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Improved fragment-based protein structure prediction by redesign of search heuristics

Kandathil, SM; Garza-Fabre, M; Handl, J; Lovell, SC; (2018) Improved fragment-based protein structure prediction by redesign of search heuristics. Scientific Reports , 8 , Article 13694. 10.1038/s41598-018-31891-8. Green open access

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Abstract

Difficulty in sampling large and complex conformational spaces remains a key limitation in fragment-based de novo prediction of protein structure. Our previous work has shown that even for small-to-medium-sized proteins, some current methods inadequately sample alternative structures. We have developed two new conformational sampling techniques, one employing a bilevel optimisation framework and the other employing iterated local search. We combine strategies of forced structural perturbation (where some fragment insertions are accepted regardless of their impact on scores) and greedy local optimisation, allowing greater exploration of the available conformational space. Comparisons against the Rosetta Abinitio method indicate that our protocols more frequently generate native-like predictions for many targets, even following the low-resolution phase, using a given set of fragment libraries. By contrasting results across two different fragment sets, we show that our methods are able to better take advantage of high-quality fragments. These improvements can also translate into more reliable identification of near-native structures in a simple clustering-based model selection procedure. We show that when fragment libraries are sufficiently well-constructed, improved breadth of exploration within runs improves prediction accuracy. Our results also suggest that in benchmarking scenarios, a total exclusion of fragments drawn from homologous templates can make performance differences between methods appear less pronounced.

Type: Article
Title: Improved fragment-based protein structure prediction by redesign of search heuristics
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1038/s41598-018-31891-8
Publisher version: https://doi.org/10.1038/s41598-018-31891-8
Language: English
Additional information: © The Author(s) 2018. This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
Keywords: Protein structure predictions, Software
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10057235
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