A knowledge-based scoring function based on residue triplets for protein structure prediction.
A knowledge-based scoring function based on residue triplets for protein structure prediction.
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DOI：
10.1093/protein/gzj018
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发表时间：
2006-05
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影响因子：
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通讯作者：
Samudrala R
中科院分区：
文献类型：
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作者：
Ngan SC;Inouye MT;Samudrala R
从头计算蛋白质结构预测的一般范例之一涉及对构象空间进行采样，使得产生一大组诱饵（候选）结构，然后使用各种评分函数从这些诱饵中选择天然样构象。在这项研究中，基于Banavar及其同事首次提出的物理/几何方法，我们制定了一个基于知识的评分函数，该函数使用蛋白质构象中残基三联体之间形成的曲率半径。通过分析其性能的各种诱饵集，我们确定了一组很好的参数-距离截止和距离箱的数量-用于配置这样的功能。此外，我们调查的效果，使用各种方法编译先验分布的性能的知识为基础的功能。目前形式的残基三联体评分功能的可能扩展进行了讨论。
One of the general paradigms for ab initio protein structure prediction involves sampling the conformational space such that a large set of decoy (candidate) structures are generated and then selecting native-like conformations from those decoys using various scoring functions. In this study, based on a physical/geometric approach first suggested by Banavar and colleagues, we formulate a knowledge-based scoring function, which uses the radii of curvature formed among triplets of residues in a protein conformation. By analyzing its performance on various decoy sets, we determine a good set of parameters—the distance cutoff and the number of distance bins—to use for configuring such a function. Furthermore, we investigate the effect of using various approaches for compiling the prior distribution on the performance of the knowledge-based function. Possible extensions to the current form of the residue triplet scoring function are discussed.