{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T10:11:00Z","timestamp":1789726260829,"version":"4.0.1"},"reference-count":48,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["febs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["FEBS Letters"],"abstract":"<jats:p>\n                    Generative AI has driven remarkable breakthroughs in protein design, enabling the rapid, computationally guided creation of high\u2010affinity binders against diverse targets. While remarkable experimental success has been demonstrated, the confidence metrics used to filter and evaluate designs remain optimized for static protein interfaces and can fail when applied to underrepresented or conformationally complex targets. In this review, we outline the current landscape of deep learning\u2010driven protein design pipelines, discuss tailored applications in peptide, small molecule, binder, vaccine, and antibody design, and argue that the integration of ensemble\u2010based methods represents a promising avenue for improving design success rates. Beyond single target binder design, we further highlight emerging strategies that expand the functional scope of designed proteins, including fold\u2010switching scaffolds and molecular glues realized through engineered cyclic peptides, which enable context\u2010dependent control of protein\u2013protein interaction networks. Together, these advances position\n                    <jats:italic>de novo<\/jats:italic>\n                    protein design as a broadly applicable technology platform at the intersection of structural biology, biophysics, and molecular medicine.\n                  <\/jats:p>","DOI":"10.1002\/1873-3468.70459","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T10:00:29Z","timestamp":1789725629000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prospecting the protein design landscape"],"prefix":"10.1002","author":[{"given":"Jakob R.","family":"Riccabona","sequence":"first","affiliation":[{"name":"Institute for Drug Discovery, Faculty of Medicine Leipzig University  Germany"},{"name":"Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI  Dresden\/Leipzig Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katharina T.","family":"Stonig","sequence":"additional","affiliation":[{"name":"Independent researcher  Copenhagen Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jens","family":"Meiler","sequence":"additional","affiliation":[{"name":"Institute for Drug Discovery, Faculty of Medicine Leipzig University  Germany"},{"name":"Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI  Dresden\/Leipzig Germany"},{"name":"School of Embedded Composite Artificial Intelligence SECAI  Dresden\/Leipzig Germany"},{"name":"Institute for Computer Science, Faculty of Chemistry University Leipzig  Germany"},{"name":"Department of Chemistry, Department of Pharmacology, Center for Structural Biology, Institute of Chemical Biology, Center for Applied Artificial Intelligence in Protein Dynamics Vanderbilt University  Nashville TN USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clara T.","family":"Schoeder","sequence":"additional","affiliation":[{"name":"Institute for Drug Discovery, Faculty of Medicine Leipzig University  Germany"},{"name":"Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI  Dresden\/Leipzig Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6811-6283","authenticated-orcid":false,"given":"Monica L.","family":"Fern\u00e1ndez\u2010Quintero","sequence":"additional","affiliation":[{"name":"Department of Microbiology and Immunology, Novo Nordisk Foundation Initiative for Vaccines and Immunity (NIVI) University of Copenhagen  Denmark"},{"name":"Department of Integrative Structural and Computational Biology The Scripps Research Institute  La Jolla CA USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1038\/s41586-026-10328-7","article-title":"The past, present and future of de novo protein design","volume":"652","author":"Yang W","year":"2026","journal-title":"Nature"},{"key":"e_1_2_10_3_1","doi-asserted-by":"crossref","unstructured":"VerkuilR KabeliO DuY WickyBIM MillesLF DauparasJ BakerD OvchinnikovS SercuTandRivesA(2022)Language models generalize beyond natural proteins. bioRxiv Preprintdoi:10.1101\/2022.12.21.521521","DOI":"10.1101\/2022.12.21.521521"},{"key":"e_1_2_10_4_1","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1126\/science.add2187","article-title":"Robust deep learning\u2013based protein sequence design using ProteinMPNN","volume":"378","author":"Dauparas J","year":"2022","journal-title":"Science"},{"key":"e_1_2_10_5_1","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1038\/s41586-023-06415-8","article-title":"De novo design of protein structure and function with RFdiffusion","volume":"620","author":"Watson JL","year":"2023","journal-title":"Nature"},{"key":"e_1_2_10_6_1","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with AlphaFold","volume":"596","author":"Jumper J","year":"2021","journal-title":"Nature"},{"key":"e_1_2_10_7_1","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1038\/s41586-025-09721-5","article-title":"Atomically accurate de novo design of\u00a0antibodies with RFdiffusion","volume":"649","author":"Bennett NR","year":"2026","journal-title":"Nature"},{"key":"e_1_2_10_8_1","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1038\/s41592-025-02626-1","article-title":"Atomic context\u2010conditioned protein sequence design using LigandMPNN","volume":"22","author":"Dauparas J","year":"2025","journal-title":"Nat Methods"},{"key":"e_1_2_10_9_1","doi-asserted-by":"crossref","unstructured":"EvansR O'NeillM PritzelA AntropovaN SeniorA GreenT \u017d\u00eddekA BatesR BlackwellS YimJet\u00a0al. 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