Single-nucleotide polymorphisms (SNPs) are single-base variants that contribute to human biological variation and pathogenesis of many human diseases. Among all SNP types, nonsynonymous single-nucleotide polymorphisms (nsSNPs) can alter many structural, biochemical, and functional features of a protein such as folding characteristics, charge distribution, stability, dynamics, and interactions with other proteins/nucleotides. These modifications in the protein structure can lead nsSNPs to be closely associated with many multifactorial diseases such as cancer, diabetes, and neurodegenerative diseases. Predicting structural and functional effects of nsSNPs with experimental approaches can be time-consuming and costly; hence, computational prediction tools and algorithms are being widely and increasingly utilized in biology and medical research. This expert review examines the in silico tools and algorithms for the prediction of functional or structural effects of SNP variants, in addition to the description of the phenotypic effects of nsSNPs on protein structure, association between pathogenicity of variants, and functional or structural features of disease-associated variants. Finally, case studies investigating the functional and structural effects of nsSNPs on selected protein structures are highlighted. We conclude that creating a consistent workflow with a combination of in silico approaches or tools should be considered to increase the performance, accuracy, and precision of the biological and clinical predictions made in silico.

中文翻译：

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用于预测单核苷酸多态性对蛋白质的功能和结构影响的计算机工具和方法：专家评论

单核苷酸多态性 (SNP) 是单碱基变异，有助于人类生物学变异和许多人类疾病的发病机制。在所有 SNP 类型中，非同义单核苷酸多态性 (nsSNP) 可以改变蛋白质的许多结构、生化和功能特征，例如折叠特性、电荷分布、稳定性、动力学以及与其他蛋白质/核苷酸的相互作用。这些蛋白质结构的修饰可以导致 nsSNPs 与许多多因素疾病密切相关，如癌症、糖尿病和神经退行性疾病。用实验方法预测 nsSNP 的结构和功能影响可能既费时又费钱；因此，计算预测工具和算法在生物学和医学研究中得到了广泛和越来越多的应用。除了描述 nsSNP 对蛋白质结构的表型影响、变异的致病性之间的关联以及疾病相关变异的功能或结构特征之外，还提供了用于预测 SNP 变异的功能或结构影响的计算机工具和算法。最后，重点介绍了调查 nsSNP 对选定蛋白质结构的功能和结构影响的案例研究。我们得出的结论是，应考虑使用计算机方法或工具的组合创建一致的工作流程，以提高计算机生物学和临床预测的性能、准确性和精确度。