DescriptionProteins carry out a staggering number of functions within the human body and the biological world at large, and are often the primary targets of drugs in treatment of disease. For this reason an understanding of protein behavior—how a protein’s sequence and structure determines its stability, function, and interactions with other molecules—is critical for pharmaceutical development and other biotech-based industries. Recent advancements in some areas of protein modelling, especially the application of neural-network based machine learning to protein structure prediction, have been very promising, but there is still much work to do to fully understand how proteins fold. Other protein modelling techniques, like molecular dynamics simulations, are powerful but hampered by very short timescales that don’t capture the full spectrum of protein behavior. Additionally, many of these techniques are computationally intensive, require a high degree of user expertise, and do not breakdown protein energy contributions at the amino acid level.
We have developed a coarse-grained protein energetics model, the Hidden Symmetry Model (HSyM), that is able to extract per-residue interaction energy data from sequence and structure data. The model is scale invariant and only requires a single structure of a protein’s native conformation; consequently, its calculations can take a matter of seconds and be done on a basic PC. We have demonstrated potential applications of HSyM by successfully using it to predict mutation-induced thermostability shifts in T4 lysozyme, and to predict the binding affinities of engineered peptides for an antibody with limited structural information for the molecules involved. Preliminary work on a fully integrated microfluidic device that would use these model-engineered peptides to carry out diagnostic blood assays is presented to show a potential clinical use for the model. We also present work to further optimize HSyM by using a simplified statistical mechanical “toy model” that takes into account solvent-residue interactions. We hope that with further refinement the Hidden Symmetry Model will have a far-reaching impact on the fields of computational drug design, protein engineering, and biomedical and biotechnology research in general.