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DTSTART;TZID=Europe/London:20220303T150000
DTEND;TZID=Europe/London:20220303T160000
DTSTAMP:20260413T002656
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SUMMARY:TYC Seminar: Interpreting experiments using simulations and using experiments to improve simulations of intrinsically-disordered proteins
DESCRIPTION:Software solutions to the challenges of materials modelling Share on X\n\nSoftware solutions to the challenges of materials modelling Share on X\n\n\n\n\nSee a recording of the seminar here \n\n\n\nKresten Lindorff-Larsen – University of CopenhagenInterpreting experiments using simulations and using experiments to improve simulations of intrinsically-disordered proteins \n\n\n\nIntrinsically disordered proteins (IDPs) and flexible regions in multi-domain proteins display substantial conformational heterogeneity. Characterizing the conformational ensembles of these proteins in solution typically requires combining data from one or more biophysical techniques with computational modelling or simulations [1\,2]. Experimental data can either be used to assess the accuracy of a computational model or to refine the computational model to get a better agreement with the experimental data. I will discuss two different approaches to integrate experiments and simulations of IDPs. \n\n\n\nIn the first approach we use experimental data to refine conformational ensembles of IDPs in a systemspecific manner. I will describe how we use our Bayesian-Maximum Entropy software to refine conformational ensembles of IDPs generated by simulations [2–6]. I will briefly touch upon a key issue regarding the so-called “forward models” that are used to calculate experimental observables from conformational ensemble and highlight how generating such models for IDPs is important but difficult [7–9]. \n\n\n\nIn the second approach we use the experimental data to refine the force field used the simulations. I will describe a Bayesian formalism we have developed and applied to optimize and parameterize force fields by targeting experimental observables [10\,11]. We have used this method to parameterize a new coarsegrained model for IDPs by targeting data from small-angle scattering experiments and nuclear magnetic resonance spectroscopy on IDPs in solution [11]. I will describe how this model enables us to study interactions between IDPs and their formation of higher-order structures in biomolecular condensates\, and discuss initial work towards improving the Martini coarse-grained model for disordered proteins [4\,12]. \n\n\n\nReferences:1. Bottaro\, Sandro\, and Kresten Lindorff-Larsen. “Biophysical experiments and biomolecular simulations: A perfect match?.” Science 361: 355-360 (2018)2. Orioli\, Simone\, et al. “How to learn from inconsistencies: Integrating molecular simulations with experimental data.” Prog Mol Biol and Transl Sci 170: 123-176 (2020)3. Bottaro\, Sandro\, Tone Bengtsen\, and Kresten Lindorff-Larsen. “Integrating molecular simulation and experimental data: A Bayesian/maximum entropy reweighting approach.” Structural Bioinformatics. Humana 219-240 (2020)4. Larsen\, Andreas Haahr\, et al. “Combining molecular dynamics simulations with small-angle X-ray and neutron scattering data to study multi-domain proteins in solution.” PLoS Comput Biol 16: e1007870 (2020)5. Ahmed\, Mustapha Carab et al. “Computing\, analyzing\, and comparing the radius of gyration and hydrodynamic radius in conformational ensembles of intrinsically disordered proteins.” Intrinsically Disordered Proteins. Humana 429-445 (2020)6. Crehuet\, Ramon\, et al. “Bayesian-maximum-entropy reweighting of IDP ensembles based on NMR chemical shifts.” Entropy 21: 898 (2019)7. Lindorff-Larsen\, Kresten\, and Birthe B. Kragelund. “On the potential of machine learning to examine the relationship between sequence\, structure\, dynamics and function of intrinsically disordered proteins.” J Mol Biol 433:167196 (2021).8. Pesce\, Francesco\, and Kresten Lindorff-Larsen. “Refining conformational ensembles of flexible proteins against small-angle X-ray scattering data.” Biophys J 120:5124–5135 (2021)9. Tesei\, Giulio\, et al. “DEER-PREdict: software for efficient calculation of Spin-Labeling EPR and NMR data from conformational ensembles.” PLoS computational biology 17: e1008551 (2021)10. Norgaard\, Anders B.