Predicting important residues and interaction pathways in proteins using Gaussian Network Model: binding and stability of HLA proteins.

PLoS Computational Biology
Turkan HalilogluB Erman

Abstract

A statistical thermodynamics approach is proposed to determine structurally and functionally important residues in native proteins that are involved in energy exchange with a ligand and other residues along an interaction pathway. The structure-function relationships, ligand binding and allosteric activities of ten structures of HLA Class I proteins of the immune system are studied by the Gaussian Network Model. Five of these models are associated with inflammatory rheumatic disease and the remaining five are properly functioning. In the Gaussian Network Model, the protein structures are modeled as an elastic network where the inter-residue interactions are harmonic. Important residues and the interaction pathways in the proteins are identified by focusing on the largest eigenvalue of the residue interaction matrix. Predicted important residues match those known from previous experimental and clinical work. Graph perturbation is used to determine the response of the important residues along the interaction pathway. Differences in response patterns of the two sets of proteins are identified and their relations to disease are discussed.

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Citations

Nov 11, 2011·The Journal of Chemical Physics·Ji Guo SuCun Xin Wang
Sep 12, 2013·PloS One·Pemra OzbekTurkan Haliloglu
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Jun 10, 2010·PloS One·E Besray UnalBurak Erman
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Sep 13, 2014·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·Ji Guo SuJi Hua Wang
Jul 5, 2013·The Journal of Physical Chemistry. B·Ji Guo SuCun Xin Wang

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Software Mentioned

GNM
ASN97
1OGT

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