Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds, LS2 9JT, UK; School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK.
Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds, LS2 9JT, UK; School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK.
Curr Opin Struct Biol. 2020 Feb;60:157-166. doi: 10.1016/j.sbi.2020.01.005. Epub 2020 Feb 19.
Protein aggregation occurs through a variety of mechanisms, initiated by the unfolded, non-native, or even the native state itself. Understanding the molecular mechanisms of protein aggregation is challenging, given the array of competing interactions that control solubility, stability, cooperativity and aggregation propensity. An array of methods have been developed to interrogate protein aggregation, spanning computational algorithms able to identify aggregation-prone regions, to deep mutational scanning to define the entire mutational landscape of a protein's sequence. Here, we review recent advances in this exciting and emerging field, focussing on protein engineering approaches that, together with improved computational methods, hold promise to predict and control protein aggregation linked to human disease, as well as facilitating the manufacture of protein-based therapeutics.
蛋白质聚集通过多种机制发生，这些机制由未折叠的、非天然的甚至天然状态本身引发。由于控制溶解度、稳定性、协同性和聚集倾向的竞争相互作用的多样性，理解蛋白质聚集的分子机制具有挑战性。已经开发了一系列方法来研究蛋白质聚集，从能够识别聚集倾向区域的计算算法，到定义蛋白质序列完整突变景观的深度突变扫描。在这里，我们回顾了这一令人兴奋和新兴领域的最新进展，重点介绍了蛋白质工程方法，这些方法与改进的计算方法相结合，有望预测和控制与人类疾病相关的蛋白质聚集，并促进基于蛋白质的治疗药物的制造。