\, Jesper Ferkinghoff-Borg\, and Kresten Lindorff-Larsen. “Experimental parameterization of an energy function for the simulation of unfolded proteins.” Biophys J 94: 182-192 (2008)11. Tesei\, Giulio\, et al. “Accurate model of liquid-liquid phase behaviour of intrinsically-disordered proteins from optimization of single-chain properties.” Proc Natl Acad Sci 118: e2111696118 (2021).12. Thomasen\, F. Emil\, et al. “Improving the global dimensions of intrinsically disordered proteins in Martini 3.” bioRxiv (2021). \n\n\n\nBiographyKresten Lindorff-Larsen trained as a biochemist at the University of Copenhagen and Carlsberg Laboratory\, and completed his Ph.D. at the University of Cambridge in 2004 under the supervision of Prof. Christopher M. Dobson. He then moved on to become an assistant professor in Copenhagen before joining D. E. Shaw Research in New York in 2007. He returned to Copenhagen in 2011\, where he now serves as a Professor of Computational Protein Biophysics at the Linderstrøm-Lang Centre For Protein Science. He received the Danish Independent Research Councils’ Young Researchers’ Award in 2006\, was a co-recipient of the 2009 Gordon Bell Prize and has received several prestigious grants including a Hallas-Møller stipend (2011)\, a Sapere Aude starting grant (2012)\, and most recently a Novo Nordisk Foundation challenge programme grant (2019). He is the director of the Lundbeck Foundation BRAINSTRUC initiative in structural biology and the Novo Nordisk Foundation PRISM (Protein Interactions and Stability in Medicine and Genomics) centre. Current research interests include developing and applying computational methods for integrative structural biology\, and the integration of biophysics and genomics research. \n\n\n\n\n\n\n\n\n\n\n\nhttps://ucl.zoom.us/j/93351179800?pwd=bVBhNjJhZURNc05wR1piTUtTWXBVUT09 \n\n\n\nMeeting ID: 933 5117 9800 Passcode: TYCIGS
URL:https://thomasyoungcentre.org/event/tyc-seminar-interpreting-experiments-using-simulations-and-using-experiments-to-improve-simulations-of-intrinsically-disordered-proteins/
CATEGORIES:Main event
ATTACH;FMTTYPE=image/jpeg:https://thomasyoungcentre.org/wp-content/uploads/2021/09/TYC-Logo-white-on-blue.jpg
ORGANIZER;CN="Edina Rosta":MAILTO:e.rosta@ucl.ac.uk
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20220330T140000
DTEND;TZID=Europe/London:20220330T170000
DTSTAMP:20260413T002656
CREATED:20220218T161617Z
LAST-MODIFIED:20220404T093632Z
UID:2494-1648648800-1648659600@thomasyoungcentre.org
SUMMARY:TYC Distinguished Speaker Symposium:  Modelling Surfaces & Catalysis
DESCRIPTION:Software solutions to the challenges of materials modelling Share on X\n\nSoftware solutions to the challenges of materials modelling Share on X\n\n\n\n\nWatch the Symposium here\n\n\n\nProfessor Dr Joachim Sauer – Humboldt Universität zu BerlinNext Generation Quantum Chemistry of Water in Acidic ZeolitesWater plays a ubiquitous role in the synthesis\, post-synthesis treatment and reactivity of zeolite catalysts. We consider the local structure of bridging OH groups (b-OH) and their interaction with water molecules for Al-O(H)-Si sites in different zeolite frameworks and at different locations.A review of past work is followed by next generation studies which go beyond previous work in several respects:(1) Structure optimizations are performed at the MP2 level using our hybrid MP2(cluster model):DFT-D(periodic) method.(2) Special attention is paid to sites with internal H-bonds across rings of corner-sharing TO4 tetrahedra to a Si-O-Si acceptor site (Fig.\, left).(3) In addition to the H-bond approach of H2O to the b-OH site (Fig.\, right)\, we consider Lewis attack to the AlO4 tetrahedron in anti-position to the b-OH site (Fig.