Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA.
Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, NY 11794, USA.
Int J Mol Sci. 2023 May 8;24(9):8450. doi: 10.3390/ijms24098450.
The insertion or deletion (indel) of amino acids has a variety of effects on protein function, ranging from disease-forming changes to gaining new functions. Despite their importance, indels have not been systematically characterized towards protein engineering or modification goals. In the present work, we focus on deletions composed of multiple contiguous amino acids (mAA-dels) and their effects on the protein (mutant) folding ability. Our analysis reveals that the mutant retains the native fold when the mAA-del obeys well-defined structural dynamics properties: localization in intrinsically flexible regions, showing low resistance to mechanical stress, and separation from allosteric signaling paths. Motivated by the possibility of distinguishing the features that underlie the adaptability of proteins to mAA-dels, and by the rapid evaluation of these features using elastic network models, we developed a positive-unlabeled learning-based classifier that can be adopted for protein design purposes. Trained on a consolidated set of features, including those reflecting the intrinsic dynamics of the regions where the mAA-dels occur, the new classifier yields a high recall of 84.3% for identifying mAA-dels that are stably tolerated by the protein. The comparative examination of the relative contribution of different features to the prediction reveals the dominant role of structural dynamics in enabling the adaptation of the mutant to mAA-del without disrupting the native fold.
氨基酸的插入或缺失(indel)对蛋白质功能有多种影响,从形成疾病的变化到获得新功能。尽管它们很重要,但 indel 尚未针对蛋白质工程或修饰目标进行系统地描述。在本工作中,我们专注于由多个连续氨基酸组成的缺失(mAA-dels)及其对蛋白质(突变体)折叠能力的影响。我们的分析表明,当 mAA-del 符合明确的结构动力学特性时,突变体保留了天然折叠:定位于固有柔性区域,表现出对机械应力的低抵抗力,并且与别构信号通路分离。由于有可能区分使蛋白质适应 mAA-dels 的特征,并且由于使用弹性网络模型可以快速评估这些特征,因此我们开发了一种基于阳性未标记学习的分类器,可用于蛋白质设计目的。在一组综合特征上进行训练,包括反映 mAA-dels 发生区域固有动力学的特征,新的分类器对识别稳定耐受蛋白质的 mAA-dels 的召回率高达 84.3%。对不同特征对预测的相对贡献的比较研究表明,结构动力学在使突变体适应 mAA-del 而不破坏天然折叠方面起着主导作用。