Chemoinformatics

Study programme: Undergraduate study “Biotechnology and drug research”

Course code: BIL307

ECTS points: 3

Course language: Croatian

Course prerequisites: None

Status: Required/Elective

Teacher: Željko M. Svedružić, Ph.D.

Course description. The aim of the course is to enable students to acquire knowledge and skills with which they can independently do basic computer analyzes of the structure and function of biomolecules. Theoretical settings for studying the structure and function of molecules in parallel with the computational approaches will be presented. The aim of the course is to enable students to visually imagine the material they have learned in previous courses.

Learning outcomes. Upon completion of the course the students will be able to:

  1. Independently search databases containing molecular structures, and identify different types of computational records of molecular structures
  2. Self-assess the physical properties of small molecules and large biomolecules
  3. Calculate molecular orbitals and electron densities on small molecules independently using quantum chemistry programs
  4. Identify functional groups and flexible parts in small molecule structures, and identify functional parts in large biomolecules (proteins, DNA and RNA molecules, biological membranes, complex carbohydrates)
  5. Numerically simulate and optimize measurements of enzyme activity and dose curve measurements

Chemoinformatics course content

Lectures:

  1. Summary of the material, through examples we will show why computational approaches are important for biochemistry and design of new drugs. Most of the examples will be from our research
  2. Calculation and presentation of different physical properties of small molecules. Names, molecular mechanics, molecular dynamics and molecular conformations, 3D overlapping of molecules, LogP and LogD values, pKa values, titration curves, tautomeric and enantiomers. NMR spectra. Presentation of databases containing small molecule structures. Software used: ChemAxon Marvin, Avogadro, and VMD
  3. QM/MM analyzes of atomic orbitals, molecular orbitals, and HOMO-LUMO orbitals. Software used: Avogadro, GAMESS (US), wxMacMolPlt, and Wavefunction Spartan
  4. Basics of crystallographic and NMR methods for macromolecule structure analysis. Presentation of databases containing structures of large biomolecules. Organization of PDB documents and algorithms for displaying molecules on computers
  5. Analyzes of structural domains, active sites, b-factors, and surface shapes, electrostatic potentials, and hydrophobicity. Basics of comparison of structures by overlaying structures based on amino acid sequence and calculating RMSD and RMSF values. Viewing and analysis of the structure of biological macromolecules. Software used: PyMOL, UCSF Chimera, and VMD
  6. Protein structures. Analyzes of the structures of different proteins and associated structural elements
  7. DNA and RNA molecules, and proteins that bind to DNA and RNA molecules
  8. DNA damage and repair, DNA methylation, and epigenetic mechanisms will be presented as an illustrative example of recent research
  9. Biological membranes and membrane proteins. Membrane receptors and ion channels, molecular basis of Alzheimer’s disease, neurochemistry and psychopharmacology
  10. Basic analyzes of enzyme activity using numerical simulations with KinTek and Microsoft Excel (or LibreOffice Calc). Students will learn what the catalytic cycle is and how enzyme and substrate concentrations affect the measurement of enzyme activity. Students will learn how to determine the concentration of an active enzyme in a reaction

Seminars:

  1. Students can choose their favorite small molecule to analyze molecular mechanics, molecular dynamics and molecular conformations
  2. Students can optionally choose their favorite small molecule to analyze 3D overlap between molecules, LogP and LogD values, pKa values, titration curves, tautomers and enantiomers. NMR spectra
  3. Students can optionally choose their favorite small molecule for QM/MM analyzes of atomic orbitals, molecular orbitals, and HOMO-LUMO orbitals
  4. Students can choose their favorite biomolecule to display crystallographic and NMR methods for analyzing the structure of macromolecules. Presentation of databases containing structures of large biomolecules. Organization of PDB documents and algorithms for displaying molecules on computers
  5. Students can optionally choose their favorite biomolecule to analyze structural domains, active sites, b-factors, and surface shapes, electrostatic potentials, and hydrophobicity. Basics of comparing structures using sequence-based stacking amino acids and calculates RMSD and RMSF values
  6. Protein structures. Analyzes of the structures of different proteins and associated structural elements
  7. DNA and RNA molecules, and proteins that bind to DNA and RNA molecules

Exercises:

  1. DNA damage and repair, DNA methylation, and epigenetic mechanisms will be presented as an illustrative example of recent research
  2. Biological membranes and membrane proteins. Membrane receptors and ion channels, molecular basis of Alzheimer’s disease, neurochemistry and psychopharmacology
  3. Basic analyzes of enzyme activity using numerical simulations with KinTek and Microsoft Excel (or LibreOffice Calc). Students will learn what the catalytic cycle is and how enzyme and substrate concentrations affect the measurement of enzyme activity. Students will learn how to determine the concentration of an active enzyme in a reaction

Computational design of biologically active molecules

Study programme: Graduate study “Drug research and development”, Graduate study “Biotechnology in medicine”, Graduate study “Medical chemistry”

Course code: MK202

ECTS points: 5

Course language: Croatian

Course prerequisites: None

Status: Required/Elective

Teacher: Željko M. Svedružić, Ph.D.

Course objectives. The course aims to provide a thorough demonstration of the methods and computational packages used in the elucidation of structure and reactivity of biologically active molecules (potential drugs). In addition, the connection between experimental measurements and the results of molecular modelling calculations will be outlined, with special emphasis on enhancing the practical use of such computational methods in the students own research. The latter will be achieved by imparting a better understanding of the systems under investigation, with the goal of increasing the efficiency of connected experimental research.

Course description. The course will cover a variety of computational approaches applicable to the design of new biologically active molecules, with a focus on modeling the 3D structure of molecules and intermolecular interactions. This will include an overview of the computational methods and programs commonly used in the elucidation of structure and reactivity of potential drugs, including examples of empirical (molecular mechanics, molecular dynamics, MC, docking, and the discrete-point-space methodology) and quantum-mechanical (ab inito, HF, DFT and semi-empirical) techniques. In addition to clarifying the mathematical and physical principles underlying these methods, their application will be clearly demonstrated through carefully selected practical examples.

The connection with the laboratory will be highlighted through a comparison of selected experimental properties (X-ray and neutron diffraction, electron microscopy, and NMR, IR, and UV spectra) with those that can be obtained through modeling. In addition, attention will be given to the productive use of known results through the interaction with databases of, for example, macromolecular structures or the properties of small molecules. Various examples will be drawn upon to demonstrate how the application of different statistical methods can establish, both qualitatively and quantitatively, the relationship between the structure and activity of molecular species. In particular, emphasis will be placed upon ways in which this information can be used, for example, to enhance the activity and/or diminish the toxicity of certain classes of compounds.

Learning outcomes. The students will become equipped with knowledge that will enable them to properly understand the results of molecular modelling investigations as well as to critically evaluate the modelling-related scientific literature. Becoming properly informed about the different modelling methods and their possibilities will assist the students in planning the appropriate application of such methods in the process of designing new biologically active molecules. Beyond this, the practical knowledge will consist of acquiring the ability to competently use at least one computational modelling software package, enabling the students to embark on preliminary modelling of the structures of small molecules alone. Importantly, successful candidates will also become comfortable in the use of molecular databases over the internet.