BEGIN:VCALENDAR VERSION:2.0 PRODID:icalendar-ruby CALSCALE:GREGORIAN METHOD:PUBLISH BEGIN:VTIMEZONE TZID:Europe/Vienna BEGIN:DAYLIGHT DTSTART:20260329T030000 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST END:DAYLIGHT BEGIN:STANDARD DTSTART:20251026T020000 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260424T143355Z UID:1761561000@ist.ac.at DTSTART:20251027T113000 DTEND:20251027T123000 DESCRIPTION:Speaker: Kresten Lindorff-Larsen\nhosted by Paul Schanda\nAbstr act: Intrinsically disordered proteins and regions (collectively IDRs) are pervasive across proteomes in all kingdoms of life\, help shape biologica l functions\, and are involved in numerous diseases [1]. IDRs populate a d iverse set of transiently formed structures yet defy commonly held sequenc e-structure-function relationships [1–3]. Recent developments in protein structure prediction have led to the ability to predict the three-dimensi onal structures of folded proteins at the proteome scale and have enabled large-scale studies of structure-function relationships. In contrast\, kno wledge of the conformational properties of fully disordered proteins and l ong disordered linkers is scarce\, in part because the sequences of disord ered proteins are poorly conserved and because only few have been characte rized experimentally. In my talk I will describe how we can use molecular simulations with coarse-grained models and machine learning to study the r elationship between sequence\, conformational properties\, and functions o f IDRs [3].First\, I will describe how we have used experimental data on c a. 100 proteins to learn a coarse-grained molecular energy function to pre dict conformational properties of IDRs [4\,5]. By globally optimizing a tr ansferable model\, called CALVADOS\, we can study the conformational ensem ble of an IDR [5\,6]\, multidomain protein [7] and IDRs interacting with d isordered RNA [8]. I will describe the Bayesian formalism we developed to parameterize CALVADOS by targeting experimental data\, and how this model enables us to study interactions within and between IDRs in biomolecular c ondensates [6–9].Second\, I will describe how CALVADOS makes it possible to perform large-scale simulations to explore the relationship between se quence\, structure\, and function of IDRs. I will describe how we have gen erated conformational ensembles of all intrinsically disordered regions of the human proteome and used these to provide insight into sequence-ensemb le relationships and evolutionary conservation of IDR properties [10].Thir d\, I will briefly describe work on how we can use the information encoded in CALVADOS to design disordered proteins with desired conformational pro perties [11]. I will outline the basic design algorithm and experimental v alidation on both single-chain compaction and measurements of phase separa tion.Finally\, I will describe how we can use CALVADOS together with activ e learning procedures to learn a quantitative model for the rules governin g homotypic phase separation of IDRs in condensates [9].References1. Holeh ouse\, A. S.\, & Kragelund\, B. B. (2023). The molecular basis for cellula r function of intrinsically disordered protein regions. Nature Reviews Mo lecular Cell Biology\, 1-25.2. Lindorff-Larsen\, K.\, & Kragelund\, B. B. (2021). On the potential of machine learning to examine the relationship b etween sequence\, structure\, dynamics and function of intrinsically disor dered proteins. Journal of Molecular Biology\, 433(20)\, 167196.3. von B ülow\, S.\, Tesei\, G.\, & Lindorff-Larsen\, K. (2025). Machine learning methods to study sequence–ensemble–function relationships in disordere d proteins. Current Opinion in Structural Biology\, 92\, 103028.4. Norgaar d\, A. B.\, Ferkinghoff-Borg\, J.\, & Lindorff-Larsen\, K. (2008). Experim ental parameterization of an energy function for the simulation of unfolde d proteins. Biophysical Journal\, 94(1)\, 182-192.5. Tesei\, G.\, Schulz e\, T. K.\, Crehuet\, R.\, & Lindorff-Larsen\, K. (2021). Accurate model o f liquid–liquid phase behavior of intrinsically disordered proteins from optimization of single-chain properties. Proceedings of the National Acad emy of Sciences\, 118(44)\, e2111696118.6. Tesei\, G.\, & Lindorff-Larsen \, K. (2023). Improved predictions of phase behaviour of intrinsically dis ordered proteins by tuning the interaction range. Open Research Europe\,  2\, 94.7. Cao\, F.\, von Bülow\, S.\, Tesei\, G.\, & LindorffLarsen\, K . (2024). A coarsegrained model for disordered and multidomain proteins. P rotein Science\, 33\, e5172.8. Yasuda\, I.\, von Bulow\, S.\, Tesei\, G.\, Yamamoto\, E.\, Yasuoka\, K.\, & Lindorff-Larsen\, K. (2025). Coarse-grai ned model of disordered RNA for simulations of biomolecular condensates.  Journal of chemical theory and computation\, 21(5)\, 2766-2779.9. von Bü low\, S.\, Tesei\, G.\, Zaidi\, F. K.\, Mittag\, T.\, & Lindorff-Larsen\, K. (2025). Prediction of phase-separation propensities of disordered prote ins from sequence. Proceedings of the National Academy of Sciences\, 122(1 3)\, e2417920122.10. Tesei\, G.\, Trolle\, A. I.\, Jonsson\, N.\, Betz\, J .\, Knudsen\, F.E.\, Pesce\, F.\, Johansson\, K. E.\, & Lindorff-Larsen\, K. (2024). Conformational ensembles of the human intrinsically disordered proteome. Nature\, 626(8000)\, 897-904.11. Pesce\, F.\, Bremer\, A.\, Tese i\, G.\, Hopkins\, J. B.\, Grace\, C. R.\, Mittag\, T.\, & Lindorff-Larsen \, K. (2024). Design of intrinsically disordered protein variants with div erse structural properties. Science Advances\, 10\, eadm9926. LOCATION:Raiffeisen Lecture Hall\, ISTA ORGANIZER:diana.gruber@ista.ac.at SUMMARY:Kresten Lindorff-Larsen: Prediction and Design of Intrinsically Dis ordered Proteins and Condensates URL:https://talks-calendar.ista.ac.at/events/5772 END:VEVENT END:VCALENDAR