Modelling based protein crystallization – molecular dynamic (MD) simulation
Protein crystallisation has been used for decades to determine the structure of proteins. What is new, however, is technical protein crystallization for purification or final formulation on an industrial scale as an alternative to chromatography. Since the molecular mechanisms of protein crystallisation can only be described incompletely so far, crystallisation conditions can only be determined empirically with great experimental expense. Despite this high effort, many technically relevant proteins often cannot be crystallized.
Predicting crystallizability of mutants with MD simulations and free energy perturbation (FEP) methods is a novel approach and could dramatically reduce the time of current empirical approaches to find and select promising mutants. Based on data from X-ray diffraction experiments the 3D structure of protein crystals is resolved. This structure is used as the input for Molecular Dynamics (MD) simulations that utilize FEP methods to test whether in silico mutations at crystal contacts lead to improved crystallizability. This approach can be used to test and validate crystallization strategies. Current work focuses on refinement of X-ray diffraction experiments, MD simulation using FEP methods and automating setup and analysis of large datasets of super cell simulations. Since immense computational resources are required the SuperMUC-NG supercomputer as well as a dedicated GPU cluster are utilized.
In this way, the empirical procedure for the identification of suitable crystallization conditions of proteins, which has been standard practice to date, is to be supplemented by a modeling-based approach. In interdisciplinary collaboration, methods from molecular biology and bioprocess engineering (research project of Brigitte Walla) will be combined with methods from theoretical biophysics (this research project).
- Walla B, Bischoff D, Corona Viramontes I, Montes Figueredo S, Weuster-Botz D (2023): Recent advances in the monitoring of protein crystallization processes in downstream processing. Crystals 13: 773.
- Bischoff D, Walla B, Weuster-Botz D (2022): Machine-learning based protein crystal detection for monitoring of crystallization processes enabled with large-scale synthetic data sets of photorealistic images. Analyt Bioanalyt Chem 414: 6379-6391.
- Walla B, Bischoff D, Janowski R, von den Eichen N, Niessing D, Weuster-Botz D (2021): Transfer of a rational crystal contact engineering strategy between diverse alcohol dehydrogenases. Crystals 11: 975.
- Hermann J, Bischoff D, Grob P, Janowski R, Hekmat D, Niessing D, Zacharias M, Weuster-Botz D (2021): Controlling protein crystallization by free energy guided design of interactions at crystal contacts. Crystals 11: 588.