{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T03:52:30Z","timestamp":1785815550003,"version":"3.56.0"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62302291"],"award-info":[{"award-number":["62302291"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12104295"],"award-info":[{"award-number":["12104295"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Computational Biology Key Program of Shanghai Science and Technology Commission","award":["23JS1400600"],"award-info":[{"award-number":["23JS1400600"]}]},{"name":"Shanghai Jiao Tong University Scientific and Technological Innovation Funds","award":["21X010200843"],"award-info":[{"award-number":["21X010200843"]}]},{"name":"Science and Technology Innovation Key R&D Program of Chongqing","award":["CSTB2022TIAD-STX0017"],"award-info":[{"award-number":["CSTB2022TIAD-STX0017"]}]},{"name":"Student Innovation Center at Shanghai Jiao Tong University"},{"name":"Shanghai Artificial Intelligence Laboratory"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Enzyme engineering is a critical approach for producing enzymes that meet industrial and research demands by modifying wild-type proteins to enhance properties such as catalytic activity and thermostability. Beyond traditional directed evolution and rational design, recent advancements in deep learning offer cost-effective and high-performance alternatives. By encoding implicit coevolutionary patterns, these pretrained models have become powerful tools, with the central challenge being to uncover the intricate relationships among protein sequence, structure, and function.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We present VenusREM, a retrieval-enhanced protein language model designed to capture local amino acid interactions in both spatial and temporal scales. VenusREM achieves state-of-the-art performance on 217 assays from the ProteinGym benchmark. Beyond high-throughput open benchmark validations, we conducted a low-throughput post hoc analysis on more than 30 mutants to verify the model\u2019s ability to improve the stability and binding affinity of a VHH antibody. We also validated the effectiveness of VenusREM by designing 10 novel mutants of a DNA polymerase and performing wet-lab experiments to evaluate their enhanced activity at elevated temperatures. Both in silico and experimental evaluations not only confirm the reliability of VenusREM as a computational tool for enzyme engineering but also demonstrate a comprehensive evaluation framework for future computational studies in mutation effect prediction.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The implementation is available at https:\/\/github.com\/tyang816\/VenusREM.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf189","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T13:02:54Z","timestamp":1752584574000},"page":"i401-i409","source":"Crossref","is-referenced-by-count":14,"title":["From high-throughput evaluation to wet-lab studies: advancing mutation effect prediction with a retrieval-enhanced model"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7261-1705","authenticated-orcid":false,"given":"Yang","family":"Tan","sequence":"first","affiliation":[{"name":"Institute of Natural Sciences, Shanghai Jiao Tong University , Shanghai, 200240,","place":["China"]},{"name":"School of Information and Science, East China University of Science and Technology , Shanghai, 200231,","place":["China"]},{"name":"Shanghai Artificial Intelligence Laboratory , Shanghai, 200232,","place":["China"]},{"name":"Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University , Shanghai, 201203,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3675-5103","authenticated-orcid":false,"given":"Ruilin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Science, East China University of Science and Technology , Shanghai, 200231,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3361-161X","authenticated-orcid":false,"given":"Banghao","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Natural Sciences, Shanghai Jiao Tong University , Shanghai, 200240,","place":["China"]},{"name":"Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University , Shanghai, 201203,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0107-336X","authenticated-orcid":false,"given":"Liang","family":"Hong","sequence":"additional","affiliation":[{"name":"Institute of Natural Sciences, Shanghai Jiao Tong University , Shanghai, 200240,","place":["China"]},{"name":"Shanghai Artificial Intelligence Laboratory , Shanghai, 200232,","place":["China"]},{"name":"Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University , Shanghai, 201203,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3897-9766","authenticated-orcid":false,"given":"Bingxin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Natural Sciences, Shanghai Jiao Tong University , Shanghai, 200240,","place":["China"]},{"name":"Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University , Shanghai, 201203,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"2025071509024507000_btaf189-B1","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1038\/s41586-024-07487-w","article-title":"Accurate structure prediction of biomolecular interactions with AlphaFold 3","volume":"630","author":"Abramson","year":"2024","journal-title":"Nature"},{"key":"2025071509024507000_btaf189-B2","doi-asserted-by":"crossref","first-page":"16506","DOI":"10.1073\/pnas.1011428107","article-title":"Improvement of \u03c629 DNA polymerase amplification performance by fusion of DNA binding motifs","volume":"107","author":"de Vega","year":"2010","journal-title":"Proc Natl Acad Sci"},{"key":"2025071509024507000_btaf189-B3","doi-asserted-by":"crossref","first-page":"e1002195","DOI":"10.1371\/journal.pcbi.1002195","article-title":"Accelerated profile HMM searches","volume":"7","author":"Eddy","year":"2011","journal-title":"PLoS Comput Biol"},{"key":"2025071509024507000_btaf189-B4","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1038\/s41586-021-04043-8","article-title":"Disease variant prediction with deep generative models of evolutionary data","volume":"599","author":"Frazer","year":"2021","journal-title":"Nature"},{"key":"2025071509024507000_btaf189-B5","author":"He","year":"2020"},{"key":"2025071509024507000_btaf189-B6","doi-asserted-by":"crossref","first-page":"e03430","DOI":"10.7554\/eLife.03430","article-title":"Sequence co-evolution gives 3D contacts and structures of protein complexes","volume":"3","author":"Hopf","year":"2014","journal-title":"eLife"},{"key":"2025071509024507000_btaf189-B7","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1038\/nbt.3769","article-title":"Mutation effects predicted from sequence co-variation","volume":"35","author":"Hopf","year":"2017","journal-title":"Nat Biotechnol"},{"key":"2025071509024507000_btaf189-B8","first-page":"8946","author":"Hsu","year":"2022"},{"key":"2025071509024507000_btaf189-B9","author":"Jing","year":"2021"},{"key":"2025071509024507000_btaf189-B10","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1016\/j.molcel.2004.10.019","article-title":"Insights into strand displacement and processivity from the crystal structure of the protein-primed DNA polymerase of bacteriophage \u03c629","volume":"16","author":"Kamtekar","year":"2004","journal-title":"Mol Cell"},{"key":"2025071509024507000_btaf189-B11","doi-asserted-by":"crossref","DOI":"10.1093\/protein\/gzad015","article-title":"Masked inverse folding with sequence transfer for protein representation learning","volume":"36","author":"Yang","year":"2023","journal-title":"Protein Eng Des Sel"},{"key":"2025071509024507000_btaf189-B12","doi-asserted-by":"crossref","first-page":"2604","DOI":"10.1093\/molbev\/msz179","article-title":"GEMME: a simple and fast global epistatic model predicting mutational effects","volume":"36","author":"Laine","year":"2019","journal-title":"Mol Biol Evol"},{"key":"2025071509024507000_btaf189-B13","author":"Li","year":"2024"},{"key":"2025071509024507000_btaf189-B14","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1126\/science.ade2574","article-title":"Evolutionary-scale prediction of atomic-level protein structure with a language model","volume":"379","author":"Lin","year":"2023","journal-title":"Science"},{"key":"2025071509024507000_btaf189-B15","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.102788","article-title":"AI-enabled alkaline-resistant evolution of protein to apply in mass production","volume":"13","author":"Kang","year":"2025","journal-title":"eLife"},{"key":"2025071509024507000_btaf189-B16","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1038\/s41586-022-04599-z","article-title":"Machine learning-aided engineering of hydrolases for PET depolymerization","volume":"604","author":"Lu","year":"2022","journal-title":"Nature"},{"key":"2025071509024507000_btaf189-B17","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1038\/s41587-022-01618-2","article-title":"Large language models generate functional protein sequences across diverse families","volume":"41","author":"Madani","year":"2023","journal-title":"Nat Biotechnol"},{"key":"2025071509024507000_btaf189-B18","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1038\/nbt.2171","article-title":"Reading DNA at single-nucleotide resolution with a mutant MspA nanopore and phi29 DNA polymerase","volume":"30","author":"Manrao","year":"2012","journal-title":"Nat Biotechnol"},{"key":"2025071509024507000_btaf189-B19","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1007\/s00439-021-02411-y","article-title":"Embeddings from protein language models predict conservation and variant effects","volume":"141","author":"Marquet","year":"2022","journal-title":"Hum Genet"},{"key":"2025071509024507000_btaf189-B20","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btae621","article-title":"Expert-guided protein language models enable accurate and blazingly fast fitness