In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 19, No. 2 ( 2023-2-2), p. e1010874-
Abstract:
Design of peptide binders is an attractive strategy for targeting “undruggable” protein-protein interfaces. Current design protocols rely on the extraction of an initial sequence from one known protein interactor of the target protein, followed by in-silico or in-vitro mutagenesis-based optimization of its binding affinity. Wet lab protocols can explore only a minor portion of the vast sequence space and cannot efficiently screen for other desirable properties such as high specificity and low toxicity, while in-silico design requires intensive computational resources and often relies on simplified binding models. Yet, for a multivalent protein target, dozens to hundreds of natural protein partners already exist in the cellular environment. Here, we describe a peptide design protocol that harnesses this diversity via a machine learning generative model. After identifying putative natural binding fragments by literature and homology search, a compositional Restricted Boltzmann Machine is trained and sampled to yield hundreds of diverse candidate peptides. The latter are further filtered via flexible molecular docking and an in-vitro microchip-based binding assay. We validate and test our protocol on calcineurin, a calcium-dependent protein phosphatase involved in various cellular pathways in health and disease. In a single screening round, we identified multiple 16-length peptides with up to six mutations from their closest natural sequence that successfully interfere with the binding of calcineurin to its substrates. In summary, integrating protein interaction and sequence databases, generative modeling, molecular docking and interaction assays enables the discovery of novel protein-protein interaction modulators.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1010874
DOI:
10.1371/journal.pcbi.1010874.g001
DOI:
10.1371/journal.pcbi.1010874.g002
DOI:
10.1371/journal.pcbi.1010874.g003
DOI:
10.1371/journal.pcbi.1010874.g004
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10.1371/journal.pcbi.1010874.g005
DOI:
10.1371/journal.pcbi.1010874.t001
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10.1371/journal.pcbi.1010874.s001
DOI:
10.1371/journal.pcbi.1010874.s002
DOI:
10.1371/journal.pcbi.1010874.s003
DOI:
10.1371/journal.pcbi.1010874.s004
DOI:
10.1371/journal.pcbi.1010874.s005
DOI:
10.1371/journal.pcbi.1010874.s006
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10.1371/journal.pcbi.1010874.s007
DOI:
10.1371/journal.pcbi.1010874.s008
DOI:
10.1371/journal.pcbi.1010874.s009
DOI:
10.1371/journal.pcbi.1010874.s010
DOI:
10.1371/journal.pcbi.1010874.s011
DOI:
10.1371/journal.pcbi.1010874.s012
DOI:
10.1371/journal.pcbi.1010874.s013
DOI:
10.1371/journal.pcbi.1010874.s014
DOI:
10.1371/journal.pcbi.1010874.s015
DOI:
10.1371/journal.pcbi.1010874.r001
DOI:
10.1371/journal.pcbi.1010874.r002
DOI:
10.1371/journal.pcbi.1010874.r003
DOI:
10.1371/journal.pcbi.1010874.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2023
detail.hit.zdb_id:
2193340-6
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