title: MH2c: Characterization of major histocompatibilityα-helices - an information criterion approach.
abstract: Major histocompatibility proteins share a common overall structure or peptide binding groove. Two binding groove domains, on the same chain for major histocompatibility class I or on two different chains for major histocompatibility class II, contribute to that structure that consists of twoα-helices ("wall") and a sheet of eight anti-parallel beta strands ("floor"). Apart from the peptide presented in the groove, the major histocompatibilityα-helices play a central role for the interaction with the T cell receptor. This study presents a generalized mathematical approach for the characterization of these helices. We employed polynomials of degree 1 to 7 and splines with 1 to 2 nodes based on polynomials of degree 1 to 7 on theα-helices projected on their principal components. We evaluated all models with a corrected Akaike Information Criterion to determine which model represents theα-helices in the best way without overfitting the data. This method is applicable for both the stationary and the dynamic characterization ofα-helices. By deriving differential geometric parameters from these models one obtains a reliable method to characterize and compareα-helices for a broad range of applications.
  
  
  
  Program title:MH2c (MH helix curves)Catalogue identifier:AELX_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AELX_v1_0.htmlProgram obtainable from:CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions:Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.:327 565No. of bytes in distributed program, including test data, etc.:17 433 656Distribution format:tar.gzProgramming language:MatlabComputer:Personal computer architecturesOperating system:Windows, Linux, Mac (all systems on which Matlab can be installed)RAM:Depends on the trajectory size, min. 1 GB (Matlab)Classification:2.1, 4.9, 4.14External routines:Curve Fitting Toolbox and Statistic Toolbox of MatlabNature of problem:Major histocompatibility (MH) proteins share a similar overall structure. However, identical MH alleles which present different peptides differ by subtle conformational alterations. One hypothesis is that such conformational differences could be another level of T cell regulation. By this software package we present a reliable and systematic way to compare different MH structures to each other.Solution method:We tested several fitting approaches on all available experimental crystal structures of MH to obtain an overall picture of how to describe MH helices. For this purpose we transformed all complexes into the same space and applied splines and polynomials of several degrees to them. To draw a general conclusion which method fits them best we employed the "corrected Akaike Information Criterion". The software is applicable for all kinds of helices of biomolecules.Running time:Depends on the data, for a single stationary structure the runtime should not exceed a few seconds.
authors.affiliation: Center for Medical Statistics, Informatics, and Intelligent Systems; Section for Biosimulation and Bioinformatics, Medical University of Vienna, Austria.
authors.forename: B
authors.initials: B
authors.lastname: Hischenhuber
authors.forename: F
authors.initials: F
authors.lastname: Frommlet
authors.forename: W
authors.initials: W
authors.lastname: Schreiner
authors.forename: B
authors.initials: B
authors.lastname: Knapp
keywords: AIC, Akaike Information Criterion; Akaike Information Criterion; CDR, complementarity determining region; Conformational changes; G, binding groove; Helix; IMGT®, the international ImMunoGeneTics information system®; MD, Molecular Dynamics; MH; MH, major histocompatibility; MH1, major histocompatibility class I; MH2, major histocompatibility class II; MH2c, MH helix curves (name of software); MHC; Minimization and fitting; Molecular dynamics simulation; PC, principal component; PCA, Principal Component Analysis; PDB, Protein Data Bank; Proteins; Secondary structure; Structure and properties; TR, T cell receptor; Theory, modeling, and computer simulation; Utility; VMD, Visual Molecular Dynamics; cAIC, corrected Akaike Information Criterion; p, peptide
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