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Integrative, AI-aided Inference of Protein Structure and Dynamics

Project description

A deeper exploration of protein dynamics

Unlocking the secrets of biological functions requires more than static protein structures. While previous research has elevated structure prediction, understanding the intricate conformational dynamics and environmental influences remains elusive. Proteins don’t perform in isolation but thrive in the complexity of cells. In this context, the ERC-funded bAIes project addresses this gap. The aim of the project is to unravel the conformational landscape of proteins, their relevant states, and pathways. Unlike AI-alone methods, bAIes tackles complexities like protein disordered regions and in situ dynamics, with a focus on SARS-CoV-2’s spike protein. The outcome: a versatile, efficient method pushing AI structure-prediction boundaries. This innovation promises transformative impacts across diverse biological systems.

Objective

The life sciences community is living in exciting times. During the past year, Artificial Intelligence (AI), and in particular AlphaFold2, has contributed to advancing our understanding of protein behaviour by enabling structure prediction with accuracy comparable to many experimental techniques at a fraction of their time and costs. However, structures are only a piece of the puzzle. To understand the mechanisms underlying biological functions, we need to characterize the conformational landscape of proteins, the population of relevant states, and their pathways of interconversion. Furthermore, we need to determine the effect of the environment in modulating structures, populations, and pathways, as biological systems perform their functions in the complexity of cells rather than in the isolation of test tubes. None of these objectives can be achieved by AI structure-prediction methods alone. In this proposal we will leverage the PIs expertise in the field of integrative computational-experimental techniques to develop, apply, and disseminate bAIes, a modelling approach that will enable attaining these goals. bAIes will make synergistic use of AI structural models, experimental data, and molecular simulations driven by accurate physico-chemical models to characterize protein structure and dynamics. We will demonstrate how bAIes can solve biological problems that exceed the capabilities of AI approaches, such as the characterization of protein disordered regions and the determination of structure and dynamics in situ, with a particular focus on the SARS-CoV-2 spike protein. The outcome of this proposal will be a versatile, accurate and efficient method that will push the boundaries of what can be achieved with AI structure-prediction methods. bAIes will be implemented in the widely used PLUMED library, of which the PI is founder and core developer, thus enabling its application to a wide variety of systems and biological problems beyond those envisioned here.

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) ERC-2022-COG

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Host institution

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 2 932 775,00
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 2 932 775,00

Beneficiaries (1)

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