Computational Drug Design - 2020 Edition

Computational Drug Design - 2020 Edition

Lecturers:

Dr. Florian Blanc, Dr. Ramachandra Bhaskara, M.Sc. Laura Schulz

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Course open to all interested students

every Tuesday 14:00 – 16:00 (s.t.)


Course Documentation - 2020

Lecture 1, 2020.11.03 (PDF) password protected

Lecture 2, 2020.11.10 (PDF) password protected

Lecture 3, 2020.11.17 (PDF) password protected


All lectures will be held via Zoom. For Zoom login information please contact the lecturers via email:

Florian Blanc: florian.blanc@biophys.mpg.de

Ramachandra Bhaskara: ramachandra.bhaskara@biophys.mpg.de

Laura Schulz: laura.schulz@biophys.mpg.de


Content covered in 2019 lectures (incl. practicals)

  • 1. Introduction to computational drug design
  • 2. Ligand-based approachees
    • a. (Quantitative) structure-activity relationship (SAR & QSAR)
    • b. Pharmacophore modeling
  • 3. Bioinformatics approaches (target recognition and structural modeling)
    • a. Sequence alignments and searches
    • b. Gene identification and prediction
    • c. Homology modeling
  • 4. Structure-based approaches
    • a. Molecular docking
      • i. Ligand docking: theory and scoring functions
      • ii. Virtual screening
      • iii. Protein-protein docking and interaction
    • b. Molecular dynamics simulation
      • i. Introduction into molecular dynamics
      • ii. Estimation of ligand binding affinity
  • 5. Free energy perturbation
  • 6. Enhance sampling methods

Topics for student presentations

QSAR

  • 1. QSAR modeling using partial least square (PLS)
  • 2. Application of neural networks in QSAR

Modeling

  • 1. Profile based methods and applications to sequence alignment
  • 2. Threading for protein structure prediction
  • 3. Using co-evolution for protein structure prediction

Docking

  • 1. Protein-protein interface prediction using 3D structure and residue conservation
  • 2. Induced-fit docking

MD simulation

  • 1. Thermodynamic integration (TI) for free energy calculation
  • 2. Non-equilibrium simulation
  • 3. Advanced methods in enhanced sampling
  • 4. Metadynamics
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