homology modelling or by homology modelling followed by docking. Several other difficult target complexes are also successfully modelled, reproducing native protein-ligand contacts with significantly different biological substrates and different binding-site conformations. These include the modelling of Cdk5 (cyclin-dependent kinase 5) from Cdk2, thymidine phosphorylase from a bacterial homologue, and dihydrofolate reductase from a recombinant variant with a markedly different inhibitor. In terms of average modelling quality across 82 targets, the ligand RMSD with respect to the experimental structure is 1.4 A (and 2.0 A for the protein binding site) for "easy" cases and 2.9 A for the ligand (and 2.7 A for the protein binding site) in "hard" cases. This demonstrates the importance of selecting an optimal template. Ligand-modelling accuracy is strongly dependent on target-template ligand structural similarity, rather than target-template sequence identity. However, protein-modelling accuracy is dependent on both. Our automated protein-ligand homology-modelling strategy generates a higher degree of accuracy than homology modelling followed by docking, generating an average ligand RMSD that is 1-2 A better than docking with homology models.

GPCR 3D homology models for ligand screening: lessons learned from blind predictions of adenosine A2a receptor complex

Proteins of the G-protein coupled receptor (GPCR) family present numerous attractive targets for rational drug design, but also a formidable challenge for identification and conformational modeling of their 3D structure. A recently performed assessment of blind predictions of adenosine A2a receptor (AA2AR) structure in complex with ZM241385 (ZMA) antagonist provided a first example of unbiased evaluation of the current modeling algorithms on a GPCR target with approximately 30% sequence identity to the closest structural template. Several of the 29 groups participating in this assessment exercise (Michino et al., doi: 10.1038/nrd2877) successfully predicted the overall position of the ligand ZMA in the AA2AR ligand binding pocket, however models from only three groups captured more than 40% the ligand-receptor contacts. Here we describe two of these top performing approaches, in which all-atom models of the AA2AR were generated by homology modeling followed by ligand guided backbone ensemble receptor optimization (LiBERO). The resulting AA2AR-ZMA models, along with the best models from other groups are assessed here for their vitual ligand screening (VLS) performance on a large set of GPCR ligands. We show that ligand guided optimization was critical for improvement of both ligand-receptor contacts and VLS performance as compared to the initial raw homology models. The best blindly predicted models performed on par with the crystal structure of AA2AR in selecting known antagonists from decoys, as well as from antagonists for other adenosine subtypes and AA2AR agonists. These results suggest that despite certain inaccuracies, the optimized homology models can be useful in the drug discovery process.

Insights into docking and scoring neuronal alpha4beta2 nicotinic receptor agonists using molecular dynamics simulations and QM/MM calculations

A combined quantum mechanical (QM)-polarized docking and molecular dynamics approach to study the binding mode and to predict the binding affinity of ligands acting at the alpha4beta2-nAChR is presented. The results obtained in this study indicate that the quantum mechanical/molecular mechanics docking protocol well describes the charge-driven interactions occurring in the binding of nicotinic agonists, and it is able to represent the polarization effects on the ligand exerted by the surrounding atoms of the receptor at the binding site. This makes it possible to properly score agonists of alpha4beta2-nAChR and to reproduce the experimental binding affinity data with good accuracy, within a mean error of 2.2 kcal/mol. Moreover, applying the QM-polarized docking to an ensemble of nAChR conformations obtained from MD simulations enabled us to accurately capture nAChR-ligand induced-fit effects.

