title: Comparison between computational alanine scanning and per-residue binding free energy decomposition for protein-protein association using MM-GBSA: application to the TCR-p-MHC complex.
abstract: Recognition by the T-cell receptor (TCR) of immunogenic peptides (p) presented by Class I major histocompatibility complexes (MHC) is the key event in the immune response against virus-infected cells or tumor cells. A study of the 2C TCR/SIYR/H-2K(b) system using a computational alanine scanning and a much faster binding free energy decomposition based on the Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) method is presented. The results show that the TCR-p-MHC binding free energy decomposition using this approach and including entropic terms provides a detailed and reliable description of the interactions between the molecules at an atomistic level. Comparison of the decomposition results with experimentally determined activity differences for alanine mutants yields a correlation of 0.67 when the entropy is neglected and 0.72 when the entropy is taken into account. Similarly, comparison of experimental activities with variations in binding free energies determined by computational alanine scanning yields correlations of 0.72 and 0.74 when the entropy is neglected or taken into account, respectively. Some key interactions for the TCR-p-MHC binding are analyzed and some possible side chains replacements are proposed in the context of TCR protein engineering. In addition, a comparison of the two theoretical approaches for estimating the role of each side chain in the complexation is given, and a new ad hoc approach to decompose the vibrational entropy term into atomic contributions, the linear decomposition of the vibrational entropy (LDVE), is introduced. The latter allows the rapid calculation of the entropic contribution of interesting side chains to the binding. This new method is based on the idea that the most important contributions to the vibrational entropy of a molecule originate from residues that contribute most to the vibrational amplitude of the normal modes. The LDVE approach is shown to provide results very similar to those of the exact but highly computationally demanding method.
authors: Swiss Institute of Bioinformatics, Molecular Modeling Group, Genopode, CH-1015 Lausanne, Switzerland. Vincent.Zoete@isb-sib.ch
authors: Vincent
authors: V
authors: Zoete
authors: Olivier
authors: O
authors: Michielin
mesh_terms: D000409:Alanine / Q000378:metabolism*; D003198:Computer Simulation; D018360:Crystallography, X-Ray; D019277:Entropy; D008285:Major Histocompatibility Complex / Q000276:immunology; D008958:Models, Molecular; D010455:Peptides / Q000737:chemistry / Q000378:metabolism; D011485:Protein Binding; D020836:Protein Structure, Quaternary; D017434:Protein Structure, Tertiary; D011948:Receptors, Antigen, T-Cell / Q000737:chemistry* / Q000276:immunology / Q000378:metabolism*; D055672:Static Electricity; D014732:Vibration
chemical_list: D010455:Peptides; D011948:Receptors, Antigen, T-Cell; D000409:Alanine