Tikader Baishakhi, Maji Samir K, Kar Sandip
Department of Chemistry, IIT Bombay Powai Mumbai - 400076 India
Department of Biosciences and Bioengineering, IIT Bombay Powai Mumbai - 400076 India.
Chem Sci. 2021 Sep 3;12(40):13530-13545. doi: 10.1039/d1sc03190b. eCollection 2021 Oct 20.
Amyloid formation is a generic property of many protein/polypeptide chains. A broad spectrum of proteins, despite having diversity in the inherent precursor sequence and heterogeneity present in the mechanism of aggregation produces a common cross β-spine structure that is often associated with several human diseases. However, a general modeling framework to interpret amyloid formation remains elusive. Herein, we propose a data-driven mathematical modeling approach that elucidates the most probable interaction network for the aggregation of a group of proteins (α-synuclein, Aβ42, Myb, and TTR proteins) by considering an ensemble set of network models, which include most of the mechanistic complexities and heterogeneities related to amyloidogenesis. The best-fitting model efficiently quantifies various timescales involved in the process of amyloidogenesis and explains the mechanistic basis of the monomer concentration dependency of amyloid-forming kinetics. Moreover, the present model reconciles several mutant studies and inhibitor experiments for the respective proteins, making experimentally feasible non-intuitive predictions, and provides further insights about how to fine-tune the various microscopic events related to amyloid formation kinetics. This might have an application to formulate better therapeutic measures in the future to counter unwanted amyloidogenesis. Importantly, the theoretical method used here is quite general and can be extended for any amyloid-forming protein.
淀粉样蛋白形成是许多蛋白质/多肽链的普遍特性。尽管多种蛋白质在前体序列固有性方面存在差异,且聚集机制具有异质性,但它们都会产生一种常见的交叉β-螺旋结构,这种结构常与多种人类疾病相关。然而,用于解释淀粉样蛋白形成的通用建模框架仍然难以捉摸。在此,我们提出一种数据驱动的数学建模方法,通过考虑一组网络模型来阐明一组蛋白质(α-突触核蛋白、Aβ42、Myb和TTR蛋白)聚集最可能的相互作用网络,这些网络模型包含了与淀粉样蛋白生成相关的大部分机制复杂性和异质性。最佳拟合模型有效地量化了淀粉样蛋白生成过程中涉及的各种时间尺度,并解释了淀粉样蛋白形成动力学对单体浓度依赖性的机制基础。此外,本模型整合了针对各蛋白质的多项突变研究和抑制剂实验,做出了实验上可行的非直观预测,并为如何微调与淀粉样蛋白形成动力学相关的各种微观事件提供了进一步的见解。这可能在未来用于制定更好的治疗措施以对抗不必要的淀粉样蛋白生成。重要的是,这里使用的理论方法非常通用,可以扩展到任何形成淀粉样蛋白的蛋白质。