21-BBSRC/NSF-BIO: Modeling of protein interactions to predict phenotypic effects of genetic mutations

21-BBSRC/NSF-BIO：蛋白质相互作用建模以预测基因突变的表型效应

• 批准号: BB/X01830X/1
• 负责人: Michael Sternberg
• 金额: $ 70.26万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX01830X%2F1
• 关键词: 21; BBSRC; NSF; BIO; Modeling

Proteins are biological molecules that are the machinery of life. Individually a protein typically consists of hundreds of atoms that fold into a complicated 3D shape. Often proteins perform their function not in isolation but via docking to other proteins in a protein-protein complex. Changes in the genetic DNA can result is an alteration of a few atoms in the protein (known as a missense variant) and these sometime disrupt the structure and thus the function of the molecule. Understanding the 3D shape of docked proteins and additionally using this information to interpret these missense variants provide major insights into fundamental biology and furthermore in humans can provide an explanation for diseases caused by changes to a person's gene.Although experimental methods has revealed the structure of several protein complexes, computational approaches can markedly extend the number of models that can be examined. Furthermore, computational approaches using the structural information can assist in evaluating if a missense variant is likely to impact on the structure and hence on the function of the protein complex.These considerations have led to a collaboration between Imperial College London and the University of Kansas to develop a web-based resource GWYRE. The development of GWYRE involves (i) computational prediction by Imperial of the shape of an individual protein, (ii) the computational docking into a complex at Kansas, (iii) the evaluation of the impact of a missense variant at Imperial and (iv) the joint development and dissemination of the web-based resource GWYRE for community use.Under this grant we will enhance the above approach. In particular there has been major developments in enhanced machine learning that has markedly improved the computational prediction of protein structure and complexes. We will build upon these developments and furthermore apply machine learning for enhanced prediction of the impact of missense variants. The user-interface to GWYRE will be extended. The GWYRE database will be extended to provide information for several model organisms which are widely studied by the bioscience community.

蛋白质是生物分子，是生命的机器。单个蛋白质通常由数百个原子组成，这些原子折叠成复杂的3D形状。蛋白质通常不是孤立地发挥其功能，而是通过与蛋白质-蛋白质复合物中的其他蛋白质对接来发挥其功能。遗传DNA的变化可能导致蛋白质中几个原子的改变（称为错义变体），这些有时会破坏分子的结构，从而破坏分子的功能。了解对接蛋白质的3D形状，并利用这些信息来解释这些错义变体，为基础生物学提供了重要的见解，此外，在人类中，可以为由人的基因变化引起的疾病提供解释。虽然实验方法已经揭示了几种蛋白质复合物的结构，但计算方法可以显着扩展可以检查的模型数量。此外，计算方法使用的结构信息可以帮助评估，如果一个错义变体可能会影响的结构，从而对蛋白质complex.These的考虑导致帝国理工学院伦敦和堪萨斯大学之间的合作，开发一个基于网络的资源GWYRE。GWYRE的开发涉及（i）帝国理工对单个蛋白质形状的计算预测，（ii）计算对接到堪萨斯的复合物中，（iii）帝国理工对错义变体的影响的评估，以及（iv）联合开发和传播基于网络的资源GWYRE供社区使用。特别是在增强机器学习方面取得了重大进展，显著改善了蛋白质结构和复合物的计算预测。我们将在这些发展的基础上，进一步应用机器学习来增强对错义变体影响的预测。GWYRE的用户界面将得到扩展。GWYRE数据库将扩大，以提供生物科学界广泛研究的几种模式生物的信息。