ContextMirror2.0 用共进化信息预测物种特异性蛋白互作网络
Sequence-based coevolutionary prediction of species-specific interactomes
作者提出基于共进化的方法 ContextMirror2.0（CM2.0），用于预测物种特异性蛋白互作（PPI）。与监督式机器学习方法不同，该方法不依赖标注数据集，可缓解物种特异性 PPI 数据稀缺的问题。对 E. coli、S. enterica 和 S. aureus 各取前 1000 个预测互作，其中约 40% 得到实验 PPI 数据库支持。对预测所得功能模块的比较分析同时显示出高度保守的互作模式与物种特异性互作。作者指出，CM2.0 在系统发育层面利用共进化信息考察蛋白互作动态，可为新功能出现、环境适应及靶向治疗研究提供线索。
来源：PLOS Computational Biology · journals.plos.org
by Miguel Fernandez-Martin, Camila Pontes, Victoria Ruiz-Serra, Alfonso Valencia
Evolutionarily conserved protein-protein interactions (PPIs) reveal fundamental biological processes. However, predicting protein function solely from these interactions provides an incomplete picture of biological systems. Computational methods for predicting PPIs often struggle due to limited functional annotations in databases, making it difficult to fully understand unique biological systems. This study introduces ContextMirror2.0 (CM2.0), a coevolution-based method designed to address these limitations. Coevolutionary approaches, unlike supervised machine learning methods, do not rely on labelled datasets, proving valuable in addressing the scarcity of data for species-specific PPIs. Around 40% of CM2.0’s top-1000 predicted PPIs for E. coli, S. enterica, and S. aureus are supported by experimental PPI databases. However, comparative analysis of the resulting functional communities further revealed both highly conserved interaction patterns and species-specific interactions. CM2.0 leverages coevolutionary information at the phylogenetic level to explore protein interaction dynamics and understand the functional consequences of species-specific variations. This information can inform further studies on the emergence of novel functions, adaptation to specific environments, and the development of targeted therapies.