Biocatalysis: chemical physics of enzymatic reactions
Doctoral study programme Materials
Objectives and competences
The aim of the course is to provide students with theoretical knowledge about the chemical physics of biomolecular (particularly enzymatic) reactions and about molecular simulation tools that can be used to elucidate the catalytic function of enzymes. Students will also gain modeling skills and apply it to a selected use case.
From examples of the application of theoretical knowledge in the design of new biomolecules or materials with desired properties, students will be able to place the modeling in a broader context and may also be able to use it in research work. This will significantly increase the interdisciplinarity of their research. With the acquired computational skills and work on a computer cluster, their knowledge and competences will be further expanded.
(1) Introduction to enzyme catalysis: typical examples of enzymatic reactions; two hypotheses on the driving force behind enzyme catalysis (preorganized electrostatics and dynamical effects); transition state theory.
(2) Basic concepts of molecular simulation: quantum chemistry (Schrödinger equation, electronic structure, Born-Oppenheimer surface, population analysis); molecular mechanics (empirical force fields, non-bonding interactions); multiscale modeling (ab initio QM/MM, Empirical Valence bond QM/MM); molecular dynamics (integration of equations of motion, temperature & pressure control, long range electrostatics, equilibration & production phase & analysis); phase space sampling (folding problem, reaction phase space, reactive force fields, ensemble averages, free energy calculations, advanced sampling techniques).
(3) Applications: analysis of electric fields in the enzyme; electrostatically guided enzyme design; design of biomolecules & materials based on electrostatic properties; drug design; understanding of pathologies associated with enzyme mutations; enzymes as anti-pollutant and decontamination agents.
(4) Hands-on tutorial: essentials in HPC/Linux; basic quantum chemistry protocols (geometry optimization, harmonic analysis, charge distribution analysis, potential energy surface scan, transition state optimization, reaction path search, gas phase & implicit solvation models); essentials in classical & EVB simulations (equilibration, sampling, calculation of free energy profiles, solvation effects).
(5) Individual work: written seminar on a featured topic related to biocatalysis; AND/OR modeling project (characterization of a selected reaction using modeling tools). The project can be tailored according to the student’s PhD program.
Intended learning outcomes
• Knowledge of physical background of enzyme catalysis.
• Knowledge of molecular simulation tools and their use in the research and design of biomolecules and biologically relevant materials.
• Basic molecular modeling skills.
• The use of simulation results for elucidation of catalytic and other functional properties of biomolecules and biomaterials.
• Improved research communication (written and oral presentation of project results).
• Familiarity with Linux environment and basic work skills on computer cluster / supercomputer (HPC environment).
• A. Warshel, Computer Modeling of Chemical Reactions in Enzymes and Solutions, J. Wiley & Sons, Inc., New York, 1991.
• J. B. Foresman, Æ Frisch, Exploring Chemistry with Electronic Structure Methods, 3rd ed., Gaussian, Inc.: Wallingford, CT, 2015. E-version
• Y. Kulkarni, S. C. L. Kamerlin, Computational physical organic chemistry using the empirical valence bond approach, Adv. Phys. Org. Chem. 2019, 53, 69-104. https://doi.org/10.1016/bs.apoc.2019.07.001
• V. V. Welborn, L. Ruiz Pestana, T. Head-Gordon, Computational Optimization of Electric Fields for Better Catalysis Design. Nat. Catal. 2018, 1, 649-655. https://doi.org/10.1038/s41929-018-0109-2
• A. Warshel, R. P. Bora, Perspective: Defining and quantifying the role of dynamics in enzyme catalysis, J. Chem. Phys. 2016, 144, 180901-1-17. https://doi.org/10.1063/1.4947037 E-version
• G. Jindal et al., Exploring the challenges of computational enzyme design by rebuilding the active site of a dehalogenase, PNAS 2019, 116, 389-394. https://doi.org/10.1073/pnas.1804979115 E-version
The literature will be regularly updated by including recent review articles and web pages related to this course, published in last five years.
