MODBASE, a database of annotated comparative protein structure models

Structure-function relationship of PE11 esterase of Mycobacterium tuberculosis with respect to its role in virulence

The lipolytic enzymes of Mycobacterium tuberculosis play a critical role in immunomodulation and virulence. Among these proteins, PE11 which also belongs to the PE/PPE family, is the smallest (∼10.8 kDa) and play a significant role in cell wall remodelling and virulence. PE11 is established to be an esterase, but its enzymatic and structural properties are not yet characterized. In this study, using homology modelling we deduced the putative structure which shows the presence of both α-helix and β-sheet structures which is in close agreement with that observed by CD spectra of the purified protein. PE11 was found to contain a Gx3Sx4G motif homologous to canonical 'GxSxG' motif present in many serin hydrolases. The catalytic triad appears to be located within this motif as substitution of Serine26 and Glycine31 residues abrogated its enzymatic activity. Gel-filtration chromatography data indicate that PE11 possibly exists as dimer and tetramer showing positive cooperativity for binding its substrates. In addition, PE11 esterase activity was found to be critical for cell wall remodelling, antibiotic resistance and conferring survival advantages to M. tuberculosis. Our data suggest that PE11 can be targeted for designing potential therapeutic strategies.

Quality assessment of VHH models

Heavy Chain Only Antibodies are specific to Camelid species. Despite the lack of the light chain variable domain, their heavy chain variable domain (VH) domain, named VHH or nanobody, has promising potential applications in research and therapeutic fields. The structural study of VHH is therefore of great interest. Unfortunately, considering the huge amount of sequences that might be produced, only about one thousand of VHH experimental structures are publicly available in the Protein Data Bank, implying that structural model prediction of VHH is a necessary alternative to obtaining 3D information besides its sequence. The present study aims to assess and compare the quality of predictions from different modelling methodologies. Established comparative & homology modelling approaches to recent Deep Learning-based modelling strategies were applied, i.e. Modeller using single or multiple structural templates, ModWeb, SwissModel (with two evaluation schema), RoseTTAfold, AlphaFold 2 and NanoNet. The prediction accuracy was evaluated using RMSD, TM-score, GDT-TS, GDT-HA and Protein Blocks distance metrics. Besides the global structure assessment, we performed specific analyses of Frameworks and CDRs structures. We observed that AlphaFold 2 and especially NanoNet performed better than the other evaluated softwares. Importantly, we performed molecular dynamics simulations of an experimental structure and a NanoNet predicted model of a VHH in order to compare the global structural flexibility and local conformations using Protein Blocks. Despite rather similar structures, substantial differences in dynamical properties were observed, which underlies the complexity of the task of model evaluation.Communicated by Ramaswamy H. Sarma.

VHH Structural Modelling Approaches: A Critical Review

VHH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved VHH structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most VHHs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given VHH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of VHHs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved VHH structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of VHH from its sequence.

Molecular Docking Analysis and Biochemical Evaluation of Levansucrase from Sphingobium chungbukense DJ77

Bacterial exopolymer Levan (β-(2,6) polyfructan) synthesized by levansucrase has attracted interest for various applications due to its low intrinsic viscosity compared with other polysaccharides. We report a novel levansucrase (Lsc) isolated from Sphingobium chunbukense DJ77 and verify its biochemical characteristics by comparative analysis of molecular docking analysis (MOE) and catalytic residue analysis. The complete sequence of the Lsc encoding gene ( lsc) was cloned under the direction of the T7 promoter and purified in an Escherichia coli BL21 (DE3) protein expression system. The enzyme activity analysis and ligand docking MOE study of S. chungbukense DJ77 Lsc revealed that Arg 77, Ser112, Arg 195, Asp196, Glu257, and Gln275 were involved in the sucrose binding and splitting as well as transfructosylation activity. A catalytic comparison of Lsc of S. chungbukense DJ77 with the results of site-directed mutational analysis indicated that Gln275 may coordinate a favorable substrate binding environment, offering broad pH resistance in the range of 5-10. The results suggest that the recombinant E. coli carrying S. chungbukense DJ77 Lsc might produce levan under the regular growth conditions with less need for pH manipulation.

The unequivocal preponderance of biocomputation in clinical virology

Bioinformatics and computer based data simulation and modeling are captivating biological research, delivering great results already and promising to deliver more. As biological research is a complex, intricate, diverse field, any available support is gladly taken. With recent outbreaks and epidemics, pathogens are a constant threat to the global economy and security. Virus related plagues are somehow the most difficult to handle. Biocomputation has provided appreciable help in resolving clinical virology related issues. This review, for the first time, surveys the current status of the role of computation in virus related research. Advances made in the fields of clinical virology, antiviral drug design, viral immunology and viral oncology, through input from biocomputation, have been discussed. The amount of progress made and the software platforms available are consolidated in this review. The limitations of computation based methods are presented. Finally, the challenges facing the future of biocomputation in clinical virology are speculated upon.

Biocomputation in clinical virology.

POLE and POLD1 screening in 155 patients with multiple polyps and early-onset colorectal cancer

Germline mutations in POLE and POLD1 have been shown to cause predisposition to colorectal multiple polyposis and a wide range of neoplasms, early-onset colorectal cancer being the most prevalent. In order to find additional mutations affecting the proofreading activity of these polymerases, we sequenced its exonuclease domain in 155 patients with multiple polyps or an early-onset colorectal cancer phenotype without alterations in the known hereditary colorectal cancer genes. Interestingly, none of the previously reported mutations in POLE and POLD1 were found. On the other hand, among the genetic variants detected, only two of them stood out as putative pathogenic in the POLE gene, c.1359 + 46del71 and c.1420G > A (p.Val474Ile). The first variant, detected in two families, was not proven to alter correct RNA splicing. Contrarily, c.1420G > A (p.Val474Ile) was detected in one early-onset colorectal cancer patient and located right next to the exonuclease domain. The pathogenicity of this change was suggested by its rarity and bioinformatics predictions, and it was further indicated by functional assays in Schizosaccharomyces pombe. This is the first study to functionally analyze a POLE genetic variant outside the exonuclease domain and widens the spectrum of genetic changes in this DNA polymerase that could lead to colorectal cancer predisposition.