prediction","volume":"40","author":"Marquet","year":"2024","journal-title":"Bioinformatics"},{"key":"2025071509024507000_btaf189-B21","first-page":"29287","article-title":"Language models enable zero-shot prediction of the effects of mutations on protein function","volume":"34","author":"Meier","year":"2021","journal-title":"Adv Neural Inform Process Syst"},{"key":"2025071509024507000_btaf189-B22","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1038\/s41592-022-01488-1","article-title":"Colabfold: making protein folding accessible to all","volume":"19","author":"Mirdita","year":"2022","journal-title":"Nat Methods"},{"key":"2025071509024507000_btaf189-B23","author":"Notin","year":"2022"},{"key":"2025071509024507000_btaf189-B24","first-page":"16990","author":"Notin","year":"2022"},{"key":"2025071509024507000_btaf189-B25","doi-asserted-by":"crossref","article-title":"ProteinGym: large-scale benchmarks for protein fitness prediction and design","author":"Notin","DOI":"10.1101\/2023.12.07.570727"},{"key":"2025071509024507000_btaf189-B26","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1016\/S0969-2126(97)00260-8","article-title":"CATH\u2014a hierarchic classification of protein domain structures","volume":"5","author":"Orengo","year":"1997","journal-title":"Structure"},{"key":"2025071509024507000_btaf189-B27","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1093\/protein\/gzw052","article-title":"In vitro evolution of phi29 DNA polymerase using isothermal compartmentalized self-replication technique","volume":"29","author":"Povilaitis","year":"2016","journal-title":"Protein Eng Des Sel"},{"key":"2025071509024507000_btaf189-B28","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1038\/s41592-018-0138-4","article-title":"Deep generative models of genetic variation capture the effects of mutations","volume":"15","author":"Riesselman","year":"2018","journal-title":"Nat Methods"},{"key":"2025071509024507000_btaf189-B29","doi-asserted-by":"crossref","first-page":"e2016239118","DOI":"10.1073\/pnas.2016239118","article-title":"Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences","volume":"118","author":"Rives","year":"2021","journal-title":"Proc Natl Acad Sci"},{"key":"2025071509024507000_btaf189-B30","first-page":"8844","author":"Roshan","year":"2021"},{"key":"2025071509024507000_btaf189-B31","author":"Su","year":"2023"},{"key":"2025071509024507000_btaf189-B32","author":"Tan"},{"key":"2025071509024507000_btaf189-B33","first-page":"223","author":"Tan","year":"2024"},{"key":"2025071509024507000_btaf189-B34","first-page":"233","author":"Tan","year":"2024"},{"key":"2025071509024507000_btaf189-B35","first-page":"77379","article-title":"PoET: a generative model of protein families as sequences-of-sequences","volume":"36","author":"Truong","year":"2023","journal-title":"Adv Neural Inform Process Syst"},{"key":"2025071509024507000_btaf189-B36","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1038\/s41587-023-01773-0","article-title":"Fast and accurate protein structure search with Foldseek","volume":"42","author":"Van Kempen","year":"2024","journal-title":"Nat Biotechnol"},{"key":"2025071509024507000_btaf189-B37","doi-asserted-by":"crossref","first-page":"8296","DOI":"10.1021\/ac5017437","article-title":"Heterologous antigen selection of camelid heavy chain single domain antibodies against tetrabromobisphenol a","volume":"86","author":"Wang","year":"2014","journal-title":"Anal Chem"},{"key":"2025071509024507000_btaf189-B38","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1002\/mlf2.12157","article-title":"Protein engineering in the deep learning era","volume":"3","author":"Zhou","year":"2024","journal-title":"mLife"},{"key":"2025071509024507000_btaf189-B39","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1038\/s41421-024-00728-2","article-title":"A conditional protein diffusion model generates artificial programmable endonuclease sequences with enhanced activity","volume":"10","author":"Zhou","year":"2024","journal-title":"Cell Discov"},{"key":"2025071509024507000_btaf189-B40","doi-asserted-by":"crossref","first-page":"3650","DOI":"10.1021\/acs.jcim.4c00036","article-title":"Protein engineering with lightweight graph denoising neural networks","volume":"64","author":"Zhou","year":"2024","journal-title":"J Chem Inf Model"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/41\/Supplement_1\/i401\/63745466\/btaf189.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/41\/Supplement_1\/i401\/63745466\/btaf189.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T13:02:58Z","timestamp":1752584578000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/41\/Supplement_1\/i401\/8199374"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"references-count":40,"journal-issue":{"issue":"Supplement_1","published-print":{"date-parts":[[2025,7,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaf189","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,7]]},"published":{"date-parts":[[2025,7,1]]}}}