论文标题
具有基于进化和物理风格的建模的计算蛋白设计：当前和未来的协同作用
Computational protein design with evolutionary-based and physics-inspired modeling: current and future synergies
论文作者
论文摘要
计算蛋白设计有助于发现具有规定结构和功能的新型蛋白质。最近，使用新型数据驱动方法进行了激动人心的设计，这些方法可以大致分为两类：基于进化和物理启发的方法。以前的推断特征序列特征由一组与进化相关的蛋白质（例如保守或共同进化位置）共享，并重组它们以生成具有相似结构和功能的候选者。后者估计了关键的生化特性，例如结构自由能，构象熵或使用机器学习替代物的结合亲和力，并优化它们以产生改进的设计。在这里，我们回顾了这两个曲目的最新进展，讨论他们的优势和劣势，并突出了协同方法的机会。
Computational protein design facilitates discovery of novel proteins with prescribed structure and functionality. Exciting designs were recently reported using novel data-driven methodologies that can be roughly divided into two categories: evolutionary-based and physics-inspired approaches. The former infer characteristic sequence features shared by sets of evolutionary-related proteins, such as conserved or coevolving positions, and recombine them to generate candidates with similar structure and function. The latter estimate key biochemical properties such as structure free energy, conformational entropy or binding affinities using machine learning surrogates, and optimize them to yield improved designs. Here, we review recent progress along both tracks, discuss their strengths and weaknesses, and highlight opportunities for synergistic approaches.
