Kocher J P, Rooman M J, Wodak S J
Unité de Conformation des Macromolécules Biologiques, Université Libre de Bruxelles, Belgium.
J Mol Biol. 1994 Feb 4;235(5):1598-613. doi: 10.1006/jmbi.1994.1109.
Several types of potentials are derived from a dataset of known protein structures by computing statistical relations between amino acid sequence and different descriptions of the protein conformation. These potentials formulate in different ways backbone dihedral angle preferences, pairwise distance-dependent interactions between amino acid residues, and solvation effects based on accessible surface area calculations. Parameters affecting the characteristics and the performance of the potentials are critically assessed by monitoring recognition of the native fold in a strict screening test, where each sequence in the dataset is threaded through a repertoire of motifs, generated from all corresponding structures. Sequence gaps are not allowed, to avoid additional approximations. Results show that residue interaction potentials computed from distances between average side-chain centroids perform significantly better on this test than those computed considering inter-C alpha or inter-C beta distances. Combining potentials that are based on different structural descriptions and different interactions is also beneficial. The performance of some of these potentials is in fact so good that they recognize the correct fold for all the tested proteins, including subunits known to be unstable in the absence of quaternary interactions. Most strikingly, potentials representing backbone dihedral angle preferences recognize as many as 68 protein chains out of a total of 74, even though they consider solely local interactions along the chain, which, being the same as those considered in secondary structure prediction methods, are well known to be incapable of determining the full three-dimensional fold. This leads us to question the ability of procedures that screen a limited repertoire of structures to act as a stringent test for the potentials. We concede, however, that they are useful and fast tests, capable of revealing gross shortcomings of the potentials, or possible biases towards native recognition due, for example, to effects of sequence memory.
通过计算氨基酸序列与蛋白质构象的不同描述之间的统计关系，从已知蛋白质结构的数据集中导出了几种类型的势。这些势以不同方式制定了主链二面角偏好、氨基酸残基之间的成对距离依赖性相互作用以及基于可及表面积计算的溶剂化效应。通过在严格的筛选测试中监测天然折叠的识别来严格评估影响势的特征和性能的参数，在该测试中，数据集中的每个序列都穿过由所有相应结构生成的一组基序。不允许序列间隙，以避免额外的近似。结果表明，根据平均侧链质心之间的距离计算的残基相互作用势在该测试中的表现明显优于考虑Cα或Cβ之间距离计算的势。结合基于不同结构描述和不同相互作用的势也是有益的。其中一些势的性能实际上非常好，以至于它们能识别所有测试蛋白质的正确折叠，包括已知在没有四级相互作用时不稳定的亚基。最引人注目的是，代表主链二面角偏好的势在总共74条蛋白质链中识别出多达68条，尽管它们仅考虑沿着链的局部相互作用，而众所周知，这种相互作用与二级结构预测方法中考虑的相互作用相同，无法确定完整的三维折叠。这使我们质疑筛选有限结构库的程序作为对势的严格测试的能力。然而，我们承认它们是有用且快速的测试，能够揭示势的严重缺陷，或例如由于序列记忆效应导致的对天然识别的可能偏差。
