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Proseminar: Molecular Modeling - Basics and Applications

General Information

Lecturer: PD Dr. Michael Hutter

Termine: Dienstags 16:00 Uhr - 17:30 Uhr

Ablaufsübersicht (Stand 24.06.2014):

03.06. Thema 1

10.06. Thema 5 und 6

17.06. kein Seminar

24.06. Thema 7

01.07. Thema 4 und 12

08.07. Thema 8

Raum: E2 1, Raum 106

Abgabe Rohfassung der Vorträge: bis 25.05. per email (z.B. als pdf) beim Dozenten.

Der Vortrag sollte auf Englisch erfolgen und etwa 25-30 Minuten dauern.

Themenvergabe:

Dienstag, 22.04.2014, 16:00 Uhr in E2 1, Raum 007

Voraussetzungen:
Vorkenntnisse entsprechend dem 4. Studiensemester. Besuch der Vorlesungen "Bioinformatik II" und "Computational Chemistry" wird dringend empfohlen.

Condition for certification: Erfolgreiche Präsentation (≥ 4.0),
regelmäßige Teilnahme (3 Termine).

Maximale Anzahl Teilnehmer: 12

Leistungspunkte/Credits:
laut Studienordnung (2006): 5

 

Topics:  

  1. Force Fields: Assigning atom types, force fields for proteins, DNA, organic compounds: AMBER, CHARMM, MM3, How to obtain parameters.
  2. Derivatives of energy in force fields: Analytical gradients for bond stretch, angle bending, torsional terms, van der Waals and electrostatic forces.
  3. Generating Partial Atomic Charges: concepts and use in force fields, electrostatic potential derived charges, constraints, RESP, Gasteiger-Marsili charges, electronegativity, differences in the CHARMM, AMBER, and MM2 force fields.
  4. Generation of 3D Molecular Structures: 3D coordinates from scratch, CORINA.
  5. Interpreting X-Ray Structure of Proteins: The. pdb file format, methods or determining the structure, synchrotron scattering, resolution, temperature factors, crystal cells, biological assemblies, alternating atom positions, missing coordinates, recreating ligands.
  6. Assigning Hydrogens and Their Networks: Finding the optimal interactions between protein residues, of side chains within proteins, pH, tritratable groups, assigning of corresponding polar hydrogens to X-ray structures. Programs GRID and WHATIF.
  7. Docking and Scoring Functions: Energy-based vs. knowledge-based scoring, estimation of entropic contributions and desolvation, DrugScore, AutoDock, treating flexibility of protein and ligand.
  8. Conformational Search: Conformational space, systematic search, tree search, random and stochastic search, genetic algortithms, distance geometry.
  9. Smallest Set of Smallest Rings: Finding and assigning cyclic structures in molecules, Hückel aromaticity.
  10. Prediction Methods for logP: Fragment-based and atom type-based additive contributions, correction factors, application of machine learning algorithms e.g. neural networks, various approaches like ClogP, XlogP. How many parameters are required?
  11. Maximium common substructure: Clique detection, colored grapghs, reduced graphs.
  12. Rotation around single bonds: Quaternions vs. Euler angles and rotation matrices, conformational search trees, dead end elimination, A* algorithm

.

References:

 

