Module Catalogue 2024/25

Pre Requisite Comment

N/A

Co Requisite Comment

N/A

Aims

The purpose of this course is to equip students with solid foundations in computational structural biology, molecular pharmacology, and proteins as drug targets – from principles governing protein structure and dynamics, drug-protein interactions, through pharmacology, to modern methods in protein structure prediction and protein simulations.

Outline Of Syllabus

Students will learn about relationships between ligand-receptor interactions, protein structure, dynamics and biological activity. The block is intended to make students familiar with the basic terminology, key concepts, data formats and algorithms of modern protein modelling and simulation methods. This will include structure prediction using homology protein modelling and building protein-ligand complexes. During this part of the course, students will learn techniques suitable for generating and visualising macromolecular data using the open-source as well as proprietary software packages.
The second part of the course will focus on concepts of molecular pharmacology, relevant in the context of modern drug chemistry. Students will learn about the methodology of preclinical studies and clinical trials.

Intended Knowledge Outcomes

Introduction to macromolecules, drug-protein interactions and molecular pharmacology
• Protein-ligand interactions, protein structure and protein dynamics
• Principles of thermodynamics in drug chemistry context
• Introduction to macromolecular modelling: key concepts, tools and techniques
• Classical mechanical force fields
• Modelling of proteins
• Sequence alignments and sequence homology
• Homology modelling and AI modelling (AlphaFold)
• Molecular docking
• Energy minimisation
• Molecular dynamics simulations
• Data analysis
• Pharmacokinetics and pharmacodynamics
• Before the molecule becomes a drug: methods in preclinical studies
• An overview of clinical trials

Intended Skill Outcomes

Developed skills in performing some simple types of molecular modelling of macromolecules, sequence analysis and structure prediction calculations: from identifying the problem and the clinical need (understanding of underlying biology and pharmacology required), through designing the computational experiment, sequence alignment, building of the homology model, model refinement, and data analysis.
Subject specific or professional skills, able to:
• Use modelling and visualisation packages (such as UCSF Chimera);
• Use online resources and databases (such as RCSB PDB, UniProt, PubMed, ChEMBL);
• Use different formats (PDB, FASTA);
• Basic computer literacy;
• Build the homology model of the macromolecule and validate the model;
• Be able to build, set up, conduct, and analyse the protein-ligand docking run and to interpret the results;
• Use molecular graphics software to generate high-quality images for the reports and publications;
Cognitive or intellectual skills, able to:
• interpret molecular docking data and extrapolate them to macroscopic observables (e.g. binding affinity);
• develop models and hypotheses based on experimental observations;
• solve problems and design experiments based on modelling outcomes;
Key skills, able to:
• Plan and organise coursework effectively;
• Take accurate notes;
• Think critically;

Teaching Rationale And Relationship

As this module is focused on linking the theory and practical skills, module team will be using a wide range of didactic methods varying between lecturing, case studies and Dry Labs, in order to introduce the core concepts and show their applications.
In order to meet the Learning Outcomes, a balance will be provided between directly taught content, independent guided reading, focused workshops, Dry Lab exercises, as well as assignment preparation and completion.

Assessment Rationale And Relationship

The Problem-Solving Assessment will test the students’ knowledge of key concepts introduced during scheduled teaching sessions and evaluate their understanding of the content of this module in the context of both concepts and practical skills introduced in Dry Labs. The in course assessment will test information literacy and problem solving skills.

General Notes

N/A