On Wednesday 8th March at 3pm in S0.09, we will have a computational chemistry seminar featuring a double bill of talks from two of our PhD students. This seminar will be followed by drinks and snacks in G block. Everyone is welcome to come along.

1. A Useful New Route for Studying the Protein-Folding Problem

Speaker: Ziad Fakhoury

Abstract:

Recent advances in machine learning methods have had a significant impact on protein structure prediction, but accurate generation and characterization of protein-folding pathways remains intractable. Here, we demonstrate how protein folding trajectories can be generated using a directed walk strategy operating in the space defined by the residue-level contact-map. This double-ended strategy views protein folding as a series of discrete transitions between connected minima on the potential energy surface. Subsequent reaction-path analysis for each transition enables thermodynamic and kinetic characterization of each protein-folding path. We validate the protein-folding paths generated by our discretized-walk strategy against direct molecular dynamics simulations for a series of model coarse-grained proteins constructed from hydrophobic and polar residues. This comparison demonstrates that ranking discretized paths based on the intermediate energy barriers provides a convenient route to generating physically-sensible folding ensembles. Importantly, by using directed walks in the protein contact-map space, we circumvent several of the traditional challenges associated with protein-folding studies, namely long time-scales required and unknown order parameters. As such, our approach offers a useful new route for studying the protein-folding problem.

2. Message-Passing Neural Networks in Chemistry

Speaker: Steven Tseng

Abstract:

Graph neural networks are now ubiquitous in chemical modeling with the most common being message passing neural networks (MPNNs). In this talk, I will briefly introduce MPNNs and discuss their strengths and limitations. I’ll then provide some examples of previous applications in chemistry and demonstrate how we’ve used it for solubility prediction.