Department of Computer Science, and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA.
Department of Computer Science, and Bioinformatics and Computational Biology Program, Worcester Polytechnic Institute, Worcester, MA, USA; Harvard Program in Therapeutic Sciences, Harvard Medical School, and Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA, USA.
Cell Rep. 2021 Nov 23;37(8):110045. doi: 10.1016/j.celrep.2021.110045.
Alternative splicing introduces an additional layer of protein diversity and complexity in regulating cellular functions that can be specific to the tissue and cell type, physiological state of a cell, or disease phenotype. Recent high-throughput experimental studies have illuminated the functional role of splicing events through rewiring protein-protein interactions; however, the extent to which the macromolecular interactions are affected by alternative splicing has yet to be fully understood. In silico methods provide a fast and cheap alternative to interrogating functional characteristics of thousands of alternatively spliced isoforms. Here, we develop an accurate feature-based machine learning approach that predicts whether a protein-protein interaction carried out by a reference isoform is perturbed by an alternatively spliced isoform. Our method, called the alternatively spliced interactions prediction (ALT-IN) tool, is compared with the state-of-the-art PPI prediction tools and shows superior performance, achieving 0.92 in precision and recall values.
可变剪接在调节细胞功能方面引入了蛋白质多样性和复杂性的额外层次，这种调节可能特定于组织和细胞类型、细胞的生理状态或疾病表型。最近的高通量实验研究通过重新连接蛋白质-蛋白质相互作用阐明了剪接事件的功能作用；然而，可变剪接对大分子相互作用的影响程度尚未完全理解。计算方法为研究数千种可变剪接异构体的功能特征提供了快速而廉价的替代方法。在这里，我们开发了一种准确的基于特征的机器学习方法，用于预测参考异构体进行的蛋白质-蛋白质相互作用是否被可变剪接异构体干扰。我们的方法称为可变剪接相互作用预测（ALT-IN）工具，与最先进的 PPI 预测工具进行了比较，表现出卓越的性能，在精度和召回值方面达到 0.92。
