Computational studies of metalloproteins, crystallography and ligand binding
||Computational studies of metalloproteins, crystallography and ligand binding|
||SNIC Large Storage|
||Ulf Ryde <firstname.lastname@example.org>|
||2022-01-01 – 2023-01-01|
||10407 10602 30103|
We will develop and apply theoretical methods for the study of structure and function of metalloproteins with high scientific, medicinal and industrial interest. For example, will study the reaction mechanisms of nitrogenases, lytic polysaccharide monooxygenases and particulate methane monooxygenases. We will also calibrate methods to calculate reduction potentials and acid constants in proteins. We will test and develop accurate multireference quantum mechanical methods to use for metal cofactors, e.g. to decide what density functional methods give 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 will 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 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 receptor (a protein or a nucleic acid). 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.