Sharma Shantanu, Ding Feng, Dokholyan Nikolay V
Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.
Front Biosci. 2008 May 1;13:4795-808. doi: 10.2741/3039.
Understanding the role of biomolecular dynamics in cellular processes leading to human diseases and the ability to rationally manipulate these processes is of fundamental importance in scientific research. The last decade has witnessed significant progress in probing biophysical behavior of proteins. However, we are still limited in understanding how changes in protein dynamics and inter-protein interactions occurring in short length- and time-scales lead to aberrations in their biological function. Bridging this gap in biology probed using computer simulations marks a challenging frontier in computational biology. Here we examine hypothesis-driven simplified protein models in conjunction with discrete molecular dynamics in the study of protein aggregation, implicated in series of neurodegenerative diseases, such as Alzheimer's and Huntington's diseases. Discrete molecular dynamics simulations of simplified protein models have emerged as a powerful methodology with its ability to bridge the gap in time and length scales from protein dynamics to aggregation, and provide an indispensable tool for probing protein aggregation.
了解生物分子动力学在导致人类疾病的细胞过程中的作用，以及合理操纵这些过程的能力，在科学研究中具有至关重要的意义。过去十年见证了蛋白质生物物理行为研究取得的重大进展。然而，我们在理解短长度和时间尺度上发生的蛋白质动力学变化和蛋白质间相互作用如何导致其生物学功能异常方面仍然存在局限。利用计算机模拟弥合生物学中的这一差距，是计算生物学中一个具有挑战性的前沿领域。在此，我们结合离散分子动力学研究假设驱动的简化蛋白质模型，以研究与一系列神经退行性疾病（如阿尔茨海默病和亨廷顿病）相关的蛋白质聚集。简化蛋白质模型的离散分子动力学模拟已成为一种强大的方法，它能够弥合从蛋白质动力学到聚集的时间和长度尺度上的差距，并为探测蛋白质聚集提供不可或缺的工具。
