In Situ Microscopy with Real-Time Image Analysis Enables Online Monitoring of Technical Protein Crystallization Kinetics in Stirred Crystallizers

Controlling protein crystallization processes is essential for improving downstream processing in biotechnology. This study investigates the combination of machine learning-based image analysis and in situ microscopy for real-time monitoring of protein crystallization kinetics. The experimental research is focused on the batch crystallization of an alcohol dehydrogenase from Lactobacillus brevis (LbADH) and two selected rational crystal contact mutants. Technical protein crystallization experiments were performed in a 1 L stirred crystallizer by adding polyethyleneglycol 550 monomethyl ether (PEG 550 MME). The estimated crystal volumes from online microscopy correlated well with the offline measured protein concentrations in solution. In addition, in situ microscopy was superior to offline data if amorphous protein precipitation occurred. Real-time image analysis provides the data basis for online estimation of important batch crystallization performance indicators like yield, crystallization kinetics, crystal size distributions, and number of protein crystals. Surprisingly, one of the LbADH mutants, which should theoretically crystallize more slowly than the wild type based on molecular dynamics (MD) simulations, showed better crystallization performance except for the yield. Thus, online monitoring of scalable protein crystallization processes with in situ microscopy and real-time image analysis improves the precision of crystallization studies for industrial settings by providing comprehensive data, reducing the limitations of traditional analytical techniques, and enabling new insights into protein crystallization process dynamics.