Scoring functions for de novo protein structure prediction revisited.
Scoring functions for de novo protein structure prediction revisited.
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DOI：
10.1007/978-1-59745-574-9_10
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发表时间：
2008
影响因子：
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通讯作者：
S. Ngan;Ling-Hong Hung;Tianyun Liu;R. Samudrala
中科院分区：
文献类型：
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作者：
S. Ngan;Ling-Hong Hung;Tianyun Liu;R. Samudrala
从头蛋白质结构预测方法尝试基于支配蛋白质折叠能量学和/或天然结构获得的构象特征的统计趋势的一般原理从序列预测三级结构，而不使用明确的模板。从头预测的一般范例涉及在评分函数和其他序列依赖性偏差的指导下对构象空间进行采样，使得产生一大组候选（“诱饵”）结构，然后使用评分函数以及构象异构体聚类从这些诱饵中选择天然样构象。高分辨率细化有时被用作微调类天然结构的最后一步。有两种主要的评分功能。基于物理的函数基于描述分子相互作用的已知物理学方面的数学模型。基于知识的功能形成与统计模型捕获天然蛋白质构象的属性方面。我们将在本章中讨论这两个类别中的一些评分函数在从头结构预测中的实现和使用。
De novo protein structure prediction methods attempt to predict tertiary structures from sequences based on general principles that govern protein folding energetics and/or statistical tendencies of conformational features that native structures acquire, without the use of explicit templates. A general paradigm for de novo prediction involves sampling the conformational space, guided by scoring functions and other sequence-dependent biases, such that a large set of candidate (“decoy”) structures are generated, and then selecting native-like conformations from those decoys using scoring functions as well as conformer clustering. High-resolution refinement is sometimes used as a final step to fine-tune native-like structures. There are two major classes of scoring functions. Physics-based functions are based on mathematical models describing aspects of the known physics of molecular interaction. Knowledge-based functions are formed with statistical models capturing aspects of the properties of native protein conformations. We discuss the implementation and use of some of the scoring functions from these two classes for de novo structure prediction in this chapter.