Open Structural Data in Precision Medicine.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Annual Review of Biomedical Data Science Pub Date : 2022-04-28 DOI:10.1146/annurev-biodatasci-122220-012951
R. Nussinov, Hyunbum Jang, G. Nir, Chung-Jung Tsai, F. Cheng
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引用次数: 7

Abstract

Three-dimensional protein structural data at the molecular level are pivotal for successful precision medicine. Such data are crucial not only for discovering drugs that act to block the active site of the target mutant protein but also for clarifying to the patient and the clinician how the mutations harbored by the patient work. The relative paucity of structural data reflects their cost, challenges in their interpretation, and lack of clinical guidelines for their utilization. Rapid technological advancements in experimental high-resolution structural determination increasingly generate structures. Computationally, modeling algorithms, including molecular dynamics simulations, are becoming more powerful, as are compute-intensive hardware, particularly graphics processing units, overlapping with the inception of the exascale era. Accessible, freely available, and detailed structural and dynamical data can be merged with big data to powerfully transform personalized pharmacology. Here we review protein and emerging genome high-resolution data, along with means, applications, and examples underscoring their usefulness in precision medicine. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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精准医学中的开放结构数据。
在分子水平上的三维蛋白质结构数据是成功的精准医疗的关键。这些数据不仅对于发现阻断目标突变蛋白活性位点的药物至关重要,而且对于向患者和临床医生阐明患者所携带的突变如何起作用至关重要。结构数据的相对缺乏反映了它们的成本、解释上的挑战以及缺乏临床应用指南。实验高分辨率结构测定技术的快速发展越来越多地产生结构。在计算方面,包括分子动力学模拟在内的建模算法正变得越来越强大,计算密集型硬件,特别是图形处理单元,也随着百亿亿次时代的开始而变得越来越强大。可访问的、免费的、详细的结构和动态数据可以与大数据相结合,有力地改变个性化药理学。在这里,我们回顾了蛋白质和新兴的基因组高分辨率数据,以及方法、应用和例子,强调了它们在精准医学中的有用性。预计《生物医学数据科学年度评论》第5卷的最终在线出版日期为2022年8月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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