PB4312 Bioinformatics and Computational Biology Laboratory Syllabus:

PB4312 Bioinformatics and Computational Biology Laboratory Syllabus – Anna University PG Syllabus Regulation 2021

COURSE OBJECTIVES:

The course aims to,
 introduce biopharma related databases, 3D structures of drugs, small molecules and targets
 get familiarized with Next Generation Sequencing Data analysis in a disease context
 perform Quantitative Structure Activity Relationship, Molecular Docking and simulations

LIST OF EXPERIMENTS

1. Introduction to Multiuser Operating System Linux.
2. Databases : Biological and Pharma related.
3. Computing molecular properties of drugs / compounds.
4. Molecular modeling of small molecules : obtaining 3D structures, understanding data formats.
5. Drug targets, Data resources and PDB structures.
6. Homology modeling of Protein Targets and Model evaluation.
7. Next Generation Sequencing Data Analysis Bioconductor Package for Differential gene expression analysis using a disease related dataset.
8. Quantitative Structure Activity relationship (QSAR) Model (partition co-efficient, dissociation constant, molar refractivity, etc.) 9. Pharmacophore identification.
10. Drug like property evaluation of compounds and ADME (Lipinski’s rule of five).
11. Methodology of building and refining protein drug targets structure models from X-ray crystallographic data using CCP4i.
12. Molecular docking : Protein – Protein, Protein-Small Molecule.
13. Molecular Dynamics Simulation using GROMACS.
14. Pharmacogenomics : Effect of SNPs / mutations on drug binding using docking approaches.

TOTAL : 90 PERIODS

COURSE OUTCOMES:

At the end of the course the student will be able to,
1. Classify different types of Biological Databases.
2. retrieve data related to small molecules, drugs and their targets, use computational tools for their analysis.
3. explain about biological macromolecular structures and structure prediction methods
4. perform basic next generation sequencing data analysis.
5. perform computational structural studies like QSAR, Molecular docking, Molecular Dynamics simulations and interpret the results.
6. Present and discuss experimental results

REFERENCES:

1. Introduction to Bioinformatics by Arthur K. Lesk, Oxford University Press.2014
2. Algorithms on Strings, Trees and Sequences by Dan Gusfield, Cambridge University Press.2004
3. Biological Sequence Analysis Probabilistic Models of proteins and nucleic acids by R.Durbin, S.Eddy, A.Krogh, G.Mitchison, Cambridge University Press,1998
4. Bioinformatics Sequence and Genome Analysis by David W. Mount, Cold Spring Harbor Laboratory Press. 2004
5. Bioinformatics The Machine Learning Approach by Pierre Baldi and Soren Brunak, Cambridge University Press,2001
6. RNA-seq Data Analysis: A Practical Approach, by Eija Korpelainen, Jarno Tuimala, Panu Somervuo, Mikael Huss and Garry Wong. CRC Press 2014
7. Next Generation Sequencing Data Analysis, by Xinkun Wang CRC Press.2016

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