确定“顽固”蛋白中的相互作用位点：HIV-1和Eiav Rev蛋白中预测的蛋白和Rna结合位点与实验数据一致
Identifying Interaction Sites in "Recalcitrant" Proteins: Predicted Protein and Rna Binding Sites in Rev Proteins of Hiv-1 and Eiav Agree with Experimental Data
摘要：
蛋白质-蛋白质和蛋白质核酸相互作用对于广泛的生物过程是至关重要的，包括基因表达的调节、蛋白质合成以及许多病毒的复制和组装。我们已经开发了机器学习方法来预测蛋白质的哪些氨基酸参与其与其他蛋白质和/或核酸的相互作用，仅使用蛋白质序列作为输入。在本文中，我们描述了一个应用程序的分类器训练的数据集的特征蛋白质-蛋白质和蛋白质-RNA复合物的实验结构是可用的。我们将这些分类器的预测蛋白质和RNA结合位点的序列中的一个临床上重要的蛋白质的结构是未知的问题：调节蛋白Rev，HIV-1和其他慢病毒的复制所必需的。我们将我们的预测与已发表的HIV-1和EIAV Rev的生化，遗传和部分结构信息进行比较，并与我们自己发表的EIAV Rev中RNA结合位点的实验映射进行比较。预测和实验确定的结合位点非常一致。可靠地预测蛋白质中直接参与特异性结合事件的残基的能力-而不需要关于蛋白质或其参与的复合物的结构信息-可以潜在地产生新的疾病干预策略。
英文摘要：
Protein-protein and protein nucleic acid interactions are vitally important for a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed machine learning approaches for predicting which amino acids of a protein participate in its interactions with other proteins and/or nucleic acids, using only the protein sequence as input. In this paper, we describe an application of classifiers trained on datasets of well-characterized protein-protein and protein-RNA complexes for which experimental structures are available. We apply these classifiers to the problem of predicting protein and RNA binding sites in the sequence of a clinically important protein for which the structure is not known: the regulatory protein Rev, essential for the replication of HIV-1 and other lentiviruses. We compare our predictions with published biochemical, genetic and partial structural information for HIV-1 and EIAV Rev and with our own published experimental mapping of RNA binding sites in EIAV Rev. The predicted and experimentally determined binding sites are in very good agreement. The ability to predict reliably the residues of a protein that directly contribute to specific binding events - without the requirement for structural information regarding either the protein or complexes in which it participates - can potentially generate new disease intervention strategies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
