1. CH 328 Biomolecular Modelling Instructors: R. Woods, E. Fadda Schedule: Lectures (24) Wednesday / Thursday 9-10 am Dillon Theatre Computer labs (24) Monday / Friday 1-5 pm Software Engineering Suite Assessment: Continuous assessment – reports based on computer labs Final written paper – Answer four questions (one question per main topic). Attendance: Attendance at all lectures and labs is compulsory. Students will not be eligible to sit the final written exam if they have missed more that 3 lectures and 3 labs without a medical cert. Course literature: A.R. Leach: Molecular Modelling – Principles and Applications, 2nd Ed. Prentice Hall 2001. Handouts and lecture notes.

2. CH 328 Biomolecular Modelling Instructors: R. Woods, E. Fadda Schedule: Lectures (24) Wednesday / Thursday 9-10 am Dillon Theatre Computer labs (24) Monday / Friday 1-5 pm Software Engineering Suite Assessment: Continuous assessment – reports based on computer labs Final written paper – Answer four questions (one question per main topic). Attendance: Attendance at all lectures and labs is compulsory. Students will not be eligible to sit the final written exam if they have missed more that 3 lectures and 3 labs without a medical cert. Course literature: A.R. Leach: Molecular Modelling – Principles and Applications, 2nd Ed. Prentice Hall 2001. Handouts and lecture notes.

3. Course Content Basic concepts of Molecular Modelling: Relative energy versus absolute energy versus thermodynamics. Potential energy functions, energy minimization and validating theory with experiment Databases as sources of information: The Cambridge Chemical Structure Database (CCSD), The Protein Data Bank (PDB) Modelling Solvent Effects in Molecular Interactions: The role of solvent: Hydrogen bonding and explicit models Implicit models and the dielectric Computing intermolecular interaction energies Challenges in Modelling Biomolecules: Protein folding and conformational sampling Levinthal’s Paradox and the theory of protein folding The fundamentals of protein structure Homology modelling: Theory, application and model validation The structure and thermodynamics of protein ligand complexes Computational Approaches to Characterize Biomolecular Interactions: The strengths and weaknesses of Computational Docking Blind versus focused docking and virtual library screening Molecular Dynamics and Monte Carlo simulation techniques The importance of convergence in molecular simulations Computing binding free energies using Molecular Mechanics-Generalized Born Solvent Accessiblity (MM-GBSA) and Thermodynamic Integration (TI).

4. Course Content Basic concepts of Molecular Modelling: Relative energy versus absolute energy versus thermodynamics. Potential energy functions, energy minimization and validating theory with experiment Databases as sources of information: The Cambridge Chemical Structure Database (CCSD), The Protein Data Bank (PDB) Modelling Solvent Effects in Molecular Interactions: The role of solvent: Hydrogen bonding and explicit models Implicit models and the dielectric Computing intermolecular interaction energies Challenges in Modelling Biomolecules: Protein folding and conformational sampling Levinthal’s Paradox and the theory of protein folding The fundamentals of protein structure Homology modelling: Theory, application and model validation The structure and thermodynamics of protein ligand complexes Computational Approaches to Characterize Biomolecular Interactions: The strengths and weaknesses of Computational Docking Blind versus focused docking and virtual library screening Molecular Dynamics and Monte Carlo simulation techniques The importance of convergence in molecular simulations Computing binding free energies using Molecular Mechanics-Generalized Born Solvent Accessiblity (MM-GBSA) and Thermodynamic Integration (TI).

5. Course Content Basic concepts of Molecular Modelling: Relative energy versus absolute energy versus thermodynamics. Potential energy functions, energy minimization and validating theory with experiment Databases as sources of information: The Cambridge Chemical Structure Database (CCSD), The Protein Data Bank (PDB) Modelling Solvent Effects in Molecular Interactions: The role of solvent: Hydrogen bonding and explicit models Implicit models and the dielectric Computing intermolecular interaction energies Challenges in Modelling Biomolecules: Protein folding and conformational sampling Levinthal’s Paradox and the theory of protein folding The fundamentals of protein structure Homology modelling: Theory, application and model validation The structure and thermodynamics of protein ligand complexes Computational Approaches to Characterize Biomolecular Interactions: The strengths and weaknesses of Computational Docking Blind versus focused docking and virtual library screening Molecular Dynamics and Monte Carlo simulation techniques The importance of convergence in molecular simulations Computing binding free energies using Molecular Mechanics-Generalized Born Solvent Accessiblity (MM-GBSA) and Thermodynamic Integration (TI).

6. Course Content Basic concepts of Molecular Modelling: Relative energy versus absolute energy versus thermodynamics. Potential energy functions, energy minimization and validating theory with experiment Databases as sources of information: The Cambridge Chemical Structure Database (CCSD), The Protein Data Bank (PDB) Modelling Solvent Effects in Molecular Interactions: The role of solvent: Hydrogen bonding and explicit models Implicit models and the dielectric Computing intermolecular interaction energies Challenges in Modelling Biomolecules: Protein folding and conformational sampling Levinthal’s Paradox and the theory of protein folding The fundamentals of protein structure Homology modelling: Theory, application and model validation The structure and thermodynamics of protein ligand complexes Computational Approaches to Characterize Biomolecular Interactions: The strengths and weaknesses of Computational Docking Blind versus focused docking and virtual library screening Molecular Dynamics and Monte Carlo simulation techniques The importance of convergence in molecular simulations Computing binding free energies using Molecular Mechanics-Generalized Born Solvent Accessiblity (MM-GBSA) and Thermodynamic Integration (TI).