\, middle). \n\n\n\n\n\n\n\nThe MP2-quality results for OH vibrational frequencies and 1H-NMR chemical shifts of different types of bridging OH groups and their interaction with one and two water molecules are compared with experimental spectroscopic results and heats of adsorption. \n\n\n\nThe conclusions are also relevant for adsorption of alcohols at b-OH sites. \n\n\n\n\n\n\n\nProfessor Adrian Mulholland – University of BristolMultiscale modelling of biocatalysts for enzyme design\, evolution and engineeringSimulations are revealing biomolecular mechanisms relevant to function\, and are contributing to catalyst and inhibitor design. Simulations can be used as computational ‘assays’ of biological activity\, e.g. to predict drug resistance or effects of mutation. Combined quantum mechanics/molecular mechanics (QM/MM) methods allow modelling of reactions in proteins: they can identify mechanisms of reaction (e.g. for targeted covalent inhibitors such as ibrutinib\, and for the SARS-CoV-2 main protease) determinants of catalytic activity and predict the activity of bacterial enzymes against antibiotics. \n\n\n\nDynamical-nonequilibrium molecular dynamics (D-NEMD) simulations show coupling between allosteric sites and the active site in beta-lactamase enzymes; the pathways identified contain positions that differ between clinically relevant variants\, indicating that allosteric effects modulate the spectrum of activity of these antibiotic resistance enzymes. The D-NEMD approach can effectively combine cloud-based and other HPC resources. \n\n\n\nIncreasingly\, simulations are contributing to the engineering of natural enzymes and de novo biocatalysts. Simulations are also contributing to the emerging evidence that activation heat capacity is an important factor in enzyme evolution and thermoadaptation. Directed evolution of a designed Kemp eliminase unexpectedly introduced curvature into the temperature dependence of reaction\, showing the emergence of an activation heat capacity. Simulations identify the dynamical networks involved\, which may provide useful targets for mutation and directed evolution experiments. \n\n\n\nVirtual reality offers new ways interact with simulations\, and new ways to collaborate. Interactive MD simulation in virtual reality (iMD-VR) allows manipulation of biological macromolecules\, going beyond mere visualization to allow e.g. fully flexible docking of drugs into protein targets. The COVID-19 pandemic has highlighted the need for effective tools for virtual collaboration. Groups of researchers can work together\, using iMD-VR for molecular problems such as catalyst and structure-based drug design. Using the cloud\, researchers in different physical locations can work together in the same virtual molecular environment. Simulations\, including iMD-VR\, with collaborative sharing of models and data\, have been brought together to design peptide inhibitors of the SARS-CoV-2 main protease. \n\n\n\nReferences‘Evolution of dynamical networks enhances catalysis in a designer enzyme HA Bunzel\, JL Anderson\, D Hilvert\, VL Arcus\, MW van der Kamp & AJ Mulholland Nature Chemistry 13\, 1017-1022 (2021)‘Designing better enzymes: Insights from directed evolution’ HA Bunzel\, JLR Anderson & AJ Mulholland Current Opinion in Structural Biology 67\, 212-218 (2021)‘Dynamical nonequilibrium molecular dynamics reveals the structural basis for allostery and signal propagation in biomolecular systems ASF Oliveira\, G Ciccotti\, S Haider\, AJ Mulholland The European Physical Journal B 94\, 1-12 (2021)‘Discovery of SARS-CoV-2 M pro peptide inhibitors from modelling substrate and ligand binding H. Chan et al. Chemical Science 12\, 13686-13703 (2021) \n\n\n\nBiography: Adrian Mulholland is a Professor of Chemistry\, University of Bristol\, UK. Following his first degree at Bristol\, he worked in a wine merchant and for ICI Pharmaceuticals before doctoral studies with Graham Richards (Oxford) and postdoctoral work with Martin Karplus (Harvard). His research focuses on mechanisms of enzyme catalysis\, biomolecular dynamics and function. He develops and applies biomolecular simulation methods to problems in antimicrobial resistance\, drug metabolism\, biocatalysis and enzyme design and evolution. He has published over 200 papers\, attracting over 10\,000 citations. He was awarded the 2020 John Meurig Thomas Medal ‘for outstanding and innovative work in catalytic science’. \n\n\n\n\n\n\n\nDr Thomas Keal – Science and Technology Facilities Council (STFC)Recent developments in QM/MM modelling with ChemShellChemShell is a scriptable computational chemistry environment with an emphasis on multiscale simulation of complex systems using combined quantum mechanical and molecular mechanical (QM/MM) methods. The QM/MM approach is well suited to studying catalysis in both biomolecular systems and materials\, where the reactive region can be treated at the QM level and the environment with classical methods. QM/MM is particularly useful when coupled with serial crystallography experiments\, as is highlighted by a case study of the mechanism of nitrite reduction in a copper nitrite reductase enzyme [1]. Recent work in QM/MM modelling of materials chemistry will also be discussed\, as well as the redevelopment of ChemShell as an open source\, python-based package\, which offers a modern platform for multiscale modelling with an emphasis on high performance computing platforms [2]. \n\n\n\nReferences:[1] K. Sen\, M.A. Hough\, R.W. Strange\, C. Yong and T.W. Keal\, J. Phys. Chem. B\, 125\, 9102 (2021).[2] Y. Lu\, M.R. Farrow\, P. Fayon\, A.J. Logsdail\, A.A. Sokol\, C.R.A. Catlow\, P. Sherwood and T.W. Keal\, J. Chem. Theory Comput.\, 15\, 1317 (2019). \n\n\n\nBiography: Thomas Keal is a Principal Scientist in the Computational Chemistry Group at STFC Daresbury Laboratory\, with responsibility for QM/MM methods development. He completed his PhD in 2005 in the group of David Tozer at Durham University\, focussing on the development of new exchange-correlation functionals for density functional theory. He then moved to a postdoctoral position in the group of Walter Thiel in Mülheim an der Ruhr\, Germany\, working on methods for excited state optimisation and dynamics of biomolecules. He joined Paul Sherwood’s group at Daresbury in 2008 to continue work on methods development in the ChemShell software package\, and now leads the team developing ChemShell. His research interests are in QM/MM methodology and its application to problems in biochemistry and materials chemistry. \n\n\n\n\n\n\n\nDr Edina Rosta – University College LondonDynamics\, function and mechanism of phosphate processing enzymesPhosphate catalytic enzymes are essential and ubiquitous to all forms of life. While structures of these proteins are typically readily available\, prediction and design of their function and activity is a key current challenge. Here we review free energy calculation methods and applications for prototype examples including HIV-1 RNase H [1]. Our work highlights the important role of coupled proton transfer steps in the catalytic mechanism using the finite-temperature string method. This allows us to use multiple collective variables that govern the reaction path. Identification of these collective variables in complex processes presents a major problem. We offer promising AI-driven algorithms to help identify essential reaction coordinates in biomolecular processes [2]. \n\n\n\nEdina Rosta1\, Department of Physics and Astronomy\, University College London\, London\, WC1E 6BT\, e.rosta@ucl.ac.uk \n\n\n\nReferences[1] S. Dürr\, O. Bohuszewicz\, R. Suardiaz\, P. G. Jambrina\, C. Peter\, Y. Shao\, and E. Rosta\, ACS Catalysis\, 10.1021/acscatal.1c01493\, 2021[2] M. Badaoui\, P. J. Buigues\, D. Berta\, G. M. Mandana\, H. Gu\, T. Földes\, C. J. Dickson\, V. Hornak\, M. Kato\, C. Molteni\, S. Parsons\, and E. Rosta\, J. Chem. Theory Comput. 10.1021/acs.jctc.1c00924\, 2022 \n\n\n\nBiography: Dr. Edina Rosta is an Associate Professor in Computational Materials Modelling at UCL. After completing her PhD at USC in the group of Arieh Warshel (2013 Chemistry Nobel Prize Laureate)\, she joined the Hummer lab as a Postdoctoral Research Fellow at the NIDDK\, NIH. She took up a lecturer position at KCL Chemistry in 2012. In 2020 she joined UCL Physics. Current research in her group focuses on atomistic molecular modeling\, including hybrid quantum mechanics/molecular mechanics (QM/MM) simulations. To quantitatively and accurately assess how enzymes achieve their extraordinary efficiency and specificity in performing chemical reactions\, she develops modern enhanced sampling methods including novel algorithms to calculate molecular kinetics from biased molecular simulations using the theoretical framework of kinetic networks. Applications of her work focus on the most prominent chemical reactions of living organisms: phosphate transfer and cleavage. She studies the key functional roles of Mg2+ cofactors in phosphate catalytic reactions.
