Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction

• Published in: Bioinformatics 2018-04, Vol.34 (7), p.1132-1140
• Main Authors: de Oliveira, Saulo H P, Law, Eleanor C, Shi, Jiye, Deane, Charlotte M
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; Caspase 12 - chemistry; Caspase 12 - metabolism; Computational Biology - methods; Humans; Original Papers; Protein Conformation; Sequence Analysis, Protein - methods; Software
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btx722

Abstract Motivation Most current de novo structure prediction methods randomly sample protein conformations and thus require large amounts of computational resource. Here, we consider a sequential sampling strategy, building on ideas from recent experimental work which shows that many proteins fold cotranslationally. Results We have investigated whether a pseudo-greedy search approach, which begins sequentially from one of the termini, can improve the performance and accuracy of de novo protein structure prediction. We observed that our sequential approach converges when fewer than 20 000 decoys have been produced, fewer than commonly expected. Using our software, SAINT2, we also compared the run time and quality of models produced in a sequential fashion against a standard, non-sequential approach. Sequential prediction produces an individual decoy 1.5-2.5 times faster than non-sequential prediction. When considering the quality of the best model, sequential prediction led to a better model being produced for 31 out of 41 soluble protein validation cases and for 18 out of 24 transmembrane protein cases. Correct models (TM-Score > 0.5) were produced for 29 of these cases by the sequential mode and for only 22 by the non-sequential mode. Our comparison reveals that a sequential search strategy can be used to drastically reduce computational time of de novo protein structure prediction and improve accuracy. Availability and implementation Data are available for download from: http://opig.stats.ox.ac.uk/resources. SAINT2 is available for download from: https://github.com/sauloho/SAINT2. Supplementary information Supplementary data are available at Bioinformatics online.