Computational studies of metalloproteins, crystallography and ligand binding
||Computational studies of metalloproteins, crystallography and ligand binding|
||SNIC Large Compute|
||Ulf Ryde <firstname.lastname@example.org>|
||2023-01-01 – 2024-01-01|
||10407 10602 30103|
We develop and apply theoretical methods for the study of structure and function of metalloproteins with high scientific, medicinal and industrial interest. For example, we study the reaction mechanisms of nitrogenases, lytic polysaccharide monooxygenase, particulate methane monooxygenase and triose phosphate isomerase. We will also calibrate methods to calculate reduction potentials and acid constants in proteins. We will test and develop accurate quantum mechanical methods (coupled cluster, selected CI and quantum Monte Carlo) to use for metal cofactors, e.g. to decide which density functional method gives the most reliable results. The project builds on the unique methods developed in our group, viz. methods to combine quantum mechanical (QM) and molecular mechanics (MM) calculations, as well as method to calculate free energies at the QM/MM level. In particular, we use calculations with very big QM systems (600–1200 atoms) to obtain stable energies.
X-ray crystallography is the main source of structural information for proteins. With the European Spallation Source (ESS), the hope is that neutron crystallography will become an important complement. We have long developed methods to use computational chemistry to interpret, complement and improve macromolecular crystal structures. We will continue this work by applying quantum refinement to structures of high scientific interest. We will extend this approach to cryo-electron-microscopy structures and to time-resolved serial crystallography. We will also develop methods to simplify and speed up the refinement of crystal structures, e.g. by identifying positions of deuterons in neutron structures or water molecules in X-ray structures.
We will also develop and improve methods to predict the binding free energy of drug candidates to their macromolecular receptor. This is one of the greatest challenges in drug development: If the binding affinity could be accurately predicted, the synthesis of most of the drug candidates could be avoided, which could save an enormous amount of money and time. We will improve free-energy perturbations by improving the potential-energy function using QM methods. We will also develop approaches based on QM or QM/MM geometry optimisations.