论文标题
蛋白质设计问题的空腔方法
The cavity method to protein design problem
论文作者
论文摘要
在这项研究中，我们提出了一种分析统计力学方法，以解决称为蛋白质设计的生物物理学中的基本问题。蛋白质设计是蛋白质结构预测的一个反问题，其溶液是最能稳定给定构象的氨基酸序列。尽管使用深度学习最近在蛋白质设计方面快速进步，但仍在探索蛋白质设计原理的挑战。与以前的计算物理研究相反，我们使用了空腔方法，这是平均场近似值的扩展，当相互作用网络是树时，该方法变得严格。我们发现，对于小的二维（2D）晶格疏水（HP）蛋白质模型，腔体方法的设计几乎与Markov Chain Carlo Monte Carlo方法相当的结果相当于计算成本较低的结果。
In this study, we propose an analytic statistical mechanics approach to solve a fundamental problem in biological physics called protein design. Protein design is an inverse problem of protein structure prediction, and its solution is the amino acid sequence that best stabilizes a given conformation. Despite recent rapid progress in protein design using deep learning, the challenge of exploring protein design principles remains. Contrary to previous computational physics studies, we used the cavity method, an extension of the mean-field approximation that becomes rigorous when the interaction network is a tree. We found that for small two-dimensional (2D) lattice hydrophobic-polar (HP) protein models, the design by the cavity method yields results almost equivalent to those from the Markov chain Monte Carlo method with lower computational cost.
