Evaluating protein structure-prediction schemes using energy landscape theory
Evaluating protein structure-prediction schemes using energy landscape theory
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DOI：
10.1147/rd.453.0475
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发表时间：
2001-05-01
影响因子：
1.3
通讯作者：
Wolynes, PG
中科院分区：
文献类型：
--
作者：
Eastwood, MP;Hardin, C;Wolynes, PG
蛋白质结构预测开始取得成功，至少是部分成功。然而，评估预测有许多主观因素，因此很难确定最需要改进的性质和程度。我们描述了用于蛋白质结构预测的能量函数的漏斗状性质如何决定它们的质量，并且可以使用景观理论和多直方图采样方法进行量化。预测算法表现出一种类似“火山口”的景观，而不是一个完美的漏斗。估计由预测算法产生的有效不同结构的预期数量。
Protein structure prediction is beginning to be, at least partially, successful. Evaluating predictions, however, has many elements of subjectivity, making it difficult to determine the nature and extent of improvements that are most needed. We describe how the funnel-like nature of energy functions used for protein structure prediction determines their quality and can be quantified using landscape theory and multiple histogram sampling methods. Prediction algorithms exhibit a "caldera"-like landscape rather than a perfectly funneled one. Estimates are made of the expected number of effectively distinct structures produced by a prediction algorithm.