This repository contains the teaching materials for the Oxford Computational Biochemistry course. This two and half day course is aimed at graduate students who have had no prior experience in the area of Computational Biochemistry. It assumes no prior knowledge and only tries to cover basic concepts.

In order to participate in these tutorials you will need specific software installations.

To get started, please see the overall setup instructions.

A set of jupyter notebooks which aim to teach the basics of python programming assuming no prior knowledge. First introducing core concepts such as variables, loops, conditionals and lists, we eventually demonstrate how molecular structures can be analysed using python libraries such as MDAnalysis and NGLView.

Contains a practical tutorial and lecture slides that aim to:

• Introduce the process of homology modelling.

• Summarise the methods for predicting the structure from sequence.

• Describe the individual steps involved in creating and optimising a protein homology model.

• Outline the methods available to evaluate the quality of homology models.

Contains lecture slides on the Molecular Dynamics method and a practical GROMACS tutorial that aims to demonstrate how to:

• Setup, equilibrate and simulate a protein-ligand system in water.
• Visualise the system and its trajectory with VMD and NGLView.
• Perform basic analysis utilising GROMACS tools, MDAnalysis, and the matplotlib plotting tool.

A tutorial which aims to teach the basics of molecular docking.

The tutorial contains:

• Lecture slides introducing the concept of molecular docking.
• A tutorial which looks at using Autodock Vina to dock small ligands to HIV-1 protease.

The Oxford Computational Biochemistry course has been written by several authors over several years. Please see individual tutorials for contributor logs.

Course leader: Professor Philip C. Biggin

The code is released under the BSD-3 license (see LICENSE file) while other content is released under the CC BY-NC 4.0 license (see below).

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.