Thermal adaptation and clinal mitochondrial DNA variation of European anchovy

Natural populations of widely distributed organisms often exhibit genetic clinal variation over their geographical ranges. The European anchovy, Engraulis encrasicolus, illustrates this by displaying a two-clade mitochondrial structure clinally arranged along the eastern Atlantic. One clade has low frequencies at higher latitudes, whereas the other has an anti-tropical distribution, with frequencies decreasing towards the tropics. The distribution pattern of these clades has been explained as a consequence of secondary contact after an ancient geographical isolation. However, it is not unlikely that selection acts on mitochondria whose genes are involved in relevant oxidative phosphorylation processes. In this study, we performed selection tests on a fragment of 1044 bp of the mitochondrial cytochrome b gene using 455 individuals from 18 locations. We also tested correlations of six environmental features: temperature, salinity, apparent oxygen utilization and nutrient concentrations of phosphate, nitrate and silicate, on a compilation of mitochondrial clade frequencies from 66 sampling sites comprising 2776 specimens from previously published studies. Positive selection in a single codon was detected predominantly (99%) in the anti-tropical clade and temperature was the most relevant environmental predictor, contributing with 59% of the variance in the geographical distribution of clade frequencies. These findings strongly suggest that temperature is shaping the contemporary distribution of mitochondrial DNA clade frequencies in the European anchovy.

Ligand-target prediction by structural network biology using nAnnoLyze

Target identification is essential for drug design, drug-drug interaction prediction, dosage adjustment and side effect anticipation. Specifically, the knowledge of structural details is essential for understanding the mode of action of a compound on a target protein. Here, we present nAnnoLyze, a method for target identification that relies on the hypothesis that structurally similar binding sites bind similar ligands. nAnnoLyze integrates structural information into a bipartite network of interactions and similarities to predict structurally detailed compound-protein interactions at proteome scale. The method was benchmarked on a dataset of 6,282 pairs of known interacting ligand-target pairs reaching a 0.96 of area under the Receiver Operating Characteristic curve (AUC) when using the drug names as an input feature for the classifier, and a 0.70 of AUC for “anonymous” compounds or compounds not present in the training set. nAnnoLyze resulted in higher accuracies than its predecessor, AnnoLyze. We applied the method to predict interactions for all the compounds in the DrugBank database with each human protein structure and provide examples of target identification for known drugs against human diseases. The accuracy and applicability of our method to any compound indicate that a comparative docking approach such as nAnnoLyze enables large-scale annotation and analysis of compound–protein interactions and thus may benefit drug development.

Description of the “mode-of-action” of a small chemical compound against a protein target is essential for the drug discovery process. Such description relies on three main steps: i) the identification of the target protein within the thousands of proteins in an organism, ii) the localization of the binding interaction site in the identified target protein, and iii) the molecular characterization of the compound’s binding mode in the binding site of the target protein. Here, we introduce a new computational method, called nAnnoLyze, which uses graph theory principles to relate compounds and target proteins based on comparative principles. nAnnoLyze aims at addressing two of the three previous steps, that is, target identification and binding site localization. Our results suggest that the nAnnoLyze accuracy and proteome-wide applicability enables the large-scale annotation and analysis of compound–protein interaction and thus may benefit drug development.

DFGmodel: predicting protein kinase structures in inactive states for structure-based discovery of type-II inhibitors

Protein kinases exist in equilibrium of active and inactive states, in which the aspartate-phenylalanine-glycine motif in the catalytic domain undergoes conformational changes that are required for function. Drugs targeting protein kinases typically bind the primary ATP-binding site of an active state (type-I inhibitors) or utilize an allosteric pocket adjacent to the ATP-binding site in the inactive state (type-II inhibitors). Limited crystallographic data of protein kinases in the inactive state hampers the application of rational drug discovery methods for developing type-II inhibitors. Here, we present a computational approach to generate structural models of protein kinases in the inactive conformation. We first perform a comprehensive analysis of all protein kinase structures deposited in the Protein Data Bank. We then develop DFGmodel, a method that takes either a known structure of a kinase in the active conformation or a sequence of a kinase without a structure, to generate kinase models in the inactive conformation. Evaluation of DFGmodel's performance using various measures indicates that the inactive kinase models are accurate, exhibiting RMSD of 1.5 Å or lower. The kinase models also accurately distinguish type-II kinase inhibitors from likely nonbinders (AUC > 0.70), suggesting that they are useful for virtual screening. Finally, we demonstrate the applicability of our approach with three case studies. For example, the models are able to capture inhibitors with unintended off-target activity. Our computational approach provides a structural framework for chemical biologists to characterize kinases in the inactive state and to explore new chemical spaces with structure-based drug design.

Insilico Structural 3D Modelling of Novel Cry1Ib9 and Cry3A Toxins from Local Isolates of Bacillus thuringiensis

Three-dimensional (3D) models for the 79.2 kDa activated Cry1Ib9 and 77.4 kDa activated Cry3A δ-endotoxins from Bacillus thuringiensis (Bt) native isolates that are specifically toxic to Coleopteran insect pests were constructed by utilizing homology modeling online tool. Evidences presented here, based on the identification of structural equivalent residues of Cry1Ib9 and Cry3A toxin through homology modelling indicate that, they share a common Bt toxin tridimensional structure. The main differences observed in Cry1I9 domain I at positions α2b (S56-I60), α4 (F78-l93) and additionally β0 (Q10-L12), α8a (T280-V282) were observed, in domain II at positions α9b (P333-L339), β6(T390-Q393), β7(V398-W404), β8 (V418-W425), β9 (E453-N454), β10 (S470-I479) where as in domain III the changes were observed at positions β19 (R601-F607), β20 (609-L613), β21 (S618-F627) and α11a (K655-F664), α13, α14 components present at downstream sites, where as in Cry3A main differences observed in domain I is at the position of α4 (P105-I152), α5 (Q163-A185), β1A(E190-L192), α6 (F193-Y217), Domain II is not consevered and main variations were observed at β2 (E292-L295), β3(V299-L308), β4(I340-F347), β5(D356-P368), β6(I375-T377), β7(V389-F394), β8(K398-N405), β9(Y416-Y427), β10 (T436-Y439), β12(G476-H495), β12A (M503-I504) where as in domain III main variations observed at positions of β18 (P583-I593), β19(F604-S610), β20(P611-L615), β21(N619-G626). Cry1Ib9 and Cry3A contain the most variable regions in the loops of domain II, which determine the specificity of these toxins. These are the first models of Coleopteran-active protein from native isolates of Bt and its importance can be perceived since members of this group of toxins are potentially important candidates for coleoptera insect pest control programs.

