{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T14:13:52Z","timestamp":1785161632140,"version":"3.55.0"},"reference-count":32,"publisher":"American Chemical Society (ACS)","issue":"14","license":[{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Drug resistance is a major challenge in cancer therapy. Cancer cells with pre-existing or acquired mutations that confer resistance to a given drug treatment outgrow the susceptible cell population and cause cancer recurrence after an initial successful treatment response. Knowledge about resistance mutations before they occur in the clinic could prevent unnecessary patient treatment with ineffective drugs, in clinical trials as well as clinical practice, or potentially speed up the development of follow-up compounds. Here, we focused on on-target amino acid mutations that confer resistance to an inhibitor compound with a known binding mode. We evaluated whether a combination of physics-based free energy perturbation (FEP) affinity estimates and protein language model-based protein fitness estimates could improve the in silico identification of resistance mutations. Validation was done with data from deep mutational scanning (DMS) experiments that tested for resistance to single amino acid mutations. A public data set testing ERK2 resistance against the inhibitor SCH772984 and an internal data set testing resistance of an EGFR_exon20 mutant against a Bayer small molecule inhibitor were used. Our results show that protein fitness estimates can facilitate the identification of resistance mutations by filtering mutations with a low estimated fitness. Even though FEP has flagged such mutations as affinity-decreasing and thus potentially resistant, they were not resistant according to the DMS experiment and therefore correctly filtered out. This indicates that protein language model-based protein fitness estimates could be a computationally efficient method to filter mutations without having to model the negative impact of mutations on native function or protein stability, which is error-prone and computationally expensive.<\/jats:p>","DOI":"10.1021\/acs.jcim.6c00768","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T12:50:42Z","timestamp":1783947042000},"page":"8284-8294","source":"Crossref","is-referenced-by-count":0,"title":["Protein Language\nModel-Based Fitness Estimates Facilitate\nResistance Mutation Identification"],"prefix":"10.1021","volume":"66","author":[{"given":"Dominik","family":"Schwarz","sequence":"first","affiliation":[{"name":"Bayer AG , , ,","place":["Berlin, Germany, 13353"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sven H.","family":"Giese","sequence":"additional","affiliation":[{"name":"Bayer AG , , ,","place":["Berlin, Germany, 13353"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akansha","family":"Gupta","sequence":"additional","affiliation":[{"name":"Broad Institute of MIT and Harvard , , ,","place":["Cambridge, Massachusetts, United States, 02142"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paulina J.","family":"Dziuban\u0301ska-Kusibab","sequence":"additional","affiliation":[{"name":"Bayer AG , , ,","place":["Berlin, Germany, 13353"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Santiago D.","family":"Villalba","sequence":"additional","affiliation":[{"name":"Bayer AG , , ,","place":["Berlin, Germany, 13353"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Cherniack","sequence":"additional","affiliation":[{"name":"Broad Institute of MIT and Harvard , , ,","place":["Cambridge, Massachusetts, United States, 02142"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoping","family":"Yang","sequence":"additional","affiliation":[{"name":"Broad Institute of MIT and Harvard , , ,","place":["Cambridge, Massachusetts, United States, 02142"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Root","sequence":"additional","affiliation":[{"name":"Broad Institute of MIT and Harvard , , ,","place":["Cambridge, Massachusetts, United States, 02142"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gizem","family":"Karsli-Uzunbas","sequence":"additional","affiliation":[{"name":"Broad Institute of MIT and Harvard , , ,","place":["Cambridge, Massachusetts, United States, 02142"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heidi","family":"Greulich","sequence":"additional","affiliation":[{"name":"Broad Institute of MIT and Harvard , , ,","place":["Cambridge, Massachusetts, United States, 