Physics of Living Systems (PoLS) Seminar -Prof. Yinglong Miao

Speaker: Prof. Yinglong Miao, University of North Carolina – Chapel Hill

Host: Prof. JC Gumbart

Zoom link: https://gatech.zoom.us/j/93533796005?pwd=bWowYWRGNGROdi9RYzlOdTJjMGJiQT09

Meeting ID: 935 3379 6005 / Passcode: 512281

Title: Accelerated Molecular Simulations: Methods and Applications

Abstract:

Remarkable advances of supercomputing and Artificial Intelligence are transforming computational chemistry and biology in studies of molecules to cells. However, large gaps remain between the time scales of supercomputer simulations (typically microseconds) and those of biological processes (milliseconds or even longer). To bridge these gaps, our research is focused on the development of novel computational methods and Deep Learning (DL) techniques, including Gaussian accelerated molecular dynamics (GaMD) and Deep Boosted Molecular Dynamics (DBMD). Our recently developed selective GaMD algorithms have unprecedentedly enabled microsecond atomic simulations to capture repetitive dissociation and binding of small-molecule ligands, highly flexible peptides and proteins, thereby allowing for highly efficient and accurate calculations of their binding free energies and kinetics. Moreover, the GaMD, DL and free energy prOfiling Workflow (GLOW) provides a systematic approach to predicting important molecular determinants and quantifying free energy profiles of biomolecules. In DBMD, probabilistic Bayesian neural network models are implemented to construct boost potentials that exhibit Gaussian distribution with minimized anharmonicity, which enables more accurate energetic reweighting and further enhanced simulations. Finally, we apply these new methods in advanced biomolecular modeling and computer-aided drug discovery. In collaboration with leading experimental groups, we combine complementary simulations and experiments to decipher functional mechanisms and design novel drug molecules of important biomolecules. Systems of our interest include membrane proteins such as G-protein-coupled receptors and membrane-embedded proteases, RNA-Binding Proteins and RNA.Remarkable advances of supercomputing and Artificial Intelligence are transforming computational chemistry and biology in studies of molecules to cells. However, large gaps remain between the time scales of supercomputer simulations (typically microseconds) and those of biological processes (milliseconds or even longer). To bridge these gaps, our research is focused on the development of novel computational methods and Deep Learning (DL) techniques, including Gaussian accelerated molecular dynamics (GaMD) and Deep Boosted Molecular Dynamics (DBMD). Our recently developed selective GaMD algorithms have unprecedentedly enabled microsecond atomic simulations to capture repetitive dissociation and binding of small-molecule ligands, highly flexible peptides and proteins, thereby allowing for highly efficient and accurate calculations of their binding free energies and kinetics. Moreover, the GaMD, DL and free energy prOfiling Workflow (GLOW) provides a systematic approach to predicting important molecular determinants and quantifying free energy profiles of biomolecules. In DBMD, probabilistic Bayesian neural network models are implemented to construct boost potentials that exhibit Gaussian distribution with minimized anharmonicity, which enables more accurate energetic reweighting and further enhanced simulations. Finally, we apply these new methods in advanced biomolecular modeling and computer-aided drug discovery. In collaboration with leading experimental groups, we combine complementary simulations and experiments to decipher functional mechanisms and design novel drug molecules of important biomolecules. Systems of our interest include membrane proteins such as G-protein-coupled receptors and membrane-embedded proteases, RNA-Binding Proteins and RNA.