| CODE | PHB2504 | ||||||||
| TITLE | Molecular Recognition and Computational Drug Design | ||||||||
| UM LEVEL | 02 - Years 2, 3 in Modular Undergraduate Course | ||||||||
| MQF LEVEL | 5 | ||||||||
| ECTS CREDITS | 6 | ||||||||
| DEPARTMENT | Physiology and Biochemistry | ||||||||
| DESCRIPTION | This study-unit shall introduce the students to the importance of Computer-Aided Drug Design (CADD) which plays an increasingly important role in improving pharmaceutical productivity. In drug discovery projects, CADD enhances the chances of success by moving experiments from in vivo and in vitro to in silico. CADD expedites failure in the drug discovery pipeline by analysing toxicity, selectivity, potency, etc. using computational approaches in earlier stages of the drug development pipeline, greatly reducing costs and time involved. Databases of small molecules are searched to find active ones which exhibit a therapeutic effect against a particular protein of interest. This subfield of CADD is known as Virtual Screening and is the focus of this study unit. This study-unit will highlight the two main approaches in Virtual Screening: structure-based and ligand-based. In structure-based virtual screening we use a protein structure to guide the computational small-molecule search. This technique uses principles of molecular recognition which govern the interactions between the small molecule and the protein. In ligand-based virtual screening, a known small-molecule active is used to guide the computational search. A general introduction of the rational drug design field will be given discussing molecular recognition principles, benchmarking techniques for CADD systems, how to represent molecules in computer systems and how a drug target is chosen. The structure based part of the study-unit will offer an overview of docking and molecular dynamics techniques, as well as, discussions on binding free energy calculation. Bioinfomartics techniques such as binding site detection, structure determination and homology modelling will also be presented. The ligand-based part of the study-unit will cover database preparation, molecular similarity and database searching. Study-unit Aims: This study-unit aims to familiarize students with the computational aspect of drug discovery and design. Through this unit, students will gain hands-on experience in rational drug design methodologies using popular scientific software which are used in industry. Students will be taught how computer models can be validated through biochemical and biological wet-lab experiments. This study-unit will focus mostly on small-molecule drug discovery efforts. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Explain how to use computer modelling techniques in drug discovery; - Describe how to run a virtual screening exercise; - Explain how to rank molecules based on their putative activity; - Discuss the distinguishing features of different protein-ligand interactions; and - Describe the limitations of CADD. 2. Skills: By the end of the study-unit the student will be able to: - Use computer modelling techniques in drug discovery; - Run a virtual screening exercise and rank molecules based on their putative activity; - Recognize different protein-ligand interactions; - classify different protein-ligand interactions; - Identify the limitations of CADD; - Contrast the different file formats used to represent molecules in computer systems; - Visualise proteins and ligands in PyMOL; - Run a structure-based docking study using AutoDock Vina; and - Run a ligand-based study using a set of RDKit based tools. Main Text/s and any supplementary readings: Main Texts: - Sliwoski, G., Kothiwale, S., Meiler, J., and Lowe, E. L. Jr. Computational Methods in Drug Discovery, Pharmacological Reviews, 66: 336-395. 2014. - Young, D. C. Computational Drug Design: A Guide for Computational and Medicinal Chemists. 1st edition. New Jersey: Wiley & Sons Publishers. 2009. Supplementary Readings: - Leach, A.R. and Gillett, V. J. An Introduction to Chemoinformatics. 1st edition. Netherlands: Springer Publishers. 2007. - Höltje, H-D., Sippl, W., Rognan, D., and Folkers, G. Molecular Modeling. 3rd edition. Wienheim: Wiley-VCH Publishers. 2008. |
||||||||
| STUDY-UNIT TYPE | Lecture, Practical, Project & Independent Study | ||||||||
| METHOD OF ASSESSMENT |
|
||||||||
| LECTURER/S | Rosalin Bonetta Jean Paul Ebejer (Co-ord.) Gary J. Hunter Brandon Charles Seychell |
||||||||
|
The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2024/5. It may be subject to change in subsequent years. |
|||||||||