02142"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Franziska","family":"Siegel","sequence":"additional","affiliation":[{"name":"Bayer AG , , ,","place":["Berlin, Germany, 13353"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atanas","family":"Kamburov","sequence":"additional","affiliation":[{"name":"Owkin France , , ,","place":["Paris, France, 75009"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ekaterina","family":"Nevedomskaya","sequence":"additional","affiliation":[{"name":"Bayer AG , , ,","place":["Berlin, Germany, 13353"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5424-9819","authenticated-orcid":true,"given":"Clara","family":"Christ","sequence":"additional","affiliation":[{"name":"Bayer AG , , ,","place":["Berlin, Germany, 13353"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matteo","family":"Aldeghi","sequence":"additional","affiliation":[{"name":"Bayer Research and Innovation Center , , , ,","place":["Cambridge, Massachusetts, United States, 02142"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8707-3867","authenticated-orcid":true,"given":"Je\u0301re\u0301mie","family":"Mortier","sequence":"additional","affiliation":[{"name":"Bayer AG , , ,","place":["Berlin, Germany, 13353"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,7,13]]},"reference":[{"key":"2026072709085340100_cit1","doi-asserted-by":"publisher","DOI":"10.1016\/j.drup.2022.100833","article-title":"The Role of Extracellular Vesicles\nin the Transfer of Drug Resistance Competences to Cancer Cells","volume":"62","author":"Xavier","year":"2022","journal-title":"Drug Resist. Updat."},{"key":"2026072709085340100_cit2","doi-asserted-by":"publisher","DOI":"10.1186\/1476-4598-9-75","article-title":"Molecular Mechanisms\nof Acquired\nResistance to Tyrosine Kinase Targeted Therapy","volume":"9","author":"Sierra","year":"2010","journal-title":"Mol. Cancer"},{"issue":"3","key":"2026072709085340100_cit3","doi-asserted-by":"publisher","first-page":"1769","DOI":"10.3390\/cancers6031769","article-title":"Drug Resistance in Cancer: An Overview","volume":"6","author":"Housman","year":"2014","journal-title":"Cancers"},{"issue":"1","key":"2026072709085340100_cit4","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1056\/NEJMoa1508887","article-title":"Resensitization\nto Crizotinib by the Lorlatinib ALK Resistance Mutation L1198F","volume":"374","author":"Shaw","year":"2016","journal-title":"N. Engl. J. Med."},{"key":"2026072709085340100_cit5","doi-asserted-by":"publisher","DOI":"10.1038\/srep46632","article-title":"Rational\nDesign of Non-Resistant Targeted Cancer Therapies","volume":"7","author":"Marti\u0301nez-Jime\u0301nez","year":"2017","journal-title":"Sci. Rep."},{"issue":"8","key":"2026072709085340100_cit6","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1038\/nmeth.3027","article-title":"Deep Mutational Scanning: A New Style of Protein Science","volume":"11","author":"Fowler","year":"2014","journal-title":"Nat. Methods"},{"issue":"4","key":"2026072709085340100_cit7","doi-asserted-by":"publisher","first-page":"1171","DOI":"10.1016\/j.celrep.2016.09.061","article-title":"Phenotypic\nCharacterization of a Comprehensive Set of MAPK1\/ERK2Missense Mutants","volume":"17","author":"Brenan","year":"2016","journal-title":"Cell Rep."},{"issue":"1","key":"2026072709085340100_cit8","doi-asserted-by":"publisher","DOI":"10.1038\/s42003-018-0075-x","article-title":"Predicting\nResistance\nof Clinical Abl Mutations to Targeted Kinase Inhibitors Using Alchemical\nFree-Energy Calculations","volume":"1","author":"Hauser","year":"2018","journal-title":"Commun. Biol."},{"issue":"11","key":"2026072709085340100_cit9","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.1016\/j.chembiol.2018.07.013","article-title":"Combining Mutational\nSignatures, Clonal Fitness, and Drug Affinity\nto Define Drug-Specific Resistance Mutations in Cancer","volume":"25","author":"Kaserer","year":"2018","journal-title":"Cell Chem. Biol."},{"issue":"8","key":"2026072709085340100_cit10","doi-asserted-by":"publisher","first-page":"1468","DOI":"10.1021\/acscentsci.9b00590","article-title":"Predicting\nKinase Inhibitor Resistance:\nPhysics-Based and Data-Driven Approaches","volume":"5","author":"Aldeghi","year":"2019","journal-title":"ACS\nCent. Sci."},{"issue":"1","key":"2026072709085340100_cit11","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1093\/bib\/bby113","article-title":"AIMMS Suite:\nA Web\nServer Dedicated for Prediction of Drug Resistance on Protein Mutation","volume":"21","author":"Wu","year":"2020","journal-title":"Brief. Bioinform."