{"entity": "researcher", "timestamp": "2026-08-15T12:40:43.384Z", "family": "Mitrovic", "given": "Darko", "initials": "D", "orcid": "0000-0002-3219-1062", "affiliations": ["Department of Applied Physics, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/126efa8019bd45748e1497d631a2f78c.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/126efa8019bd45748e1497d631a2f78c"}}, "publications": [{"entity": "publication", "iuid": "aa4d649386164bacac3a9d0d54c7cc90", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/aa4d649386164bacac3a9d0d54c7cc90.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/aa4d649386164bacac3a9d0d54c7cc90"}}, "title": "The full spectrum of SLC22 OCT1 mutations illuminates the bridge between drug transporter biophysics and pharmacogenomics.", "authors": [{"family": "Yee", "given": "Sook Wah", "initials": "SW"}, {"family": "Macdonald", "given": "Christian B", "initials": "CB"}, {"family": "Mitrovic", "given": "Darko", "initials": "D", "orcid": "0000-0002-3219-1062", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/126efa8019bd45748e1497d631a2f78c.json"}}, {"family": "Zhou", "given": "Xujia", "initials": "X"}, {"family": "Koleske", "given": "Megan L", "initials": "ML"}, {"family": "Yang", "given": "Jia", "initials": "J", "orcid": "0000-0002-4497-9615", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/813a309f6b5c46ff9bff0ac129512c48.json"}}, {"family": "Buitrago Silva", "given": "Dina", "initials": "D"}, {"family": "Rockefeller Grimes", "given": "Patrick", "initials": "P"}, {"family": "Trinidad", "given": "Donovan D", "initials": "DD"}, {"family": "More", "given": "Swati S", "initials": "SS"}, {"family": "Kachuri", "given": "Linda", "initials": "L"}, {"family": "Witte", "given": "John S", "initials": "JS"}, {"family": "Delemotte", "given": "Lucie", "initials": "L", "orcid": "0000-0002-0828-3899", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/87eaf619d7bd487ebdbe68c46b827e66.json"}}, {"family": "Giacomini", "given": "Kathleen M", "initials": "KM"}, {"family": "Coyote-Maestas", "given": "Willow", "initials": "W"}], "type": "journal article", "published": "2024-05-16", "journal": {"title": "Mol. Cell", "issn": "1097-4164", "volume": "84", "issue": "10", "pages": "1932-1947.e10", "issn-l": "1097-2765"}, "abstract": "Mutations in transporters can impact an individual's response to drugs and cause many diseases. Few variants in transporters have been evaluated for their functional impact. Here, we combine saturation mutagenesis and multi-phenotypic screening to dissect the impact of 11,213 missense single-amino-acid deletions, and synonymous variants across the 554 residues of OCT1, a key liver xenobiotic transporter. By quantifying in parallel expression and substrate uptake, we find that most variants exert their primary effect on protein abundance, a phenotype not commonly measured alongside function. Using our mutagenesis results combined with structure prediction and molecular dynamic simulations, we develop accurate structure-function models of the entire transport cycle, providing biophysical characterization of all known and possible human OCT1 polymorphisms. This work provides a complete functional map of OCT1 variants along with a framework for integrating functional genomics, biophysical modeling, and human genetics to predict variant effects on disease and drug efficacy.", "doi": "10.1016/j.molcel.2024.04.008", "pmid": "38703769", "labels": {"Lucie Delemotte": null, "SciLifeLab Fellow": null}, "xrefs": [{"db": "mid", "key": "NIHMS2011805"}, {"db": "pmc", "key": "PMC11382353"}, {"db": "pii", "key": "S1097-2765(24)00323-X"}], "notes": [], "created": "2024-11-26T05:26:48.790Z", "modified": "2025-04-08T06:10:56.050Z"}, {"entity": "publication", "iuid": "aa1c9aa2f1474e5d8aff2a2a0be9bf08", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/aa1c9aa2f1474e5d8aff2a2a0be9bf08.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/aa1c9aa2f1474e5d8aff2a2a0be9bf08"}}, "title": "Coevolution-Driven Method for Efficiently Simulating Conformational Changes in Proteins Reveals Molecular Details of Ligand Effects in the \u03b22AR Receptor.", "authors": [{"family": "Mitrovic", "given": "Darko", "initials": "D", "orcid": "0000-0002-3219-1062", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/126efa8019bd45748e1497d631a2f78c.json"}}, {"family": "Chen", "given": "Yue", "initials": "Y"}, {"family": "Marciniak", "given": "Antoni", "initials": "A", "orcid": "0000-0002-6859-869X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/38424a2ca7a845af9a88b4366a4549ee.json"}}, {"family": "Delemotte", "given": "Lucie", "initials": "L", "orcid": "0000-0002-0828-3899", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/87eaf619d7bd487ebdbe68c46b827e66.json"}}], "type": "journal article", "published": "2023-11-23", "journal": {"title": "J Phys Chem B", "issn": "1520-5207", "volume": "127", "issue": "46", "pages": "9891-9904", "issn-l": "1520-5207"}, "abstract": "With the advent of AI-powered structure prediction, the scientific community is inching closer