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 Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=660616
• 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.

蛋白质运动通过折叠形成键和角振动、侧链重排、环或结构域运动的连续体。近年来，对蛋白质功能动力学的重要性的认识正在增长。我们在计算结构生物学的研究旨在了解分子识别，即，通过开发创新方法及其在四个研究方向的实验验证，研究影响分子相互作用中选择性和特异性的因素：重建代谢网络、对接模拟、检测分子相似性和模拟大分子中多尺度的动力学。我们的研究目标是：** 1。开发方法来模拟大分子的动力学，并将其应用于了解蛋白质功能。我们最近开发了第一个正常模式分析（NMA）方法称为ENCoM，能够说明氨基酸的性质，从而允许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生成。我们正在开发TENCoM，ENCoM的一个版本，其中运动发生在扭转角空间（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是特别适合于预测稳定的突变，如组成型活性或非活性突变的G蛋白偶联受体（GPCR）。这些程序将用于研究GPCR中的偏倚信号传导以及远端突变对配体结合的动态影响。** 2.解释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，使我们能够将FlexAID应用于寻找变构小分子抑制剂以及进行蛋白质-蛋白质对接模拟。其次，在我们的小组中，我们开发了IsoCleft和IsoMIF，两个基于图匹配的方法，分别用于检测局部3D原子和分子相互作用的相似性。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性靶点。目前，IsoCleft和IsoMIF仅通过相关参数中的阈值非常简单地考虑灵活性，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（例如循环）。3.实验验证我们的方法。我们小组的实验分支负责克隆、表达和纯化感兴趣的蛋白质，其中预测的结合小分子可以使用生物物理方法如圆二色性和差示扫描荧光进行验证。我们目前正在研究C. difficile以及STK 38和BUB 1，这两种人蛋白激酶参与几种类型的癌症。该研究项目在加拿大是独一无二的，处于全球计算生物学的前沿，将帮助我们了解分子识别中动力学的影响。此外，在我们的小组整合计算和实验的多学科方法允许形成高素质的人员准备在化学，分子生物学，物理学和计算机科学的接口工作。