Machine Learning-Assisted simulation of Metalloenzyme’s Reactivity

Metalloenzymes play a crucial role in various biological functions, going from small molecule transportation to catalyzing essential metabolic ingredients.

Understanding their reactivity is essential for advancing a broad range of fields and related industries, such as medicine, biotechnology, environmental science, and catalysis.

The computational modelling of biomolecules is a pillar of new drug and catalyst design, but the common methods used for purely organic-based compounds, such as empirical force fields, cannot simply be applied in the presence of a metal centre due to their complex electronic structure.

On the other hand, hybrid Quantum Mechanical/Molecular Mechanics (QM/MM) methods provide a way to study metalloenzymes, but their computational overheads prevent their large-scale use for molecular dynamics simulations and reactivity studies.

The project MAchine Learning-Assisted simulation of MEtalloenzyme’s Reactivity (MALAMER), aims to revolutionize the simulation of metalloenzymes' reactivity by integrating machine-learning force fields with QM/MM methods, and advanced sampling techniques, allowing for an unprecedentedly accurate description of their activity.

This innovative approach will lead to significant insights and applications in catalyst development and drug discovery, with a potential impact on several EU priority areas. The project will commence in September 2024 under the supervision of Prof.

Lunghi at Trinity College Dublin, with a planned 3-month secondment at University College London to gain additional expertise in advanced sampling simulations from Prof. Salvalaglio.

A comprehensive set of training, dissemination, exploitation, and communication activities are planned and they will be implemented with the help of the supervisors and Trinity College Dublin's human resources, training office, and innovation and technology transfer office.