Toward the atomistic simulation of T cell epitopes Automated construction of MHC: Peptide structures for free energy calculations
Department of Crystallography, University of London, Birkbeck College, Malet Street, London WC1E 7HX, United Kingdom. Journal of Molecular Graphics and Modelling
(Impact Factor: 1.72).
03/2008; 26(6). DOI: 10.1016/j.jmgm.2007.07.005
Epitopes mediated by T cells lie at the heart of the adaptive immune response and form the essential nucleus of anti-tumour peptide or epitope-based vaccines. Antigenic T cell epitopes are mediated by major histocompatibility complex (MHC) molecules, which present them to T cell receptors. Calculating the affinity between a given MHC molecule and an antigenic peptide using experimental approaches is both difficult and time consuming, thus various computational methods have been developed for this purpose. A server has been developed to allow a structural approach to the problem by generating specific MHC:peptide complex structures and providing configuration files to run molecular modelling simulations upon them. A system has been produced which allows the automated construction of MHC:peptide structure files and the corresponding configuration files required to execute a molecular dynamics simulation using NAMD. The system has been made available through a web-based front end and stand-alone scripts. Previous attempts at structural prediction of MHC:peptide affinity have been limited due to the paucity of structures and the computational expense in running large scale molecular dynamics simulations. The MHCsim server (http://igrid-ext.cryst.bbk.ac.uk/MHCsim) allows the user to rapidly generate any desired MHC:peptide complex and will facilitate molecular modelling simulation of MHC complexes on an unprecedented scale.
Available from: Marialva Sinigaglia
- "We have considered these issues to develop a reliable technique for building pMHC-I complexes (D1-EM-D2) (12), which has been validated for human (HLA-B*27:05 and HLA-A*02:01) and murine alleles (H2-Kb and H2-Db). Some alternative in silico techniques to construct pMHC complexes have already been developed (14–16), but the question relying above all current available approaches is how reliable are these modelled structures, considering the importance of the information that they carry? In immunology, it is not enough for a model to just meet the basic stereochemical conditions of molecular modelling. "
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ABSTRACT: The CrossTope is a highly curate repository of three-dimensional structures of peptide:major histocompatibility complex (MHC) class I complexes (pMHC-I). The complexes hosted by this databank were obtained in protein databases and by large-scale in silico construction of pMHC-I structures, using a new approach developed by our group. At this moment, the database contains 182 ‘non-redundant’ pMHC-I complexes from two human and two murine alleles. A web server provides interface for database query. The user can download (i) structure coordinate files and (ii) topological and charges distribution maps images from the T-cell receptor-interacting surface of pMHC-I complexes. The retrieved structures and maps can be used to cluster similar epitopes in cross-reactivity approaches, to analyse viral escape mutations in a structural level or even to improve the immunogenicity of tumour antigens.
Database The Journal of Biological Databases and Curation 01/2013; 2013:bat002. DOI:10.1093/database/bat002 · 3.37 Impact Factor
Available from: Priyanka Chaubey
- "The selected peptides obtained by the above bioinformatics tools were listed with their anchor residues. For structural simulations, the molecular models of the peptides were constructed using a web based interface MHCsim (Todman et al., 2008) available online. Sample peptides of HIGH affinity binders for a few alleles whose crystal structures are known (HLA-B*5102, HLA-B*2705, HLA-DRBI*0101) were obtained from the Protein Data Bank (Bergman et al., 2000) and modelled employing structural templates (viz. "
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ABSTRACT: The present work uses a structural simulation approach to identify the potential target vaccine candidates or T cell epitopes (antigenic region that can activate T cell response) in two iron acquisition proteins from Neisseria. An iron regulated outer membrane protein frpB: extracellular, [NMB1988], and a Major ferric Iron-binding protein fbpA: periplasmic, [NMB0634] critical for the survival of the pathogen in the host were used. Ten novel promiscuous epitopes from the two iron acquisition proteins were identified using bioinformatics interface. Of these epitopes, 630VQKAVGSIL638 present on frpB with high binding affinity for allele HLA*DR1 was identified with an anchor position at P2, an aliphatic residue at P4 and glycine at P6 making it thereby a potential quality choice for linking peptide-loaded MHC dynamics to T-cell activation and vaccine constructs. The feasibility and structural binding of predicted peptide to the respective HLA allele
was investigated by molecular modeling and template-based structural simulation. The conformational properties of the linear peptide were investigated by molecular dynamics using GROMOS96 package and Swiss PDB viewer.
Available from: Gajendra Pal Singh Raghava
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ABSTRACT: Genome sequences from many organisms, including humans, have been completed, and high-throughput analyses have produced burgeoning volumes of 'omics' data. Bioinformatics is crucial for the management and analysis of such data and is increasingly used to accelerate progress in a wide variety of large-scale and object-specific functional analyses. Refined algorithms enable biotechnologists to follow 'computer-aided strategies' based on experiments driven by high-confidence predictions. In order to address compound problems, current efforts in immuno-informatics and reverse vaccinology are aimed at developing and tuning integrative approaches and user-friendly, automated bioinformatics environments. This will herald a move to 'computer-aided biotechnology': smart projects in which time-consuming and expensive large-scale experimental approaches are progressively replaced by prediction-driven investigations.
Trends in Biotechnology 05/2008; 26(4):190-200. DOI:10.1016/j.tibtech.2007.12.006 · 11.96 Impact Factor
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