Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue-residue contacts
Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue-residue contacts
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DOI：
10.1093/bioinformatics/btp149
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发表时间：
2009-05-15
期刊：
影响因子：
5.8
通讯作者：
Hvidsten, Torgeir R.
中科院分区：
文献类型：
--
作者：
Bjorkholm, Patrik;Daniluk, Pawel;Hvidsten, Torgeir R.
动机：正确预测缺乏已知结构的良好模板的蛋白质中的残基-残基接触将使从头开始的蛋白质结构预测向前迈出一大步。缺乏正确的接触，特别是长距离接触，被认为是这些方法经常失败的主要原因。结果：我们提出了一种基于隐马尔可夫模型（HMM）的新颖方法，用于使用同源序列、预测的二级结构和局部邻域库（蛋白质结构的局部描述符）作为训练数据来预测蛋白质序列中的残基-残基接触。该库由包含短程、中程和长程相互作用的重复结构实体组成，并且足够通用，可以重新组装 PDB 中几乎所有蛋白质的核心。该方法在 606 个与训练集没有显着序列相似性的域以及 151 个训练集中不存在 SCOP 折叠的域的外部测试集上进行了测试。考虑到顶部 0.2 。 L 预测（L = 序列长度），我们的 HMM 在新折叠目标中的长程相互作用方面获得了 22.8% 的准确度，对于长程、中程和短程接触的平均准确度为 28.6%。与文献中发表的结果相比，这比当前可用的方法有显着的性能提升。
Motivation: Correct prediction of residue-residue contacts in proteins that lack good templates with known structure would take ab initio protein structure prediction a large step forward. The lack of correct contacts, and in particular long-range contacts, is considered the main reason why these methods often fail.Results: We propose a novel hidden Markov model (HMM)based method for predicting residue-residue contacts from protein sequences using as training data homologous sequences, predicted secondary structure and a library of local neighborhoods (local descriptors of protein structure). The library consists of recurring structural entities incorporating short-, medium- and long-range interactions and is general enough to reassemble the cores of nearly all proteins in the PDB. The method is tested on an external test set of 606 domains with no significant sequence similarity to the training set as well as 151 domains with SCOP folds not present in the training set. Considering the top 0.2 . L predictions (L = sequence length), our HMMs obtained an accuracy of 22.8% for long-range interactions in new fold targets, and an average accuracy of 28.6% for long-, medium- and short- range contacts. This is a significant performance increase over currently available methods when comparing against results published in the literature.