BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//THOMAS YOUNG CENTRE - ECPv6.15.17//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:THOMAS YOUNG CENTRE
X-ORIGINAL-URL:https://thomasyoungcentre.org
X-WR-CALDESC:Events for THOMAS YOUNG CENTRE
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/London
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20240331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20241027T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20251026T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20261025T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20250409T160000
DTEND;TZID=Europe/London:20250409T173000
DTSTAMP:20260411T030316
CREATED:20250313T164029Z
LAST-MODIFIED:20250324T113633Z
UID:6447-1744214400-1744219800@thomasyoungcentre.org
SUMMARY:MMM Hub Software Spotlight: Intel HBM
DESCRIPTION:Venue: ONLINE \n\n\n\n\n\n\n\n\n\n\nMMM Hub Software Spotlight: Intel HBM Share on X\n\n\n\n\nFouzhan Hosseini from Intel will give an overview of the Intel HBM hardware and how to use it both in cache and flat mode\, and demonstrate benchmarks for a different relevant codes. \n\n\n\nFuture talks aim to include commonly codes used on Young\, such as Quantum ESPRESSO and Casino and include some emerging technologies such as machine learning with Keras\, Tensorflow and Torch \n\n\n\nJoin Zoom Meeting: \n\n\n\nMeeting ID: 991 6854 2304Passcode: TYCSWS
URL:https://thomasyoungcentre.org/event/mmm-hub-software-spotlight-intel-hbm/
CATEGORIES:Main event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20250410T150000
DTEND;TZID=Europe/London:20250410T180000
DTSTAMP:20260411T030316
CREATED:20250313T150330Z
LAST-MODIFIED:20250407T104534Z
UID:6434-1744297200-1744308000@thomasyoungcentre.org
SUMMARY:TYC Recently Appointed Academic Talks: Carla de Tomas\, King's\, Gabriella Heller\, UCL & Ivan Palaia\, King’s
DESCRIPTION:TYC Recently Appointed Academic Talks: Carla de Tomas\, King's\, Gabriella Heller\, UCL & Ivan Palaia\, King’s Share on X\n\n\n\n\n\n\n\n\n\nRegister\n\n\n\n\n\n\n\n\nTo welcome new PIs to the TYC\, and to introduce them and their research to existing members\, the Thomas Young Centre runs a continuous programme of Recently Recruited events. \n\n\n\nThis session introduces Carla de Tomas\, Gabriella Heller and Ivan Palaia to the TYC. \n\n\n\nDesigning next-generation carbon materials – Carla de Tomas\, King’s College LondonNanoporous carbon materials play a crucial role in a range of industrial applications\, including water purification\, gas separation\, and energy storage—particularly as electrodes in alkali-ion batteries. Their exceptional performance stems from a complex internal structure featuring a diverse distribution of pore sizes and geometries. As a result\, detailed structuralcharacterization is essential to assess\, optimize\, and scale up their synthesis and functional performance. \n\n\n\nOver the past few decades\, a variety of methods have been employed to study porosity and related properties—such as surface area\, pore size distribution (PSD)\, and real density—using gas adsorption techniques. However\, these models typically rely on simplified one-dimensional representations of carbon nanopores\, such as slit-pore geometries. In reality\, the adsorption and separation behavior of nanoporous carbons is governed by their three-dimensional pore architecture. Advancing techniques to estimate the 3D nanostructure of these materials from gas adsorption data would significantly accelerate both fundamental research and the optimization of nanoporous carbon electrodes. \n\n\n\nDrugging Intrinsically Disordered Proteins – Gabriella Heller\, UCL & Bind ResearchIntrinsically disordered proteins are highly dynamic biomolecules that rapidly interconvert among many structural conformations. These dynamic proteins are involved in cancers\, neurodegeneration\, cardiovascular illnesses\, and viral infections. Despite their enormous therapeutic potential\, intrinsically disordered