},{"issue":"10","key":"2026072709085340100_cit12","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1016\/j.cels.2022.09.003","article-title":"Resistor:\nAn Algorithm for Predicting Resistance Mutations Using Pareto Optimization\nover Multistate Protein Design and Mutational Signatures","volume":"13","author":"Guerin","year":"2022","journal-title":"Cell Syst."},{"issue":"5","key":"2026072709085340100_cit13","doi-asserted-by":"publisher","first-page":"2543","DOI":"10.1021\/acs.jcim.4c02313","article-title":"Predicting Resistance\nto Small Molecule\nKinase Inhibitors","volume":"65","author":"Nagarajan","year":"2025","journal-title":"J. Chem. Inf. Model."},{"issue":"11","key":"2026072709085340100_cit14","doi-asserted-by":"publisher","first-page":"2882","DOI":"10.1021\/acs.jpcb.4c07794","article-title":"Prospective Evaluation of Structure-Based\nSimulations Reveal Their Ability to Predict the Impact of Kinase Mutations\non Inhibitor Binding","volume":"129","author":"Singh","year":"2025","journal-title":"J. Phys. Chem. B"},{"issue":"8","key":"2026072709085340100_cit15","doi-asserted-by":"publisher","first-page":"1468","DOI":"10.1021\/acscentsci.9b00590","article-title":"Predicting Kinase Inhibitor Resistance:\nPhysics-Based and Data-Driven Approaches","volume":"5","author":"Aldeghi","year":"2019","journal-title":"ACS\nCent. Sci."},{"issue":"1","key":"2026072709085340100_cit16","doi-asserted-by":"publisher","DOI":"10.1002\/wcms.1563","article-title":"Computational\nStudies of Protein\u2013Drug Binding Affinity Changes upon Mutations\nin the Drug Target","volume":"12","author":"Friedman","year":"2022","journal-title":"WIREs Comput. Mol. Sci."},{"issue":"7","key":"2026072709085340100_cit17","doi-asserted-by":"publisher","first-page":"2695","DOI":"10.1021\/ja512751q","article-title":"Accurate and Reliable\nPrediction of Relative Ligand Binding Potency in Prospective Drug\nDiscovery by Way of a Modern Free-Energy Calculation Protocol and\nForce Field","volume":"137","author":"Wang","year":"2015","journal-title":"J. Am. Chem. Soc."},{"issue":"12","key":"2026072709085340100_cit18","doi-asserted-by":"publisher","first-page":"1708","DOI":"10.1021\/acscentsci.8b00717","article-title":"Accurate\nEstimation of Ligand Binding\nAffinity Changes upon Protein Mutation","volume":"4","author":"Aldeghi","year":"2018","journal-title":"ACS\nCent. Sci."},{"issue":"1","key":"2026072709085340100_cit19","doi-asserted-by":"publisher","DOI":"10.1038\/s42004-023-01019-9","article-title":"The Maximal and Current Accuracy\nof Rigorous Protein-Ligand Binding Free Energy Calculations","volume":"6","author":"Ross","year":"2023","journal-title":"Commun. Chem."},{"key":"2026072709085340100_cit20","unstructured":"Notin, P.; Dias, M.; Frazer, J.; Marchena-Hurtado, J.; Gomez, A. N.; Marks, D.; Gal, Y. In  Tranception: Protein Fitness Prediction\nwith Autoregressive\nTransformers and Inference-Time Retrieval, Proceedings of\nthe 39th International Conference on Machine Learning; PMLR, 2022; pp 16990\u201317017."},{"issue":"9","key":"2026072709085340100_cit21","doi-asserted-by":"publisher","first-page":"1512","DOI":"10.1038\/s41588-023-01465-0","article-title":"Genome-Wide\nPrediction of Disease Variant Effects with a Deep Protein Language\nModel","volume":"55","author":"Brandes","year":"2023","journal-title":"Nat. Genet."},{"issue":"15","key":"2026072709085340100_cit22","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2016239118","article-title":"Biological Structure\nand Function Emerge from Scaling\nUnsupervised Learning to 250 Million Protein Sequences","volume":"118","author":"Rives","year":"2021","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"issue":"7463","key":"2026072709085340100_cit23","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1038\/nature12477","article-title":"Signatures of Mutational\nProcesses in Human Cancer","volume":"500","author":"Alexandrov","year":"2013","journal-title":"Nature"},{"issue":"2","key":"2026072709085340100_cit24","doi-asserted-by":"publisher","DOI":"10.1016\/j.xpro.2023.102170","article-title":"Protocol for Predicting Drug-Resistant\nProtein Mutations to an ERK2 Inhibitor Using RESISTOR","volume":"4","author":"Guerin","year":"2023","journal-title":"STAR Protoc."},{"issue":"1","key":"2026072709085340100_cit25","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1111\/pcmr.12171","article-title":"Resistance\nto Vemurafenib Resulting from a Novel Mutation in the BRAFV600E Kinase\nDomain","volume":"27","author":"Wagenaar","year":"2014","journal-title":"Pigm. Cell Melanoma Res."