I decided to kick off the new year with 3 new lead-author publications, each using a different perspective to model and understand how proteins interact and respond to their environment.
First, we replicate the results from simulations done using more traditional Molecular Dynamics combined with Monte Carlo methods, but this time only using interaction rates between monomers to produce the resulting distributions of protein aggregation. We also show that these coarsened models are capable of simulating aggregation systems that are orders of magnitude larger than those achievable using MD.
Network Hamiltonian Models for Unstructured Protein Aggregates, with Application to γD-Crystallin
Next, we take a deep dive into the effect of residue mutations on the structure and dynamics of a protein that is vital to replication of the SARS-CoV-2 virus within an infected cell. Analyses show how individual mutations contribute to changes in the protein using clinically relevant sequences collected over the span of the first year of the global pandemic as the virus continues to adapt to its human hosts.
Mutation Effects on Structure and Dynamics: Adaptive Evolution of the SARS-CoV-2 Main Protease
Finally, we examine how extreme thermal environments contribute to differences in protein structure for a family of serine proteases. Our results are compared with similar studies on the adaptations of proteins in extreme conditions, and show the dependence of such analyses on knowledge of protein function and interactions. D-ala-D-ala carboxypeptidases defy commonly held beliefs about protein adaptation at extreme temperatures, urging us to rethink the assumptions that uphold those beliefs.