proteins have generally been considered undruggable because of their lack of classical long-lived binding pockets for small molecules. Currently\, only a few instances are known where small molecules have been observed to interact with intrinsically disordered proteins\, and this situation is further exacerbated by the limited sensitivity of experimental techniques to detect such binding events. I will share our recent work which combines all-atom metadynamic simulations and nuclear magnetic resonance spectroscopy to characterise the interactions between small molecules and intrinsically disordered proteins towards the discovery and development of new therapeutics targeting these highly dynamic biomolecules. \n\n\n\nDividing cells in silico: how actin constricts the membrane – Ivan Palaia\, King’s College LondonTo divide\, our cells must generate coherent forces across their entire diameter. They do so by assembling nm-sized building blocks into a contractile ring\, which pinches the cell membrane from the inside. How can such small building blocks produce reliable\, coherent forces over distances of tens of µm?  \n\n\n\nIn this talk\, we will build a minimal computational model to study the mechanics of cell division\, involving actin filaments\, molecular motors and a fluid membrane. After identifying the key principle behind a functional contractile ring\, we will show that the constriction mechanism is robust against a plethora of perturbations\, particularly in filament dynamics. This robustness may explain why this fundamental machinery has been conserved through evolution for over a billion years. \n\n\n\nPalaia\, Šarić\, in preparation \n\n\n\nDar\, Tesoro-Moreno\, Palaia\, Gopalan\, Sun\, Strauss\, Sprenger\, Belmonte\, Foster\, Murrell\, Ejsing\, Šarić\, Leptin\, Diz-Muñoz\, bioRxiv:10.1101/2024.10.14.618153
URL:https://thomasyoungcentre.org/event/tyc-recently-appointed-academic-talks-carla-de-tomas-ivan-palaia-kings-college-london/
LOCATION:K-1.56 King’s building\, King's College London\, Strand Campus\, Strand\, London\, WC2R 2LS
CATEGORIES:Main event
ORGANIZER;CN="Jan Tomczak":MAILTO:jan.tomczak@kcl.ac.uk
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/London:20250424T150000
DTEND;TZID=Europe/London:20250424T180000
DTSTAMP:20260411T030316
CREATED:20250204T161400Z
LAST-MODIFIED:20250324T110315Z
UID:6326-1745506800-1745517600@thomasyoungcentre.org
SUMMARY:TYC Mini-Symposium Bio Interest Group: AI-based approaches for biomolecular modelling and simulation - Chris Oostenbrink\, BOKU & Franca Fraternali\, UCL
DESCRIPTION:TYC Mini-Symposium Bio Interest Group: Biomolecules and their modelling using AI – Chris Oostenbrink\, BOKU & Franca Fraternali\, UCL Share on X\n\n\n\n\nDrinks reception will be held in Physics E3/7 ground floor \n\n\n\n\nRegister here\n\n\n\n\n\n\n\n\nUse of machine-learned potentials in QM/MM settings: the BuRNN methodology – Chris OostenbrinkInstitute for Molecular Modelling and Simulation\, Department of Natural Sciences and Sustainable Resources\, BOKU University\, Vienna\, Austria \n\n\n\nIn hybrid quantum mechanics / molecular mechanics (QM/MM) approaches\, the molecular system is partitioned into regions that are treated at different levels of theory. At the interfaces between these regions\, artifacts may occur. Examples are an overpolarization of the QM region due to near partial charges in the MM region\, the lack of polarization in the MM region or unbalanced interactions between particles in the different regions\, leading to an intrusion of MM particles into the QM region\, or an accumulation or depletion of QM particles if particles are allowed to change character. \n\n\n\nWe have recently introduced a buffered embedding scheme\, in which a buffer region between the inner (QM) and outer (MM) region is defined for which the interactions are computed both at the QM and MM level. This comes at the cost of introducing a second QM-calculation at every timestep of the simulation. The use of