Date: 26 May 2026

Time: 15:00 CET

Registration

Abstract

Intrinsically disordered proteins and regions (collectively IDRs) are pervasive across proteomes in all kingdoms of life, help shape biological functions, and are involved in numerous diseases [1]. IDRs populate a diverse set of transiently formed structures yet defy commonly held sequence-structure-function relationships [1–3]. Recent developments in protein structure prediction have led to the ability to predict the three-dimensional structures of folded proteins at the proteome scale and have enabled large-scale studies of structure-function relationships. In contrast, knowledge of the conformational properties of fully disordered proteins and long disordered linkers is scarce, in part because the sequences of disordered proteins are poorly conserved and because only few have been characterized experimentally. In my talk I will describe how we can use molecular simulations with coarse-grained models and machine learning to study the relationship between sequence, conformational properties, and functions of IDRs [3].

First, I will describe how we have used experimental data on more than 100 proteins to learn a coarse-grained molecular energy function to predict conformational properties of IDRs [4,5]. By globally optimizing a transferable model, called CALVADOS, we can study the conformational ensemble of an IDR [5,6], multidomain protein [7] and IDRs interacting with disordered 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 condensates [6–9].

Second, I will describe how CALVADOS makes it possible to perform large-scale simulations to explore the relationship between sequence, structure, and function of IDRs. I will describe how we have generated conformational ensembles of all intrinsically disordered regions of the human proteome and used these to provide insight into sequence-ensemble relationships and evolutionary conservation of IDR properties [10].

Third, I will describe how we can integrate AlphaFold with CALVADOS to generate conformational ensembles of proteins with both folded and disordered regions, how we benchmarked the method using data for hundreds of proteins, and how the speed and scalability enables us to construct conformational ensembles at the proteome level [11].

References

  1. Holehouse, A. S., & Kragelund, B. B. (2023). The molecular basis for cellular function of intrinsically disordered protein regions. Nature Reviews Molecular Cell Biology, 1-25.
  2. Lindorff-Larsen, K., & Kragelund, B. B. (2021). On the potential of machine learning to examine the relationship between sequence, structure, dynamics and function of intrinsically disordered proteins. Journal of Molecular Biology, 433(20), 167196.
  3. von Bülow, S., Tesei, G., & Lindorff-Larsen, K. (2025). Machine learning methods to study sequence–ensemble–function relationships in disordered proteins. Current Opinion in Structural Biology, 92, 103028.
  4. Norgaard, A. B., Ferkinghoff-Borg, J., & Lindorff-Larsen, K. (2008). Experimental parameterization of an energy function for the simulation of unfolded proteins. Biophysical Journal, 94(1), 182-192.
  5. Tesei, G., Schulze, T. K., Crehuet, R., & Lindorff-Larsen, K. (2021). Accurate model of liquid–liquid phase behavior of intrinsically disordered proteins from optimization of single-chain properties. Proceedings of the National Academy of Sciences, 118(44), e2111696118.
  6. Tesei, G., & Lindorff-Larsen, K. (2023). Improved predictions of phase behaviour of intrinsically disordered proteins by tuning the interaction range. Open Research Europe, 2, 94.
  7. Cao, F., von Bülow, S., Tesei, G., & Lindorff‐Larsen, K. (2024). A coarse‐grained model for disordered and multi‐domain proteins. Protein Science, 33, e5172.
  8. Yasuda, I., von Bülow, S., Tesei, G., Yamamoto, E., Yasuoka, K., & Lindorff-Larsen, K. (2025). Coarse-grained model of disordered RNA for simulations of biomolecular condensates. Journal of chemical theory and computation, 21(5), 2766-2779.
  9. von Bülow, S., Tesei, G., Zaidi, F. K., Mittag, T., & Lindorff-Larsen, K. (2025). Prediction of phase-separation propensities of disordered proteins from sequence. Proceedings of the National Academy of Sciences, 122(13), 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. von Bülow, S., Johansson, K. E., & Lindorff-Larsen, K. (2025). AF-CALVADOS: AlphaFold-guided simulations of multi-domain proteins at the proteome level. bioRxiv, 2025-10.

Presenters

Kresten Lindorff-Larsen

Kresten 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 and is the director of the PRISM (Protein Interactions and Stability in Medicine and Genomics) centre.

Kresten received the Danish Independent Research Councils’ Young Researchers’ Award in 2006, was a co-recipient of the 2009 Gordon Bell Prize and the 2025 Novo Nordisk Foundation’s Prize for Natural Science Teachers at Universities, and has received several prestigious grants including a Hallas-Møller stipend (2011), a Sapere Aude starting grant (2012), a Novo Nordisk Foundation challenge programme grant (2019), and an ERC Synergy Grant (2023).

He was elected as a fellow of the Biophysical Society in 2024 and the same year he also became a member of the Royal Danish Academy of Sciences and Letters. Current research interests include developing and applying computational methods for studying the structure and dynamics of proteins, and the integration of biophysics and genomics research.