Literature DB >> 15322290

Modeling the structure of bound peptide ligands to major histocompatibility complex.

Joo Chuan Tong1, Tin Wee Tan, Shoba Ranganathan.   

Abstract

In this article, we present a new technique for the rapid and precise docking of peptides to MHC class I and class II receptors. Our docking procedure consists of three steps: (1) peptide residues near the ends of the binding groove are docked by using an efficient pseudo-Brownian rigid body docking procedure followed by (2) loop closure of the intervening backbone structure by satisfaction of spatial constraints, and subsequently, (3) the refinement of the entire backbone and ligand interacting side chains and receptor side chains experiencing atomic clash at the MHC receptor-peptide interface. The method was tested by remodeling of 40 nonredundant complexes of at least 3.00 A resolution for which three-dimensional structural information is available and independently for docking peptides derived from 15 nonredundant complexes into a single template structure. In the first test, 33 out of 40 MHC class I and class II peptides and in the second test, 11 out of 15 MHC-peptide complexes were modeled with a Calpha RMSD < 1.00 A.

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Year:  2004        PMID: 15322290      PMCID: PMC2279999          DOI: 10.1110/ps.04631204

Source DB:  PubMed          Journal:  Protein Sci        ISSN: 0961-8368            Impact factor:   6.725


  29 in total

1.  Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins.

Authors:  D Rognan; S L Lauemoller; A Holm; S Buus; V Tschinke
Journal:  J Med Chem       Date:  1999-11-04       Impact factor: 7.446

2.  Flexible docking of peptide ligands to proteins.

Authors:  J Desmet; M De Maeyer; J Spriet; I Lasters
Journal:  Methods Mol Biol       Date:  2000

3.  Soft protein-protein docking in internal coordinates.

Authors:  Juan Fernández-Recio; Maxim Totrov; Ruben Abagyan
Journal:  Protein Sci       Date:  2002-02       Impact factor: 6.725

4.  Prediction of promiscuous peptides that bind HLA class I molecules.

Authors:  Vladimir Brusic; Nikolai Petrovsky; Guanglan Zhang; Vladimir B Bajic
Journal:  Immunol Cell Biol       Date:  2002-06       Impact factor: 5.126

5.  MPID: MHC-Peptide Interaction Database for sequence-structure-function information on peptides binding to MHC molecules.

Authors:  Kunde Ramamoorthy Govindarajan; Pandjassarame Kangueane; Tin Wee Tan; Shoba Ranganathan
Journal:  Bioinformatics       Date:  2003-01-22       Impact factor: 6.937

Review 6.  Databases and data mining for computational vaccinology.

Authors:  Darren R Flower
Journal:  Curr Opin Drug Discov Devel       Date:  2003-05

7.  Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models.

Authors:  H Mamitsuka
Journal:  Proteins       Date:  1998-12-01

8.  Modeling of the TCR-MHC-peptide complex.

Authors:  O Michielin; I Luescher; M Karplus
Journal:  J Mol Biol       Date:  2000-07-28       Impact factor: 5.469

9.  Binding free energy differences in a TCR-peptide-MHC complex induced by a peptide mutation: a simulation analysis.

Authors:  Olivier Michielin; Martin Karplus
Journal:  J Mol Biol       Date:  2002-11-29       Impact factor: 5.469

10.  Prediction of MHC class I binding peptides, using SVMHC.

Authors:  Pierre Dönnes; Arne Elofsson
Journal:  BMC Bioinformatics       Date:  2002-09-11       Impact factor: 3.169

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  29 in total

1.  Design of enhanced agonists through the use of a new virtual screening method: application to peptides that bind class I major histocompatibility complex (MHC) molecules.

Authors:  Sergio Madurga; Ignasi Belda; Xavier Llorà; Ernest Giralt
Journal:  Protein Sci       Date:  2005-08       Impact factor: 6.725

2.  Mass spectral data for 64 eluted peptides and structural modeling define peptide binding preferences for class I alleles in two chicken MHC-B haplotypes associated with opposite responses to Marek's disease.

Authors:  Mark A Sherman; Ronald M Goto; Roger E Moore; Henry D Hunt; Terry D Lee; Marcia M Miller
Journal:  Immunogenetics       Date:  2008-07-09       Impact factor: 2.846

3.  Large-scale characterization of peptide-MHC binding landscapes with structural simulations.

Authors:  Chen Yanover; Philip Bradley
Journal:  Proc Natl Acad Sci U S A       Date:  2011-04-08       Impact factor: 11.205

4.  Prediction of peptide binding to a major histocompatibility complex class I molecule based on docking simulation.

Authors:  Takeshi Ishikawa
Journal:  J Comput Aided Mol Des       Date:  2016-09-13       Impact factor: 3.686

Review 5.  Emerging Concepts in TCR Specificity: Rationalizing and (Maybe) Predicting Outcomes.

Authors:  Nishant K Singh; Timothy P Riley; Sarah Catherine B Baker; Tyler Borrman; Zhiping Weng; Brian M Baker
Journal:  J Immunol       Date:  2017-10-01       Impact factor: 5.422

6.  Structural allele-specific patterns adopted by epitopes in the MHC-I cleft and reconstruction of MHC:peptide complexes to cross-reactivity assessment.

Authors:  Dinler A Antunes; Gustavo F Vieira; Maurício M Rigo; Samuel P Cibulski; Marialva Sinigaglia; José A B Chies
Journal:  PLoS One       Date:  2010-04-26       Impact factor: 3.240

7.  pDOCK: a new technique for rapid and accurate docking of peptide ligands to Major Histocompatibility Complexes.

Authors:  Javed Mohammed Khan; Shoba Ranganathan
Journal:  Immunome Res       Date:  2010-09-27

8.  Predicting HLA class I non-permissive amino acid residues substitutions.

Authors:  T Andrew Binkowski; Susana R Marino; Andrzej Joachimiak
Journal:  PLoS One       Date:  2012-08-08       Impact factor: 3.240

Review 9.  T-cell epitope vaccine design by immunoinformatics.

Authors:  Atanas Patronov; Irini Doytchinova
Journal:  Open Biol       Date:  2013-01-08       Impact factor: 6.411

10.  Development of a novel in silico docking simulation model for the fine HIV-1 cytotoxic T lymphocyte epitope mapping.

Authors:  Masahiko Mori; Kei Matsuki; Tomoyuki Maekawa; Mari Tanaka; Busarawan Sriwanthana; Masaru Yokoyama; Koya Ariyoshi
Journal:  PLoS One       Date:  2012-07-27       Impact factor: 3.240

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