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DTSTART;TZID=Europe/Stockholm:20221109T080000
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DTSTAMP:20260404T003611
CREATED:20221003T134428Z
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UID:144116-1667980800-1668013200@www.scilifelab.se
SUMMARY:Workshop on biomolecular simulations
DESCRIPTION:The main focus of ‘Biomoelcular simulations workshop’ is on molecular dynamics simulations of biomolecular systems. The target audience of this workshop is the researchers working in Sweden on biomolecular simulations. The goal of this workshop is to bring together all the researchers from Sweden interested in biomolecular simulations and to provide an opportunity to junior researchers in the field to present their work. This will be a single day event with some talks by other researchers from abroad and ample opportunities for discussion and interactions among the people in the field. \n\n\n\nConfirmed speakers\n\n\n\nSimon Olsson\, Chalmers\, GothenburgMilosz Wieczor\, IRB BarcelonaMercedes Alfonso-Prieto\, Forschungszentrum Jülich\n\n\n\n\n\n\n\nProgram and abstracts are collapsible items – click to expand. \n\n\n\nProgram\n09:30Morning Coffee\, MingleInghesalen\, Karolinska Institutet09:55Welcome from organizersOrganizing Committee10:00Multiscale simulations to study proton transfer: from enzymes to membrane proteinsMercedes Alfonso-Prieto\, Forschungszentrum Jülich\, Germany10:40Exploring the selectivity profile of plant UDP dependent glycosyl transferases (UGTs) by computational and experimental studiesJunhao Li\, Uppsala University11:00Multi-scale Modeling of Axonal Injury (coupling of finite element analysis with molecular dynamics simulations)Maryam Majdolhosseini\, KTH Royal Institute of Technology11:20Coffee Break11:40A Machine-Learned Atomistic Force Field for Nucleic AcidsMiłosz Wieczór\, IRB Barcelona12:20Combining evolution and physics with coevolution-powered machine learningDarko Mitrovic\, KTH Royal Institute of Technology12:40Lipid-ethanol mixtures for topical drug delivery applications: A molecular dynamics and NMR studyAmanda Quinones Vallin\, Stockholm University13:00Lunch14:00Machine learning to reconcile experiment and simulationSimon Olsson\, Chalmers University of Technology14:40Effective Molecular Dynamics from Neural-Network Based Structure Prediction ModelsAlexander Jussupow\, Stockholm University15:00Opportunities and Challenges in GPCR SBDD: Finding the Sweet SpotsPierre Matricon\, Sosei Heptares15:20Wrap up\, Thank youOrganizing Committee\n\n\n\n\nMultiscale simulations to study proton transfer: from enzymes to membrane proteins\nMercedes Alfonso-Prieto\, Assistant Professor\, Forschungszentrum Jülich\, Germany \n\n\n\nProton transfer (PT) is one the most common processes in biology. Studying PT at the molecular level is a challenging task\, due to the intrinsic complexity of proteins\, including their functional dynamics and the presence of several titratable residues. Therefore\, combination of experimental and computational techniques is needed to obtain a more comprehensive picture. Recent methodological and software advancements have enabled the efficient application of quantum mechanics/molecular mechanics (QM/MM) simulations to (large) biological systems. This multiscale approach allows treating the region of the protein where PT takes place at the QM level\, while including the effect of the rest of the protein and its environment at the MM level. In this talk\, I will show some examples of QM/MM simulations applied to PT processes in different systems\, from enzymes to membrane proteins. Such hybrid simulations have provided atomistic insights complementary to experiments. \n\n\n\n\nA Machine-Learned Atomistic Force Field for Nucleic Acids\nMiłosz Wieczór\, Postdoctoral fellow\, IRB Barcelona \n\n\n\nWe present a new paradigm for the parametrization of nonbonded interactions in molecular force fields directly from quantum chemical data. The proposed machine-learned force field (ML-FF) accurately reproduces well-validated quantum chemical interaction energies\, as well as allows for the systematic incorporation of experimental data such as measured affinity constants. Contrary to many recently published general ML-FFs\, our force field ensures stability in molecular dynamics simulations thanks to a combination of regularization and active learning\, and permits gradual upgrades should issues arise in previously untested systems. Meanwhile\, a simple embedding scheme allows the parameters learned from minimal methyl-nucleobase systems to be employed in simulations of large biomacromolecules at virtually no computational overhead. We also present several key directions for the future evolution of our force field\, both in terms of accuracy and applicability. \n\n\n\n\nMachine learning to reconcile experiment and simulation\nSimon Olsson\, Assistant Professor\, Chalmers University of Technology \n\n\n\nLong-time-scale and enhanced sampling molecular dynamics simulations reveal the approximations of classical molecular dynamics force fields disagreeing with experimental data. However\, under ideal conditions\, experiments and simulations observe the same process — consequently\, by optimally balancing experimental and simulation data\, we may build models extracting helpful information from both. In this talk\, I will overview efforts in this direction and talk about Augmented Markov models\, and their recent extension dynamic Augmented Markov models. \n\n\n\n\n\n\n\n\nRegistration
URL:https://www.scilifelab.se/event/workshops-on-biomolecular-simulations/
LOCATION:Inghesalen\, Tomtebodavägen 18a\,\, Solna\, 171 65 171 65\, Sweden
CATEGORIES:Event
ORGANIZER;CN="Biomolecular Simulations Network":MAILTO:lucie.delemotte@scilifelab.se
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