Dynamics in molecular recognition

分子识别动力学

• 批准号: RGPIN-2014-05766
• 负责人: Najmanovich, Rafael
• 金额: $ 2.55万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=611830
• 关键词: Dynamics; molecular; recognition

Proteins movements form a continuum from bond and angle vibrations, side-chain rearrangements, loop or domain movements through folding. In recent years the appreciation of the importance of dynamics in protein function is growing. Our research in computational structural biology aims to understand molecular recognition, i.e., the factors affecting selectivity and specificity in molecular interactions, through the development of innovative methods and their experimental validation in four research axes: reconstructed metabolic networks, docking simulations, the detection of molecular similarities and the simulation of dynamics across multiple scales in macromolecules. The goals of our research program are: 1. To develop methods to simulate dynamics in macromolecules and apply them to understand protein function. We have recently developed the first normal modes analysis (NMA) method called ENCoM, able to account for the nature of amino acids thus permitting to assess the effect of mutations on dynamics. A second application for a NMA method is in the generation of conformational ensembles. We are developing TENCoM, a version of ENCoM where movements occur in torsional angle space (rather than Cartesian space) to generate geometrically correct conformational ensembles. Initial results show that ENCoM is particularly apt at predicting stabilizing mutations such as constitutively active or inactive mutations in G-protein Coupled Receptors (GPCRs). These programs will be used to study biased signalling in GPCRs and the dynamic effect of distal mutations on ligand binding. 2. To account for the dynamic nature of proteins and its effect on molecular recognition. One goal is to introduce full protein backbone movements to our docking program FlexAID using normal modes. This will allow exploring the target conformational space more thoroughly in a computationally efficient manner enabling us to apply FlexAID in the search for allosteric small-molecule inhibitors as well as perform protein-protein docking simulations. Secondly, in our group we develop IsoCleft and IsoMIF, two graph-matching based methods for the detection of local 3D atomic and molecular interaction similarities respectively. These programs can be used to predict binding ligands, protein function as well as potential cross-reactivity targets. Currently, IsoCleft and IsoMIF consider flexibility only very simplistically via thresholds values in pertinent parameters irrespective of the actual local flexibility. Yet, the explicit consideration of flexibility through the use of Gaussian distributions derived from normal modes will permit us to treat flexibility in a realistic manner and thus detect similarities more accurately (e.g. loops). 3. To validate experimentally our methods. The experimental branch of our group is responsible for cloning, expression and purification of proteins of interest where predicted binding small molecules can be validated using biophysical methods such as circular dichroism and differential scanning fluorescence. We are currently working on the Germination Protease of C. difficile as well as STK38 and BUB1, two human protein kinases involved in several types of cancer. This research program is unique in Canada and at the cutting edge of computational biology worldwide and will help us understand the effect of dynamics in molecular recognition. Furthermore, the multidisciplinary approach in our group integrating calculations and experiments allows for the formation of high quality personnel ready to work at the interface of chemistry, molecular biology, physics and computer science.

蛋白质的运动从键和角振动、侧链重排、通过折叠的环或结构域运动形成一个连续体。近年来，人们越来越认识到动力学在蛋白质功能中的重要性。我们在计算结构生物学方面的研究旨在通过创新方法的发展及其在四个研究轴线上的实验验证来理解分子识别，即分子相互作用中影响选择性和特异性的因素：重建代谢网络、对接模拟、分子相似性检测和大分子多尺度动力学模拟。我们研究计划的目标是： 1.发展模拟大分子动力学的方法，并将其应用于了解蛋白质功能。我们最近开发了第一种名为Encom的简正模式分析(NMA)方法，能够解释氨基酸的性质，从而能够评估突变对动力学的影响。NMA方法的第二个应用是构象系综的产生。我们正在开发TENCoM，这是Encom的一个版本，其中运动发生在扭角空间(而不是笛卡尔空间)，以生成几何正确的构象系综。初步结果表明，Encom特别适合预测稳定型突变，如G蛋白偶联受体(GPCRs)的结构性活跃突变或非活跃突变。这些程序将被用来研究GPCRs中的偏向信号以及末端突变对配体结合的动态影响。 2.说明蛋白质的动态性质及其对分子识别的影响。一个目标是使用正常模式将完整的蛋白质骨架运动引入我们的对接程序FlexAID。这将允许以计算高效的方式更彻底地探索目标构象空间，从而使我们能够将FlexAID应用于寻找变构小分子抑制剂以及执行蛋白质-蛋白质对接模拟。其次，在本课题组中，我们开发了两种基于图匹配的局部3D原子和分子相互作用相似性检测方法IsoCLeft和IsoMIF。这些程序可用于预测结合配体、蛋白质功能以及潜在的交叉反应靶标。目前，IsoCleft和IsoMIF仅非常简单地通过相关参数中的阈值来考虑灵活性，而不考虑实际的局部灵活性。然而，通过使用从正常模式导出的高斯分布来明确考虑灵活性，将允许我们以现实的方式处理灵活性，从而更准确地检测相似性(例如环路)。 3.对我们的方法进行了实验验证。我们小组的实验分支负责感兴趣的蛋白质的克隆、表达和纯化，其中预测的结合小分子可以使用生物物理方法，如圆二色谱和差示扫描荧光来验证。我们目前正在研究艰难梭菌的萌发蛋白酶以及STK38和Bub1，这两种人类蛋白激酶与几种类型的癌症有关。 这一研究计划在加拿大是独一无二的，在世界范围内处于计算生物学的前沿，将帮助我们了解分子识别中的动力学效应。此外，我们小组将计算和实验相结合的多学科方法允许形成准备在化学、分子生物学、物理和计算机科学的界面上工作的高素质人员。