Protein structure modeling in the proteomics era

Structural proteomics aims to understand the structural basis of protein interactions and functions. A prerequisite for this is the availability of 3D protein structures that mediate the biochemical interactions. The explosion in the number of available gene sequences set the stage for the next step in genome-scale projects -- to obtain 3D structures for each protein. To achieve this ambitious goal, the slow and costly structure determination experiments are supplemented with theoretical approaches. The current state and recent advances in structure modeling approaches are reviewed here, with special emphasis on comparative protein structure modeling techniques.

Structural insights into a novel interkingdom signaling circuit by cartography of the ligand-binding sites of the homologous quorum sensing LuxR-family

Recent studies have identified a novel interkingdom signaling circuit, via plant signaling molecules, and a bacterial sub-family of LuxR proteins, bridging eukaryotes and prokaryotes. Indeed pivotal plant-bacteria interactions are regulated by the so called Plant Associated Bacteria (PAB) LuxR solo regulators that, although closely related to the quorum sensing (QS) LuxR family, do not bind or respond to canonical quorum sensing N-acyl homoserine lactones (AHLs), but only to specific host plant signal molecules. The large body of structural data available for several members of the QS LuxR family complexed with different classes of ligands (AHLs and other compounds), has been exploited to dissect the cartography of their regulatory domains through structure-based multiple sequence alignments, structural superimposition and a comparative analysis of the contact residues involved in ligand binding. In the absence of experimentally determined structures of members of the PAB LuxR solos subfamily, an homology model of its prototype OryR is presented, aiming to elucidate the architecture of its ligand-binding site. The obtained model, in combination with the cartography of the regulatory domains of the homologous QS LuxRs, provides novel insights into the 3D structure of its ligand-binding site and unveils the probable molecular determinants responsible for differences in selectivity towards specific host plant signal molecules, rather than to canonical QS compounds.

Biodegradation of single-walled carbon nanotubes by eosinophil peroxidase

Eosinophil peroxidase (EPO) is one of the major oxidant-producing enzymes during inflammatory states in the human lung. The degradation of single-walled carbon nanotubes (SWCNTs) upon incubation with human EPO and H₂O₂ is reported. Biodegradation of SWCNTs is higher in the presence of NaBr, but neither EPO alone nor H₂O₂ alone caused the degradation of nanotubes. Molecular modeling reveals two binding sites for SWCNTs on EPO, one located at the proximal side (same side as the catalytic site) and the other on the distal side of EPO. The oxidized groups on SWCNTs in both cases are stabilized by electrostatic interactions with positively charged residues. Biodegradation of SWCNTs can also be executed in an ex vivo culture system using primary murine eosinophils stimulated to undergo degranulation. Biodegradation is proven by a range of methods including transmission electron microscopy, UV-visible-NIR spectroscopy, Raman spectroscopy, and confocal Raman imaging. Thus, human EPO (in vitro) and ex vivo activated eosinophils mediate biodegradation of SWCNTs: an observation that is relevant to pulmonary responses to these materials.

Comprehensive database of Chorismate synthase enzyme from shikimate pathway in pathogenic bacteria

Background

Infectious diseases are major public health problem. It is increasingly affecting more than 50 million people worldwide. Targeting shikimate pathway could be efficiently used for the development of broad spectrum antimicrobial compound against variety of infectious diseases. Chorismate synthase is an enzyme in shikimate pathway that catalyzes Phosphoenol pyruvate to chorismate in most of the prokaryotic bacteria. This step is crucial for its growth, since Chorismate acts as a precursor molecule for the synthesis of aromatic amino acids. Hence, we present a comprehensive database of Chorismate Synthase Database (CSDB) which is a manually curated database. It provides information on the sequence, structure and biological activity of chorismate synthase from shikimate pathway of pathogenic bacteria. Design of suitable inhibitors for this enzyme, hence could be a probable solution to destroy its proteomic machinery and thereby inhibit the bacterial growth.

Description

The aim of this study was to characterise chorismate synthase enzyme belonging to pathogenic bacteria to analysis the functional and structural characterization of chorismate synthase is very important for both structure-based and ligand based drug design.

Conclusions

The broad range of data easy to use user interface makes csdb.in a useful database for researchers in designing drugs.

Function of human Rh based on structure of RhCG at 2.1 A

In humans, NH(3) transport across cell membranes is facilitated by the Rh (rhesus) family of proteins. Human Rh C glycoprotein (RhCG) forms a trimeric complex that plays an essential role in ammonia excretion and renal pH regulation. The X-ray crystallographic structure of human RhCG, determined at 2.1 A resolution, reveals the mechanism of ammonia transport. Each monomer contains 12 transmembrane helices, one more than in the bacterial homologs. Reconstituted into proteoliposomes, RhCG conducts NH(3) to raise internal pH. Models of the erythrocyte Rh complex based on our RhCG structure suggest that the erythrocytic Rh complex is composed of stochastically assembled heterotrimers of RhAG, RhD, and RhCE.

SKPDB: a structural database of shikimate pathway enzymes

BACKGROUND

The functional and structural characterisation of enzymes that belong to microbial metabolic pathways is very important for structure-based drug design. The main interest in studying shikimate pathway enzymes involves the fact that they are essential for bacteria but do not occur in humans, making them selective targets for design of drugs that do not directly impact humans.

DESCRIPTION

The ShiKimate Pathway DataBase (SKPDB) is a relational database applied to the study of shikimate pathway enzymes in microorganisms and plants. The current database is updated regularly with the addition of new data; there are currently 8902 enzymes of the shikimate pathway from different sources. The database contains extensive information on each enzyme, including detailed descriptions about sequence, references, and structural and functional studies. All files (primary sequence, atomic coordinates and quality scores) are available for downloading. The modeled structures can be viewed using the Jmol program.

CONCLUSIONS

The SKPDB provides a large number of structural models to be used in docking simulations, virtual screening initiatives and drug design. It is freely accessible at http://lsbzix.rc.unesp.br/skpdb/.

