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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.

本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。生物物理适能景观(BFL)的新概念是蛋白质/核酸图酸性分子特性对健康有好处。我们通过发现一个简单的BFL来证明BFL的概念有效性大肠杆菌适合度与重要岩心分子性质之间的准确定量关系代谢酶。这一发现将健身景观的概念从一个巧妙的比喻转变为一个用于预测基因-表型关系(GPR)的定量易处理的工具。在这里，我们将这些发现作为进一步扩展我们对生物物理和种群因素之间相互作用的理解的基础它们决定了适应性进化的动力和结果。我们将应用生物物理分析，自动化机器人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种群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，以适应从抗生素胁迫与必需蛋白质二氢叶酸还原酶的结构不稳定性。我们的特点是所有尺度--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样性新出现的细菌菌株的抗药性和适应的进化路径，并确定在哪个水平描述(基因、生物物理特性、系统响应)进化变得可重现--并通过言外之意是可以预见的。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逼真的方式呈现，模型生物的适合性从它的分子特征使用了实验中获得的BFL。可能逃脱的全面分子图谱路线将提供一个机会，合理地设计新类别的化合物--“进化药物”-- 全面阻断病原菌的抗性。在相关的工作中，我们将探索生物物理基础密码子适应的研究，以辨别它们对信使核糖核酸和共翻译蛋白折叠的影响。紧密结合理论和实验之间的联系将为开发可预测的进化模型提供机会。提高精确度和真实性。沿着这些思路取得的进展将改变我们研究的方法从描述性到预测性和量化的进化动力学，这将有助于开发新的方法来对抗抗生素耐药性和潜在的病毒逃避应激源比如药物和免疫反应。

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。