High Fidelity Protein Interaction Prediction with Applications in Novel Biomaterials

Recent advances in protein modeling (specifically, AlphaFold2 from Google, DeepMind) have made it possible to predict protein structures with high fidelity from alignments of homologous protein sequences by using significant computational resources. While groundbreaking, three outstanding challenges remain unaddressed by these systems: (i) accurate protein-protein interaction, (ii) allostery-driven changes in protein structure, and (iii) scientific understanding of the sequence-to-structure relationships that underpin protein-material interactions (such as with biomimetic block copolymers).

My research will be geared towards (a) predicting protein structure from single protein sequences in context with binding partners such as other proteins or organic/ inorganic materials, and (b) create bioactive scaffolds to enable applications such as, (a) membrane separation, (b) DNA nanopore sequencing, (c) porin-based separation devices, and (d) antigen-presenting cell mimics for CAR-T cell therapy. My brand of research will create strong collaborations across disciplines of computer science, chemical engineering and biomedical sciences and will utilize the power of machine learning and optimization to explore hitherto unknown biology.