Summary
Complete understanding of how complex biological objects operate and fulfill their cellular role requires at its core a detailed picture of the millisecond (ms) conformational transitions between functional states. Computational investigation of such ms–events is thwarted by our difficulty—and often impossibility—to identify and sample efficiently the relevant degrees of freedom at play, as well as the current limitation of all-atom molecular dynamics to the microsecond (μs) timescale on common computer architectures. Guided by concrete biological questions that experiment alone has hitherto proven unable to address, we propose a holistic approach to bridge affordable μs–computer simulations and ms–biological processes without the aid of a special-purpose supercomputer. To meet this grand theoretical challenge, we will associate two powerful developments to make the quantum leap, and open a breadth of applications, scaling up to very large biological objects, so far inaccessible to μs–timescale computer simulations. First, we will determine the reaction coordinate in an unprecedented combination of data- driven discovery of collective variables and advanced algorithms to find the minimum free-energy pathway that connects the end states of the conformational transition. Second, we will accelerate sampling along this pathway by associating ergodic schemes to a novel approach that maps complex free-energy landscapes significantly faster than its competitors. We will apply this methodology to a V1Vo–ATPase, a complete ATP–driven biological motor that converts over the ms–timescale the chemical energy of ATP hydrolysis into mechanical work, with minimalist dissipation. Beyond illuminating the allosteric pathways that underlie the conformational transition, atomic-level description of the rotary-catalysis milestones will shed new light on the effects of pathological mutations altering ATP activity, while helping engineer artificial cells with accelerated ATP turnover.
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| Web resources: | https://cordis.europa.eu/project/id/101097272 |
| Start date: | 01-12-2023 |
| End date: | 30-11-2028 |
| Total budget - Public funding: | 2 134 529,00 Euro - 2 134 529,00 Euro |
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Original description
Complete understanding of how complex biological objects operate and fulfill their cellular role requires at its core a detailed picture of the millisecond (ms) conformational transitions between functional states. Computational investigation of such ms–events is thwarted by our difficulty—and often impossibility—to identify and sample efficiently the relevant degrees of freedom at play, as well as the current limitation of all-atom molecular dynamics to the microsecond (μs) timescale on common computer architectures. Guided by concrete biological questions that experiment alone has hitherto proven unable to address, we propose a holistic approach to bridge affordable μs–computer simulations and ms–biological processes without the aid of a special-purpose supercomputer. To meet this grand theoretical challenge, we will associate two powerful developments to make the quantum leap, and open a breadth of applications, scaling up to very large biological objects, so far inaccessible to μs–timescale computer simulations. First, we will determine the reaction coordinate in an unprecedented combination of data- driven discovery of collective variables and advanced algorithms to find the minimum free-energy pathway that connects the end states of the conformational transition. Second, we will accelerate sampling along this pathway by associating ergodic schemes to a novel approach that maps complex free-energy landscapes significantly faster than its competitors. We will apply this methodology to a V1Vo–ATPase, a complete ATP–driven biological motor that converts over the ms–timescale the chemical energy of ATP hydrolysis into mechanical work, with minimalist dissipation. Beyond illuminating the allosteric pathways that underlie the conformational transition, atomic-level description of the rotary-catalysis milestones will shed new light on the effects of pathological mutations altering ATP activity, while helping engineer artificial cells with accelerated ATP turnover.Status
SIGNEDCall topic
ERC-2022-ADGUpdate Date
12-03-2024
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