Oral exam, Written project report / seminar, Presentation of project & discussion. 40/30/30
Janez Mavri is head of the Theoretical Department at the National Institute of Chemistry. At the same time he is head of Laboratory for Computational Biochemistry and Drug Design and leader of the program group "Molecular Simulations, Bioinformatics and Drug Design" at the Slovenian Research Agency. The central part of the research activity of dr. Mavri are biomolecular simulations of enzyme and receptor activity, focusing in particular on the monoaminergic system and the metabolism of neurotransmitters dopamine, serotonin, norepinephrine, and neurologically active endogenous and synthetic analogs in the central nervous system. Investigations of monoaminergic system and the reactions involved there are important in clinical and molecular medicine and pharmacology. New findings delivered by the research of dr. Mavri and his group provide links between chemical physics and medical sciences, which is of great importance for the understanding of clinical effects at the molecular level and is a good example of interdisciplinarity.
Janez Mavri graduated from the Faculty of Chemistry and Chemical Technology of the University of Ljubljana in 1987 and received his PhD there in 1992. The topic of the doctoral thesis was short hydrogen bonds, which he studied with molecular modeling tools under supervision of acad. prof. dr. Dušan Hadži. Theoretical research on the hydrogen bond was the leading part of his activities in next ten years, including postdoctoral training at the University of Groningen, the Netherlands with prof. H. J. C. Berendsen.
In 2004, dr. Mavri focused on investigations of the function of enzymes using multiscale simulation methods. As a Fullbright Scholar, he was a guest at the University of Southern California with prof. Arieh Warshel, who later became Nobel Prize Laureate in Chemistry. He transferred state-of-the art simulation techniques for the studies of enzymatic reactions to the Slovenian research environment, where he and his research group upgraded and improved them and placed them in the context of biomedical issues.
Janez Mavri leads or. has led several research projects, including the ARRS program over several periods, and has also hosted an individual Marie Curie project. Since 2006, he has held the research title of Scientific Councellor.
Pedagogical work of dr. Mavri includes mentoring or. co-mentorship of six doctoral students and eleven undergraduate or masters students. Since 2019, dr. Mavri is a full professor of pharmaceutical chemistry at the Faculty of Pharmacy, University of Ljubljana.
Janez Mavri is the author of 117 original scientific papers that collected 2128 pure citations, h-index = 28.
- R. Vianello, M. Repič and J. Mavri, How are biogenic amines metabolized by monoamine oxidases? Eur. J. Org. Chem. (2012) 7057-7065. IF=3,34
- A. Prah, E. Frančišković, J. Mavri, J. Stare, Electrostatics as the Driving Force Behind the Catalytic Function of the Monoamine Oxidase A Enzyme Confirmed by Quantum Computations, ACS Catal. 9 (2019) 1231-1240. IF=12,35
- M. Kržan, J. Keuschler, J. Mavri, R. Vianello, Relevance of Hydrogen Bonds for the Histamine H2 Receptor-Ligand Interactions: A Lesson from Deuteration, Molecules 10 (2020) 196. IF= 4,08
- M. Pavlin, M. Repič, R. Vianello, J. Mavri, The Chemistry of Neurodegeneration: Kinetic Data and Their Implications, Mol. Neurobiol. 53 (2016) 3400–3415. IF=6,19
- R. Borštnar, M. Repič, S.C.L. Kamerlin, R. Vianello, and J. Mavri, Computational Study of the pKa Values of Potential Catalytic Residues in the Active Site of Monoamine Oxidase B, J. Chem. Theor. Comput. 8 (2012) 3864–3870. IF=5,39
University course code: 3MAi13
Year of study: 1. year
- Lectures: 30 hours
- Exercises: 30 hours
- Individual work: 210 hours
Course kind: elective
Languages: slovene, english
Learning and teaching methods:
lectures, hands-on tutorials, individual project work / seminar, communication and discussion of project results with the lecturer and other students.