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Detecting selection for negative design in proteins through an improved model of the misfolded state
Author(s) -
Minning Jonas,
Porto Markus,
Bastolla Ugo
Publication year - 2013
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.24244
Subject(s) - protein folding , stability (learning theory) , protein design , energy landscape , gaussian , selection (genetic algorithm) , protein stability , computer science , statistical physics , protein structure , chemistry , physics , biology , artificial intelligence , computational chemistry , machine learning , thermodynamics , microbiology and biotechnology , biochemistry
Proteins that need to be structured in their native state must be stable both against the unfolded ensemble and against incorrectly folded (misfolded) conformations with low free energy. Positive design targets the first type of stability by strengthening native interactions. The second type of stability is achieved by destabilizing interactions that occur frequently in the misfolded ensemble, a strategy called negative design. Here, we investigate negative design adopting a statistical mechanical model of the misfolded ensemble, which improves the usual Gaussian approximation by taking into account the third moment of the energy distribution and contact correlations. Applying this model, we detect and quantify selection for negative design in most natural proteins, and we analytically design protein sequences that are stable both against unfolding and against misfolding. Proteins 2013; 81:1102–1112. © 2013 Wiley Periodicals, Inc.