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首页> 外文期刊>Journal of Molecular Biology >The DBSAV Database: Predicting Deleteriousness of Single Amino Acid Variations in the Human Proteome
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The DBSAV Database: Predicting Deleteriousness of Single Amino Acid Variations in the Human Proteome

机译：DBSAV数据库：预测人蛋白质组的单氨基酸变化的有害性

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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.
机译：有害的单氨基酸变异（SAV）是人类疾病的主要原因之一。评估SAVs的功能影响对于诊断遗传性疾病至关重要。我们之前开发了一种深度卷积神经网络预测器DeepSAV，用于基于各种序列、结构和功能特性评估Sav对蛋白质功能的有害影响。在人群中观察到的罕见SAV的DeepSAV分数被聚合为一个称为GTS（罕见SAV的基因耐受性）的基因水平分数，该分数反映了基因对有害错义突变的耐受性，并作为研究基因疾病关联的有用工具。在这项研究中，我们的目标是通过使用致病性和良性变异的扩展数据集、更多特征和神经网络优化来提高DeepSAV的性能。我们发现，与哺乳动物水平的同源序列相比，从脊椎动物水平的同源序列构建的多序列比对产生了更好的预测结果。对于通过BLAST搜索构建的多序列比对，通过50%的序列识别截止值来去除远处的同源物，实现了最佳性能。在SAV有害影响的独立预测因子中，新版本的DeepSAV表现出最好的性能。我们开发了DBSAV数据库(http://prodata.swmed.edu/DBSAV)报告了人类基因的GTS评分和人类蛋白质组中SAV的DeepSAV评分，包括致病性和良性SAV、群体水平SAV，以及所有可能的单核苷酸变异SAV。该数据库为研究人类SAV及其与蛋白质功能和人类疾病的关系提供了有用的资源。（c）2021爱思唯尔有限公司保留所有权利。

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