Towards Data-Driven Discovery of Solid Binding Peptides
Speaker
Jim Pfaendtner
Dean of Engineering
Professor of Chemical and Biomolecular Engineering
NC State University
Abstract
Towards Data-Driven Discovery of Solid Binding Peptides
Solid binding peptides (SBPs) are a versatile class of engineered macromolecules with a huge range of applications including biomimetic mineralization, shape-selective nanoparticle synthesis, biomedical coatings, stimulus responsive particle assembly and more. Their tremendous power and diversity of application stems from the unique properties of biomolecules and the enormous phase space available to intrinsically disordered peptides. However, this massive application space is a double-edged sword as the properties of SBPs arise from the overlapping features of the sequence, surface and environmental conditions. Further, experimental probes of the structure and dynamics of nano-bio interfaces involving SBPs are costly, slow and extremely difficult to perform at scales required for phenomenological modeling.
Physics-based modeling tools such as molecular dynamics (MD) simulations are an important complement to experiments. MD simulations can predict important thermodynamic and kinetic quantities that reveal mechanisms of binding and help identify sequence-structure-energy relationships. However, the application of MD simulations is computationally demanding and requires significant expert knowledge, which can blunt the limit of these approaches. These limitations naturally raise questions of if and how data driven tools based like machine learning (ML) could be used to augment the limitations of MD and provide practical solutions to the challenge of SBP design.
This seminar will provide an overview of the three areas of the Pfaendtner research group’s efforts in application of ML/AI to SBP design. First, I will discuss the fundamentals of molecular data science. Second, through the lens of a data driven molecular optimization scheme, I will highlight contributions our group has made in the area of physics-based modeling of SBP/surface interactions. Finally, I will describe how this comes together in recent projects that leverage high throughput simulations and data driven modeling for SBP design and discovery.
Bio
Jim Pfaendtner is the Louis Martin-Vega Dean of Engineering and Professor of Chemical and Biomolecular Engineering at NC State University. Prior to joining NC State in 2023, Jim was Chair and Professor of Chemical Engineering and Professor of Chemistry at the University of Washington in Seattle, WA. At the UW, Jim also served as the University’s first Associate Vice Provost for Research Computing. Jim received his Ph.D. in Chemical Engineering from Northwestern University and was previously a research associate at University of Utah and ETH Zürich. Jim’s research program is broadly in the area of computational molecular science including methods and applications in the use of machine learning and AI for molecular design, applications in biomineralization, interfacial phenomena of biomolecules, biomimetic materials, and reaction engineering. From 2016-2022, Jim was the PI and director of an interdisciplinary NSF graduate training program at the nexus of molecular engineering and data science. In 2022 he was elected to the Washington State Academy of Sciences and also was selected as the recipient of the AIChE/CoMSEF Impact Award.