Stochastic Dynamic MOdeling of Cellular Protein Interactions

细胞蛋白质相互作用的随机动态建模

基本信息

批准号：
9916770
负责人：
Mark Andrew Kon
金额：
$ 10.78万
依托单位：
BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-01 至 2022-04-30
项目状态：
已结题

项目摘要

Stochastic methods for modeling molecular-protein interactions are entirely new approaches to the important biological goal of simulating cellular biology in silico. Though great progress has been made in this direction by computational biologists over the past 15 years, the goal of "siliconizing" cellular molecular interactions still remains remote. More precisely, the current level of model realism has led to a plateau in the prediction accuracy of molecular interactions. This motivates the development of novel dynamic models that incorporate more extensive biological details and can add layers of realism to the simulations. However, such models are computationally daunting, so that it is critical to develop efficient and accurate computational methods. The purpose is to augment molecular-level understanding and simulation of biological interactions. In particular, by exploiting novel developments in stochastic optimi?ation, the investigators shall significantly improve the prediction of interactions by adding a new dimension of realism. However, for many practical cases the stochastic objective function will become a high dimensional, nonGaussian, nonlinear random field that will be computationally very challenging to optimize. This is a hard problem that the investigators plan to address by developing novel Uncertainty Quantification (UQ) mathematical theory. The specific aims are to: (i) Develop a compact dynamic surrogate model of the stochastic objective function that incorporates the molecular structure uncertainty and molecular properties such as the electrostatic fields by solving the nonlinear Poisson Boltzmann (PB) equation. The stochastic optimization is solved efficiently with a surrogate model. (ii) Analyze the complex analytic regularity properties of the solution of the nonlinear Poisson-Boltzmann equation (and the other molecular properties) with respect to the probabilistic molecular conformation model. (iii) Develop convergence rates of the surrogate model from the complex analytic regularity with respect to the number of realizations of the protein structure (computational complexity). Most protein interactions models based on molecular. structure assume a rigid shape thus leading to erroneous predictions. The investigators propose to significantly improve the prediction of protein interactions by incorporating dynamic uncertainty of the molecular conformational shape. The theory and application of UQ to protein interactions is at its infancy.

模拟分子-蛋白质相互作用的随机方法是研究分子-蛋白质相互作用的全新方法。在计算机中模拟细胞生物学的重要生物学目标。虽然已经取得了很大的进步计算生物学家在过去 15 年里一直在朝着这个方向发展，目标是“硅化”细胞分子互动仍然很遥远。更准确地说，当前的模型现实水平已经导致了分子相互作用的预测准确性。这推动了新型动态模型的开发包含更广泛的生物细节，并可以为模拟添加真实感。然而，此类模型的计算量巨大，因此开发高效且准确的模型至关重要计算方法。目的是增强分子水平的理解和模拟的生物相互作用。特别是，通过利用随机优化的新颖发展，研究人员应通过增加现实主义的新维度来显着改进相互作用的预测。然而，对于许多实际情况，随机目标函数将变成高维、非高斯、非线性随机场的优化在计算上非常具有挑战性。这是一个艰难的研究人员计划通过开发新颖的不确定性量化（UQ）来解决这个问题数学理论。具体目标是： (i) 开发一个紧凑的动态代理模型包含分子结构不确定性和分子性质的随机目标函数例如通过求解非线性泊松玻尔兹曼 (PB) 方程来求解静电场。随机性通过代理模型可以有效地解决优化问题。 (ii) 分析复杂的解析规律非线性泊松-玻尔兹曼方程解的性质（以及其他分子性质）关于概率分子构象模型。 (iii) 发展收敛速度从关于实现数量的复杂分析规律的代理模型蛋白质结构（计算复杂性）。大多数蛋白质相互作用模型基于分子。结构呈现刚性形状，从而导致错误的预测。调查人员建议通过结合动态不确定性显着改善蛋白质相互作用的预测分子构象形状。 UQ 在蛋白质相互作用方面的理论和应用还处于起步阶段。