Molecular Modelling and Drug Discovery16 Mai 2023

Training Neural Network Potentials: Bayesian and Simulation-based Approaches | Stephan Thaler

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Abstract: Cryptic pockets, which are absent in ligand-free structures and have the potential to be used as drug targets, are often challenging to access through conventional biomolecular simulations due to their slow motions. To overcome this limitation, we have combined AlphaFold and Markov State modelling (MSM) to accelerate the discovery of cryptic pockets. AlphaFold was used to generate a diverse structural ensemble with open or partially open pockets that can serve as starting points for molecular dynamics simulations which were later stitched together using MSM to predict free energy and kinetics associated with cryptic pocket opening. Our approach explored known cryptic pockets, as well as discovered new cryptic pockets which were absent in PDB. Our study highlighted the power of AlphaFold and MSM to discover novel cryptic pockets which can unlock development of next-gen therapeutics.

Speaker: Stephan Thaler

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Episoder(60)

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20 Jun 20231h

Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics | Albert Musaelian

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Multimodal Deep Learning for Protein Engineering | Kevin K. Yang

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Systematic Analysis of Biomolecular Conformational Ensembles with PENSA | Martin Vögele

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30 Mai 202350min

Accelerating Cryptic Pocket Discovery Using Alphafold and Markov State Modelling | Soumendranath Bhakat

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Protein Representation Learning by Geometric Structure Pretraining | Zuobai Zhang

[DISCLAIMER] - For the full visual experience, we recommend you tune in through our ⁠⁠⁠⁠YouTube channel ⁠⁠⁠⁠to see the presented slides. Try datamol.io - the open source toolkit that simplifies molecu...

19 Apr 202353min