UNLABELLED: Sequence-structure-function information is critical in understanding the mechanism of pMHC and TR/pMHC binding and recognition. A database for sequence-structure-function information on pMHC and TR/pMHC interactions, MHC-Peptide Interaction Database-TR version 2 (MPID-T2), is now available augmented with the latest PDB and IMGT/3Dstructure-DB data, advanced features and new parameters for the analysis of pMHC and TR/pMHC structures. AVAILABILITY: http://biolinfo.org/mpid-t2. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The increasing infectious disease outbreaks has led to a need for new research to better understand the disease's origins, epidemiological features and pathogenicity caused by fast-mutating, fast-spreading viruses. Traditional sequence analysis methods do not take into account the spatio-temporal dynamics of rapidly evolving and spreading viral species. They are also focused on identifying single-point mutations. In this paper, we propose a novel approach that incorporates space-time relationships for studying changes in protein sequences from fast mutating viruses. We aim to detect both single-point mutations as well as k-mutations in the viral sequences. We define the problem of mutation chain pattern mining and design algorithms to discover valid mutation chains. Compact data structures to facilitate the mining process as well as pruning strategies to increase the scalability of the algorithms are devised. Experiments on both synthetic datasets and real world influenza A virus dataset show that our algorithms are scalable and effective in discovering mutations that occur geographically over time.

Modern drug discovery is characterized by the production of vast quantities of compounds and the need to examine these huge libraries in short periods of time. The need to store, manage and analyze these rapidly increasing resources has given rise to the field known as computer-aided drug design (CADD). CADD represents computational methods and resources that are used to facilitate the design and discovery of new therapeutic solutions. Digital repositories, containing detailed information on drugs and other useful compounds, are goldmines for the study of chemical reactions capabilities. Design libraries, with the potential to generate molecular variants in their entirety, allow the selection and sampling of chemical compounds with diverse characteristics. Fold recognition, for studying sequence-structure homology between protein sequences and structures, are helpful for inferring binding sites and molecular functions. Virtual screening, the in silico analog of high-throughput screening, offers great promise for systematic evaluation of huge chemical libraries to identify potential lead candidates that can be synthesized and tested. In this article, we present an overview of the most important data sources and computational methods for the discovery of new molecular entities. The workflow of the entire virtual screening campaign is discussed, from data collection through to post-screening analysis.

SUMMARY: A variety of specialist databases have been developed to facilitate the study of allergens. However, these databases either contain different subsets of allergen data or are deficient in tools for assessing potential allergenicity of proteins. Here, we describe Allergen Atlas, a comprehensive repository of experimentally validated allergen sequences collected from in-house laboratory, online data submission, literature reports and all existing general-purpose and specialist databases. Each entry was manually verified, classified and hyperlinked to major databases including Swiss-Prot, Protein Data Bank (PDB), Gene Ontology (GO), Pfam and PubMed. The database is integrated with analysis tools that include: (i) keyword search, (ii) BLAST, (iii) position-specific iterative BLAST (PSI-BLAST), (iv) FAO/WHO criteria search, (v) graphical representation of allergen information network and (vi) online data submission. The latest version contains information of 1593 allergen sequences (496 IUIS allergens, 978 experimentally verified allergens and 119 new sequences), 56 IgE epitope sequences, 679 links to PDB structures and 155 links to Pfam domains. AVAILABILITY: Allergen Atlas is freely available at http://tiger.dbs.nus.edu.sg/ATLAS/.

BACKGROUND: Type I signal peptidases (SPases) are essential membrane-bound serine proteases responsible for the cleavage of signal peptides from proteins that are translocated across biological membranes. The crystal structure of SPase in complex with signal peptide has not been solved and their substrate-binding site and binding specificities remain poorly understood. We report here a structure-based model for Escherichia coli DsbA 13-25 in complex with its endogenous type I SPase. RESULTS: The bound structure of DsbA 13-25 in complex with its endogenous type I SPase reported here reveals the existence of an extended conformation of the precursor protein with a pronounced backbone twist between positions P3 and P1'. Residues 13-25 of DsbA occupy, and thereby define 13 subsites, S7 to S6', within the SPase substrate-binding site. The newly defined subsites, S1' to S6' play critical roles in the substrate specificities of E. coli SPase. Our results are in accord with available experimental data. CONCLUSION: Collectively, the results of this study provide interesting new insights into the binding conformation of signal peptides and the substrate-binding site of E. coli SPase. This is the first report on the modeling of a precursor protein into the entire SPase binding site. Together with the conserved precursor protein binding conformation, the existing and newly identified substrate binding sites readily explain SPase cleavage fidelity, consistent with existing biochemical results and solution structures of inhibitors in complex with E. coli SPase. Our data suggests that both signal and mature moiety sequences play important roles and should be considered in the development of predictive tools.

BACKGROUND: Bioinformatics tools are commonly used for assessing potential protein allergenicity. While these methods have achieved good accuracies for highly conserved sequences, they are less effective when the overall similarity is low. In this study, we assessed the feasibility of using position-specific scoring matrices as a basis for predicting potential allergenicity in proteins. RESULTS: Two simple methods for predicting potential allergenicity in proteins, based on general and group-specific allergen profiles, are presented. Testing results indicate that the performances of both methods are comparable to the best results of other methods. The group-specific profile approach, with a sensitivity of 84.04% and specificity of 96.52%, gives similar results as those obtained using the general profile approach (sensitivity = 82.45%, specificity = 96.92%). CONCLUSION: We show that position-specific scoring matrices are highly promising for constructing computational models suitable for allergenicity assessment. These data suggest it may be possible to apply a targeted approach for allergenicity assessment based on the profiles of allergens of interest.

