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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.

这项研究的首要目标是开发预测性多尺度生物物理模型的适应性进化动力学生物物理适应度景观（BFL）的新概念是蛋白质/核酸的地图酸性分子的性质来适应。我们通过发现一个简单的和E.大肠杆菌及其重要核心的分子特性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预测基因型-表型关系（GPR）的定量易处理工具。我们把这些发现作为进一步扩大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沿着，实验室实验的条件下，允许严格控制所有规模-从分子到人口。作为一个关键模型，我们进行了一系列的进化实验与适应人口的E。大肠杆菌从抗生素应激和必需蛋白二氢叶酸还原酶的结构不稳定性。我们的特点是所有量表-基因分型、分子性状、系统蛋白质组学和代谢组学以及群体-多重新出现的细菌菌株的耐药性和适应性的进化路径，并确定描述（基因型，生物物理特性，系统响应）进化变得可重复-并通过含义可预测。我们使用多尺度模型模拟进化动力学，模型细胞以生物药理学现实的方式呈现，并且模型生物体的适合度由它的分子性状使用实验衍生的BFL。可能逃逸的综合分子图谱路线将提供一个机会，合理地设计新的化合物类-“进化药物”，全面阻断病原菌的抗药性。在相关的努力中，我们将探索生物物理基础的密码子适应，以辨别其对mRNA和共翻译蛋白质折叠的影响。的紧密集成理论和实验之间的联系将提供一个机会，开发预测性的进化模型，提高准确性和真实性。沿着这些路线的进展将改变我们的研究方法从描述性到预测性和定量的进化动力学，这将有助于开发新的方法来对抗抗生素耐药性，并可能使病毒逃离压力源如药物和免疫反应。

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。