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Article

Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues

1
School of Mathematical Sciences, Nankai University, Tianjin 300071, China
2
Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao 266237, China
3
Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
4
Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Liam James McGuffin
Biomolecules 2021, 11(9), 1337; https://doi.org/10.3390/biom11091337
Received: 9 August 2021 / Revised: 4 September 2021 / Accepted: 6 September 2021 / Published: 9 September 2021
(This article belongs to the Collection Feature Papers in Bioinformatics and Systems Biology Section)
Non-synonymous single nucleotide polymorphisms (nsSNPs) may result in pathogenic changes that are associated with human diseases. Accurate prediction of these deleterious nsSNPs is in high demand. The existing predictors of deleterious nsSNPs secure modest levels of predictive performance, leaving room for improvements. We propose a new sequence-based predictor, DMBS, which addresses the need to improve the predictive quality. The design of DMBS relies on the observation that the deleterious mutations are likely to occur at the highly conserved and functionally important positions in the protein sequence. Correspondingly, we introduce two innovative components. First, we improve the estimates of the conservation computed from the multiple sequence profiles based on two complementary databases and two complementary alignment algorithms. Second, we utilize putative annotations of functional/binding residues produced by two state-of-the-art sequence-based methods. These inputs are processed by a random forests model that provides favorable predictive performance when empirically compared against five other machine-learning algorithms. Empirical results on four benchmark datasets reveal that DMBS achieves AUC > 0.94, outperforming current methods, including protein structure-based approaches. In particular, DMBS secures AUC = 0.97 for the SNPdbe and ExoVar datasets, compared to AUC = 0.70 and 0.88, respectively, that were obtained by the best available methods. Further tests on the independent HumVar dataset shows that our method significantly outperforms the state-of-the-art method SNPdryad. We conclude that DMBS provides accurate predictions that can effectively guide wet-lab experiments in a high-throughput manner. View Full-Text
Keywords: mutation; sequence profile; binding site mutation; sequence profile; binding site
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MDPI and ACS Style

Song, R.; Cao, B.; Peng, Z.; Oldfield, C.J.; Kurgan, L.; Wong, K.-C.; Yang, J. Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues. Biomolecules 2021, 11, 1337. https://doi.org/10.3390/biom11091337

AMA Style

Song R, Cao B, Peng Z, Oldfield CJ, Kurgan L, Wong K-C, Yang J. Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues. Biomolecules. 2021; 11(9):1337. https://doi.org/10.3390/biom11091337

Chicago/Turabian Style

Song, Ruiyang, Baixin Cao, Zhenling Peng, Christopher J. Oldfield, Lukasz Kurgan, Ka-Chun Wong, and Jianyi Yang. 2021. "Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues" Biomolecules 11, no. 9: 1337. https://doi.org/10.3390/biom11091337

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