T-cell recognition of peptide/major histocompatibility complex (MHC) is a prerequisite for cellular immunity. Recently, there has been an influx of bioinformatics tools to facilitate the identification of T-cell epitopes to specific MHC alleles. This article examines existing computational strategies for the study of peptide/MHC interactions. The most important bioinformatics tools and methods with relevance to the study of peptide/MHC interactions have been reviewed. We have also provided guidelines for predicting antigenic peptides based on the availability of existing experimental data.

MOTIVATION: Classification of human leukocyte antigen (HLA) proteins into supertypes underpins the development of epitope-based vaccines with wide population coverage. Current methods for HLA supertype definition, based on common structural features of HLA proteins and/or their functional binding specificities, leave structural interaction characteristics among different HLA supertypes with antigenic peptides unexplored. METHODS: We describe the use of structural interaction descriptors for the analysis of 68 peptide/HLA class I crystallographic structures. Interaction parameters computed include the number of intermolecular hydrogen bonds between each HLA protein and its corresponding bound peptide, solvent accessibility, gap volume and gap index. RESULTS: The structural interactions patterns of peptide/HLA class I complexes investigated herein vary among individual alleles and may be grouped in a supertype dependent manner. Using the proposed methodology, eight HLA class I supertypes were defined based on existing experimental crystallographic structures which largely overlaps (77% consensus) with the definitions by binding motifs. This mode of classification, which considers conformational information of both peptide and HLA proteins, provides an alternative to the characterization of supertypes using either peptide or HLA protein information alone.

UNLABELLED: Assessment of potential allergenicity and patterns of cross-reactivity is necessary whenever novel proteins are introduced into human food chain. Current bioinformatic methods in allergology focus mainly on the prediction of allergenic proteins, with no information on cross-reactivity patterns among known allergens. In this study, we present AllerTool, a web server with essential tools for the assessment of predicted as well as published cross-reactivity patterns of allergens. The analysis tools include graphical representation of allergen cross-reactivity information; a local sequence comparison tool that displays information of known cross-reactive allergens; a sequence similarity search tool for assessment of cross-reactivity in accordance to FAO/WHO Codex alimentarius guidelines; and a method based on support vector machine (SVM). A 10-fold cross-validation results showed that the area under the receiver operating curve (A(ROC)) of SVM models is 0.90 with 86.00% sensitivity (SE) at specificity (SP) of 86.00%. AVAILABILITY: AllerTool is freely available at http://research.i2r.a-star.edu.sg/AllerTool/.

MOTIVATION: While processing of MHC class II antigens for presentation to helper T-cells is essential for normal immune response, it is also implicated in the pathogenesis of autoimmune disorders and hypersensitivity reactions. Sequence-based computational techniques for predicting HLA-DQ binding peptides have encountered limited success, with few prediction techniques developed using three-dimensional models. METHODS: We describe a structure-based prediction model for modeling peptide-DQ3.2beta complexes. We have developed a rapid and accurate protocol for docking candidate peptides into the DQ3.2beta receptor and a scoring function to discriminate binders from the background. The scoring function was rigorously trained, tested and validated using experimentally verified DQ3.2beta binding and non-binding peptides obtained from biochemical and functional studies. RESULTS: Our model predicts DQ3.2beta binding peptides with high accuracy [area under the receiver operating characteristic (ROC) curve A(ROC) > 0.90], compared with experimental data. We investigated the binding patterns of DQ3.2beta peptides and illustrate that several registers exist within a candidate binding peptide. Further analysis reveals that peptides with multiple registers occur predominantly for high-affinity binders.

BACKGROUND: Pemphigus vulgaris (PV) is a severe autoimmune blistering skin disorder that is strongly associated with major histocompatibility complex class II alleles DRB1*0402 and DQB1*0503. The target antigen of PV, desmoglein 3 (Dsg3), is crucial for initiating T-cell response in early disease. Although a number of T-cell specificities within Dsg3 have been reported, the number is limited and the role of T-cells in the pathogenesis of PV remains poorly understood. We report here a structure-based model for the prediction of peptide binding to DRB1*0402 and DQB1*0503. The scoring functions were rigorously trained, tested and validated using experimentally verified peptide sequences. RESULTS: High predictivity is obtained for both DRB1*0402 (r2 = 0.90, s = 1.20 kJ/mol, q2 = 0.82, s(press) = 1.61 kJ/mol) and DQB1*0503 (r2 = 0.95, s = 1.20 kJ/mol, q2 = 0.75, s(press) = 2.15 kJ/mol) models, compared to experimental data. We investigated the binding patterns of Dsg3 peptides and illustrate the existence of multiple immunodominant epitopes that may be responsible for both disease initiation and propagation in PV. Further analysis reveals that DRB1*0402 and DQB1*0503 may share similar specificities by binding peptides at different binding registers, thus providing a molecular mechanism for the dual HLA association observed in PV. CONCLUSION: Collectively, the results of this study provide interesting new insights into the pathology of PV. This is the first report illustrating high-level of cross-reactivity between both PV-implicated alleles, DRB1*0402 and DQB1*0503, as well as the existence of a potentially large number of T-cell epitopes throughout the entire Dsg3 extracellular domain (ECD) and transmembrane region. Our results reveal that DR4 and DR6 PV may initiate in the ECD and transmembrane region respectively, with implications for immunotherapeutic strategies for the treatment of this autoimmune disease.