{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T15:46:21Z","timestamp":1768405581002,"version":"3.49.0"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T00:00:00Z","timestamp":1674000000000},"content-version":"vor","delay-in-days":17,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000024","name":"Canadian Institute for Health Research","doi-asserted-by":"crossref","award":["#PJT-166008"],"award-info":[{"award-number":["#PJT-166008"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institute for Health Research","doi-asserted-by":"crossref","award":["#PJT-153279"],"award-info":[{"award-number":["#PJT-153279"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Protein and peptide engineering has become an essential field in biomedicine with therapeutics, diagnostics and synthetic biology applications. Helices are both abundant structural feature in proteins and comprise a major portion of bioactive peptides. Precise design of helices for binding or biological activity is still a challenging problem.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we present HelixGAN, the first generative adversarial network method to generate de novo left-handed and right-handed alpha-helix structures from scratch at an atomic level. We developed a gradient-based search approach in latent space to optimize the generation of novel \u03b1-helical structures by matching the exact conformations of selected hotspot residues. The designed \u03b1-helical structures can bind specific targets or activate cellular receptors. There is a significant agreement between the helix structures generated with HelixGAN and PEP-FOLD, a well-known de novo approach for predicting peptide structures from amino acid sequences. HelixGAN outperformed RosettaDesign, and our previously developed structural similarity method to generate D-peptides matching a set of given hotspots in a known L-peptide. As proof of concept, we designed a novel D-GLP1_1 analog that matches the conformations of critical hotspots for the GLP1 function. MD simulations revealed a stable binding mode of the D-GLP1_1 analog coupled to the GLP1 receptor. This novel D-peptide analog is more stable than our previous D-GLP1 design along the MD simulations. We envision HelixGAN as a critical tool for designing novel bioactive peptides with specific properties in the early stages of drug discovery.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/xxiexuezhi\/helix_gan.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad036","type":"journal-article","created":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T04:50:07Z","timestamp":1674017407000},"source":"Crossref","is-referenced-by-count":20,"title":["HelixGAN a deep-learning methodology for conditional <i>de novo<\/i> design of \u03b1-helix structures"],"prefix":"10.1093","volume":"39","author":[{"given":"Xuezhi","family":"Xie","sequence":"first","affiliation":[{"name":"Donnelly Centre for Cellular and Biomolecular Research, University of Toronto , Toronto, ON M5S 3E1, Canada"},{"name":"Department of Computer Science, University of Toronto , Toronto, ON M5S 3E1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4776-6017","authenticated-orcid":false,"given":"Pedro A","family":"Valiente","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Toronto , Toronto, ON M5S 3E1, Canada"}]},{"given":"Philip M","family":"Kim","sequence":"additional","affiliation":[{"name":"Donnelly Centre for Cellular and Biomolecular Research, University of Toronto , Toronto, ON M5S 3E1, Canada"},{"name":"Department of Computer Science, University of Toronto , Toronto, ON M5S 3E1, Canada"},{"name":"Department of Molecular Genetics, University of Toronto , Toronto, ON M5S 3E1, Canada"}]}],"member":"286","published-online":{"date-parts":[[2023,1,18]]},"reference":[{"key":"2023013106121540000_btad036-B1","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.softx.2015.06.001","article-title":"GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers","volume":"1\u20132","author":"Abraham","year":"2015","journal-title":"SoftwareX"},{"key":"2023013106121540000_btad036-B2","doi-asserted-by":"crossref","first-page":"3031","DOI":"10.1021\/acs.jctc.7b00125","article-title":"The Rosetta all-atom energy function for macromolecular modeling and design","volume":"13","author":"Alford","year":"2017","journal-title":"J. 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