Mechanistic Dissection of Conformational Transitions in a Bicyclic Peptide via Molecular Modeling and Deep Learning

Experimental and computational approaches can characterize conformations and provide insight into why certain conformations are thermodynamically preferred over others. However, examining static conformations alone may not fully explain why subtle differences, such as a single LEU-to-ILE mutation in a bicyclic peptide, can produce markedly distinct conformational ensembles.

Furthermore, analyzing transition pathways between conformations reveals the mechanisms that shape these ensembles. Here, we introduce a deep learning model, termed ICoN-v1, trained on molecular dynamics (MD) simulation data to learn the underlying physics that governs cyclic peptide conformational dynamics. We examined cyclic hexapeptides with Nuclear Magnetic Resonance (NMR)-determined structures, and MYC-targeting bicyclic peptides that are stereo-diversified or have a single LEU-to-ILE mutation.