Modeling of the full-length Escherichia coli SeqA protein, in complex with DNA

The Escherichia coli SeqA protein, a negative regulator of chromosome DNA replication, prevents the overinitiation of replication within one cell cycle by binding to hemimethylated GATC sequences in the replication origin, oriC. In addition to the hemimethylated DNA-binding activity, the SeqA protein has a self-association activity, which is also considered to be essential for its regulatory function in replication initiation. To study the SeqA protein biological activity, we performed a SeqA protein model to examine its architecture. SeqA has a bipartite structure composed of a large and small lobe. The SeqA spatial conformation contributes to its ability to bind to a pair of hemimethylated GATC sequences and to its cooperative behavior.

Dopamine transporter comparative molecular modeling and binding site prediction using the LeuT(Aa) leucine transporter as a template

Pharmacological and behavioral studies indicate that binding of cocaine and the amphetamines by the dopamine transporter (DAT) protein is principally responsible for initiating the euphoria and addiction associated with these drugs. The lack of an X-ray crystal structure for the DAT or any other member of the neurotransmitter:sodium symporter (NSS) family has hindered understanding of psychostimulant recognition at the atomic level; structural information has been obtained largely from mutagenesis and biophysical studies. The recent publication of a crystal structure for the bacterial leucine transporter LeuT(Aa), a distantly related NSS family homolog, provides for the first time a template for three-dimensional comparative modeling of NSS proteins. A novel computational modeling approach using the capabilities of the Molecular Operating Environment program MOE 2005.06 in conjunction with other comparative modeling servers generated the LeuT(Aa)-directed DAT model. Probable dopamine and amphetamine binding sites were identified within the DAT model using multiple docking approaches. Binding sites for the substrate ligands (dopamine and amphetamine) overlapped substantially with the analogous region of the LeuT(Aa) crystal structure for the substrate leucine. The docking predictions implicated DAT side chains known to be critical for high affinity ligand binding and suggest novel mutagenesis targets in elucidating discrete substrate and inhibitor binding sites. The DAT model may guide DAT ligand QSAR studies, and rational design of novel DAT-binding therapeutics.

Neurokinin-3 receptor-specific antagonists talnetant and osanetant show distinct mode of action in cellular Ca2+ mobilization but display similar binding kinetics and identical mechanism of binding in ligand cross-competition

Talnetant and osanetant, two structurally diverse antagonists of neurokinin-3 receptor (NK3), displayed distinct modes of action in Ca2+ mobilization. Although talnetant showed a normal Schild plot with a slope close to unity and a Kb similar to its Ki value in binding, osanetant presented an aberrant Schild with a steep slope (3.3 +/- 0.5) and a Kb value (12 nM) significantly elevated compared with its Ki value (0.8 nM) in binding. Kinetic binding experiments indicated a simple one-step binding mechanism with relatively fast on- and off-rates for both antagonists, arguing against slow onset of antagonism as the reason for abnormal Schild. This conclusion was supported by prolonged preincubation of antagonist that failed to improve the observed aberrant Schild. In ligand cross-competition binding, both talnetant and osanetant displayed linear reciprocal plots of identical slope when [MePhe7]neurokinin B (NKB) was used as the other competition partner with 125I-[MePhe7]NKB as the radioligand, indicating competitive binding of either antagonist with regard to [MePhe7]NKB. Similar patterns were obtained when talnetant was tested against osanetant, indicating competitive binding between the two antagonists as well. These results were reproduced when [3H]4-quinolinecarboxamide (SB222200), a close derivative of talnetant, was used as the radioligand. Taken together, these data strongly suggest binding of both talnetant and osanetant at the orthosteric binding site with similar kinetic properties and do not support the hypothesis that the aberrant Schild observed in functional assays for osanetant is derived from differences in the mechanism of binding for these NK3 antagonists.

Biological units and their effect upon the properties and prediction of protein-protein interactions

Structural data as collated in the Protein Data Bank (PDB) have been widely applied in the study and prediction of protein-protein interactions. However, since the basic PDB Entries contain only the contents of the asymmetric unit rather than the biological unit, some key interactions may be missed by analysing only the PDB Entry. A total of 69,054 SCOP (Structural Classification of Proteins) domains were examined systematically to identify the number of additional novel interacting domain pairs and interfaces found by considering the biological unit as stored in the PQS (Protein Quaternary Structure) database. The PQS data adds 25,965 interacting domain pairs to those seen in the PDB Entries to give a total of 61,783 redundant interacting domain pairs. Redundancy filtering at the level of the SCOP family shows PQS to increase the number of novel interacting domain-family pairs by 302 (13.3%) from 2277, but only 16/302 (1.4%) of the interacting domain pairs have the two domains in different SCOP families. This suggests the biological units add little to the elucidation of novel biological interaction networks. However, when the orientation of the domain pairs is considered, the PQS data increases the number of novel domain-domain interfaces observed by 1455 (34.5%) to give 5677 non-redundant domain-domain interfaces. In all, 162/1455 novel domain-domain interfaces are between domains from different families, an increase of 8.9% over the PDB Entries. Overall, the PQS biological units provide a rich source of novel domain-domain interfaces that are not seen in the studied PDB Entries, and so PQS domain-domain interaction data should be exploited wherever possible in the analysis and prediction of protein-protein interactions.

Improving the accuracy of protein secondary structure prediction using structural alignment

Background

The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3) of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence) database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences), the probability of a newly identified sequence having a structural homologue is actually quite high.

Results

We have developed a method that performs structure-based sequence alignments as part of the secondary structure prediction process. By mapping the structure of a known homologue (sequence ID >25%) onto the query protein's sequence, it is possible to predict at least a portion of that query protein's secondary structure. By integrating this structural alignment approach with conventional (sequence-based) secondary structure methods and then combining it with a "jury-of-experts" system to generate a consensus result, it is possible to attain very high prediction accuracy. Using a sequence-unique test set of 1644 proteins from EVA, this new method achieves an average Q3 score of 81.3%. Extensive testing indicates this is approximately 4–5% better than any other method currently available. Assessments using non sequence-unique test sets (typical of those used in proteome annotation or structural genomics) indicate that this new method can achieve a Q3 score approaching 88%.

Conclusion

By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called PROTEUS, that performs these secondary structure predictions is accessible at . For high throughput or batch sequence analyses, the PROTEUS programs, databases (and server) can be downloaded and run locally.