7. Course Content Basic concepts of Molecular Modelling: Relative energy versus absolute energy versus thermodynamics. Potential energy functions, energy minimization and validating theory with experiment Databases as sources of information: The Cambridge Chemical Structure Database (CCSD), The Protein Data Bank (PDB) Modelling Solvent Effects in Molecular Interactions: The role of solvent: Hydrogen bonding and explicit models Implicit models and the dielectric Computing intermolecular interaction energies Challenges in Modelling Biomolecules: Protein folding and conformational sampling Levinthal’s Paradox and the theory of protein folding The fundamentals of protein structure Homology modelling: Theory, application and model validation The structure and thermodynamics of protein ligand complexes Computational Approaches to Characterize Biomolecular Interactions: The strengths and weaknesses of Computational Docking Blind versus focused docking and virtual library screening Molecular Dynamics and Monte Carlo simulation techniques The importance of convergence in molecular simulations Computing binding free energies using Molecular Mechanics-Generalized Born Solvent Accessiblity (MM-GBSA) and Thermodynamic Integration (TI).

8. Learning Outcomes Students will gain an understanding of: Potential Energy Functions Energy Minimization: Steepest Descent/Conjugate Gradient/Grid Searching Automated ligand docking Molecular dynamics (MD) simulations Computing ligand binding energies from MD data The importance of water in modelling Approaches to predicting the 3D structure of proteins The structure and thermodynamic properties of protein secondary elements The structure and thermodynamics of protein-ligand interactions How to compare theoretical and experimental data

9. Practicals The Molecular Modelling Practical Course will take place over a 12 week period (6 hrs per week). Attendance records are taken at practical classes and performance at each laboratory class will be assessed on a weekly basis. Part of the marks will be awarded for this continuous assessment. The principal objectives of the CH328 laboratory course are: To develop a practical capability to visualize and modify molecular structures on a computer. To be able to compute binding energies. To be able to perform and analyse data from MD simulations. To be able to critically compare theoretical and experimental molecular data. To illustrate the principles dealt with in the lecture course. The practicals are to be written up as a separate Report and handed up in the lab each week. The experiments are to be done in sequence. To derive full benefit from the course the student should, before coming to the laboratory, read details of the experiment to be performed.

10. Human being 1-2 m Human cell 0.0001 m Drug mol. 0.000000001 m

11. Possible Drug Targets Cell wall (lipid membrane) DNAProteins (enzymes) DoxorubicinTamiflu Penicillin

14. What is Molecular Modelling? Develop a mathematical model based on physics for the physical property of interest. That is, describe ”our surrounding” in mathematical terms. Convert the mathematical description into computer algorithms (numerical methods) Apply this theory to model systems of interest (form hypothesis) Perform calculations to generate data that allows us to interpret (validate/falsify) our hypothesis and/or our model system Analyse the results Compare with experiments, whenever possible

15. The Toolbox Quantum Chemistry: (explicitly solving the Schrodinger equation for different systems); practical limit 100 atoms (good for chemical reactions) Molecular mechanics / dynamics: (classical mechanics; potential functions; time resolved processes such as diffusion); 100,000 atoms. Can extend with coarse-graining methods (good for molecular shape and motion) Bioinformatics: modelling new protein structures; docking from large databases (500,000 molecules) in search of lead compounds; pharmacophore models (good for discovery) All can require a significant amount of computing time

16. or Thermally averaged structure from a mixture in equilibrium at non-zero temp How to compare with experiment? Isolated single molecule in vacuum at zero degrees Cartesian coordinates (x,y,z; absolute positions of all atoms in space) or Internal coordinates (relative positions of all atoms, defined by bond lengths, angles and torsions) What does the molecule ’look like’) Computable Quantities: Structure

17. Energy surface (hypersurface) of 3N-6 dimensions. Helps to relate (map) variation of Energy as function of one or two structural variables; e.g. bond distances, bond angles or a more ’non-specific’ reaction coordinate. Computable Quantities: Potential Energy Surfaces (PES) Energies are usually defined relative to one of the points on the surface. The lowest energy point is called the global minimum Identifies stable conformations and barriers to reactions

18. ’Single molecule’ type properties (typically by QM): Spectral quantities: NMR chemical shifts and couplings, EPR hyperfine couplings, Vibrational spectra (IR)Absorption spectra, (electronic excitations, UV/Vis) Other: Electron Affinities and Ionization Potentials, Molecular Dipoles Thermodynamic type quantities (by QM or MM/MD): Enthalpy; heat of formation, Free energy; reaction thermochemistry, Kinetic isotope effects, Complexation energies, Acidity/basicity (pKa), Hydrogen bonding, Solvation effects Properties without physical observables (by QM): Bond order; Aromaticity; Isoelectronic behaviour; Partial charges, ’conceptual properties’, Bond Dipoles Computable Quantities: Molecular Properties

19. Reaction studies / enzymatic mechanisms Protein structure and function Homology modelling Docking / Drug design MD simulations Simulations of large systems (membranes, colloids, fibres) Applications of Molecular Modelling