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PreprintCited by 28Protein Structure and DynamicsComputational Drug Discovery MethodsChemical Synthesis and Analysis

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PepFlow: direct conformational sampling from peptide energy landscapes through hypernetwork-conditioned diffusion

Open access
Abdin, Osama, Kim, Philip M.·2023·Nature Machine Intelligence
DOI 10.1101/2023.06.25.546443

Preprint — published as 10.1038/s42256-024-00860-4

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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 hyper-network 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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Metadata

Authors
Abdin, O., & Kim, P. M.
Affiliations
University of Toronto (CA)
Corresponding author
Kim, Philip M.
ORCID
Venue
Nature Machine Intelligence
Publisher
openRxiv
Published
2023-06-26
Volume
6
Issue
7
Pages
775-786
ISSN
2522-5839 (ISSN-L)
DOI
10.1101/2023.06.25.546443
OpenAlex
W4400090810
Cited by
28
References
2
Citations by year
2026: 8 · 2025: 15 · 2024: 3 · 2023: 2
FWCI
3.54
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Nature Machine Intelligence (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
Funders
  • Canadian Institutes of Health Research (166008_1)
  • Natural Sciences and Engineering Research Council of Canada (RGPIN-2017-064)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Physical Sciences › Chemistry › Organic Chemistry › Chemical Synthesis and Analysis
Keywords
peptide conformational sampling, generative models, macrocyclization

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