Stöckelmaier Johannes, Oostenbrink Chris
Institute of Molecular Modeling and Simulation (MMS), BOKU University, Vienna, Austria.
Christian Doppler Laboratory Molecular Informatics in the Biosciences, BOKU University, Vienna, Austria.
Phys Chem Chem Phys. 2025 Jul 17;27(28):14704-14717. doi: 10.1039/d5cp01263e.
To elucidate the connection between the structure and function of intrinsically disordered proteins (IDPs) a description of their conformational ensembles is crucial. These are typically characterized by an extremely large number of similarly low energy conformations, which can hardly be captured by either experimental or computational means only. Rather, the combination of data from both simulation studies and experimental research offers a way towards a more complete understanding of these proteins. Over the last decade, a number of methods have been developed to integrate experimental data and simulations into one model to describe the conformational diversity. While many of these methods have been successfully applied, they often remain black-boxes for the scientist applying them. In this work, we review maximum entropy methods to optimize conformational ensembles of proteins. From a didactical perspective, we aim to present the mathematical concepts and the optimization processes in a common framework, to increase the understanding of these methods.
为阐明内在无序蛋白质（IDP）的结构与功能之间的联系，描述其构象集合至关重要。这些构象集合的典型特征是存在大量能量相近的低能构象，仅通过实验或计算手段很难全面捕捉。相反，将模拟研究和实验研究的数据相结合，为更全面理解这些蛋白质提供了一条途径。在过去十年中，已开发出多种方法，将实验数据和模拟整合到一个模型中，以描述构象多样性。虽然其中许多方法已成功应用，但对于使用它们的科学家来说，这些方法往往仍是黑箱操作。在这项工作中，我们综述了用于优化蛋白质构象集合的最大熵方法。从教学的角度来看，我们旨在将数学概念和优化过程呈现于一个通用框架中，以增进对这些方法的理解。
