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Instantaneous Flexible and Coarse-Grained Docking based on Novel Deep Neural Network Approaches

Research Project
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01.12.2020
 - 30.11.2024

The goal of this proposal is to develop novel computational methods for flexible docking combining modern deep neural network approaches with detailed physicochemical models of protein-ligand complexes with consideration of hydration phenomena. The methodology includes completely novel concepts such as the prediction of intermolecular distance matrices based on graph-convolutional methods, efficient coarse-grained docking to implicitly model protein flexibility, and on-the-spot prediction of peptide configurations binding to protein surfaces. The methodology will be retrospectively validated and prospectively applied together with our experimental collaborator. The developed concepts have broad impact to drug discovery, for example for the design of peptides as protein-protein interaction modulators or for rapid structure-based vaccine design, a task of crucial need in context of the current COVID-19 pandemic.

Funding

Instantaneous Flexible and Coarse-Grained Docking based on Novel Deep Neural Network Approaches

SNF Projekt (GrantsTool), 12.2020-11.2024 (48)
PI : Lill, Markus A..

Members (4)

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Markus A. Lill

Principal Investigator
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Daniel Ricklin

Co-Investigator
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Justin Diamond

Project Member
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Florian Benjamin Hinz

Project Member