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Computational Proteomics

3 Credit Hour Course

Prerequisite:   None

Introduction: DNA, RNA, gene, aminoacid, protein, protein synthesis, structural and functional analysis of proteins, proteome, proteomics, computational proteomics; Protein sequence comparison: sequence alignment, sequence identity, heuristic algorithms, protein profile, profile-profile comparison, Universal Protein Resource (UniProt), BLASTP, FASTA, CD-HIT; Peptide sequencing: mass spectrometry, ideal spectrum, real spectrum, suffix peptide, peptide vector, spectral alignment; Protein structure analysis: protein folding, primary structure, secondary structure, tertiary structure, quaternary structure, protein structure prediction, Protein Data Bank (PDB), post-translational modification(PTM), PTM Structural Database(PTM-SD); Protein families: super families (structural homology), families (sequence homology), sub-families, Hidden Markov Models (HMM) for protein families, Protein family databases (Pfam, PROSITE); Protein sample representation: AAC, PseAAC, SAAC, di-peptides, gappeddi-peptides, physicochemical and biochemical properties of aminoacids, AAIndex database, PSI-BLAST, Position Specific Scoring Matrix (SSM), structural features; Protein subcellular localization prediction; Protein function prediction; Protein-protein interactions (PPI)?: introduction, experimental detection, prediction, dynamic PPI, PPI network; Computer-aided drug design: reverse vaccinology, antigen prediction, epitope prediction, protein-liganddocking, protein-protein docking, BioLip;

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