Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences

JOURNAL OF COMPUTATIONAL PHYSICS

卷:276

Article

Ekeberg, Magnus^1,2  Hartonen, Tuomo^3,4  Aurell, Erik^2,3,5 

[1] KTH Royal Inst Technol, Engn Phys Program, SE-10077 Stockholm, Sweden

[2] AlbaNova Univ Ctr, Dept Computat Biol, S-10691 Stockholm, Sweden

[3] Aalto Univ, Dept Informat & Comp Sci, FI-00076 Aalto, Finland

[4] Univ Helsinki, Biomedicum Helsinki, Masters Degree Programme Translat Med, FI-00014 Helsinki, Finland

[5] Aalto Sci Inst, FI-00076 Aalto, Finland

关键词: Protein structure prediction;    Contact map;    Direct-coupling analysis;    Potts model;    Pseudolikelihood;    Inference;   

DOI  :  10.1016/j.jcp.2014.07.024

来源: Elsevier

【 摘 要 】

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. (C) 2014 Elsevier Inc. Allrightsreserved.

【 授权许可】

Free