Advances in homology protein structure modeling

Homology modeling plays a central role in determining protein structure in the structural genomics project. The importance of homology modeling has been steadily increasing because of the large gap that exists between the overwhelming number of available protein sequences and experimentally solved protein structures, and also, more importantly, because of the increasing reliability and accuracy of the method. In fact, a protein sequence with over 30% identity to a known structure can often be predicted with an accuracy equivalent to a low-resolution X-ray structure. The recent advances in homology modeling, especially in detecting distant homologues, aligning sequences with template structures, modeling of loops and side chains, as well as detecting errors in a model, have contributed to reliable prediction of protein structure, which was not possible even several years ago. The ongoing efforts in solving protein structures, which can be time-consuming and often difficult, will continue to spur the development of a host of new computational methods that can fill in the gap and further contribute to understanding the relationship between protein structure and function.

Production of a novel class of polyreactive pathogenic autoantibodies in BXD2 mice causes glomerulonephritis and arthritis

OBJECTIVE

The BXD2 mouse strain spontaneously develops glomerulonephritis and erosive arthritis. The goal of this study was to identify the antigenic target proteins and epitopes and to unravel the mechanisms by which the related conditions arise in BXD2 mice.

METHODS

Individual hybridomas isolated from the spleen of a 10-month-old BXD2 mouse were injected intraperitoneally into nonautoimmune mice for evaluation of pathogenicity of each autoantibody. Autoantigens were immunoprecipitated with the pathogenic autoantibody L3A4. Autoantigens were identified using enzyme-linked immunosorbent assay, Western blotting, 2-dimensional gel electrophoresis, and matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MS) and tandem MS. Antigenic epitopes were determined using a high-throughput epitope mapping method.

RESULTS

The production of autoantibodies in BXD2 mice occurred in an orderly progression, with peak levels of autoantibodies to nitrotyrosine (NT)-modified enolase, Ro, alpha-actin, and heat-shock proteins (HSPs) preceding peak levels of antihistone, anti-DNA, and rheumatoid factor. Two monoclonal autoantibodies, L3A4 and T56G10, were identified that could induce immune complexes, renal disease, and/or arthritis. Both L3A4 and T56G10 were polyreactive, and each reacted with separate sets of autoantigens. The antigenic targets of L3A4 consisted of NT-modified enolase, ATP5b, alpha-actin, and Hsp70 family proteins including Hspa5 and Hsp74. The antigenic epitopes of NT-modified enolase and Hspa5 exhibited sequence homology and cross-reactivity, suggesting that epitope spreading may occur through a molecular mimicry mechanism.

CONCLUSION

The polyreactivity of autoantibodies that target a novel class of autoantigens may enable these autoantibodies to induce erosive arthritis or glomerulonephritis either by direct pathogenic mechanisms or indirectly via Fc or immune complex deposition.

MODBASE: a database of annotated comparative protein structure models and associated resources

MODBASE () is a database of annotated comparative protein structure models for all available protein sequences that can be matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on MODELLER for fold assignment, sequence–structure alignment, model building and model assessment (). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, and improvements in the software for calculating the models. MODBASE currently contains 3 094 524 reliable models for domains in 1 094 750 out of 1 817 889 unique protein sequences in the UniProt database (July 5, 2005); only models based on statistically significant alignments and models assessed to have the correct fold despite insignificant alignments are included. MODBASE also allows users to generate comparative models for proteins of interest with the automated modeling server MODWEB (). Our other resources integrated with MODBASE include comprehensive databases of multiple protein structure alignments (DBAli, ), structurally defined ligand binding sites and structurally defined binary domain interfaces (PIBASE, ) as well as predictions of ligand binding sites, interactions between yeast proteins, and functional consequences of human nsSNPs (LS-SNP, ).

Benchmarking of dimeric threading and structure refinement

The understanding of protein-protein interactions is a major goal in the postgenomic era. The prediction of interaction from sequence and the subsequent generation of full-length dimeric models is therefore of great interest especially because the number of structurally characterized protein-protein complexes is sparse. A quality assessment of a benchmark comprised of 170 weakly homologous dimeric target-template pairs is presented. They are predicted in a two-step method, similar to the previously described MULTIPROSPECTOR algorithm: each target sequence is assigned to a monomeric template structure by threading; then, those templates that belong to the same physically interacting dimer template are selected. Additionally we use structural alignments as the "gold standard" to assess the percentage of correctly assigned monomer and dimer templates and to evaluate the threading results with a focus on the quality of the alignments in the interfacial region. This work aims to give a quantitative picture of the quality of dimeric threading. Except for one, all monomer templates are identified correctly, but approximately 40% of the dimer templates are still problematic or incorrect. Preliminary results for three full-length dimeric models generated with the TASSER method show on average a significant improvement of the final model over the initial template.

Alteration of the stereo- and regioselectivity of alkene monooxygenase based on coupling protein interactions

Alkene monooxygenase from Xanthobacter autotrophicus Py2 (XAMO) catalyses the asymmetric epoxidation of a broad range of alkenes. As well as the electron transfer components (a NADH-oxidoreductase and a Rieske-type ferredoxin) and the terminal oxygenase containing the binuclear non-haem iron active site, it requires a small catalytic coupling/effector protein, AamD. The effect of changing AamD stoichiometry and substitution with effector protein homologues on the regioselectivity of toluene hydroxylation and stereoselectivity of styrene epoxidation has been studied. At sub-optimal stoichiometries, there was a marked change in regioselectivity, but no significant change in epoxidation stereoselectivity. Recombinant coupling proteins from a number of phylogenetically related oxygenases were investigated for their ability to functionally replace AamD. Substitution of AamD with IsoD, the coupling protein from the closely related isoprene monooxygenase, changed the regioselectivity of toluene hydroxylation and stereoselectivity of styrene epoxidation, although this was accompanied by a high level of uncoupling. This indicates the importance of coupling protein interaction in controlling the catalytic specificity. Sequence analysis suggests that interaction between Asn34 and Arg57 is important for complementation specificity of the coupling proteins, providing a candidate for site-directed mutagenesis studies.

