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Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues
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 r...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8469993/ https://www.ncbi.nlm.nih.gov/pubmed/34572550 http://dx.doi.org/10.3390/biom11091337 |
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author | Song, Ruiyang Cao, Baixin Peng, Zhenling Oldfield, Christopher J. Kurgan, Lukasz Wong, Ka-Chun Yang, Jianyi |
author_facet | Song, Ruiyang Cao, Baixin Peng, Zhenling Oldfield, Christopher J. Kurgan, Lukasz Wong, Ka-Chun Yang, Jianyi |
author_sort | Song, Ruiyang |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8469993 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84699932021-09-27 Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues Song, Ruiyang Cao, Baixin Peng, Zhenling Oldfield, Christopher J. Kurgan, Lukasz Wong, Ka-Chun Yang, Jianyi Biomolecules Article 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. MDPI 2021-09-09 /pmc/articles/PMC8469993/ /pubmed/34572550 http://dx.doi.org/10.3390/biom11091337 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Song, Ruiyang Cao, Baixin Peng, Zhenling Oldfield, Christopher J. Kurgan, Lukasz Wong, Ka-Chun Yang, Jianyi Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues |
title | Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues |
title_full | Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues |
title_fullStr | Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues |
title_full_unstemmed | Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues |
title_short | Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues |
title_sort | accurate sequence-based prediction of deleterious nssnps with multiple sequence profiles and putative binding residues |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8469993/ https://www.ncbi.nlm.nih.gov/pubmed/34572550 http://dx.doi.org/10.3390/biom11091337 |
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