Emergent Properties of Complex Systems: From Atoms to Macromolecules; from Humans to Societies

复杂系统的涌现性质：从原子到大分子；

• 批准号: 10622276
• 负责人: MICHAEL LEVITT
• 金额: $ 55.93万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10622276
• 关键词: 2019-nCoV; Accidents; Behavior; Biology; COVID-19; Cessation of life; Collaborations; Community Services; Complex; Computational Biology; Computers; Coronavirus; DNA Structure; Data; Data Analyses; Drug Targeting; Epidemic; Future; Gases; Growth; Health; Human; Infection; Lead; Logistics; Mentors; Modeling; Modernization; Molecular; Peptides; Performance; Persons; Philosophy; Population; Principal Investigator; Productivity; Property; Protein Conformation; Proteins; Public Health; Recovery; Research; Ribosomes; Sampling; Scientist; Societies; Structure; System; Techniques; Time; Viral; Virus; Virus Diseases; Work; Workplace; atomic interactions; career; computer code; computer framework; curve fitting; deep learning; drug development; drug discovery; experience; fascinate; interdisciplinary approach; macromolecule; molecular dynamics; neural network architecture; novel; outreach; particle; protein structure; respiratory virus; simulation; transmission process

Project Summary (30 lines) With its revised title “Emergent Properties of Complex Systems: From Atoms to Macromolecules; from Humans to Societies” this proposal has been broadened by adding data-analysis & simulation on a problem of grave current concern: namely how an air-borne virus like SARS-2-CoV spread in human population. Getting involved by accident, I became fascinated with how the numbers of daily cases & deaths group with time and what is the physical mechanism that make the data follow the Gompertz function. Michael Levitt, the Principal Investigator has a long career of independent scientific research that started in 1967 when he was one of the first to work in computational biology. His early work set up the conceptual, theoretical and computational framework for protein and DNA structure refinement, structure analysis and macromolecular simulations. He makes computer codes available and has been productive, scientifically rigorous and impactful for half a century. This approaches is continued here by a PI committed to mentoring young scientists as well as engaging in sustained research-community service and public outreach. 1. Protein Structure Refinement with Deep Equivariant Networks. We propose to use Deep Learning technique to refine models of proteins. We anticipate that such an approach, combined with the power of modern neural net architectures and computational performance of hardware will enable efficient sampling of the protein conformational space near the native state and will systematically provide structures with accuracy useful for drug development purposes. 2. Functional Dynamics of Ribosome. Our experience with structure curation will lead to a useful computer package for others. Our work on Ribosome dynamics will provide a model of how peptides such as SecM can stall the ribosome. Structures sampled from our MD simulations could also be used as potential targets for drug discovery. 3. Epidemic Analysis, Curve-Fitting and Simulation. Applied to SARS-Cov-2 and COVID-19, we show that viral spread follows the Gompertz growth function rather than commonly assumed Logistics or Exponential functions. This means that the population transmitting the infection is not uniform. Network simulation of viral spread shows that only when the connection network is scale-free does the simulated epidemic follow the Gompertz function. We will model a physical system with scale-free connectivity using molecular dynamics to simulate a 2D gas of particles with a wide range of masses. This novel multi- disciplinary approach may also apply to future respiratory viruses to enable better control of their spread. Studying biomedically significant systems in collaboration with experimental colleagues will reveal fascinating details of biology in action. We expect this work will help elucidate the relationship between underlying structure and function in complex systems, extending from macromolecular machines to human societies.