  1. Force Fields: Atom types, force fields for proteins, DNA, organic and inorganic compounds: AMBER, CHARMM, MM3, UFF
    • Andrew Leach, Molecular Modelling, 2nd ed. Chapter on Empirical Force Field Models: Molecular Mechanics.
    • AMBER: S. Weiner, P. Kollman, D. Case, U. Chandra Singh, C. Ghio, G. Alagona, S. Profeta Jr., P. Weiner, A New Force Field for Molecular Mechanical Simulation of Nucleic Acids and Proteins, Journal of the American Chemical Society, 106 (1984) 765-784
    • B.R. Brooks, R.E. Bruccoleri, B.D. Olafson, D.J. States, S. Swaminathan, M. Karplus, CHARMM: A Program for Macromolecular Energy, Minimization, and Dynamics Calculations, Journal of Computational Chemistry 4 (1983) 187-217.
    • N. Allinger, Y Yuh, J.-H. Lii, Molecular Mechanics. The MM3 Force Fields for Hydrocarbons I. Journal of the American Chemical Society, 111 (1989) 8551-8134.
  2. Derivatives of energy in force fields: Analytical gradients for bond stretch, angle bending, torsional terms, van der Waals and electrostatic forces, local, global, with and without the use of gradients, DFP, eigenvector following, simplex, simulated annealing, genetic algorithms
    • Andrew Leach, Molecular Modelling, 2nd ed. Chapter on Derivatives (4.16).
    • B.R. Brooks, R.E. Bruccoleri, B.D. Olafson, D.J. States, S. Swaminathan, M. Karplus, CHARMM: A Program for Macromolecular Energy, Minimization, and Dynamics Calculations, Journal of Computational Chemistry 4 (1983) 187-217.
    • W. Press, S. Teukolsky, W. Vetterling, B. Flannery, Numerical Recipies, 2nd ed.
    • J. Baker, An Algorithm for the Localization of Transition-States, Journal of Computational Chemistry 7 (1986) 385-395.
  3. Generating Partial Atomic Charges: concepts and use in force fields, electrostatic potential derived charges, constraints, RESP, Gasteiger-Marsili charges, electronegativity, differences in the CHARMM, AMBER, and MM2 force fields
    • Andrew Leach, Molecular Modelling, 2nd ed. Chapter on Electrostatic Interactions
    • RESP: U.C. Singh, P.A. Kollman, An Approach to Computing Electrostatic Charges for Molecules, Journal of Computational Chemistry 5 (1984) 129-145.
    • J. Gasteiger, M. Marsili, Iterative Partial Equalization of Orbital Electronegativity–Rapid Access to Atomic Charges, Tetrahedron 36 (1980) 3219-3228.
    • See also those for the topic “Force Fields” above
  4. Generation of 3D Molecular Structures: 3D coordinates from scratch, CORINA
    • J. Gasteiger, C. Rudolph, J. Sadowski, Automatic Generation of 3D-Atomic Coordinates for Organic Molecules, Tetrahedron Computer Methodology 3 (1990) 537-547.
    • J. Sadowski, J. Gasteiger, From Atoms and Bonds to Three-Dimensional Atomic Coordinates: Automatic Model Builders, Chemical Reviews 93 (1993) 2567-2581.
  5. Interpreting X-Ray Structure of Proteins: The. pdb file format , methods or determining the structure, synchrotron scattering, resolution, temperature factors, crystal cells, biological assemblies, alternating atom positions, missing coordinates, recreating ligands.
    • http://www.pdb.org/pdb/static.do?p=education_discussion/Looking-at-Structures/intro.html
    • common text books: X-ray and synchrotron scattering
    • Chemistry text books: van der Waals and covalent bond radii for assigning bonds between atoms.
    • S. Urbaczek, A. Kolodzik et al. Reading PDB: Preception of Molecules from 3D Atomic Coordinates Journal of Chemical Information and Modeling 53 (2013) 76-87.
  6. Assigning Hydrogens and Their Networks: Finding the optimal interactions between protein residues, flip of side chains within proteins, pH, tritratable groups, assigning of corresponding polar hydrogens to X-ray structures. Programs GRID and WHATIF.
    • Andrew Leach, Molecular Modelling, 2nd ed. Chapter on Hydrogen Bonding (4.13).
    • GRID P. J. Goodford, A Computational Procedure for Determining Energetically Favorable Binding Sites on Biologically Important Macromolecules. Journal of Medicinal Chemistry 28 (1985) 849-857.
    • WHATIF: R.W.W. Hooft, C. Sander, G. Vriend, Positioning Hydrogen Atoms by Optiming Hydrogen-Bond Networks in Protein Structures, Proteins: Structure, Function, and Genetics 26 (1996) 363-376.