MollDE: a homology modeling framework you can click with

Molecular Integrated Development Environment (MolIDE) is an integrated application designed to provide homology modeling tools and protocols under a uniform, user-friendly graphical interface. Its main purpose is to combine the most frequent modeling steps in a semi-automatic, interactive way, guiding the user from the target protein sequence to the final three-dimensional protein structure. The typical basic homology modeling process is composed of building sequence profiles of the target sequence family, secondary structure prediction, sequence alignment with PDB structures, assisted alignment editing, side-chain prediction and loop building. All of these steps are available through a graphical user interface. MolIDE's user-friendly and streamlined interactive modeling protocol allows the user to focus on the important modeling questions, hiding from the user the raw data generation and conversion steps. MolIDE was designed from the ground up as an open-source, cross-platform, extensible framework. This allows developers to integrate additional third-party programs to MolIDE.

AVAILABILITY

http://dunbrack.fccc.edu/molide/molide.php

CONTACT

rl_dunbrack@fccc.edu.

Comprehensive search for cysteine cathepsins in the human genome

Our study was aimed at examinating whether or not the human genome encodes for previously unreported cysteine cathepsins. To this end, we used analyses of the genome sequence and mRNA expression levels. The program TBLASTN was employed to scan the draft sequence of the human genome for the 11 known cysteine cathepsins. The cathepsin-like segments in the genome were inspected, filtered, and annotated. In addition to the known cysteine cathepsins, the scan identified three pseudogenes, closely related to cathepsin L, on chromosome 10, as well as two remote homologs, tubulointerstitial protein antigen and tubulointerstitial protein antigen-related protein. No new members of the family were identified. mRNA expression profiles for 10 known human cysteine cathepsins showed varying expression levels in 46 different human tissues and cell lines. No expression of any of the three cathepsin L-like pseudogenes was found. Based on these results, it is likely that to date all human cysteine cathepsins are known.

Systematic analysis of added-value in simple comparative models of protein structure

Added-value is the additional information that a model carries with respect to the template structure used for model building. Thousands of single-template models, corresponding to proteins of known structure, were analyzed. The accuracy of structure-derived properties, such as residue accessibility, surface area, electrostatic potential, and others, was determined as a function of template:target sequence identity by comparing the models with their corresponding experimental structures. Added-value was determined by comparing the accuracy in models with that from templates. Geometry-dependent properties such as neighborhood of buried residues and accessible surface area showed low added-value. Properties that also depend on the protein sequence, such as presence of polar areas and electrostatic potential, showed high added-value. In general added-value increases when template:target sequence identity decreases, but it is also affected by alignment errors. This study justifies the use of models instead of the use of templates to estimate structure-derived properties of a target protein.

Virtual screening of chemical libraries

Virtual screening uses computer-based methods to discover new ligands on the basis of biological structures. Although widely heralded in the 1970s and 1980s, the technique has since struggled to meet its initial promise, and drug discovery remains dominated by empirical screening. Recent successes in predicting new ligands and their receptor-bound structures, and better rates of ligand discovery compared to empirical screening, have re-ignited interest in virtual screening, which is now widely used in drug discovery, albeit on a more limited scale than empirical screening.

Protein fragment reconstruction using various modeling techniques

Recently developed reduced models of proteins with knowledge-based force fields have been applied to a specific case of comparative modeling. From twenty high resolution protein structures of various structural classes, significant fragments of their chains have been removed and treated as unknown. The remaining portions of the structures were treated as fixed - i.e., as templates with an exact alignment. Then, the missed fragments were reconstructed using several modeling tools. These included three reduced types of protein models: the lattice SICHO (Side Chain Only) model, the lattice CABS (Calpha + Cbeta + Side group) model and an off-lattice model similar to the CABS model and called REFINER. The obtained reduced models were compared with more standard comparative modeling tools such as MODELLER and the SWISS-MODEL server. The reduced model results are qualitatively better for the higher resolution lattice models, clearly suggesting that these are now mature, competitive and complementary (in the range of sparse alignments) to the classical tools of comparative modeling. Comparison between the various reduced models strongly suggests that the essential ingredient for the sucessful and accurate modeling of protein structures is not the representation of conformational space (lattice, off-lattice, all-atom) but, rather, the specificity of the force fields used and, perhaps, the sampling techniques employed. These conclusions are encouraging for the future application of the fast reduced models in comparative modeling on a genomic scale.

Successful virtual screening for a submicromolar antagonist of the neurokinin-1 receptor based on a ligand-supported homology model

The neurokinin-1 (NK1) receptor belongs to the family of G-protein-coupled receptors (GPCRs), which represents one of the most relevant target families in small-molecule drug design. In this paper, we describe a homology modeling of the NK1 receptor based on the high-resolution X-ray structure of rhodopsin and the successful virtual screening based on this protein model. The NK1 receptor model has been generated using our new MOBILE (modeling binding sites including ligand information explicitly) approach. Starting with preliminary homology models, it generates improved models of the protein binding pocket together with bound ligands. Ligand information is used as an integral part in the homology modeling process. For the construction of the NK1 receptor, antagonist CP-96345 was used to restrain the modeling. The quality of the obtained model was validated by probing its ability to accommodate additional known NK1 antagonists from structurally diverse classes. On the basis of the generated model and on the analysis of known NK1 antagonists, a pharmacophore model was deduced, which subsequently guided the 2D and 3D database search with UNITY. As a following step, the remaining hits were docked into the modeled binding pocket of the NK1 receptor. Finally, seven compounds were selected for biochemical testing, from which one showed affinity in the submicromolar range. Our results suggest that ligand-supported homology models of GPCRs may be used as effective platforms for structure-based drug design.

Two novel mutations of Wiskott-Aldrich syndrome: the molecular prediction of interaction between the mutated WASP L101P with WASP-interacting protein by molecular modeling

Wiskott-Aldrich syndrome (WAS) is an X-linked disorder characterized by eczema, thrombocytopenia and increased susceptibility of infections, with mutations of the WAS gene being responsible for WAS and X-linked thrombocytopenia. Herein, two novel mutations of WAS at T336C on exon 3, and at 1326-1329, a G deletion on exon 10, resulting in L101P missense mutation and frameshift mutation 444 stop, respectively, are reported. The affected patients with either mutation showed severe suppression of WAS protein (WASP) levels, T cell proliferation, and CFSE-labeled T cells division. Because WASP L101 have not shown direct nuclear Overhauser effect (NOE) contact with the WASP-interacting protein (WIP) in NMR spectroscopy, molecular modeling was performed to evaluate the molecular effect of WASP P101 to WIP peptide. It is presumed that P101 induced a conformational change in the Q99 residue of WASP and made the side chain of Q99 move away from the WIP peptide, resulting in disruption of the hydrogen bond between Q99 WASP and Y475 WIP. A possible model for the molecular pathogenesis of WAS has been proposed by analyzing the interactions of WASP and WIP using a molecular modeling study.

