Lemer C M, Rooman M J, Wodak S J
Unité de Conformation de Macromolécules Biologiques, Brussels, Belgium.
Proteins. 1995 Nov;23(3):337-55. doi: 10.1002/prot.340230308.
This paper evaluates the results of a protein structure prediction contest. The predictions were made using threading procedures, which employ techniques for aligning sequences with 3D structures to select the correct fold of a given sequence from a set of alternatives. Nine different teams submitted 86 predictions, on a total of 21 target proteins with little or no sequence homology to proteins of known structure. The 3D structures of these proteins were newly determined by experimental methods, but not yet published or otherwise available to the predictors. The predictions, made from the amino acid sequence alone, thus represent a genuine test of the current performance of threading methods. Only a subset of all the predictions is evaluated here. It corresponds to the 44 predictions submitted for the 11 target proteins seen to adopt known folds. The predictions for the remaining 10 proteins were not analyzed, although weak similarities with known folds may also exist in these proteins. We find that threading methods are capable of identifying the correct fold in many cases, but not reliably enough as yet. Every team predicts correctly a different set of targets, with virtually all targets predicted correctly by at least one team. Also, common folds such as TIM barrels are recognized more readily than folds with only a few known examples. However, quite surprisingly, the quality of the sequence-structure alignments, corresponding to correctly recognized folds, is generally very poor, as judged by comparison with the corresponding 3D structure alignments. Thus, threading can presently not be relied upon to derive a detailed 3D model from the amino acid sequence. This raises a very intriguing question: how is fold recognition achieved? Our analysis suggests that it may be achieved because threading procedures maximize hydrophobic interactions in the protein core, and are reasonably good at recognizing local secondary structure.
本文评估了一场蛋白质结构预测竞赛的结果。这些预测是使用穿线法做出的，穿线法采用将序列与三维结构进行比对的技术，以便从一组备选结构中选择给定序列的正确折叠方式。九个不同的团队提交了86个预测结果，涉及总共21个与已知结构蛋白质几乎没有或完全没有序列同源性的目标蛋白质。这些蛋白质的三维结构是通过实验方法新确定的，但尚未发表，预测者也无法通过其他途径获取。因此，仅根据氨基酸序列做出的预测代表了对当前穿线法性能的真实测试。这里仅评估了所有预测中的一个子集。它对应于为11个被认为采用已知折叠方式的目标蛋白质提交的44个预测。尽管其余10个蛋白质可能也与已知折叠方式存在微弱相似性，但未对针对它们的预测进行分析。我们发现穿线法在许多情况下能够识别出正确的折叠方式，但目前还不够可靠。每个团队正确预测的目标集各不相同，几乎所有目标都至少被一个团队正确预测。此外，像TIM桶这样的常见折叠方式比只有少数已知实例的折叠方式更容易被识别。然而，非常令人惊讶的是，与相应的三维结构比对相比，对应于正确识别的折叠方式的序列-结构比对质量通常非常差。因此，目前不能依靠穿线法从氨基酸序列推导出详细的三维模型。这就引出了一个非常有趣的问题：折叠识别是如何实现的？我们的分析表明，这可能是因为穿线法使蛋白质核心中的疏水相互作用最大化，并且在识别局部二级结构方面表现相当出色。
