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Protein structure prediction enhanced with evolutionary diversity: SPEED
Author(s) -
DeBartolo Joe,
Hocky Glen,
Wilde Michael,
Xu Jinbo,
Freed Karl F.,
Sosnick Tobin R.
Publication year - 2010
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1002/pro.330
Subject(s) - structural alignment , dihedral angle , protein structure prediction , protein secondary structure , threading (protein sequence) , protein tertiary structure , homology modeling , multiple sequence alignment , protein structure , cluster analysis , loop modeling , protein folding , protein data bank , computational biology , sequence alignment , sequence (biology) , monte carlo method , protein superfamily , computer science , peptide sequence , biology , chemistry , artificial intelligence , genetics , mathematics , statistics , molecule , hydrogen bond , biochemistry , organic chemistry , gene , enzyme
For naturally occurring proteins, similar sequence implies similar structure. Consequently, multiple sequence alignments (MSAs) often are used in template‐based modeling of protein structure and have been incorporated into fragment‐based assembly methods. Our previous homology‐free structure prediction study introduced an algorithm that mimics the folding pathway by coupling the formation of secondary and tertiary structure. Moves in the Monte Carlo procedure involve only a change in a single pair of ϕ,ψ backbone dihedral angles that are obtained from a Protein Data Bank‐based distribution appropriate for each amino acid, conditional on the type and conformation of the flanking residues. We improve this method by using MSAs to enrich the sampling distribution, but in a manner that does not require structural knowledge of any protein sequence (i.e., not homologous fragment insertion). In combination with other tools, including clustering and refinement, the accuracies of the predicted secondary and tertiary structures are substantially improved and a global and position‐resolved measure of confidence is introduced for the accuracy of the predictions. Performance of the method in the Critical Assessment of Structure Prediction (CASP8) is discussed.