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

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

Tutkijat

Organisaatiot

  • Royal Institute of Technology
  • University of Helsinki
  • AlbaNova

Kuvaus

Direct-coupling analysis is a group of methods to harvest information about coevolving residues in a protein fsamily 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 l2-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.

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut341-356
Sivumäärä16
JulkaisuJournal of Computational Physics
Vuosikerta276
TilaJulkaistu - 1 marraskuuta 2014
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

ID: 9363523