Walking the surfaces with AI-powered MD simulations

Peptides and enzymes have emerged as incredibly promising building blocks for materials with tunable properties. Self-assembly-driven peptide materials have a broad range of applications such as drug delivery, sensing, and separations. Enzymes too have several biotechnological applications including sensing and degrading plastics. In all these applications, it is crucial to understand the behavior of these peptides/enzymes as a function of their sequences (i.e., composition) and solution conditions such as temperature, pressure, additives, and surfaces. In our work, we explore these aspects using molecular simulations. We use various computational techniques such as molecular dynamics, network analysis, path sampling, and machine-learning enhanced sampling. In my talk, I will talk about our recent work on developing ML-enhanced methods to study peptides on interfaces. We will illustrate that our methodology has the potential to overcome the challenge of conformation sampling on interfaces. This enables us to study large conformational changes that could occur when proteins interact with surfaces. We will demonstrate the power of such approaches through studies of proteins on polymeric surfaces motivated by biodegradable polymer design applications.