Soleymani Farzan, Paquet Eric, Viktor Herna Lydia, Michalowski Wojtek, Spinello Davide
Department of Mechanical Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
National Research Council, 1200 Montreal Road, Ottawa, ON K1A 0R6, Canada.
Comput Struct Biotechnol J. 2023 Jan 25;21:1324-1348. doi: 10.1016/j.csbj.2023.01.028. eCollection 2023.
Proteins mainly perform their functions by interacting with other proteins. Protein-protein interactions underpin various biological activities such as metabolic cycles, signal transduction, and immune response. However, due to the sheer number of proteins, experimental methods for finding interacting and non-interacting protein pairs are time-consuming and costly. We therefore developed the ProtInteract framework to predict protein-protein interaction. ProtInteract comprises two components: first, a novel autoencoder architecture that encodes each protein's primary structure to a lower-dimensional vector while preserving its underlying sequence attributes. This leads to faster training of the second network, a deep convolutional neural network (CNN) that receives encoded proteins and predicts their interaction under three different scenarios. In each scenario, the deep CNN predicts the class of a given encoded protein pair. Each class indicates different ranges of confidence scores corresponding to the probability of whether a predicted interaction occurs or not. The proposed framework features significantly low computational complexity and relatively fast response. The contributions of this work are twofold. First, ProtInteract assimilates the protein's primary structure into a pseudo-time series. Therefore, we leverage the nature of the time series of proteins and their physicochemical properties to encode a protein's amino acid sequence into a lower-dimensional vector space. This approach enables extracting highly informative sequence attributes while reducing computational complexity. Second, the ProtInteract framework utilises this information to identify protein interactions with other proteins based on its amino acid configuration. Our results suggest that the proposed framework performs with high accuracy and efficiency in predicting protein-protein interactions.
蛋白质主要通过与其他蛋白质相互作用来发挥其功能。蛋白质-蛋白质相互作用支撑着各种生物活动，如代谢循环、信号转导和免疫反应。然而，由于蛋白质数量众多，寻找相互作用和非相互作用蛋白质对的实验方法既耗时又昂贵。因此，我们开发了ProtInteract框架来预测蛋白质-蛋白质相互作用。ProtInteract由两个组件组成：首先，一种新颖的自动编码器架构，它将每个蛋白质的一级结构编码为低维向量，同时保留其潜在的序列属性。这使得第二个网络（一个深度卷积神经网络（CNN））的训练速度更快，该网络接收编码后的蛋白质并在三种不同场景下预测它们的相互作用。在每种场景中，深度CNN预测给定编码蛋白质对的类别。每个类别表示对应于预测相互作用是否发生的概率的不同置信度分数范围。所提出的框架具有显著低的计算复杂度和相对较快的响应速度。这项工作的贡献有两方面。首先，ProtInteract将蛋白质的一级结构同化为伪时间序列。因此，我们利用蛋白质时间序列的性质及其物理化学性质将蛋白质的氨基酸序列编码到低维向量空间中。这种方法能够在降低计算复杂度的同时提取高度信息丰富的序列属性。其次，ProtInteract框架利用这些信息根据其氨基酸构型识别与其他蛋白质的相互作用。我们的结果表明，所提出的框架在预测蛋白质-蛋白质相互作用方面具有高精度和高效率。