neural networks to describe molecular potential energies\, allows for an elegant solution to this problem. We train a neural network directly on the difference between the two QM calculations\, ensuring that the network reproduces the QM-interactions of the inner region\, with itself and with the buffer region as well as the polarization of the buffer region due to the inner region. Any remaining artifacts largely cancel in the trained differences and are far removed from the inner region of interest. The use of the Buffer Region Neural Network (BuRNN) approach\, furthermore\, allows us to apply alchemical free-energy calculations at the QM-level of theory. In this presentation\, I will demonstrate our most recent advances with BuRNN. \n\n\n\n\nLier\, B.\, Poliak\, P.\, Marquetand\, P.\, Westermayr\, J.\, Oostenbrink\, C. (2022) BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network/Molecular Mechanics Simulations. J Phys Chem Lett 13\, 3812-3818. doi: 10.1021/acs.jpclett.2c00654\n\n\n\nCrha\,R.\, Poliak\, P.\, Gillhofer\, M.\, Oostenbrink C. (2025) Alchemical Free-Energy Calculations at Quantum-Chemical Precision. J. Phys. Chem. Lett 16\, 863–869. doi: 10.1021/acs.jpclett.4c03213\n\n\n\n\nPredicting cognate pairing of heavy and light immunoglobulin chains using single-cell antibody repertoire data – Franca Fraternali – Division of Biosciences\, Department of Structure and Molecular Biology\, University College London \n\n\n\nThe formation of stable antibodies through compatible heavy (H) and light (L) chain pairing is essential for both the natural maturation of antibody-producing cells in vivo and the engineered development of therapeutic antibodies ex vivo. Here\, we introduce ImmunoMatch\, a novel machine learning framework designed to decode the molecular principles underlying antibody chain pairing. Leveraging an antibody-specific language model\, ImmunoMatch is trained on paired H and L sequences from single human B cells\, enabling it to differentiate between cognate H-L pairs and randomly paired sequences.The application of ImmunoMatch is crucial in understanding how V(D)J usage preference drives differences in chain pairing propensities\, and how this in turn affects in vivo antibody repertoire formation. Furthermore\, ImmunoMatch opens up avenues to optimise chain pairing and facilitate in silico antibody design: while this has recently received much attention\, research effort focuses almost exclusively on antigen affinity. \n\n\n\nGuo D\, Dunn-Walters DK\, Fraternali F+ \, Ng JCF+ . (2025). ImmunoMatch learns and predicts cognate pairing of heavy and light immunoglobulin chains. bioRxiv\, doi: https://doi.org/10.1101/2025.02.11.637677 \n\n\n\nNg JCF(*\,+)\, Montamat Garcia G(+)\, Stewart AT(+)\, … Fraternali F(*). (2024). sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data\, Nature Methods\, 21(5):823-834\, doi: https://doi.org/10.1038/s41592-023-02060 \n\n\n\nGuo D\, Ng JCF\, Dunn-Walters DK\, Fraternali F. VCAb: a web-tool for structure-guided exploration of antibodies. Bioinform Adv. 2024 Sep 24;4(1):vbae137 \n\n\n\nBio: Professor Franca Fraternali is currently Chair of Integrative Computational Biology at UCL\, and Head of the Institute of Structural and Molecular Biology. \n\n\n\nHer group research focuses on the study of physical interactions of proteins and their interaction networks by combining information theoretic methods\, statistical analyses and molecular simulations. Recently the group’s research introduced AI methods and multiscale approaches bridging atomistic and cellular protein function in Computational Systems Immunology. \n\n\n\nWeb-site: https://fraternalilab.github.io/
URL:https://thomasyoungcentre.org/event/tyc-mini-symposium-bio-interest-group-chris-oostenbrink-boku-franca-fraternali-ucl/
LOCATION:UCL Physics A1/3\, Physics Building\, Gower Street\, London\, WC1E 6BT\, United Kingdom
CATEGORIES:Main event
ORGANIZER;CN="Edina Rosta":MAILTO:e.rosta@ucl.ac.uk
END:VEVENT
END:VCALENDAR