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

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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.