Structural determinants of species-selective substrate recognition in human and Drosophila serotonin transporters revealed through computational docking studies

To identify potential determinants of substrate selectivity in serotonin (5-HT) transporters (SERT), models of human and Drosophila serotonin transporters (hSERT, dSERT) were built based on the leucine transporter (LeuT(Aa)) structure reported by Yamashita et al. (Nature 2005;437:215-223), PBDID 2A65. Although the overall amino acid identity between SERTs and the LeuT(Aa) is only 17%, it increases to above 50% in the first shell of the putative 5-HT binding site, allowing de novo computational docking of tryptamine derivatives in atomic detail. Comparison of hSERT and dSERT complexed with substrates pinpoints likely structural determinants for substrate binding. Forgoing the use of experimental transport and binding data of tryptamine derivatives for construction of these models enables us to critically assess and validate their predictive power: A single 5-HT binding mode was identified that retains the amine placement observed in the LeuT(Aa) structure, matches site-directed mutagenesis and substituted cysteine accessibility method (SCAM) data, complies with support vector machine derived relations activity relations, and predicts computational binding energies for 5-HT analogs with a significant correlation coefficient (R = 0.72). This binding mode places 5-HT deep in the binding pocket of the SERT with the 5-position near residue hSERT A169/dSERT D164 in transmembrane helix 3, the indole nitrogen next to residue Y176/Y171, and the ethylamine tail under residues F335/F327 and S336/S328 within 4 A of residue D98. Our studies identify a number of potential contacts whose contribution to substrate binding and transport was previously unsuspected.

Molecular modeling and characterization of the B. thuringiensis and B. thuringiensis LDC-9 cytolytic proteins

The Cyt toxins are able to lyse a wide range of cell types in vitro, unlike the Cry delta-endotoxins. It exerts its activity by the formation of pores within target cell membranes. The structural information available for Cyt2Aa (PDB id: 1CBY) consists of a single domain in which two outer layers of alpha-helix wrap around a mixed beta-sheet. Beta-barrel was suggested as a possible structure of the pores. Hence, this study seeks to investigate the structural properties of other Cytolytic proteins by predicting the three-dimensional (3D) model using Cyt2Aa as template. The predicted models are expected to be significantly more accurate as all the Cyt proteins showed significant similarity with the template (PDB id: 1CBY). The refined homology models revealed similar secondary structures (alpha-helices and beta-sheets) and tertiary features as Cyt2Aa. The variation in the loop regions of the tertiary structure accounts for the differential toxicity.

A method to enhance the hit ratio by a combination of structure-based drug screening and ligand-based screening

We examined the procedures to combine two different in silico drug-screening results to achieve a high hit ratio. When the 3D structure of the target protein and some active compounds are known, both structure-based and ligand-based in silico screening methods can be applied. In the present study, the machine-learning score modification multiple target screening (MSM-MTS) method was adopted as a structure-based screening method, and the machine-learning docking score index (ML-DSI) method was adopted as a ligand-based screening method. To combine the predicted compound’s sets by these two screening methods, we examined the product of the sets (consensus set) and the sum of the sets. As a result, the consensus set achieved a higher hit ratio than the sum of the sets and than either individual predicted set. In addition, the current combination was shown to be robust enough for the structural diversities both in different crystal structure and in snapshot structures during molecular dynamics simulations.

Preservation of protein clefts in comparative models

BACKGROUND

Comparative, or homology, modelling of protein structures is the most widely used prediction method when the target protein has homologues of known structure. Given that the quality of a model may vary greatly, several studies have been devoted to identifying the factors that influence modelling results. These studies usually consider the protein as a whole, and only a few provide a separate discussion of the behaviour of biologically relevant features of the protein. Given the value of the latter for many applications, here we extended previous work by analysing the preservation of native protein clefts in homology models. We chose to examine clefts because of their role in protein function/structure, as they are usually the locus of protein-protein interactions, host the enzymes' active site, or, in the case of protein domains, can also be the locus of domain-domain interactions that lead to the structure of the whole protein.

RESULTS

We studied how the largest cleft of a protein varies in comparative models. To this end, we analysed a set of 53507 homology models that cover the whole sequence identity range, with a special emphasis on medium and low similarities. More precisely we examined how cleft quality - measured using six complementary parameters related to both global shape and local atomic environment, depends on the sequence identity between target and template proteins. In addition to this general analysis, we also explored the impact of a number of factors on cleft quality, and found that the relationship between quality and sequence identity varies depending on cleft rank amongst the set of protein clefts (when ordered according to size), and number of aligned residues.

