Journal
BIOINFORMATICS
Volume 35, Issue 17, Pages 3013-3019Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btz026
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Funding
- Spanish grant [BFU2016-76220-P]
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Motivation: Knowledge-based statistical potentials constitute a simpler and easier alternative to physics-based potentials in many applications, including folding, docking and protein modeling. Here, to improve the effectiveness of the current approximations, we attempt to capture the six-dimensional nature of residue-residue interactions from known protein structures using a simple backbone-based representation. Results: We have developed KORP, a knowledge-based pairwise potential for proteins that depends on the relative position and orientation between residues. Using a minimalist representation of only three backbone atoms per residue, KORP utilizes a six-dimensional joint probability distribution to outperform state-of-the-art statistical potentials for native structure recognition and best model selection in recent critical assessment of protein structure prediction and loop-modeling benchmarks. Compared with the existing methods, our side-chain independent potential has a lower complexity and better efficiency. The superior accuracy and robustness of KORP represent a promising advance for protein modeling and refinement applications that require a fast but highly discriminative energy function.
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