HomeAll softwareProductsNew ProductsServicesManagement teamCorporate ProfileContact

Test online

Gene finding
Gene finding with similarity
Gene finding in Bacteria
Gene finding in Viruses
Next Generation
Gene search
Gene explorer
Promoter
Protein location
RNA structure
Protein structure
3d-explorer
SeqMan
Multiple alignment
Analysis of expression data
Plant promoter database
Search and map repeats
Extracting known SNPs

 

 

NNSSP Program Description Version 2.

NNSSP - Prediction of protein secondary structure by combining nearest-neighbor algorithms and multiply sequence alignments

Version 2

Method description:

Yi and Lander (*) developed a neural-network and nearest-neighbor method with a scoring system that combined a sequence similarity matrix with the local structural environment scoring scheme of Bowie et al.(**) for predicting protein secondary structure. We have improved their scoring system by taking into consideration N- and C-terminal positions of a-helices and b-strands and also b-turns as distinctive types of secondary structure. Another improvement, which also significantly decrease the time of computation, is performed by restricting a data base with a smaller subset of proteins which are similar with a query sequence. Using multiple sequence alignments rather than single sequences and a simple jury decision method we achieved an over all three-state accuracy of 72.2%, which is better than that observed for the most accurate multilayered neural network approach, tested on the same data set of 126 non-homologous protein chains.

(*) Yi T-M., Lander E.S. (1993)
Protein secondary structure prediction using nearest-neighbor methods.
J.Mol.Biol.,232:1117-1129.

(**) Bowie J.U., Luthy R., Eisenberg D. (1991)
A method to identify protein sequences that fold into a known three-dimensional structure.
Science, 253, 164-170.)

Accuracy:
Overall 3-states (a, b, c) prediction gives ~67.6% correctly predicted residues on 126 non-homologous proteins using the jack-knife test procedure. Using multiple sequence alignments instead of single sequences increases prediction accuracy up to 72.2%.

SEE ALSO "ssp" program of this server.

Example of NNSSP output: This output contains probabilities (Pa and Pb) of a and b structures in 0-9 scale. Probability of c is approximately 10 - Pa - Pb.

ADENYLATE KINASE ISOENZYME-3, /GTP:AMP$
 L=  214 SS content: a-  0.43 b=  0.05 c=  0.52
                    10        20        30        40        50
 PredSS     aaaaaaa           aaaaaa         aaaaaaaa       aa
 AA seq     RLLRAIMGAPGSGKGTVSSRITKHFELKHLSSGDLLRDNMLRGTEIGVLA
 Prob a     99888651000001112244545422211111346775554221332335
 Prob b     00001221000001134422321222233221001110010101134443
                    60        70        80        90       100
 PredSS     aaaa        aaaaaaaaaaaaaaaa             aaaaaaaaa
 AA seq     KTFIDQGKLIPDDVMTRLVLHELKNLTQYNWLLDGFPRTLPQAEALDRAY
 Prob a     54543201110346789888877545553334210001113588888875
 Prob b     22221001210001111000000000111233410101110000000011
                   110       120       130       140       150
 PredSS         bb     aaaaaaaa   bb      bbbb
 AA seq     QIDTVINLNVPFEVIKQRLTARWIHPGSGRVYNIEFNPPKTMGIDDLTGE
 Prob a     32111111111466766643321110001100000000000111111111
 Prob b     12135643321222110122245531001478764210013333211101
                   160       170       180       190       200
 PredSS               aaaaaaaaaaaaaaaaaaaaaaa   bbb          a
 AA seq     PLVQREDDRPETVVKRLKAYEAQTEPVLEYYRKKGVLETFSGTETNKIWP
 Prob a     23433211146788999997765577888886621121111111123335
 Prob b     12321000001110000000000000000000101365542111111221
                   210
 PredSS     aaaaaaa
 AA seq     HVYAFLQTKLPQRS
 Prob a     46687764210111
 Prob b     22211110110001

Reference:

Salamov A.A., Solovyev V.V.
Prediction of protein secondary sturcture by combining nearest-neighbor algorithms and multiply sequence alignments.
J.Mol.Biol.,1995, 247, 11-15.

© 2020 www.softberry.com