},{"issue":"2","key":"2026072709085340100_cit26","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad033","article-title":"Bioinformatics\nToolbox for Exploring Target Mutation-Induced Drug Resistance","volume":"24","author":"Huang","year":"2023","journal-title":"Briefings Bioinf."},{"key":"2026072709085340100_cit27","doi-asserted-by":"crossref","unstructured":"Cagiada, M.; Jonsson, N.; Lindorff-Larsen, K.\n          Decoding Molecular\nMechanisms for Loss of Function Variants in the Human Proteome\n          bioRxiv\n          2024\n          10.1101\/2024.05.21.595203.","DOI":"10.1101\/2024.05.21.595203"},{"issue":"11","key":"2026072709085340100_cit28","doi-asserted-by":"publisher","first-page":"2604","DOI":"10.1093\/molbev\/msz179","article-title":"GEMME: A Simple\nand Fast Global Epistatic\nModel Predicting Mutational Effects","volume":"36","author":"Laine","year":"2019","journal-title":"Mol. Biol.\nEvol."},{"issue":"10","key":"2026072709085340100_cit29","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1038\/nchembio.1629","article-title":"A Unique Inhibitor Binding Site in\nERK1\/2 Is Associated with Slow Binding Kinetics","volume":"10","author":"Chaikuad","year":"2014","journal-title":"Nat. Chem. Biol."},{"issue":"1","key":"2026072709085340100_cit30","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1158\/2159-8290.CD-25-0605","article-title":"Sevabertinib, a Reversible\nHER2 Inhibitor\nwith Activity in Lung Cancer","volume":"16","author":"Siegel","year":"2026","journal-title":"Cancer Discovery"},{"issue":"7511","key":"2026072709085340100_cit31","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1038\/nature13385","article-title":"The Cancer Genome Atlas\nResearch Network; Disease analysis working group; Genome sequencing\ncentres: The Eli & Edythe L. Broad Institute; Washington University\nin St. Louis; Baylor College of Medicine; Genome characterization\ncentres: Canada\u2019s Michael Smith Genome Sciences Centre, B.\nC. C. A.; The Eli & Edythe L. Broad Institute; Harvard Medical\nSchool\/Brigham & Women\u2019s Hospital\/MD Anderson Cancer Center;\nUniversity of North Carolina, C. H.; University of Kentucky; The USC\/JHU\nEpigenome Characterization Center; Genome data analysis centres: The\nEli & Edythe L. Broad Institute; Memorial Sloan-Kettering Cancer\nCenter; University of California, S. C. I.; Oregon Health & Sciences\nUniversity; The University of Texas MD Anderson Cancer Center; Biospecimen\ncore resource: International Genomics Consortium; Tissue source sites:\nAnalytical Biological Service, Inc.; Brigham & Women\u2019s\nHospital; University of Alabama at Birmingham; Cleveland Clinic; Christiana\nCare; Cureline; Emory University; Fox Chase Cancer Center; ILSbio;\nIndiana University; Indivumed; John Flynn Hospital. Comprehensive\nMolecular Profiling of Lung Adenocarcinoma","volume":"511","author":"Collisson","year":"2014","journal-title":"Nature"},{"key":"2026072709085340100_cit32","doi-asserted-by":"crossref","unstructured":"Meier, J.; Rao, R.; Verkuil, R.; Liu, J.; Sercu, T.; Rives, A. In  Language Models Enable Zero-Shot Prediction\nof the Effects\nof Mutations on Protein Function, Proceedings of the 35th\nInternational Conference on Neural Information Processing Systems; Curran Associates Inc.: Red Hook,\nNY, USA, 2021; pp 29287\u201329303.","DOI":"10.1101\/2021.07.09.450648"}],"container-title":["Journal of Chemical Information\nand Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.6c00768","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article-pdf\/66\/14\/8284\/65891046\/acs.jcim.6c00768.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article-pdf\/66\/14\/8284\/65891046\/acs.jcim.6c00768.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T13:32:48Z","timestamp":1785159168000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article\/66\/14\/8284\/5207423\/Protein-Language-Model-Based-Fitness-Estimates"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,13]]},"references-count":32,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2026,7,13]]},"published-print":{"date-parts":[[2026,7,27]]}},"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.6c00768","relation":{"has-preprint":[{"id-type":"doi","id":"10.26434\/chemrxiv.15000911\/v1","asserted-by":"object"}]},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"value":"1549-9596","type":"print"},{"value":"1549-960X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,13]]}}}