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Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB.
KTH, Skolan för datavetenskap och kommunikation (CSC), Beräkningsbiologi, CB. Aalto University, Finland.
2014 (engelsk)Inngår i: Journal of Computational Physics, ISSN 0021-9991, E-ISSN 1090-2716, Vol. 276, s. 341-356Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Direct-coupling analysis is a group of methods to harvest information about coevolving residues in a protein family by learning a generative model in an exponential family from data. In protein families of realistic size, this learning can only be done approximately, and there is a trade-off between inference precision and computational speed. We here show that an earlier introduced l(2)-regularized pseudolikelihood maximization method called plmDCA can be modified as to be easily parallelizable, as well as inherently faster on a single processor, at negligible difference in accuracy. We test the new incarnation of the method on 143 protein family/structure-pairs from the Protein Families database (PFAM), one of the larger tests of this class of algorithms to date.

sted, utgiver, år, opplag, sider
2014. Vol. 276, s. 341-356
Emneord [en]
Contact map, Direct-coupling analysis, Inference, Potts model, Protein structure prediction, Pseudolikelihood
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-152551DOI: 10.1016/j.jcp.2014.07.024ISI: 000341310100014Scopus ID: 2-s2.0-84905637666OAI: oai:DiVA.org:kth-152551DiVA, id: diva2:752712
Merknad

QC 20141006

Tilgjengelig fra: 2014-10-06 Laget: 2014-09-29 Sist oppdatert: 2018-01-11bibliografisk kontrollert

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