I’m exploring whether we can recover protein conformational ensembles from existing experimental data using steered generative models. See this post on diffuse.science for recent work.
Mutations in the kinase and juxtamembrane domains of the MET Receptor Tyrosine Kinase are responsible for oncogenesis in various cancers and can drive resistance to MET-directed treatments. Determining the most effective inhibitor for each mutational profile is a major challenge for MET-driven cancer treatment in precision medicine. Here, we used a deep mutational scan (DMS) of ~5764 MET kinase domain variants to profile the growth of each mutation against a panel of 11 inhibitors that are reported to target the MET kinase domain. We validate previously identified resistance mutations, pinpoint common resistance sites across type I, type II, and type I \frac12 inhibitors, unveil unique resistance and sensitizing mutations for each inhibitor, and verify non-cross-resistant sensitivities for type I and type II inhibitor pairs. We augment a protein language model with biophysical and chemical features to improve the predictive performance for inhibitor-treated datasets. Together, our study demonstrates a pooled experimental pipeline for identifying resistance mutations, provides a reference dictionary for mutations that are sensitized to specific therapies, and offers insights for future drug development.
@article{estevamMappingKinaseDomain2025,title={Mapping Kinase Domain Resistance Mechanisms for the {{MET}} Receptor Tyrosine Kinase via Deep Mutational Scanning},author={Estevam, Gabriella O and Linossi, Edmond and Rao, Jingyou and Macdonald, Christian B and Ravikumar, Ashraya and Chrispens, Karson M and Capra, John A and {Coyote-Maestas}, Willow and Pimentel, Harold and Collisson, Eric A and Jura, Natalia and Fraser, James S},editor={Seeliger, Markus A and Andreotti, Amy H},year={2025},month=feb,journal={eLife},volume={13},pages={RP101882},publisher={eLife Sciences Publications, Ltd},issn={2050-084X},doi={10.7554/eLife.101882},urldate={2025-03-30},langid={english},keywords={drug discovery,mutagenesis,protein kinase},}
Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of nine different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.
@article{petersenIntegratedTechnologyQuantitative2024,title={An Integrated Technology for Quantitative Wide Mutational Scanning of Human Antibody {{Fab}} Libraries},author={Petersen, Brian M. and Kirby, Monica B. and Chrispens, Karson M. and Irvin, Olivia M. and Strawn, Isabell K. and Haas, Cyrus M. and Walker, Alexis M. and Baumer, Zachary T. and Ulmer, Sophia A. and Ayala, Edgardo and Rhodes, Emily R. and Guthmiller, Jenna J. and Steiner, Paul J. and Whitehead, Timothy A.},year={2024},month=may,journal={Nature Communications},volume={15},number={1},pages={3974},publisher={Nature Publishing Group},issn={2041-1723},doi={10.1038/s41467-024-48072-z},urldate={2024-05-16},copyright={2024 The Author(s)},langid={english},keywords={Antibodies,Applied immunology,Assay systems,Molecular engineering},}