  7. Docking and Scoring Functions: Energy-based vs. knowledge-based scoring, estimation of entropic contributions and desolvation, DrugScore, AutoDock, treating flexibility of protein and ligands.
    • Andrew Leach, Molecular Modelling, 2nd ed.
    • G.M. Morris, D.S. Goodsell et al., AutoDock User’s Guide Version 3.0.5
    • G.M. Morris, D.S. Goodsell et al., Automated Docking Using a Lamarckian Genetic Algorithm and an Empirical Binding Free Energy Function, Journal of Computational Chemistry 19 (1998) 1639-1662
    • S. Forli, M. Botta, Lennard-Jones Potential and Dummy Atom Settings to Overcome the AutoDock Limitation in Treating Flexible Ring Systems, Journal of Chemical Information and Modeling 47 (2007) 1481-1492.
    • H. Velec, H. Gohlke, G. Klebe, DrugScoreCSD – Knowledge-Based Scoring Function Derived from Small Molecule Crystal Data with Superior Recognition Rate of Near-Native Poses and Better Affinity Prediction, Journal of Medicinal Chemistry 48 (2005) 6296-6303.
  8. Conformational Search: Conformational space, systematic search, tree search, random and stochastic search, genetic algortithms, distance geometry
    • Andrew Leach, Molecular Modelling, 2nd ed.
  9. Smallest set of smallest rings: Finding and assigning cyclic structures in, Hückel aromaticity.
    • J. Figueras, Ring Preception Using Breadth-First Search, Journal of Chemical Information and Computer Science, 36 (1996) 986-991.
    • Chemistry text books: Hückel aromaticity
  10. Prediction Methods for logP: fragment-based and atom type-based additive contributions, correction factors, application of machine learning algorithms e.g. neural networks, various approaches like ClogP, XlogP. How many parameters are required?
    • Overview: R. Mannhold, H. van de Waterbeemd, Substructure and Whole Molecule Approaches for Calculating logP, Journal of Computer-Aided Drug Design, 15 (2001) 337-354.
    • ClogP: A. Leo, P.Y.C. Jow, C. Silipo, C. Hansch, Calculation of Hydrophobic Constant (logP) from pi and f Constants, Journal of Medicinal Chemistry, 18 (1975) 865-868.
    • S.A. Wildman, G.M. Crippen, Prediction fo Physicochemical Parameters by Atomic Contributions, Journal of Chemical Information and Computer Science 39 (1999) 868-873.
    • A. Breindl, B. Beck, T. Clark, R. Glen, Prediction of the n-Octanol/Water Partition Coefficient, logP, Using a Combination of Semiempirical MO-Calculations and a Neural Network, Journal of Molecular Modeling, 3 (1997) 142-155.
    • XlogP v2.0: R. Wang, Y. Gao, L. Lai, Calculating Partition Coefficient by Atom-Additive Method, Perspectives in Drug Discovery and Design, 19 (2000) 47-66.
    • XlogP v3.0: T. Cheng, Y. Zhao, X. Li, F. Lin, Y. Xu, X. Zhang, Y. Li, R. Wang, L. Lai, Computation of Octanol-Water Partition Coefficients by Guiding an Additive Model with Knowledge, Journal of Chemical Information and Modeling, 47 (2007) 2140-2148.
  11. Maximium common substructure: Clique detection, colored graphs, reduced graphs.
    • Andrew Leach, Molecular Modelling, 2nd ed. Chapter on 2D Substructure Searching (12.2).
    • J. M. Barnard, Substructure Searching Methods: Old and New, Journal of Chemical Information and Computer Science 33 (1993) 532-538.
    • J.W. Raymond, P. Willett, Maximum Common Subgraph Isomorphism Algorithms for the Matching of Chemical Structures, Journal of Computer-Aided Molecular Design, 16 (2002) 521-533.
    • V. J. Gillet, P. Willett, J. Bradshaw, Similarity Searching Using Reduced Graphs, Journal of Chemical Information and Computer Science 43 (2003) 338-345.
    • http://en.wikipedia.org/wiki/Clique_(graph_theory)
  12. Rotation around single bonds: Quaternions vs. Euler angles and rotation matrices, conformational search trees, dead end elimination, A* algorithm.
    • Andrew Leach, Molecular Modelling, 2nd ed. Chapter on Conformational Analysis (9.1, 9.2)
    • http://en.wikipedia.org/wiki/Quaternion
    • http://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation
    • http://en.wikipedia.org/wiki/Rotation_matrix
    • http://en.wikipedia.org/wiki/A*_search_algorithm and references therein
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