High-throughput computational and experimental techniques in structural genomics

Structural genomics has as its goal the provision of structural information for all possible ORF sequences through a combination of experimental and computational approaches. The access to genome sequences and cloning resources from an ever-widening array of organisms is driving high-throughput structural studies by the New York Structural Genomics Research Consortium. In this report, we outline the progress of the Consortium in establishing its pipeline for structural genomics, and some of the experimental and bioinformatics efforts leading to structural annotation of proteins. The Consortium has established a pipeline for structural biology studies, automated modeling of ORF sequences using solved (template) structures, and a novel high-throughput approach (metallomics) to examining the metal binding to purified protein targets. The Consortium has so far produced 493 purified proteins from >1077 expression vectors. A total of 95 have resulted in crystal structures, and 81 are deposited in the Protein Data Bank (PDB). Comparative modeling of these structures has generated >40,000 structural models. We also initiated a high-throughput metal analysis of the purified proteins; this has determined that 10%-15% of the targets contain a stoichiometric structural or catalytic transition metal atom. The progress of the structural genomics centers in the U.S. and around the world suggests that the goal of providing useful structural information on most all ORF domains will be realized. This projected resource will provide structural biology information important to understanding the function of most proteins of the cell.

Alignment of protein sequences by their profiles

The accuracy of an alignment between two protein sequences can be improved by including other detectably related sequences in the comparison. We optimize and benchmark such an approach that relies on aligning two multiple sequence alignments, each one including one of the two protein sequences. Thirteen different protocols for creating and comparing profiles corresponding to the multiple sequence alignments are implemented in the SALIGN command of MODELLER. A test set of 200 pairwise, structure-based alignments with sequence identities below 40% is used to benchmark the 13 protocols as well as a number of previously described sequence alignment methods, including heuristic pairwise sequence alignment by BLAST, pairwise sequence alignment by global dynamic programming with an affine gap penalty function by the ALIGN command of MODELLER, sequence-profile alignment by PSI-BLAST, Hidden Markov Model methods implemented in SAM and LOBSTER, pairwise sequence alignment relying on predicted local structure by SEA, and multiple sequence alignment by CLUSTALW and COMPASS. The alignment accuracies of the best new protocols were significantly better than those of the other tested methods. For example, the fraction of the correctly aligned residues relative to the structure-based alignment by the best protocol is 56%, which can be compared with the accuracies of 26%, 42%, 43%, 48%, 50%, 49%, 43%, and 43% for the other methods, respectively. The new method is currently applied to large-scale comparative protein structure modeling of all known sequences.

Comparative structural modeling and inference of conserved protein classes in Drosophila seminal fluid

The constituents of seminal fluid are a complex mixture of proteins and other molecules, most of whose functions have yet to be determined and many of which are rapidly evolving. As a step in elucidating the roles of these proteins and exposing potential functional similarities hidden by their rapid evolution, we performed comparative structural modeling on 28 of 52 predicted seminal proteins produced in the Drosophila melanogaster male accessory gland. Each model was characterized by defining residues likely to be important for structure and function. Comparisons of known protein structures with predicted accessory gland proteins (Acps) revealed similarities undetectable by primary sequence alignments. The structures predict that Acps fall into several categories: regulators of proteolysis, lipid modifiers, immunity/protection, sperm-binding proteins, and peptide hormones. The comparative structural modeling approach indicates that major functional classes of mammalian and Drosophila seminal fluid proteins are conserved, despite differences in reproductive strategies. This is particularly striking in the face of the rapid protein sequence evolution that characterizes many reproductive proteins, including Drosophila and mammalian seminal proteins.

SDPMOD: an automated comparative modeling server for small disulfide-bonded proteins

Small disulfide-bonded proteins (SDPs) are rich sources for therapeutic drugs. Designing drugs from these proteins requires three-dimensional structural information, which is only available for a subset of these proteins. SDPMOD addresses this deficit in structural information by providing a freely available automated comparative modeling service to the research community. For expert users, SDPMOD offers a manual mode that permits the selection of a desired template as well as a semi-automated mode that allows users to select the template from a suggested list. Besides the selection of templates, expert users can edit the target-template alignment, thus allowing further customization of the modeling process. Furthermore, the web service provides model stereochemical quality evaluation using PROCHECK. SDPMOD is freely accessible to academic users via the web interface at http://proline.bic.nus.edu.sg/sdpmod.

Detection of homologous proteins by an intermediate sequence search

We developed a variant of the intermediate sequence search method (ISS(new)) for detection and alignment of weakly similar pairs of protein sequences. ISS(new) relates two query sequences by an intermediate sequence that is potentially homologous to both queries. The improvement was achieved by a more robust overlap score for a match between the queries through an intermediate. The approach was benchmarked on a data set of 2369 sequences of known structure with insignificant sequence similarity to each other (BLAST E-value larger than 0.001); 2050 of these sequences had a related structure in the set. ISS(new) performed significantly better than both PSI-BLAST and a previously described intermediate sequence search method. PSI-BLAST could not detect correct homologs for 1619 of the 2369 sequences. In contrast, ISS(new) assigned a correct homolog as the top hit for 121 of these 1619 sequences, while incorrectly assigning homologs for only nine targets; it did not assign homologs for the remainder of the sequences. By estimate, ISS(new) may be able to assign the folds of domains in approximately 29,000 of the approximately 500,000 sequences unassigned by PSI-BLAST, with 90% specificity (1 - false positives fraction). In addition, we show that the 15 alignments with the most significant BLAST E-values include the nearly best alignments constructed by ISS(new).

