COMPUTATIONAL BIOPHYSICS : TOOLS and METHODS

AIM

It is aimed to teach the students some widely-used computational techniques such as molecular modeling, molecular docking and molecular dynamics simulations along with the parameters used to optimize simulations. In this way, students are expected to run a molecular dynamics simulation by their own.

CONTENT

This course contains; Introduction to Quantum Chemistry,An overview to the Quantum Chemical Methods,Introduction to Statistical Mechanics,Comperative Study of Classical and Enhanced Molecular Dynamics Simulations,Analysis of force fields used in molecular dynamics simulations and investigation of the Transferability of the force fields,Comperative Study of classical and polarizable water models used in molecular dynamics simulations,Derivation of potentials used to calculate long-range electrostatic interactions,Calculation of free energies using metadynamics, thermodynamics integration and umbrella sampling and comperative interpretation of the results.,Investigation of the impact of enhanced sampling techniques on the conformational energy landscapes of proteins,Investigation of the impact of hybrid potentials, which are formed by quantum and classical mechanics, on the structure and dynamics of proteins.,Investigation of coarse grained models that are used to achieve long time scales in biological systems.,Investigation of free and constrained molecular docking calculations and impact of water therein.,Discussion on the widely and currently used computational biophysical techniques -I,Discussion on the widely and currently used computational biophysical techniques-II.

1. 1

   The student understands the differences between molecular mechanics and quantum mechanics and decides which method is appropriate for solving a given scientific problem.

   Taught by: Discussion Method, Case Study Method, Self Study Method, Question - Answer Technique, Brainstorming Technique, Project Based Learning Model, Simulation Technique, Computer-Internet Supported Instruction · Assessed by: Project Task

2. 2

   The student learns about the force fields and water models used in molecular dynamics simulations, and thus decides the parameter set that should be used in the simulation.

   Taught by: Discussion Method, Case Study Method, Self Study Method, Question - Answer Technique, Project Based Learning Model, Lecture Method · Assessed by: Project Task

3. 3

   At the end of the course the student gains basic knowledge on the Linux operating system.

   Taught by: Self Study Method, Question - Answer Technique, Experiential Learning, Lecture Method · Assessed by: Homework

4. 4

   At the end of the course, the student gains knowledge on how to run molecular dynamics simulations using parallel-computing systems.

   Taught by: Discussion Method, Self Study Method, Project Based Learning Model, Experiential Learning, Lecture Method

5. 5

   At the end of the course, the student gains ability to perform molecular dynamics simulation and to analyze the resulting trajectory by her/his own.

   Taught by: Discussion Method, Self Study Method, Question - Answer Technique, Brainstorming Technique, Project Based Learning Model, Reverse Brainstorming Technique · Assessed by: Project Task

WEEKLY PLAN

1. WEEK 1

   Introduction to Quantum Chemistry

   Preparation: Understanding Molecular Simulation : From Algorithms to Applications - Chapter 1

2. WEEK 2

   An overview to the Quantum Chemical Methods

   Preparation: Understanding Molecular Simulation : From Algorithms to Applications - Chapter 2

3. WEEK 3

   Introduction to Statistical Mechanics

   Preparation: Understanding Molecular Simulation : From Algorithms to Applications - Chapter 3

4. WEEK 4

   Comperative Study of Classical and Enhanced Molecular Dynamics Simulations

   Preparation: Understanding Molecular Simulation : From Algorithms to Applications - Chapter 4

5. WEEK 5

   Analysis of force fields used in molecular dynamics simulations and investigation of the Transferability of the force fields

   Preparation: Understanding Molecular Simulation : From Algorithms to Applications - Chapter 5

6. WEEK 6

   Comperative Study of classical and polarizable water models used in molecular dynamics simulations

   Preparation: Understanding Molecular Simulation : From Algorithms to Applications - Chapter 6

7. WEEK 7

   Derivation of potentials used to calculate long-range electrostatic interactions

   Preparation: Understanding Molecular Simulation : From Algorithms to Applications - Chapter 7

8. WEEK 8

   Calculation of free energies using metadynamics, thermodynamics integration and umbrella sampling and comperative interpretation of the results.

   Preparation: Understanding Molecular Simulation : From Algorithms to Applications - Chapter 8

9. WEEK 9

   Investigation of the impact of enhanced sampling techniques on the conformational energy landscapes of proteins

   Preparation: Understanding Molecular Simulation : From Algorithms to Applications - Chapter 9

10. WEEK 10

    Investigation of the impact of hybrid potentials, which are formed by quantum and classical mechanics, on the structure and dynamics of proteins.

    Preparation: Understanding Molecular Simulation : From Algorithms to Applications - Chapter 10

11. WEEK 11

    Investigation of coarse grained models that are used to achieve long time scales in biological systems.

    Preparation: Understanding Molecular Simulation : From Algorithms to Applications - Chapter 11

12. WEEK 12

    Investigation of free and constrained molecular docking calculations and impact of water therein.

    Preparation: Understanding Molecular Simulation : From Algorithms to Applications - Chapter 12

13. WEEK 13

    Discussion on the widely and currently used computational biophysical techniques -I

    Preparation: Literature research: modern applications of the methods used in the Computational Biophysics field.

14. WEEK 14

    Discussion on the widely and currently used computational biophysical techniques-II

    Preparation: Literature research: modern applications of the methods used in the Computational Biophysics field.

ASSESSMENT

• Rate of Midterm Exam to Success50%
• Rate of Final Exam to Success50%

WORKLOAD

READING

• Frenkel and Smit, Understanding Molecular Simulation : From Algorithms to Applications, , Academic Press, Computational Science Series Sunum
• 1) Frenkel and Smit, Understanding Molecular Simulation : From Algorithms to Applications, , Academic Press, Computational Science Series 2)Allen and Tildesley, Computer Simulation of Liquids, Clarendon Press 3)Zhou, Molecular Modeling at the Atomic Scale, CRC Press, Taylor & Francis.

TEACHING STAFF

• Assoc.Prof. Özge ŞENSOYCOORDINATOR
• Assoc.Prof. Özge ŞENSOY