Peter Chen, Professor of Physical-Organic Chemistry, will be delivering a farewell lecture to mark his upcoming retirement. Chen is a man with a remarkable history who has played a significant role in...

The 2025 call for applications for the NOMIS–ETH Fellowship Programme within the Centre for Origin and Prevalence of Life is open. Researchers are invited to apply until 7 November 2025, at 17:00 Zuri...

Die Nacht wird zum Tag! Entdecke die ausserordentliche Vielfalt der Zürcher Museen am 7. September von 18-02 Uhr.

Protein–protein interactions (PPIs) play an essential role in biological processes. Molecules that stabilize or induce PPIs in ternary complexes have received growing attention for their therapeutic potential in engaging “undruggable” targets and their high selectivity. Here, we investigate the thermodynamics of the cooperative phenomenon in ternary complexes. The thermodynamics of cooperativity are characterized by the cooperative free energy, which comprises induced PPIs, cooperative solvation free energy, ligand-associated geometric free-energy costs, and gas-phase correlation. Importantly, the induced PPIs only account for the binding affinity between stabilized conformations of the protein partners, i.e., the free-energy change associated with the conformational transition during protein–ligand binding is not accounted for. By introducing an approximated expression for the cooperative free energy, we developed a rapid computational method, which allowed us to crudely predict cooperativity in eight ternary complexes (Kendall τ = 0.79). We highlight that the term cooperativity used in protein–protein stabilization does not represent the cooperativity phenomenon in three-body systems. We also critically discuss the counterintuitive interpretation of cooperative free energy due to its asymmetric nature. Our study shows how cooperativity stabilizes ternary complexes and provides a thermodynamic basis of cooperativity in protein–ligand–protein complexes.

The conformational ensemble of a molecule is strongly influenced by the surrounding environment. Correctly modeling the effect of any given environment is, hence, of pivotal importance in computationa...

Calculating free-energy differences using molecular dynamics (MD) simulations is an important task in computational chemistry. In practice, the accuracy of the results is limited by model approximatio...

Zurich has a prominent place in the history of quantum science. We profile a few of the protagonists who shaped the field over the past century — with many more stories yet to be told.

Understanding and manipulating the conformational behavior of a molecule in different solvent environments is of great interest in the fields of drug discovery and organic synthesis. Molecular dynamics (MD) simulations with solvent molecules explicitly present are the gold standard to compute such conformational ensembles (within the accuracy of the underlying force field), complementing experimental findings and supporting their interpretation. However, conventional methods often face challenges related to computational cost (explicit solvent) or accuracy (implicit solvent). Here, we showcase how our graph neural network (GNN)-based implicit solvent (GNNIS) approach can be used to rapidly compute small molecule conformational ensembles in 39 common organic solvents reproducing explicit-solvent simulations with high accuracy. We validate this approach using nuclear magnetic resonance (NMR) measurements, thus identifying the conformers contributing most to the experimental observable. The method allows the time required to accurately predict conformational ensembles to be reduced from days to minutes while achieving results within one kBT of the experimental values.