Department of Computer and Information Science, University of Delaware, Newark, DE 19716, USA.
IEEE Trans Nanobioscience. 2013 Sep;12(3):158-64. doi: 10.1109/TNB.2013.2263810. Epub 2013 Aug 15.
Identification of interacting residues involved in protein-protein and protein-ligand interaction is critical for the prediction and understanding of the interaction and has practical impact on mutagenesis and drug design. In this work, we introduce a new decoding algorithm, ETB-Viterbi, with an early traceback mechanism, and apply it to interaction profile hidden Markov models (ipHMMs) to enable optimized incorporation of long-distance correlations between interacting residues, leading to improved prediction accuracy. The method was applied and tested to a set of domain-domain interaction families from the 3DID database, and showed statistically significant improvement in accuracy measured by F-score. To gauge and assess the method's effectiveness and robustness in capturing the correlation signals, sets of simulated data based on the 3DID dataset with controllable correlation between interacting residues were also used, as well as reversed sequence orientation. It was demonstrated that the prediction consistently improves as the correlations increase and is not significantly affected by sequence orientation.
鉴定蛋白质-蛋白质和蛋白质-配体相互作用涉及的相互作用残基对于预测和理解相互作用至关重要，并且对诱变和药物设计具有实际影响。在这项工作中，我们引入了一种新的解码算法 ETB-Viterbi，它具有早期回溯机制，并将其应用于相互作用谱隐马尔可夫模型 (ipHMM)，以优化整合相互作用残基之间的远距离相关性，从而提高预测精度。该方法应用于 3DID 数据库中的一组域-域相互作用家族，并通过 F 分数测量的准确性显示出统计学上的显著提高。为了评估和评估该方法在捕获相关性信号方面的有效性和稳健性，还使用了基于 3DID 数据集的具有可控相互作用残基相关性的模拟数据集以及反转序列方向。结果表明，随着相关性的增加，预测性能持续提高，并且不受序列方向的显著影响。
