Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.06.25.546443v1?rss=1
Authors: Abdin, O., Kim, P. M.
Abstract:
Deep learning approaches have spurred substantial advances in the single-state prediction of biomolecular structures. The function of biomolecules is, however, dependent on the range of conformations they can assume. This is especially true for peptides, a highly flexible class of molecules that are involved in numerous biological processes and are of high interest as therapeutics. Here, we introduce PepFlow, a generalized Boltzmann generator that enables direct all-atom sampling from the allowable conformational space of input peptides. We train the model in a diffusion framework and subsequently use an equivalent flow to perform conformational sampling. To overcome the prohibitive cost of generalized all-atom modelling, we modularize the generation process and integrate a hypernetwork to predict sequence-specific network parameters. PepFlow accurately predicts peptide structures and effectively recapitulates experimental peptide ensembles at a fraction of the running time of traditional approaches. PepFlow can additionally be used to sample conformations that satisfy constraints such as macrocyclization.
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