Computational studies of metalloproteins, crystallography and ligand binding
Title: Computational studies of metalloproteins, crystallography and ligand binding
DNr: NAISS 2024/4-21
Project Type: NAISS Large Storage
Principal Investigator: Ulf Ryde <ulf.ryde@compchem.lu.se>
Affiliation: Lunds universitet
Duration: 2025-01-01 – 2026-01-01
Classification: 10407 10408 10601
Homepage: https://www.compchem.lu.se/research/research-groups/ulf-ryde/
Keywords:

Abstract

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, particulate methane monooxygenase and DNA polymerase. 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 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 (called quantum refinement) 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 X-ray free-electron laser, electron diffraction and 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 also develop and improve methods to predict the binding free energy of drug candidates to a 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.