项目摘要（30行） 《复杂系统的涌现性质：从原子到大分子》（Emergent Properties of Complex Systems：From Atoms to Macromolecules） 从人类到社会”的建议通过增加对问题的数据分析和模拟而得到了扩展 目前严重关注的问题：即像SARS-2-CoV这样的空气传播病毒如何在人群中传播。 偶然参与进来，我开始着迷于每日病例和死亡人数如何与 是什么物理机制使数据遵循Gompertz函数。 首席研究员迈克尔·莱维特（Michael Levitt）有着长期的独立科学研究生涯， 1967年，他是最早从事计算生物学工作的人之一。他的早期工作建立了概念， 蛋白质和DNA结构精修、结构分析和 大分子模拟他使计算机代码可用，并一直是富有成效的，科学的 严谨而有影响力长达半个世纪。这种方法在这里继续由PI致力于指导 青年科学家以及从事持续的研究-社区服务和公共宣传。 1.使用深度等变网络进行蛋白质结构优化。我们建议使用深度学习 改进蛋白质模型的技术。我们预计，这种方法，结合 现代神经网络架构和硬件的计算性能将使有效的采样成为可能 蛋白质构象空间接近天然状态，并将系统地提供结构， 准确性有助于药物开发目的。 2.核糖体的功能动力学。我们在结构管理方面的经验将导致一个有用的计算机 包给别人。我们对核糖体动力学的研究将提供一个模型， 可以阻止核糖体从我们的MD模拟中取样的结构也可以用作潜在的 药物发现的目标。 3.流行病分析、曲线拟合和模拟。应用于SARS-Cov-2和COVID-19，我们显示 病毒传播遵循Gompertz增长函数，而不是通常假设的物流或 指数函数。这意味着传播感染的人群是不均匀的。网络 病毒传播的模拟表明，只有当连接网络是无标度的， 流行遵循Gompertz函数我们将使用以下方法对具有无标度连接的物理系统进行建模： 分子动力学来模拟具有广泛质量范围的粒子的2D气体。这部小说多- 纪律措施亦可应用于日后的呼吸道病毒，以更有效地控制其传播。 与实验同事合作研究具有生物医学意义的系统将揭示令人着迷的 生物学的细节我们希望这项工作将有助于阐明潜在的 复杂系统的结构和功能，从大分子机器到人类社会。

项目成果

Insights on cross-species transmission of SARS-CoV-2 from structural modeling.

• DOI: 10.1371/journal.pcbi.1008449
• 发表时间: 2020-12
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Rodrigues JPGLM;Barrera-Vilarmau S;M C Teixeira J;Sorokina M;Seckel E;Kastritis PL;Levitt M
• 通讯作者: Levitt M

Probing Interplays between Human XBP1u Translational Arrest Peptide and 80S Ribosome.

• DOI: 10.1021/acs.jctc.1c00796
• 发表时间: 2022-03-08
• 期刊: Journal of chemical theory and computation
• 影响因子: 5.5
• 作者: Di Palma F;Decherchi S;Pardo-Avila F;Succi S;Levitt M;von Heijne G;Cavalli A
• 通讯作者: Cavalli A

Intermolecular correlations are necessary to explain diffuse scattering from protein crystals.

分子间相关性对于解释蛋白质晶体的漫散射是必要的。

• DOI: 10.1107/s2052252518001124
• 发表时间: 2018
• 期刊: IUCrJ
• 影响因子: 3.9
• 作者: Peck,Ariana;Poitevin,Frédéric;Lane,ThomasJ
• 通讯作者: Lane,ThomasJ

Aminoglycoside ribosome interactions reveal novel conformational states at ambient temperature.

氨基糖苷核糖体相互作用揭示了环境温度下的新构象状态。

• DOI: 10.1093/nar/gky693
• 发表时间: 2018
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: O'Sullivan,MaryE;Poitevin,Frédéric;Sierra,RaymondG;Gati,Cornelius;Dao,EHan;Rao,Yashas;Aksit,Fulya;Ciftci,Halilibrahim;Corsepius,Nicholas;Greenhouse,Robert;Hayes,Brandon;Hunter,MarkS;Liang,Mengling;McGurk,Alex;Mbgam,Paul;O
• 通讯作者: O

Protein-Ligand Binding Free-Energy Calculations with ARROW─A Purely First-Principles Parameterized Polarizable Force Field.

• DOI: 10.1021/acs.jctc.2c00930
• 发表时间: 2022-12-13
• 期刊: JOURNAL OF CHEMICAL THEORY AND COMPUTATION
• 影响因子: 5.5
• 作者: Nawrocki, Grzegorz;Leontyev, Igor;Sakipov, Serzhan;Darkhovskiy, Mikhail;Kurnikov, Igor;Pereyaslavets, Leonid;Kamath, Ganesh;Voronina, Ekaterina;Butin, Oleg;Illarionov, Alexey;Olevanov, Michael;Kostikov, Alexander;Ivahnenko, Ilya;Patel, Dhilon S.;Sankaranarayanan, Subramanian K. . R. S.;Kurnikova, Maria G.;Lock, Christopher;Crooks, Gavin E.;Levitt, Michael;Kornberg, Roger D.;Fain, Boris
• 通讯作者: Fain, Boris