2026-10-11T14:45:46Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33777956 2026-09-15T08:13:26Z category_1 category_4 category_915 category_873 category_734 category_135 portal_63 item_type_2 month_year_09_2026

Mechanistic Dissection of Conformational Transitions in a Bicyclic Peptide via Molecular Modeling and Deep Learning Ta I Hung (17729875) Raghu Venkatesan (24917756) Chia-en A. Chang (237574) Biophysics Biochemistry Physical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Computational Biology ultimately therapeutic efficacy nuclear magnetic resonance latent space constructed key residues governing examined cyclic hexapeptides deep learning model informing molecular design determining molecular properties targeting bicyclic peptides concerted torsional motions smooth transition pathways analyzing transition pathways observed using icon molecular dynamics energy pathways observed conformations concerted backbone bicyclic peptide underlying physics thermodynamically preferred thereby elucidating termed icon subtle differences single leu simulation data provide insight nonlinearly combined membrane permeability mechanistic dissection ile mutation fully explain following minimum energy minima critical role conformations reveals conformational transitions computational approaches characterize conformations certain conformations binding affinity Molecular conformations play a critical role in determining molecular properties, such as membrane permeability, binding affinity, and ultimately therapeutic efficacy. 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. By following minimum-energy pathways in the latent space constructed by ICoN-v1, we efficiently generated smooth conformational transition paths. These pathways reveal sequential sets of concerted backbone and side-chain torsional rotations moving between energy minima. Notably, smooth transition pathways that are absent from MD output were observed using ICoN-v1. Our results identify various sets of concerted torsional motions that are nonlinearly combined during conformational transitions and reveal the key residues governing each stage of the transition, thereby elucidating how the observed conformations are generated and informing molecular design. 2026-09-15T00:00:00Z Dataset Media 10.1021/acs.jctc.6c01233.s003 https://figshare.com/articles/media/Mechanistic_Dissection_of_Conformational_Transitions_in_a_Bicyclic_Peptide_via_Molecular_Modeling_and_Deep_Learning/33777956 CC BY-NC 4.0