Chemical cooperativity is a widespread yet poorly understood phenomenon

Multicomponent systems are very common in biochemistry: from allosteric modulation to organelle assembly, the underlying principle is the simultaneous interaction of more than two partners. Often mutual affinities are extremely affected by the presence of the remaining members of the multicomponent complex. This phenomenon, known as cooperativity, is widespread and impacts many biological processes, yet its molecular determinants are poorly understood.

A relevant case of this is the ability of some small molecules to stabilize the interaction between macromolecules, acting as so-called molecular glues. This mechanism is often exploited by nature to modulate biological responses, and a few noteworthy examples have been developed into drugs. Nevertheless,our lack of understanding of the cooperativity phenomenon has to date prevented the purposeful and reliable development of molecular glues. This research project aims at developing new computational tools that will grant better understanding the cooperativity phenomenon and enable its exploitation for drug design purposes.

Targeted protein degradation exemplifies the potential of cooperativity to impact human health

The development of small molecules able to selectively trigger protein degradation by hijacking the Ubiquitin-Proteasome System (UPS) is a frontier challenge in current pharmaceutical sciences. A crucial step for a molecule to trigger protein degradation is to bring the target protein into close proximity of an E3 ubiquitin ligase protein in order to be tagged by polyubiquitination and most protein degraders act as cooperative stabilizers of the multicomponent interaction between the substrate protein and the E3 ligase complex. Therefore, only a better understanding of the cooperativity phenomenon will allow us to fully leverage the potential of targeted protein degradation to impact human health.

The successful candidate will contribute to the project development by combining computational structure-based techniques (Virtual Screening, Molecular Dynamics,.) and knowledge-based approaches (supervised and unsupervised ML). In this sense, this project represents an optimal training environment for a predoctoral researcher, who will receive advanced training through research in the following subjects:

• Computer-Aided Drug Design techniques.
• Molecular Dynamics Simulations and enhanced sampling techniques
• Medicinal Chemistry implications in Drug Design.
• Physical chemistry principles and applications to molecular simulations.
• Good practices in open-source software development.
• Development of hybrid ML/MD methods.

Additionally, the project offers opportunities for the candidate to undertake short term placements in biophysical labs, which will perfectly complement a strong computational background, therefore building a multidisciplinary profile that is coveted both in industry and academic positions.

Moreover, through the development of the project, the fellow will be trained in a variety of transferable skills in order to widen their future career opportunities. Specifically, the candidate will be responsible for developing their own scripts for data production and analysis, which is a highly transferable skill with applications outside the field of computational chemistry.  Furthermore, the fellow will be actively encouraged to present their work at international conferences either as poster presentations or oral communications, in order to develop their communication and networking skills. Finally, to foster decision-making and critical thinking, they will be encouraged to pursue side projects of their choosing with the support of the PI.

Applications from underrepresented collectives are especially encouraged, as the CMD lab is a safe and welcoming environment for everybody regardless of gender, sexual orientation, religion or ethnicity.