Background: Typically, proteins perform key biological functions by interacting with each other. As a consequence, predicting which protein pairs interact is a fundamental problem. Experimental methods are slow, expensive, and may be error prone. Many computational methods have been proposed to identify candidate interacting pairs. When accurate, they can serve as an inexpensive, preliminary filtering stage, to be followed by downstream experimental validation. Among such methods, sequence-based ones are very promising. Results: We present MPS(T&B) (Maximum Protein Similarity Topological and Biological), a new algorithm that leverages both topological and biological information to predict protein-protein interactions. We comprehensively compare MPS(T) and MPS(T&B) with state-of-the-art approaches on reliable PPIs datasets, showing that they have competitive or higher accuracy on biologically validated test sets. Conclusion: MPS(T) and MPS(T&B) are topological only and topological plus sequence-based computational methods that can effectively predict the entire human interactome.

中文翻译：

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蛋白质-蛋白质相互作用的基于网络和序列的预测

背景：通常，蛋白质通过相互作用来执行关键的生物学功能。因此，预测哪些蛋白质对相互作用是一个基本问题。实验方法缓慢、昂贵，并且可能容易出错。已经提出了许多计算方法来识别候选相互作用对。如果准确，它们可以作为廉价的初步过滤阶段，随后进行下游实验验证。在这些方法中，基于序列的方法非常有前途。结果：我们提出了 MPS(T&B)（最大蛋白质相似性拓扑和生物），这是一种利用拓扑和生物信息来预测蛋白质-蛋白质相互作用的新算法。我们在可靠的 PPI 数据集上全面比较 MPS(T) 和 MPS(T&B) 与最先进的方法，表明他们在生物学验证的测试集上具有竞争性或更高的准确性。结论： MPS(T) 和 MPS(T&B) 是纯拓扑和基于拓扑加序列的计算方法，可以有效预测整个人类相互作用组。