C8 BIOCAT – Molecular modelling

Key information

Due to the coronavirus situation, the 2nd part of this course will be held remotely in week 18 (beginning April 27th). Contact the course responsible with questions.

Dates: Bergen week 10 ( 2-6. March) and Tromsø, week 14 (30. March-3.April) 2020

Place: Realfagbygget, UiB (the first week) and Institute for Chemistry, UiT (the second week)

Course responsible: Nathalie Reuter (UiB)

Exam: Oral exam, tentatively May 2020 (might be over a video system/Skype)

Credits: 10 ECTS 

Registration: Deadline 12.02.2020

Content

The course will present central methods in molecular modeling for the analysis of enzyme structure-function relationship and the modeling of their catalytic activity.


Teaching

The course will be given over two weeks from 9 to 16 every day, not necessarily consecutive, and happening on two different sites (UiB and UiT). Mornings will be dedicated to lectures (3 hours) and afternoons to hands-on sessions (4 hours).

Week 1

D1: Computational methods for the investigation of structure-function relationships of enzymes

D2: Force fields (FF)

D3: Geometry optimization/Molecular simulations

D4: Enhanced sampling methods

D5: Normal mode analysis

WEEK 2 (35 hours lectures+hands on)

D1: Docking

D2: Free energy calculations

D3: Empirical Valence Bond (EVB)

D4: Quantum mechanical calculations for the purpose of FF/EVD parameterization

D5: Validation of computer simulations

Work requirement

The students will be given two independent assignments to work on. One will focus on the learning outcomes from week #1 and the other one on the projects from week #2. Each project will require that the students perform computations on their own and will be independent of the exercises done during the hands-on session. We estimate that each project will require about 3-5 days of work

Exam and evaluation

Assignments will count for 50% of the final evaluation. Oral exam will count for the other half (might be over a video conferencing system)

Syllabus

Learning outcome

The candidate..

Knowledge

Skills

General competence