Partner-Aware Prediction of Interacting Residues in Protein-Protein Complexes from Sequence Data

Computational prediction of residues that participate in protein-protein interactions is a difficult task, and state of the art methods have shown only limited success in this arena. One possible problem with these methods is that they try to predict interacting residues without incorporating information about the partner protein, although it is unclear how much partner information could enhance prediction performance. To address this issue, the two following comparisons are of crucial significance: (a) comparison between the predictability of inter-protein residue pairs, i.e., predicting exactly which residue pairs interact with each other given two protein sequences; this can be achieved by either combining conventional single-protein predictions or making predictions using a new model trained directly on the residue pairs, and the performance of these two approaches may be compared: (b) comparison between the predictability of the interacting residues in a single protein (irrespective of the partner residue or protein) from conventional methods and predictions converted from the pair-wise trained model. Using these two streams of training and validation procedures and employing similar two-stage neural networks, we showed that the models trained on pair-wise contacts outperformed the partner-unaware models in predicting both interacting pairs and interacting single-protein residues. Prediction performance decreased with the size of the conformational change upon complex formation; this trend is similar to docking, even though no structural information was used in our prediction. An example application that predicts two partner-specific interfaces of a protein was shown to be effective, highlighting the potential of the proposed approach. Finally, a preliminary attempt was made to score docking decoy poses using prediction of interacting residue pairs; this analysis produced an encouraging result.

蛋白质-蛋白质相互作用（protein-protein interactions）相关残基的计算预测是一项颇具挑战性的任务，当前最先进的方法在该领域仅取得了有限的成果。此类方法存在一处潜在局限：其在预测相互作用残基时未引入伴侣蛋白（partner protein）的相关信息，尽管目前尚不明确伴侣蛋白信息能够在多大程度上提升预测性能。为解决这一问题，以下两项对比研究至关重要：(a) 蛋白质间残基对（inter-protein residue pairs）可预测性的对比，即给定两条蛋白质序列时精准预测哪些残基对会发生相互作用，该任务可通过两种路径实现——一是结合传统的单蛋白质预测结果，二是使用直接基于残基对训练的全新模型进行预测，我们可对这两种方法的性能进行对比；(b) 传统方法与从成对训练模型转换得到的预测结果，在单蛋白质内相互作用残基（不考虑伴侣残基或伴侣蛋白）的可预测性上的对比。借助这两类训练与验证流程，并采用相似的两阶段神经网络（two-stage neural networks），我们证实：基于成对接触训练的模型，在预测相互作用残基对与单蛋白质相互作用残基两方面，均优于未引入伴侣蛋白信息的模型。预测性能会随复合物形成时的构象变化幅度提升而下降，这一趋势与分子对接（docking）领域的规律相似，尽管我们的预测未使用任何结构信息。我们展示了一个预测蛋白质的两种伴侣特异性结合界面的应用实例，该实例效果良好，凸显了所提方法的应用潜力。最后，我们尝试通过相互作用残基对的预测结果对对接诱饵构象（docking decoy poses）进行评分，该分析取得了令人振奋的结果。