Document Type
Article
Publication Date
6-28-2018
Publication Title
PLoS One
Department
Department of Computer Science
Additional Department
Department of Biological Sciences
Abstract
Co-evolution between pairs of residues in a multiple sequence alignment (MSA) of homologous proteins has long been proposed as an indicator of structural contacts. Recently, several methods, such as direct-coupling analysis (DCA) and MetaPSICOV, have been shown to achieve impressive rates of contact prediction by taking advantage of considerable sequence data. In this paper, we show that prediction success rates are highly sensitive to the structural definition of a contact, with more permissive definitions (i.e., those classifying more pairs as true contacts) naturally leading to higher positive predictive rates, but at the expense of the amount of structural information contributed by each contact. Thus, the remaining limitations of contact prediction algorithms are most noticeable in conjunction with geometrically restrictive contacts—precisely those that contribute more information in structure prediction. We suggest that to improve prediction rates for such “informative” contacts one could combine co-evolution scores with additional indicators of contact likelihood. Specifically, we find that when a pair of co-varying positions in an MSA is occupied by residue pairs with favorable statistical contact energies, that pair is more likely to represent a true contact. We show that combining a contact potential metric with DCA or MetaPSICOV performs considerably better than DCA or MetaPSICOV alone, respectively. This is true regardless of contact definition, but especially true for stricter and more informative contact definitions. In summary, this work outlines some remaining challenges to be addressed in contact prediction and proposes and validates a promising direction towards improvement.
DOI
10.1371/journal.pone.0199585
Original Citation
Holland J, Pan Q, Grigoryan G. Contact prediction is hardest for the most informative contacts, but improves with the incorporation of contact potentials. PLoS One. 2018 Jun 28;13(6):e0199585. doi: 10.1371/journal.pone.0199585. PMID: 29953468; PMCID: PMC6023208.
Dartmouth Digital Commons Citation
Holland, Jack; Pan, Qinxin; and Grigoryan, Gevorg, "Contact Prediction is Hardest for the Most Informative Contacts, but Improves with the Incorporation of Contact Potentials" (2018). Dartmouth Scholarship. 2845.
https://digitalcommons.dartmouth.edu/facoa/2845
