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Protein identication characterization

This document provides information on various computational tools and methods for protein identification, characterization, and structure prediction. It discusses tools that use amino acid composition, sequence alignment, peptide mass fingerprinting, and physico-chemical properties to identify proteins. It also describes methods such as Chou-Fasman, GOR, and neural networks that predict protein secondary structure and properties based on amino acid order, propensities, and probabilities.

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Protein Identification & Characterization
AACompIdent
AACompIdent
AACompIdent
AACompIdent TagIdent
To find the putative protein family if querying a new sequence has failed using alignment methods By neglecting the order of amino acid residues in a sequence, it uses the amino acid composition 144 properties like mol.wt, hydrophobicity, average charge etc., are weighted individually and are used as query vector PROPSEARCH
PROPSEARCH-DISTANCE
Tool for identification by peptide mapping or peptide Sequencing PepSea
PepMAPPER: Takes peptide mass as the input Mascot: Can take the following as input, 1) Peptide mass fingerprint 2) Sequence query 3) MS/MS ion search Peptide mass fingerprinting tools
PepMAPPER
MASCOT
MASCOT
To identify peptides that from unspecific cleavage of proteins from their experimental masses Takes chemical modifications, post-translational modifications(PTM) and protease autolytic cleavage in account Findpept
TMAP TMHMM TMPRED TopPred2 PHDhtm DAS Transmembrane helices prediction
Compute pI/Mw: Calculates pI and mol.wt ProtParam: • Computation of physical and chemical parameters for a protein sequence • Computed parameters include, Mol.Wt, Theoretical pI, amino acid composition, atomic composition, estimated half-life etc., Primary structure analysis and prediction
PHDacc: Simple hydrophobicity analysis ProtScale: • Computation of physical and chemical parameters for a protein sequence • Computed parameters include, Mol.Wt, Theoretical pI, amino acid composition, atomic composition, estimated half-life etc., Hydrophobicity prediction
Identifies possible PEST regions PEST
Chou-Fasman method GOR (Garnier, Osguthorpe and Robson) Method Nearest Neighbour Mthod Hidden Markov Models Neural networks SOPMA (self-Optimized Prediction method based on MSA) Secondary structure prediction
Relative frequencies of each Amino acid in different secondary structures Less accurate than GOR method Amino acid propensities is the basis Chou-Fasman method
Ala, Glu, Leu, Met  Helix formers Pro, Gly  Helix breakers Amino acid propensities
Four out of six amino acids have high probability >1.03  α helix Three out of five amino acids with a probability of >1.00  β Sheet Predictive values
Amino acid propensities & conditional probabilities are the basis GOR Method