Albanaz Amanda T S, Rodrigues Carlos H M, Pires Douglas E V, Ascher David B
a Centro de Pesquisas René Rachou, FIOCRUZ , Belo Horizonte , MG , Brazil.
b Department of Biochemistry and Immunology , Universidade Federal de Minas Gerais , Belo Horizonte , Minas Gerais , Brazil.
Expert Opin Drug Discov. 2017 Jun;12(6):553-563. doi: 10.1080/17460441.2017.1322579.
Mutations introduce diversity into genomes, leading to selective changes and driving evolution. These changes have contributed to the emergence of many of the current major health concerns of the 21st century, from the development of genetic diseases and cancers to the rise and spread of drug resistance. The experimental systematic testing of all mutations in a system of interest is impractical and not cost-effective, which has created interest in the development of computational tools to understand the molecular consequences of mutations to aid and guide rational experimentation. Areas covered: Here, the authors discuss the recent development of computational methods to understand the effects of coding mutations to protein function and interactions, particularly in the context of the 3D structure of the protein. Expert opinion: While significant progress has been made in terms of innovative tools to understand and quantify the different range of effects in which a mutation or a set of mutations can give rise to a phenotype, a great gap still exists when integrating these predictions and drawing causality conclusions linking variants. This often requires a detailed understanding of the system being perturbed. However, as part of the drug development process it can be used preemptively in a similar fashion to pharmacokinetics predictions, to guide development of therapeutics to help guide the design and analysis of clinical trials, patient treatment and public health policy strategies.
突变使基因组产生多样性，导致选择性变化并推动进化。这些变化促成了21世纪当前许多主要健康问题的出现，从遗传疾病和癌症的发展到耐药性的产生和传播。对感兴趣系统中的所有突变进行实验性系统测试既不切实际也不具成本效益，这引发了人们对开发计算工具的兴趣，以了解突变的分子后果，从而辅助和指导合理实验。涵盖领域：在此，作者讨论了用于理解编码突变对蛋白质功能和相互作用影响的计算方法的最新进展，特别是在蛋白质三维结构的背景下。专家观点：虽然在开发创新工具以理解和量化突变或一组突变产生表型的不同程度影响方面取得了重大进展，但在整合这些预测并得出将变体与因果关系联系起来的结论时，仍然存在很大差距。这通常需要对受干扰的系统有详细了解。然而，作为药物开发过程的一部分，它可以以类似于药代动力学预测的方式预先使用，以指导治疗方法的开发，帮助指导临床试验的设计和分析、患者治疗以及公共卫生政策策略。
