About event
The seminar will be onsite and also live-streamed on this link.
This is part of the Principal Investigator Seminar Series.
Abstract
Transient Kinetics, Microfluidics and Machine Learning in Protein Engineering
Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
International Centre for Clinical Research, St. Ann’s Hospital, Brno, Czech Republic
In recent years, the trend in miniaturization and automation of laboratory processes has emerged in life sciences and biomedicine leding to the development of lab-on-a-chip microfluidic systems.[1] Microfluidic devices can perform thousands of analyzes per second, and in a combinatorial manner, they can analyze reaction volumes ranging from nano- to femtoliters. Microfluidics thus significantly outperforms the conventional analytical techniques in terms of throughput and time, and sample consumption. While the potential of microfluidic methods is already well exploited in nucleic acid analysis and screening of large metagenomic and directed evolution libraries,[2] there is still room for broader applications in high-throughput biochemical characterization. The lecture will focus on droplet microfluidics methods and their utilisation in transient kinetic analysis providing detailed insight into the mechanisms of enzyme catalysis[3] and protein stability[4]. The presented examples will show that synergy in the combination of high-throughput microfluidic analysis, modern numerical methods for global data analysis, and molecular modelling provide a deep understanding of the specific protein property essential for its rational engineering. The lecture will also focuse the perspective potential of combining automated data collection by microfluidics with machine learning methods.[5]
References:
[1] G. M. Whitesides. The origins and the future of microfluidics, Nature 2006, 442, 368–373.
[2] E. M. Payne, D. A. Holland-Moritz, S. Sun and R. T. Kennedy. High-throughput screening by droplet microfluidics: perspective into key challenges and future prospects, Lab Chip 2020, 20, 2247-2262.
[3] D. Hess, V. Dockalova, P. Kokkonen, D. Bednar, J. Damborsky, A. deMello, Z. Prokop, S. Stavrakis. Exploring Mechanism of Enzyme Catalysis by On-Chip Transient Kinetics Coupled with Global Data Analysis and Molecular Modelling, Chem 2021, 7, 1066-1079.
[4] T. Yang, A. Villois, A. Kunka, F. Grigolato, P. Arosio, Z. Prokop, A. deMello, S. Stavrakis. A Microfluidic Temperature Jump Droplet-based Microfluidic Reactor for Rapid Biomolecular Kinetics, under review.
[5] S. Mazurenko, Z. Prokop, J. Damborsky. Machine Learning in Enzyme Engineering, ACS Catalysis 2019, 10, 1210-1223.
Web: https://loschmidt.chemi.muni.cz/peg/
Correspondence address: zbynek@chemi.muni.cz
