Howladar Nayan, Kabir Md Wasi Ul, Hoque Foyzul, Katebi Ataur, Hoque Md Tamjidul
Department of Computer Science, University of New Orleans, New Orleans, LA, USA.
Department of Computer Science & Engineering, Independent University, Bangladesh.
Comput Biol Med. 2025 Mar;186:109678. doi: 10.1016/j.compbiomed.2025.109678. Epub 2025 Jan 19.
Protein-protein interactions within a cell are essential for various fundamental biological processes. Computational techniques have arisen in bioinformatics due to the challenging and resource-intensive nature of experimental protein pair interaction studies. This research seeks to create a cutting-edge machine learning method for predicting protein pair interactions using carefully chosen input features and leveraging evolutionary data. PPILS leverages evolutionary knowledge from the protein language model. It develops an encoder-decoder architecture with light attention. The trained model obtains protein embeddings from a language model and employs a light attention-based encoder, where a single convolution operation generates attention. A subsequent convolution is applied to input features, creating a representative construct for the protein interaction prediction. These encoded representations are then channeled into the decoder to predict protein interactions. Our findings indicated that PPILS outperformed existing methods in PPI prediction. The proposed method could be essential in protein-protein interaction prediction, further accelerating the discovery of protein-based drugs.
细胞内的蛋白质-蛋白质相互作用对于各种基本生物学过程至关重要。由于实验性蛋白质对相互作用研究具有挑战性且资源密集，计算技术在生物信息学中应运而生。本研究旨在创建一种前沿的机器学习方法，通过精心选择输入特征并利用进化数据来预测蛋白质对相互作用。PPILS利用来自蛋白质语言模型的进化知识。它开发了一种具有轻量级注意力的编码器-解码器架构。经过训练的模型从语言模型中获取蛋白质嵌入，并采用基于轻量级注意力的编码器，其中单个卷积操作生成注意力。随后对输入特征应用卷积，创建用于蛋白质相互作用预测的代表性结构。然后将这些编码表示输入到解码器中以预测蛋白质相互作用。我们的研究结果表明，PPILS在蛋白质-蛋白质相互作用预测方面优于现有方法。所提出的方法在蛋白质-蛋白质相互作用预测中可能至关重要，可进一步加速基于蛋白质的药物发现。
