| Project Detail |
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 PI’s 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. |
