巨正则模型下蛋白质序列设计与景观分析的组合优化
Combinatorial Toolbox for Protein Sequence Design and Landscape Analysis in the Grand Canonical Model
摘要：
在现代生物学中，最重要的研究问题之一是了解蛋白质序列如何折叠成其天然的3D结构。为了在高水平上研究这个问题，人们希望分析蛋白质景观，即，所有蛋白质序列的空间结构及其天然3D结构。也许在这个水平上最基本的计算问题是将目标3D结构作为输入，并设计相对于目标3D结构的一个或多个适应度函数最适合的蛋白质序列。我们开发了一个工具箱的组合技术的蛋白质景观分析的太阳，Brem，陈，迪尔的大典模型。该工具箱基于线性规划、网络流和网络所有最小割集的线性尺寸表示。它不仅大大扩展了网络流技术的蛋白质序列设计在克莱因伯格的开创性工作，但也适用于一个相当广泛的收集计算问题比克莱因伯格所考虑的。我们已经使用这个工具箱，以获得一些有效的算法和硬度的结果。我们进一步使用该算法来分析蛋白质数据库中的3D结构，并发现了这些原生3D结构与Grand Canonical模型之间的一些新关系。
英文摘要：
In modern biology, one of the most important research problems is to understand how protein sequences fold into their native 3D structures. To investigate this problem at a high level, one wishes to analyze the protein landscapes, i.e., the structures of the space of all protein sequences and their native 3D structures. Perhaps the most basic computational problem at this level is to take a target 3D structure as input and design a fittest protein sequence with respect to one or more fitness functions of the target 3D structure. We develop a toolbox of combinatorial techniques for protein landscape analysis in the Grand Canonical model of Sun, Brem, Chan, and Dill. The toolbox is based on linear programming, network flow, and a linear-size representation of all minimum cuts of a network. It not only substantially expands the network flow technique for protein sequence design in Kleinberg's seminal work but also is applicable to a considerably broader collection of computational problems than those considered by Kleinberg. We have used this toolbox to obtain a number of efficient algorithms and hardness results. We have further used the algorithms to analyze 3D structures drawn from the Protein Data Bank and have discovered some novel relationships between such native 3D structures and the Grand Canonical model.
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