CONCLUSION

We have examined cleft quality in homology models at a range of seq.id. levels. Our results provide a detailed view of how quality is affected by distinct parameters and thus may help the user of comparative modelling to determine the final quality and applicability of his/her cleft models. In addition, the large variability in model quality that we observed within each sequence bin, with good models present even at low sequence identities (between 20% and 30%), indicates that properly developed identification methods could be used to recover good cleft models in this sequence range.

The assessment of methods for protein structure prediction

Methods for protein structure prediction are flourishing and becoming widely available to both experimentalists and computational biologists. But, how good are they? What is their range of applicability and how can we know which method is better suited for the task at hand? These are the questions that this chapter tries to address, by describing automatic evaluation methods as well as the world-wide Critical Assessment of Techniques for Protein Structure Prediction (CASP) initiative and focusing on the specific problems of assessing the quality of a protein 3D model.

Assessing model accuracy using the homology modeling automatically software

Homology modeling is a powerful technique that greatly increases the value of experimental structure determination by using the structural information of one protein to predict the structures of homologous proteins. We have previously described a method of homology modeling by satisfaction of spatial restraints (Li et al., Protein Sci 1997;6:956-970). The Homology Modeling Automatically (HOMA) web site, <http://www-nmr.cabm.rutgers.edu/HOMA>, is a new tool, using this method to predict 3D structure of a target protein based on the sequence alignment of the target protein to a template protein and the structure coordinates of the template. The user is presented with the resulting models, together with an extensive structure validation report providing critical assessments of the quality of the resulting homology models. The homology modeling method employed by HOMA was assessed and validated using twenty-four groups of homologous proteins. Using HOMA, homology models were generated for 510 proteins, including 264 proteins modeled with correct folds and 246 modeled with incorrect folds. Accuracies of these models were assessed by superimposition on the corresponding experimentally determined structures. A subset of these results was compared with parallel studies of modeling accuracy using several other automated homology modeling approaches. Overall, HOMA provides prediction accuracies similar to other state-of-the-art homology modeling methods. We also provide an evaluation of several structure quality validation tools in assessing the accuracy of homology models generated with HOMA. This study demonstrates that Verify3D (Luthy et al., Nature 1992;356:83-85) and ProsaII (Sippl, Proteins 1993;17:355-362) are most sensitive in distinguishing between homology models with correct or incorrect folds. For homology models that have the correct fold, the steric conformational energy (including primarily the Van der Waals energy), MolProbity clashscore (Word et al., Protein Sci 2000;9:2251-2259), and the PROCHECK G-factors (Laskowski et al., J Biomol NMR 1996;8:477-486) provide sensitive and consistent methods for assessing accuracy and can distinguish between homology models of higher and lower accuracy. As demonstrated in the accompanying paper (Bhattacharya et al., accompanying paper), combinations of these scores for models generated with HOMA provide a basis for distinguishing low from high accuracy models.

Fine-tuning of intrinsic N-Oct-3 POU domain allostery by regulatory DNA targets

The 'POU' (acronym of Pit-1, Oct-1, Unc-86) family of transcription factors share a common DNA-binding domain of approximately 160 residues, comprising so-called 'POUs' and 'POUh' sub-domains connected by a flexible linker. The importance of POU proteins as developmental regulators and tumor-promoting agents is due to linker flexibility, which allows them to adapt to a considerable variety of DNA targets. However, because of this flexibility, it has not been possible to determine the Oct-1/Pit-1 linker structure in crystallographic POU/DNA complexes. We have previously shown that the neuronal POU protein N-Oct-3 linker contains a structured region. Here, we have used a combination of hydrodynamic methods, DNA footprinting experiments, molecular modeling and small angle X-ray scattering to (i) structurally interpret the N-Oct-3-binding site within the HLA DRalpha gene promoter and deduce from this a novel POU domain allosteric conformation and (ii) analyze the molecular mechanisms involved in conformational transitions. We conclude that there might exist a continuum running from free to 'pre-bound' N-Oct-3 POU conformations and that regulatory DNA regions likely select pre-existing conformers, in addition to molding the appropriate DBD structure. Finally, we suggest that a specific pair of glycine residues in the linker might act as a major conformational switch.

