Improving Contact Prediction along Three Dimensions

Research output: Contribution to journalArticleScientificpeer-review

Researchers

  • Christoph Feinauer
  • Marcin J. Skwark
  • Andrea Pagnani
  • Erik Aurell

Research units

Abstract

Correlation patterns in multiple sequence alignments of homologous proteins can be exploited to infer information on the three-dimensional structure of their members. The typical pipeline to address this task, which we in this paper refer to as the three dimensions of contact prediction, is to (i) filter and align the raw sequence data representing the evolutionarily related proteins; (ii) choose a predictive model to describe a sequence alignment; (iii) infer the model parameters and interpret them in terms of structural properties, such as an accurate contact map. We show here that all three dimensions are important for overall prediction success. In particular, we show that it is possible to improve significantly along the second dimension by going beyond the pair-wise Potts models from statistical physics, which have hitherto been the focus of the field. These (simple) extensions are motivated by multiple sequence alignments often containing long stretches of gaps which, as a data feature, would be rather untypical for independent samples drawn from a Potts model. Using a large test set of proteins we show that the combined improvements along the three dimensions are as large as any reported to date.

Details

Original languageEnglish
Article numbere1003847
Pages (from-to)1-13
JournalPLoS computational biology
Volume10
Issue number10
Publication statusPublished - 2014
MoE publication typeA1 Journal article-refereed

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