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Título: | KORP: knowledge-based 6D potential for fast protein and loop modeling |
Autor: | López-Blanco, José R. CSIC ORCID; Chacón, Pablo CSIC ORCID | Fecha de publicación: | 14-ene-2019 | Editor: | Oxford University Press | Citación: | Bioinformatics 35: 3013-3019 (2019) | Resumen: | 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. | Descripción: | 7 pags., 8 figs., 4 tabs. | Versión del editor: | http://dx.doi.org/10.1093/bioinformatics/btz026 | URI: | http://hdl.handle.net/10261/206345 | DOI: | 10.1093/bioinformatics/btz026 | Identificadores: | doi: 10.1093/bioinformatics/btz026 issn: 1367-4803 |
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