Mapping antigenic diversity and strain specificity of mumps virus: A bioinformatics approach

Mumps is an acute infectious disease caused by mumps virus, a member of the family Paramyxoviridae. With the implementation of vaccination programs, mumps infection is under control. However, due to resurgence of mumps epidemics, there is a renewed interest in understanding the antigenic diversity of mumps virus. Hemagglutinin-neuraminidase (HN) is the major surface antigen and is known to elicit neutralizing antibodies. Mutational analysis of HN of wild-type and vaccine strains revealed that the hypervariable positions are distributed over the entire length with no detectable pattern. In the absence of experimentally derived 3D structure data, the structure of HN protein of mumps virus was predicted using homology modeling. Mutations mapped on the predicted structures were found to cluster on one of the surfaces. A predicted conformational epitope encompasses experimentally characterized epitopes suggesting that it is a major site for neutralization. These analyses provide rationale for strain specificity, antigenic diversity and varying efficacy of mumps vaccines.

Localization of binding sites in protein structures by optimization of a composite scoring function

The rise in the number of functionally uncharacterized protein structures is increasing the demand for structure-based methods for functional annotation. Here, we describe a method for predicting the location of a binding site of a given type on a target protein structure. The method begins by constructing a scoring function, followed by a Monte Carlo optimization, to find a good scoring patch on the protein surface. The scoring function is a weighted linear combination of the z-scores of various properties of protein structure and sequence, including amino acid residue conservation, compactness, protrusion, convexity, rigidity, hydrophobicity, and charge density; the weights are calculated from a set of previously identified instances of the binding-site type on known protein structures. The scoring function can easily incorporate different types of information useful in localization, thus increasing the applicability and accuracy of the approach. To test the method, 1008 known protein structures were split into 20 different groups according to the type of the bound ligand. For nonsugar ligands, such as various nucleotides, binding sites were correctly identified in 55%-73% of the cases. The method is completely automated (http://salilab.org/patcher) and can be applied on a large scale in a structural genomics setting.

Rigorous performance evaluation in protein structure modelling and implications for computational biology

In principle, given the amino acid sequence of a protein, it is possible to compute the corresponding three-dimensional structure. Methods for modelling structure based on this premise have been under development for more than 40 years. For the past decade, a series of community wide experiments (termed Critical Assessment of Structure Prediction (CASP)) have assessed the state of the art, providing a detailed picture of what has been achieved in the field, where we are making progress, and what major problems remain. The rigorous evaluation procedures of CASP have been accompanied by substantial progress. Lessons from this area of computational biology suggest a set of principles for increasing rigor in the field as a whole.

Crystallizing new approaches for antimicrobial drug discovery

Over the past decade, the sequences of microbial genomes have accumulated, changing the strategies for the discovery of novel anti-infective agents. Targets have become plentiful, yet new antimicrobial agents have been slow to emerge from this effort. In part, this reflects the long discovery and development times needed to bring new drugs to market. In addition, bottlenecks have been revealed in the antimicrobial drug discovery process at the steps of identifying good leads, and optimizing those leads into drug candidates. The fruit of structural genomics may provide opportunities to overcome these bottlenecks and fill the antimicrobial pipeline, by using the tools of structure guided drug discovery (SGDD).

Comparative modeling for protein structure prediction

With the progression of structural genomics projects, comparative modeling remains an increasingly important method of choice. It helps to bridge the gap between the available sequence and structure information by providing reliable and accurate protein models. Comparative modeling based on more than 30% sequence identity is now approaching its natural template-based limits and further improvements require the development of effective refinement techniques capable of driving models toward native structure. For difficult targets, for which the most significant progress in recent years has been observed, optimal template selection and alignment accuracy are still the major problems.

