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1. Summary


Frag1D is a web server for predicting one-dimensional (1D) structure of proteins from amino acid sequences. It predicts three types of 1D structures, i.e. three-state secondary structure (H: Helix, S: Sheet, R: Random Coil), eight-state Shape Strings (R, S, U, V, A, K, G and T, see the definition in Figure 1) and three-state Shape Strings (R, S, U and V to S; A and K to H; G and T to T).

Definition of Shape Strings

Figure 1: Assignment of eight-state Shape Strings as eight clustered regions with specific boundaries on the Ramachandran plot.

Background: The precise prediction of one-dimensional (1D) protein structure as represented by the protein secondary structure and 1D string of discrete state of dihedral angles (i.e. Shape Strings) is a prerequisite for the successful prediction of three-dimensional (3D) structure as well as protein-protein interaction. We have developed a novel 1D structure prediction method, called Frag1D, based on a straightforward fragment matching algorithm and demonstrated its success in the prediction of three sets of 1D structural alphabets, i.e. the classical three-state secondary structure, three- and eight-state Shape Strings. By exploiting the vast protein sequence and protein structure data available, we have brought secondary-structure prediction closer to the expected theoretical limit. When tested by a leave-one-out cross validation on a non-redundant set of PDB cutting at 30% sequence identity containing 5860 protein chains, the overall per-residue accuracy for secondary-structure prediction, i.e. Q3 is 82.9%. The overall per-residue accuracy for three- and eight-state Shape Strings are 85.1 and 71.5%, respectively. We have also benchmarked our program with the latest version of PSIPRED for secondary structure prediction and our program predicted 0.3% better in Q3 when tested on 2241 chains with the same training set. For Shape Strings, we compared our method with a recently published method with the same dataset and definition as used by that method. Our program predicted at 2.2% better in accuracy for three-state Shape Strings. By quantitatively investigating the effect of data base size on 1D structure prediction we show that the accuracy increases by 1% with every doubling of the database size.



3. Usage


Input to the server is one or several amino acid sequences (up to 10) in FASTA format. The user can either paste one or more sequences in the text-area provided, or, alternatively, upload a file in ASCII format.

Example input:
>2az4_A mol:protein length:429 hypothetical protein EF2904
MESKAKTTVTFHSGILTIGGTVIEVAYKDAHIFFDFGTEFRPELDLPDDHIETLINNRLVPELKD
LYDPRLGYEYHGAEDKDYQHTAVFLSHAHLDHSRMINYLDPAVPLYTLKETKMILNSLNRKGDFL
IPSPFEEKNFTREMIGLNKNDVIKVGEISVEIVPVDHDAYGASALLIRTPDHFITYTGDLRLHGH
NREETLAFCEKAKHTELLMMEGVSISFPEREPDPAQIAVVSEEDLVQHLVRLELENPNRQITFNG
YPANVERFAKIIEKSPRTVVLEANMAALLLEVFGIEVRYYYAESGKIPELNPALEIPYDTLLKDK
TDYLWQVVNQFDNLQEGSLYIHSDAQPLGDFDPQYRVFLDLLAKKDITFVRLACSGHAIPEDLDK
IIALIEPQVLVPIHTLKPEKLENPYGERILPERGEQIVL



4. Output


The result file has eight columns, they are Num, AA, Sec, ConfSec, S8, ConfS8, S3 and ConfS3.

Explanation:
 Num        residue index in the sequence.
 AA         one-letter amino acid code.
 Sec        Predicted three state secondary structure,
            (H, S and R).
 ConfSec    Confidence of the predicted secondary structure.
 S8         Predicted 8 state Shape String,
            (R, S, U, V, A, K, G and T).
 ConfS8     Confidence of the predicted 8 state Shape String.
 S3         Predicted 3 state Shape String
            (R, S, U, V -> S; A, K -> H; G, T -> T). 
 ConfS3     Confidence of the predicted 3 state Shape String.
The output for the example sequence can be found here



5. References


Frag1D: [Please cite this paper if you find PredZinc useful in your research]

Tuping Zhou*, Nanjiang Shu* and Sven Hovmöller. A Novel Method for Accurate One-dimensional Protein Structure Prediction based on Fragment Matching, Bioinformatics, 2010;26(4):470-477. (*Co-first author) [PubMed]


5. Contact


Nanjiang Shu

Department for Biochemistry and Biophysics
The Arrhenius Laboratories for Natural Sciences
Stockholm University
SE-106 91 Stockholm, Sweden

Science for Life Laboratory
Box 1031, 17121 Solna, Sweden

E-mail:   nanjiang.shu@scilifelab.se



 
© Nanjiang Shu

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