Di Francesco V, Geetha V, Garnier J, Munson P J
Analytical Biostatistics Section, National Institutes of Health, Bethesda, Maryland, USA.
Proteins. 1997;Suppl 1:123-8. doi: 10.1002/(sici)1097-0134(1997)1+<123::aid-prot16>3.3.co;2-#.
We present an analysis of the blind predictions submitted to the fold recognition category for the second meeting on the Critical Assessment of techniques for protein Structure Prediction. Our method achieves fold recognition from predicted secondary structure sequences using hidden Markov models (HMMs) of protein folds. HMMs are trained only with experimentally derived secondary structure sequences of proteins having similar fold, therefore protein structures are described by the models at a remarkably simplified level. We submitted predictions for five target sequences, of which four were later found to be suitable for threading. Our approach correctly predicted the fold for three of them. For a fourth sequence the fold could have been correctly predicted if a better model for its structure was available. We conclude that we have additional evidence that secondary structure information represents an important factor for achieving fold recognition.
我们对提交给蛋白质结构预测技术关键评估第二次会议折叠识别类别的盲测预测进行了分析。我们的方法利用蛋白质折叠的隐马尔可夫模型（HMM）从预测的二级结构序列实现折叠识别。HMM仅使用具有相似折叠的蛋白质的实验衍生二级结构序列进行训练，因此模型以显著简化的水平描述蛋白质结构。我们提交了五个目标序列的预测结果，其中四个后来被发现适合穿线法。我们的方法正确预测了其中三个的折叠。对于第四个序列，如果有更好的其结构模型，折叠本可以被正确预测。我们得出结论，我们有更多证据表明二级结构信息是实现折叠识别的一个重要因素。
