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• W2006184433 abstract "An increasing number of functional studies of proteins have shown that sequence and structural similarities alone may not be sufficient for reliable prediction of their interaction properties. This is particularly true for proteins recognizing specific antibodies, where the prediction of antibody-binding sites, called epitopes, has proven challenging. The antibody-binding properties of an antigen depend on its structure and related dynamics. Aiming to predict the antibody-binding regions of a protein, we investigate a new approach based on the integrated analysis of the dynamical and energetic properties of antigens, to identify nonoptimized, low-intensity energetic interaction networks in the protein structure isolated in solution. The method is based on the idea that recognition sites may correspond to localized regions with low-intensity energetic couplings with the rest of the protein, which allows them to undergo conformational changes, to be recognized by a binding partner, and to tolerate mutations with minimal energetic expense. Upon analyzing the results on isolated proteins and benchmarking against antibody complexes, it is found that the method successfully identifies binding sites located on the protein surface that are accessible to putative binding partners. The combination of dynamics and energetics can thus discriminate between epitopes and other substructures based only on physical properties. We discuss implications for vaccine design." @default.
• W2006184433 created "2016-06-24" @default.
• W2006184433 creator A5046491394 @default.
• W2006184433 creator A5064391171 @default.
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• W2006184433 date "2010-05-01" @default.
• W2006184433 modified "2023-10-16" @default.
• W2006184433 title "Predicting Interaction Sites from the Energetics of Isolated Proteins: A New Approach to Epitope Mapping" @default.
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• W2006184433 doi "https://doi.org/10.1016/j.bpj.2010.01.014" @default.
• W2006184433 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/2862194" @default.
• W2006184433 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/20441761" @default.
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