Journal Article

Prediction of protein β-residue contacts by Markov logic networks with grounding-specific weights

Marco Lippi and Paolo Frasconi

in Bioinformatics

Volume 25, issue 18, pages 2326-2333
Published in print September 2009 | ISSN: 1367-4803
Published online July 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp421
Prediction of protein β-residue contacts by Markov logic networks with grounding-specific weights

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Motivation: Accurate prediction of contacts between β-strand residues can significantly contribute towards ab initio prediction of the 3D structure of many proteins. Contacts in the same protein are highly interdependent. Therefore, significant improvements can be expected by applying statistical relational learners that overcome the usual machine learning assumption that examples are independent and identically distributed. Furthermore, the dependencies among β-residue contacts are subject to strong regularities, many of which are known a priori. In this article, we take advantage of Markov logic, a statistical relational learning framework that is able to capture dependencies between contacts, and constrain the solution according to domain knowledge expressed by means of weighted rules in a logical language.

Results: We introduce a novel hybrid architecture based on neural and Markov logic networks with grounding-specific weights. On a non-redundant dataset, our method achieves 44.9% F1 measure, with 47.3% precision and 42.7% recall, which is significantly better (P < 0.01) than previously reported performance obtained by 2D recursive neural networks. Our approach also significantly improves the number of chains for which β-strands are nearly perfectly paired (36% of the chains are predicted with F1 ≥ 70% on coarse map). It also outperforms more general contact predictors on recent CASP 2008 targets.

Contact: lippi@dsi.unifi.it

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  7040 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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