to solving protein folding. An unresolved enigma, however, is to accurately, reliably, and deterministically predict alternative conformational states that are crucial for the function of, e.g., transporters, receptors, or ion channels where conformational cycling is innately coupled to protein function. Accurately discovering and exploring all conformational states of membrane proteins has been challenging due to the need to retain atomistic detail while enhancing the sampling along interesting degrees of freedom. The challenges include but are not limited to finding which degrees of freedom are relevant, how to accelerate the sampling along them, and then quantifying the populations of each micro- and macrostate. In this work, we present a methodology that finds relevant degrees of freedom by combining evolution and physics through machine learning and apply it to the conformational sampling of the \u03b22 adrenergic receptor. In addition to predicting new conformations that are beyond the training set, we have computed free energy surfaces associated with the protein's conformational landscape. We then show that the methodology is able to quantitatively predict the effect of an array of ligands on the \u03b22 adrenergic receptor activation through the discovery of new metastable states not present in the training set. Lastly, we also stake out the structural determinants of activation and inactivation pathway signaling through different ligands and compare them to functional experiments to validate our methodology and potentially gain further insights into the activation mechanism of the \u03b22 adrenergic receptor.", "doi": "10.1021/acs.jpcb.3c04897", "pmid": "37947090", "labels": {"Lucie Delemotte": null, "SciLifeLab Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC10683026"}], "notes": [], "created": "2024-11-26T05:25:29.698Z", "modified": "2024-11-26T05:25:29.747Z"}, {"entity": "publication", "iuid": "216dbcef31b94bf48f30025b122712a8", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/216dbcef31b94bf48f30025b122712a8.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/216dbcef31b94bf48f30025b122712a8"}}, "title": "Reconstructing the transport cycle in the sugar porter superfamily using coevolution-powered machine learning.", "authors": [{"family": "Mitrovic", "given": "Darko", "initials": "D", "orcid": "0000-0002-3219-1062", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/126efa8019bd45748e1497d631a2f78c.json"}}, {"family": "McComas", "given": "Sarah E", "initials": "SE"}, {"family": "Alleva", "given": "Claudia", "initials": "C", "orcid": "0000-0001-8595-9250", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9507e83a9f924c83832d047c6db02906.json"}}, {"family": "Bonaccorsi", "given": "Marta", "initials": "M"}, {"family": "Drew", "given": "David", "initials": "D", "orcid": "0000-0001-8866-6349", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e80593c5f1ec4f87a38b079e5909f0b0.json"}}, {"family": "Delemotte", "given": "Lucie", "initials": "L", "orcid": "0000-0002-0828-3899", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/87eaf619d7bd487ebdbe68c46b827e66.json"}}], "type": "journal article", "published": "2023-07-05", "journal": {"title": "Elife", "issn": "2050-084X", "volume": "12", "issn-l": "2050-084X"}, "abstract": "Sugar porters (SPs) represent the largest group of secondary-active transporters. Some members, such as the glucose transporters (GLUTs), are well known for their role in maintaining blood glucose homeostasis in mammals, with their expression upregulated in many types of cancers. Because only a few sugar porter structures have been determined, mechanistic models have been constructed by piecing together structural states of distantly related proteins. Current GLUT transport models are predominantly descriptive and oversimplified. Here, we have combined coevolution analysis and comparative modeling, to predict structures of the entire sugar porter superfamily in each state of the transport cycle. We have analyzed the state-specific contacts inferred from coevolving residue pairs and shown how this information can be used to rapidly generate free-energy landscapes consistent with experimental estimates, as illustrated here for the mammalian fructose transporter GLUT5. By comparing many different sugar porter models and scrutinizing their sequence, we have been able to define the molecular determinants of the transport cycle, which are conserved throughout the sugar porter superfamily. We have also been able to highlight differences leading to the emergence of proton-coupling, validating, and extending the previously proposed latch mechanism. Our computational approach is transferable to any transporter, and to other protein families in general.", "doi": "10.7554/eLife.84805", "pmid": "37405846", "labels": {"Lucie Delemotte": null, "SciLifeLab Fellow": null}, "xrefs": [{"db": "pmc", "key": "PMC10322152"}, {"db": "pii", "key": "84805"}], "notes": [], "created": "2024-11-26T05:25:10.265Z", "modified": "2024-11-26T05:25:10.535Z"}]}