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.

1. Protein
Identification
&
Characterization

2. AACompIdent

3. AACompIdent

4. AACompIdent

5. AACompIdent
TagIdent

6. 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

7. PROPSEARCH-DISTANCE

8. Tool for identification by peptide mapping or peptide
Sequencing
PepSea

9. 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

10. PepMAPPER

11. MASCOT

12. MASCOT

13. 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

14. TMAP
TMHMM
TMPRED
TopPred2
PHDhtm
DAS
Transmembrane helices prediction

15. 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

16. 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

17. Identifies possible PEST regions
PEST

18. 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

19. Relative frequencies of each Amino acid in different
secondary structures
Less accurate than GOR method
Amino acid propensities is the basis
Chou-Fasman method

20. Ala, Glu, Leu, Met  Helix formers
Pro, Gly  Helix breakers
Amino acid propensities

21. 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

22. Amino acid propensities & conditional probabilities
are the basis
GOR Method