URL:https://thomasyoungcentre.org/event/tyc-distinguished-speaker-symposium-modelling-surfaces-catalysis/
LOCATION:XLG1 Lecture Theatre\, Christopher Ingold Building\, 20 Gordon Street\, London\, WC1H 0AJ\, United Kingdom
CATEGORIES:Main event
ATTACH;FMTTYPE=image/jpeg:https://thomasyoungcentre.org/wp-content/uploads/2021/11/TYC-Logo_blue_on_white_2.jpg
ORGANIZER;CN="Professor Sir Richard Catlow":MAILTO:tyc-administrator@ucl.ac.uk
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20220331T090000
DTEND;TZID=Europe/London:20220331T160000
DTSTAMP:20260413T002656
CREATED:20220307T104950Z
LAST-MODIFIED:20220404T154009Z
UID:2632-1648717200-1648742400@thomasyoungcentre.org
SUMMARY:MMM Hub: HPE / NVIDIA GPU Training Day
DESCRIPTION:Software solutions to the challenges of materials modelling Share on X\n\nSoftware solutions to the challenges of materials modelling Share on X\n\n\n\n\n \n\n\n\n09:00 – 12:00 (UK time) Technology and Partner sessions (UCL\, HPE & NVIDIA) chaired by Owain Kenway – UCL \n\n\n\nRecording: Introduction to the MMM Hub GPU Training Day – Owain Kenway\, UCLRecording: HPE HPC/AI EMEA Research Lab – Tim Dykes\, HPERecording: Overview of the new MMM Hub system – Owain Kenway\, UCLRecording: HPE CRAY Programming Environment – Tim Dykes\, HPERecording: Multi-GPU programming with MPI and NCCL – Jiri Kraus\, NVIDIARecording: Developer Tools: Nsight Product Review – Sanjiv Satoor\, NVIDIA \n\n\n\n13:00 – 16:00 (UK time) Materials community codes\, experiences & lessons learned (invited speakers) chaired by Filippo Spiga – NVIDIA \n\n\n\nRecording: Tools and Techniques to port codes on GPU – Introduction by Filippo Spiga\, NVIDIARecording: Tools & Techniques to port CASTEP – Phil Hasnip\, YorkRecording: Lessons learned from porting VASP to GPUs – Stefan Maintz\, NVIDIARecording: Current state of CP2K on GPU – Matthieu Talletumier\, CSCSRecording: Challenges and lesons from using GPUs in GSGW – Dimitar Pashov\, King’s College LondonRecording: Getting QMCpack ready to model material properties at Exascale – Ye Luo\, Argonne National Laboratories
URL:https://thomasyoungcentre.org/event/mmm-hub-hpe-nvidia-gpu-training-day/
CATEGORIES:Main event
ATTACH;FMTTYPE=image/jpeg:https://thomasyoungcentre.org/wp-content/uploads/2022/01/MMM-no-description.jpg
ORGANIZER;CN="Dr Owain Kenway":MAILTO:o.kenway@ucl.ac.uk
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