Structure-based assessment of missense mutations in human BRCA1: implications for breast and ovarian cancer predisposition

The BRCA1 gene from individuals at risk of breast and ovarian cancers can be screened for the presence of mutations. However, the cancer association of most alleles carrying missense mutations is unknown, thus creating significant problems for genetic counseling. To increase our ability to identify cancer-associated mutations in BRCA1, we set out to use the principles of protein three-dimensional structure as well as the correlation between the cancer-associated mutations and those that abolish transcriptional activation. Thirty-one of 37 missense mutations of known impact on the transcriptional activation function of BRCA1 are readily rationalized in structural terms. Loss-of-function mutations involve nonconservative changes in the core of the BRCA1 C-terminus (BRCT) fold or are localized in a groove that presumably forms a binding site involved in the transcriptional activation by BRCA1; mutations that do not abolish transcriptional activation are either conservative changes in the core or are on the surface outside of the putative binding site. Next, structure-based rules for predicting functional consequences of a given missense mutation were applied to 57 germ-line BRCA1 variants of unknown cancer association. Such a structure-based approach may be helpful in an integrated effort to identify mutations that predispose individuals to cancer.

Comprehensive de novo structure prediction in a systems-biology context for the archaea Halobacterium sp. NRC-1

BACKGROUND

Large fractions of all fully sequenced genomes code for proteins of unknown function. Annotating these proteins of unknown function remains a critical bottleneck for systems biology and is crucial to understanding the biological relevance of genome-wide changes in mRNA and protein expression, protein-protein and protein-DNA interactions. The work reported here demonstrates that de novo structure prediction is now a viable option for providing general function information for many proteins of unknown function.

RESULTS

We have used Rosetta de novo structure prediction to predict three-dimensional structures for 1,185 proteins and protein domains (<150 residues in length) found in Halobacterium NRC-1, a widely studied halophilic archaeon. Predicted structures were searched against the Protein Data Bank to identify fold similarities and extrapolate putative functions. They were analyzed in the context of a predicted association network composed of several sources of functional associations such as: predicted protein interactions, predicted operons, phylogenetic profile similarity and domain fusion. To illustrate this approach, we highlight three cases where our combined procedure has provided novel insights into our understanding of chemotaxis, possible prophage remnants in Halobacterium NRC-1 and archaeal transcriptional regulators.

CONCLUSIONS

Simultaneous analysis of the association network, coordinated mRNA level changes in microarray experiments and genome-wide structure prediction has allowed us to glean significant biological insights into the roles of several Halobacterium NRC-1 proteins of previously unknown function, and significantly reduce the number of proteins encoded in the genome of this haloarchaeon for which no annotation is available.

Structural proteomics of the poxvirus family

Recent concerns over the potential use of variola virus-commonly known as smallpox-and other orthopox viruses as weapons of bioterrorism have increased research efforts towards creating new antiviral drugs and safer more effective vaccines. Here we introduce a new resource for structural information of poxvirus proteins: the poxvirus proteomics database (PPDB). In the PPDB, we leverage recently developed bioinformatics structure prediction tools on a genomic scale and provide results in a publicly accessible format. The current version of the system contains both experimentally determined and predicted information about protein structural features, such as secondary structure and relative solvent accessibility, as well as tertiary structure and homology information. The system is automated to read the primary sequences from the database, produce the new information for each sequence, and update the database monthly and as new tools are incorporated. The PPDB contains detailed information on the open reading frames (ORFs) in the Copenhagen strain of the vaccinia virus genome. The contents of the PPDB can be accessed through a simple web interface. Inclusion of additional poxvirus genomes in the PPDB is in progress. The PPDB has an upward scalable informatics infrastructure that can readily be applied to viral, bacterial, as well as eukaryotic genomes.

Structures and technology for biologists

Structural genomics efforts are already producing a quarter of all 'new' macromolecular structures (<30% sequence identity to previously solved structures) and are stimulating development of systematic and automated approaches to structure determination. The thousands of new structures likely to be determined and the technologies and infrastructure likely to be developed over the next decade will benefit all biologists.

MODBASE, a database of annotated comparative protein structure models, and associated resources

MODBASE (http://salilab.org/modbase) is a relational database of annotated comparative protein structure models for all available protein sequences matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on the MODELLER package for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE uses the MySQL relational database management system for flexible querying and CHIMERA for viewing the sequences and structures (http://www.cgl.ucsf.edu/chimera/). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, as well as improvements in the software for calculating the models. For ease of access, MODBASE is organized into different data sets. The largest data set contains 1,26,629 models for domains in 659,495 out of 1,182,126 unique protein sequences in the complete Swiss-Prot/TrEMBL database (August 25, 2003); only models based on alignments with significant similarity scores and models assessed to have the correct fold despite insignificant alignments are included. Another model data set supports target selection and structure-based annotation by the New York Structural Genomics Research Consortium; e.g. the 53 new structures produced by the consortium allowed us to characterize structurally 24,113 sequences. MODBASE also contains binding site predictions for small ligands and a set of predicted interactions between pairs of modeled sequences from the same genome. Our other resources associated with MODBASE include a comprehensive database of multiple protein structure alignments (DBALI, http://salilab.org/dbali) as well as web servers for automated comparative modeling with MODPIPE (MODWEB, http://salilab. org/modweb), modeling of loops in protein structures (MODLOOP, http://salilab.org/modloop) and predicting functional consequences of single nucleotide polymorphisms (SNPWEB, http://salilab. org/snpweb).

The SWISS-MODEL Repository of annotated three-dimensional protein structure homology models

The SWISS-MODEL Repository is a database of annotated three-dimensional comparative protein structure models generated by the fully automated homology-modelling pipeline SWISS-MODEL. The Repository currently contains about 300,000 three-dimensional models for sequences from the Swiss-Prot and TrEMBL databases. The content of the Repository is updated on a regular basis incorporating new sequences, taking advantage of new template structures becoming available and reflecting improvements in the underlying modelling algorithms. Each entry consists of one or more three-dimensional protein models, the superposed template structures, the alignments on which the models are based, a summary of the modelling process and a force field based quality assessment. The SWISS-MODEL Repository can be queried via an interactive website at http://swissmodel.expasy. org/repository/. Annotation and cross-linking of the models with other databases, e.g. Swiss-Prot on the ExPASy server, allow for seamless navigation between protein sequence and structure information. The aim of the SWISS-MODEL Repository is to provide access to an up-to-date collection of annotated three-dimensional protein models generated by automated homology modelling, bridging the gap between sequence and structure databases.