Doing Charges Right: Modelling Ion-Controlled Biological Processes with the Correct Toolbox
Develop a machine learning-based force field that incorporates electronic polarization to enhance ion modeling in biological systems, improving understanding and applications in various ion-related processes.
Projectdetails
Introduction
Electrical stimuli are essential for a plethora of biological functions. Unlike in electronics, where electrons form currents, nature rather exploits ions as charge carriers. Lack of a consistent molecular picture of the action of ions impairs progress in fundamental understanding of ion-controlled biological processes and in designing smart strategies for fixing ion-related pathological conditions.
Molecular Simulations
Molecular simulations represent a powerful tool for modelling such processes; however, they can only be as good as the underlying interaction model (force field). A major drawback of commonly used force fields is the lack of description of electronic polarization, which results in severe artifacts such as a dramatic over-binding of ions. This prevents, for example, accurate modelling of calcium signalling processes. This now well-recognized deficiency hampers faithful modelling of complex ion-involving biological processes.
Proposed Solution
We will employ machine learning techniques to build a de novo comprehensive force field for biological systems that accounts for electronic polarization in a mean field way via charge scaling. This approach will qualitatively improve modelling of ions in biological contexts without additional computational costs.
Relevant Ion-Specific Processes
This will allow us to address accurately the following highly relevant ion-specific processes of increasing complexity from molecular over cellular to organ levels:
- Dissolution of radical anions of aromatic molecules as key intermediates in technologically and biologically important non-enzymatic and enzymatic Birch reduction processes.
- Direct membrane translocation of cationic cell-penetrating peptides with a potential for drug delivery.
- Circulation of calcium ions as signalling charge carriers through ion channels of hair cells in the cochlea.
Community Contribution
At the same time, the newly developed charge scaled force field will be made freely available to the community for further development and ready to be used within major simulation program packages.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 2.499.115 |
Totale projectbegroting | € 2.499.115 |
Tijdlijn
Startdatum | 1-10-2023 |
Einddatum | 30-9-2028 |
Subsidiejaar | 2023 |
Partners & Locaties
Projectpartners
- USTAV ORGANICKE CHEMIE A BIOCHEMIE, AV CR, V.V.I.penvoerder
Land(en)
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Field-Theory Approach to Molecular Interactions
The FITMOL project aims to revolutionize modeling of large molecular complexes through a new field-theory approach, enhancing accuracy and efficiency in quantum calculations for intricate biological systems.
Metal-Induced Energy Transfer based Electrometry and Nanometry: Dissecting Electrostatic Phenomena in Biological Processes
The project aims to develop MIETEN technology to quantitatively measure biomolecule and membrane electrical charges, enhancing our understanding of biological processes and advancing biomedical research.
Probing and controlling ultrafast electron and ion dynamics in operating battery electrodes and interfaces
FemtoCharge aims to elucidate ultrafast interfacial dynamics in batteries using femtosecond spectroscopy to enhance charge transport and develop new electrode/electrolyte materials.
A holistic approach to bridge the gap between microsecond computer simulations and millisecond biological events
This project aims to bridge μs computer simulations and ms biological processes by developing methods to analyze conformational transitions in V1Vo–ATPase, enhancing understanding of ATP-driven mechanisms.
Bottom-up assembly of synthetic neural networks from biological matter
The project aims to construct synthetic neural networks from biological materials by studying action potential propagation in lipid nanotubes to advance sustainable computing and understanding of biological networks.