Biophysical foundations of evolutionary dynamics

进化动力学的生物物理学基础

基本信息

批准号：
10633124
负责人：
EUGENE I SHAKHNOVICH
金额：
$ 76.02万
依托单位：
HARVARD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2026-05-31
项目状态：
未结题

项目摘要

The overarching goal of this research is to develop predictive multiscale biophysical models of adaptive evolutionary dynamics. The new concept of Biophysical Fitness Landscape (BFL) is a map of protein/nucleic acid molecular properties to fitness. We demonstrated the conceptual validity of BFL by discovering a simple and accurate quantitative relationship between fitness of E. coli and molecular properties of important core metabolic enzymes. This finding transforms the concept of fitness landscape from an artful metaphor into a quantitative tractable tool to predict the genotype-phenotype relationship (GPR). Here we take these findings as a foundation to further extend our understanding of interplay between biophysical and population factors that determine the dynamics and outcome of adaptive evolution. We will apply biophysical analysis, automated robotics setup along with protein engineering and genomic editing tools to explore evolutionary dynamics in laboratory experiments under conditions that allow tight control on all scales – from molecules to populations. As a key model we carry out a set of evolution experiments with adapting populations of E. coli escaping from antibiotic stress and structural instability of the essential protein Dihydrofolate Reductase. We characterize on all scales – genotyping, molecular traits, systems proteomics and metabolomics and population - multiple evolutionary paths to resistance and adaption of emerging bacterial strains and determine at which level of description (genotype, biophysical properties, systems responses) evolution becomes reproducible – and by implication predictable. We model the evolutionary dynamics using multiscale models where cytoplasm of model cells is presented in a biophysically realistic manner, and fitness of model organisms is predicted from its molecular traits using experimentally derived BFL. Comprehensive molecular mapping of possible escape routes will provide an opportunity to rationally design new class of compounds – “evolution drugs” - that comprehensively block pathogen’s resistance. In a related effort we will explore the biophysical underpinnings of codon adaptation to discern their effects on mRNA and cotranslational protein folding. A tight integration between theory and experiment will provide an opportunity to develop predictive evolutionary models of ever increasing accuracy and realism. Progress along these lines will transform our approaches to study evolutionary dynamics from descriptive into predictive and quantitative, which will be instrumental to the development of novel approaches to fight antibiotic resistance and, potentially, viral escape from stressors such as drugs and immune response.

这项研究的总体目标是开发适应性的预测性多尺度生物物理模型进化动力学。生物物理适应性景观（BFL）的新概念是蛋白质/核酸图谱。我们通过发现一个简单的方法证明了 BFL 的概念有效性。以及大肠杆菌适应度与重要核心分子特性之间的准确定量关系这一发现将健身景观的概念从艺术隐喻转变为一种健康景观。预测基因型-表型关系（GPR）的易于处理的工具在这里我们定量地获取这些发现。作为进一步扩展我们对生物物理和人口因素之间相互作用的理解的基础决定适应性进化的动态和结果我们将应用自动化的生物物理分析。机器人技术与蛋白质工程和基因组编辑工具一起探索进化动力学实验室实验条件允许对从分子到群体的所有尺度进行严格控制。作为一个关键模型，我们进行了一系列进化实验，以适应从环境中逃脱的大肠杆菌种群。我们表征了必需蛋白二氢叶酸还原酶的抗生素应激和结构不稳定性。所有尺度 - 基因分型、分子特征、系统蛋白质组学和代谢组学以及群体 - 多个新出现的细菌菌株的耐药性和适应的进化路径，并确定在哪个水平上描述（基因型、生物物理特性、系统反应）进化变得可重复——并且通过我们使用多尺度模型对进化动力学进行建模，其中细胞质模型细胞以生物物理现实的方式呈现，模型生物体的适应性是根据使用实验得出的 BFL 对其分子特征进行可能逃逸的全面分子图谱分析。路线将为合理设计新型化合物——“进化药物”——提供机会在相关工作中，我们将探索其生物物理基础。密码子适应以辨别它们对 mRNA 和共翻译蛋白折叠的影响。理论与实验之间的结合将为开发预测进化模型提供机会提高准确性和现实性将改变我们的研究方法。进化动力学从描述性到预测性和定量性，这将有助于开发新方法来对抗抗生素耐药性以及潜在的病毒逃避压力源例如药物和免疫反应。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Enzymatic metabolons dramatically enhance metabolic fluxes of low-efficiency biochemical reactions.

酶代谢显着增强低效生化反应的代谢通量。

DOI：
10.1016/j.bpj.2023.10.033
发表时间：
2023
期刊：
Biophysical journal
影响因子：
3.4
作者：
Ranganathan,Srivastav;Liu,Junlang;Shakhnovich,Eugene
通讯作者：
Shakhnovich,Eugene

A native chemical chaperone in the human eye lens.

DOI：
10.7554/elife.76923
发表时间：
2022-06-20
期刊：
ELIFE
影响因子：
7.7
作者：
Serebryany, Eugene;Chowdhury, Sourav;Woods, Christopher N.;Thorn, David C.;Watson, Nicki E.;McClelland, Arthur A.;Klevit, Rachel E.;Shakhnovich, Eugene, I
通讯作者：
Shakhnovich, Eugene, I

The physics of liquid-to-solid transitions in multi-domain protein condensates.

多域蛋白质凝聚物中液体到固体转变的物理学。