期刊
JOURNAL OF MOLECULAR BIOLOGY
卷 433, 期 11, 页码 -出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jmb.2021.166915
关键词
genetic variations; pathogenic variants; benign variants; variant deleteriousness prediction; neural network predictor
资金
- National Institutes of Health [GM127390]
- Welch Foundation [I-1505]
The study aimed to enhance the performance of DeepSAV by using expanded datasets, more features, and neural network optimization. It was found that multiple sequence alignments built from vertebrate-level orthologs yield better prediction results compared to those built from mammalian-level orthologs, and optimal performance was achieved with a sequence identity cutoff of 50%. The new version of DeepSAV exhibits the best performance among standalone predictors of deleterious effects of SAVs.
Deleterious single amino acid variation (SAV) is one of the leading causes of human diseases. Evaluating the functional impact of SAVs is crucial for diagnosis of genetic disorders. We previously developed a deep convolutional neural network predictor, DeepSAV, to evaluate the deleterious effects of SAVs on protein function based on various sequence, structural, and functional properties. DeepSAV scores of rare SAVs observed in the human population are aggregated into a gene-level score called GTS (Gene Tolerance of rare SAVs) that reflects a gene's tolerance to deleterious missense mutations and serves as a useful tool to study gene-disease associations. In this study, we aim to enhance the performance of DeepSAV by using expanded datasets of pathogenic and benign variants, more features, and neural network optimization. We found that multiple sequence alignments built from vertebrate-level orthologs yield better prediction results compared to those built from mammalian-level orthologs. For multiple sequence alignments built from BLAST searches, optimal performance was achieved with a sequence identify cutoff of 50% to remove distant homologs. The new version of DeepSAV exhibits the best performance among standalone predictors of deleterious effects of SAVs. We developed the DBSAV database (http://prodata.swmed.edu/DBSAV) that reports GTS scores of human genes and DeepSAV scores of SAVs in the human proteome, including pathogenic and benign SAVs, population-level SAVs, and all possible SAVs by single nucleotide variations. This database serves as a useful resource for research of human SAVs and their relationships with protein functions and human diseases. (C) 2021 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据