# Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns.

 Title Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns. Publication Type Journal Article Year of Publication 2014 Authors Skwark, MJ, Raimondi, D, Michel, M, Elofsson, A Journal PLoS computational biology Volume 10 Pagination e1003889 ISSN 1553-7358 Abstract Given sufficient large protein families, and using a global statistical inference approach, it is possible to obtain sufficient accuracy in protein residue contact predictions to predict the structure of many proteins. However, these approaches do not consider the fact that the contacts in a protein are neither randomly, nor independently distributed, but actually follow precise rules governed by the structure of the protein and thus are interdependent. Here, we present PconsC2, a novel method that uses a deep learning approach to identify protein-like contact patterns to improve contact predictions. A substantial enhancement can be seen for all contacts independently on the number of aligned sequences, residue separation or secondary structure type, but is largest for $\beta$-sheet containing proteins. In addition to being superior to earlier methods based on statistical inferences, in comparison to state of the art methods using machine learning, PconsC2 is superior for families with more than 100 effective sequence homologs. The improved contact prediction enables improved structure prediction. URL http://www.ncbi.nlm.nih.gov/pubmed/25375897 DOI 10.1371/journal.pcbi.1003889

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