MTech Computational Biology syllabus for 1 Sem 2018 scheme 18BBC152

Module-1 MODULE – 1 10 hours

Sequence databases Formats, querying and retrieval, Nucleic acid & Protein sequence databases, Genome Databases, NCBI, EBI, TIGR, SANGER ; Various file formats for bio-molecular sequences: Similarity matrices; Pair-wise alignment; BLAST; Statistical significance of alignment; Sequence assembly; multiple sequence alignment; Tools and techniques. Phylogenetics: distance based and character based approaches. Discussions with Case studies.

Module-2 MODULE – 2 10 hours

SEQUENCE PATTERNS AND PROFILES:

Basic concept and definition of sequence patterns, motifs and profiles, various types of pattern representations viz. consensus, regular expression (Prosite-type) and sequence profiles; trees Motif representation: consensus, regular expressions; PSSMs; Markov models; Regulatory sequence identification using Meme; Gene finding: composition based finding, sequence motif-based finding. Profile-based database searches using PSI-BLAST, analysis and interpretation of profile-based searches. Discussions with Case studies.

A d v e r t i s e m e n t
Module-3 MODULE – 3 10 hours

DATABASES:

PDB, NDB, Chemical Structure database. Pubchem, Gene Expression database: GEO, SAGE, InterPro, Prosite, Pfam, ProDom, Gene Ontology Structure classification database: CATH, SCOP, FSSP, Protein-Protein interaction databases. Representation of molecular structures (DNA, mRNA, protein), secondary structures, domains and motifs; Protein structure classification, evolution; structural quality assessment; structure comparison and alignment; Visualization software (Pymol, Rasmol etc.); 3-D structure comparison and concepts, CE, VAST and DALI, concept of coordinate transformation, RMSD, Z-score for structural comparison. Discussions with Case studies.

Module-4 MODULE – 4 10 hours

STRUCTURE PREDICTION:

Chou Fasman, GOR methods; analysis of results and measuring the accuracy of predictions. Prediction of membrane helices, solvent accessibility; RNA structure prediction; Mfold; Fundamentals of the methods for 3D structure prediction (sequence similarity/identity of target proteins of known structure, fundamental principles of protein folding etc.) Homology/comparative modelling, fold recognition, threading approaches, and ab initio structure prediction methods. Force fields, backbone conformer generation by Monte Carloapproaches, sidechain packing; Energy minimization; Structure analysis and validation: Pdbsum, Whatcheck, Procheck, Verify3D and ProsaII; Rosetta; Discussions with Case studies.

Module-5 MODULE – 5 10 hours

COMPUTATIONAL BIOLOGY IN DRUG DESIGN:

Target identification, validation and Identification and Analysis of Binding sites; virtual screening, lead optimization. Ligand based drug design: QSARs and QSPRs, In silico prediction ADMET properties for Drug Molecules. Pharmacophore identification. Protein-ligand docking; Rigid and Semi Flexible Molecular Docking. Studying Protein-Protein interactions via computational biology tools. Computational Biology applications for proteomics, Comparative genomics, Transcriptomics, Microarray technology, expression profiles data analysis; SAGE; MS Data analysis, Probabilistic Models of Evolution, Protein arrays; Metabolomics, Gene Mapping, SNP analysis, Systems Biology. Discussions with case studies.