Screening Drug-Like Compounds by Docking to Homology Models: A Systematic Study

In the absence of an experimentally solved structure, a homology model of a protein target can be used instead for virtual screening of drug candidates by docking and scoring. This approach poses a number of questions regarding the choice of the template to use in constructing the model, the accuracy of the screening results, and the importance of allowing for protein flexibility. The present study addresses such questions with compound screening calculations for multiple homology models of five drug targets. A central result is that docking to homology models frequently yields enrichments of known ligands as good as that obtained by docking to a crystal structure of the actual target protein. Interestingly, however, standard measures of the similarity of the template used to build the homology model to the targeted protein show little correlation with the effectiveness of the screening calculations, and docking to the template itself often is as successful as docking to the corresponding homology model. Treating key side chains as mobile produces a modest improvement in the results. The reasons for these sometimes unexpected results, and their implications for future methodologic development, are discussed.

Protein Family Clustering for Structural Genomics

A major goal of structural genomics is the provision of a structural template for a large fraction of protein domains. The magnitude of this task depends on the number and nature of protein sequence families. With a large number of bacterial genomes now fully sequenced, it is possible to obtain improved estimates of the number and diversity of families in that kingdom. We have used an automated clustering procedure to group all sequences in a set of genomes into protein families. Bench-marking shows the clustering method is sensitive at detecting remote family members, and has a low level of false positives. This comprehensive protein family set has been used to address the following questions. (1) What is the structure coverage for currently known families? (2) How will the number of known apparent families grow as more genomes are sequenced? (3) What is a practical strategy for maximizing structure coverage in future? Our study indicates that approximately 20% of known families with three or more members currently have a representative structure. The study indicates also that the number of apparent protein families will be considerably larger than previously thought: We estimate that, by the criteria of this work, there will be about 250,000 protein families when 1000 microbial genomes have been sequenced. However, the vast majority of these families will be small, and it will be possible to obtain structural templates for 70-80% of protein domains with an achievable number of representative structures, by systematically sampling the larger families.

A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction

For the past ten years, CASP (Critical Assessment of Structure Prediction) has monitored the state of the art in modeling protein structure from sequence. During this period, there has been substantial progress in both comparative modeling of structure (using information from an evolutionarily related structural template) and template-free modeling. The quality of comparative models depends on the closeness of the evolutionary relationship on which they are based. Template-free modeling, although still very approximate, now produces topologically near correct models for some small proteins. Current major challenges are refining comparative models so that they match experimental accuracy, obtaining accurate sequence alignments for models based on remote evolutionary relationships, and extending template-free modeling methods so that they produce more accurate models, handle parts of comparative models not available from a template and deal with larger structures.

The advantages and limitations of protein crystal structures

Crystal structure analysis using X-ray diffraction is, in many cases, the most advanced method available for obtaining high-resolution structural information about biological macromolecules. The ways in which X-ray diffraction data are collected and refined have a strong impact on the final quality of the structural models and the type and magnitude of their associated errors. It is becoming increasingly necessary for both structural and non-structural biologists to judge the reliability and accuracy of these models, which are being used in many aspects of research, including structure-based drug design, and to address detailed functional biological questions. In this article, we discuss how errors in these models arise and how they can be evaluated, and we argue for even more stringent validation checks and documentation of structures before deposition with the Protein Data Bank.

Seeing is believing: the impact of structural genomics on antimicrobial drug discovery

Over the past decade, the availability of complete microbial genome sequences has led to changes in the strategies that are used to search for novel anti-infectives. However, despite the identification of many new potential drug targets, novel antimicrobial agents have been slow to emerge from these efforts. In part, this reflects the long discovery and development times that are needed to bring new drugs to market and the bottlenecks at the stages of identifying good lead compounds and optimizing these leads into drug candidates. Structural genomics will hopefully provide opportunities to overcome these bottlenecks and populate the antimicrobial pipeline.