{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T20:35:57Z","timestamp":1767731757245,"version":"build-2238731810"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1012695","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T00:00:00Z","timestamp":1735862400000}}],"reference-count":56,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/W000326\/1"],"award-info":[{"award-number":["EP\/W000326\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/X017982\/1"],"award-info":[{"award-number":["EP\/X017982\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Directed evolution can enable engineering of biological systems with minimal knowledge of their underlying sequence-to-function relationships. A typical directed evolution process consists of iterative rounds of mutagenesis and selection that are designed to steer changes in a biological system (e.g. a protein) towards some functional goal. Much work has been done, particularly leveraging advancements in machine learning, to optimise the process of directed evolution. Many of these methods, however, require DNA sequencing and synthesis, making them resource-intensive and incompatible with developments in targeted\n                    <jats:italic>in vivo<\/jats:italic>\n                    mutagenesis. Operating within the experimental constraints of established sorting-based directed evolution techniques (e.g. Fluorescence-Activated Cell Sorting, FACS), we explore approaches for optimisation of directed evolution that could in future be implemented without sequencing information. We then expand our methods to the context of emerging experimental techniques in directed evolution, which allow for single-cell selection based on fitness objectives defined from any combination of measurable traits. Finally, we explore these alternative strategies on the GB1 and TrpB empirical landscapes, demonstrating that they could lead to up to 19-fold and 7-fold increases respectively in the probability of attaining the global fitness peak.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1012695","type":"journal-article","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T13:53:26Z","timestamp":1734616406000},"page":"e1012695","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":4,"title":["Optimisation strategies for directed evolution without sequencing"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8416-2321","authenticated-orcid":true,"given":"Jessica","family":"James","sequence":"first","affiliation":[]},{"given":"Sebastian","family":"Towers","sequence":"additional","affiliation":[]},{"given":"Jakob","family":"Foerster","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9625-4755","authenticated-orcid":true,"given":"Harrison","family":"Steel","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2024,12,19]]},"reference":[{"key":"pcbi.1012695.ref001","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1038\/s41392-023-01440-5","article-title":"Applications of synthetic biology in medical and pharmaceutical fields","volume":"8","author":"X Yan","year":"2023","journal-title":"Signal Transduct Target Ther"},{"key":"pcbi.1012695.ref002","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1038\/s41587-022-01248-8","article-title":"Sustainable manufacturing with synthetic biology","volume":"40","author":"CD Scown","year":"2022","journal-title":"Nat Biotechnol"},{"issue":"2","key":"pcbi.1012695.ref003","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1002\/sae2.12014","article-title":"Synthetic biology and opportunities within agricultural crops","volume":"1","author":"D Sargent","year":"2022","journal-title":"Journal of Sustainable Agriculture and Environment"},{"issue":"6615","key":"pcbi.1012695.ref004","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":"J Dauparas","year":"2022","journal-title":"Sci"},{"issue":"1","key":"pcbi.1012695.ref005","doi-asserted-by":"crossref","first-page":"4348","DOI":"10.1038\/s41467-022-32007-7","article-title":"ProtGPT2 is a deep unsupervised language model for protein design","volume":"13","author":"N Ferruz","year":"2022","journal-title":"Nat. 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