Domain Linker Prediction using SVM

What is DLP-SVM? Run DLP-SVM Reference

What is DLP-SVM

DLP-SVM is a domain linker predictor.
It is composed of three loop-length dependent SVM predictors of domain linkers (SVM-All, SVM-Long and SVM-Short), and SVM-Joint, which combines
the results of SVM-Short and SVM-Long into a single consolidated prediction (Ebina et al, 2009, Biopolymers ref 1).

The performances of our predictor appear to be largely related to the
quality of the domain data base, we developed in previous studies (ref 2 and 3).
An essential point of DLP is that only strictly-defined domain linkers were used to
train the predictor. Our definition of a domain linker is a loop sequence separating
two structural domains which can fold independently.

The traininf dataset is available here: LinkerList.txt

Ebina T, Toh H, Kuroda Y: Loop-length dependent SVM prediction of domain linkers for high-throughput structural proteomics. Biopolymers 2009;92(1):1-8.

Run DLP-SVM

Sequence Input a single protein sequence using single-letter AA code.
Input more than 120 and less than 2,000 residues.
You can use only AA code, space and return keys for sequence.

Sample image is here.
Prediction result of 2cwg_A

SVMs SVM-All SVM-Long SVM-Short SVM-Joint Select SVMs
Threshold Threshold for output value of candidate region
Offset Length of terminal region to be ignored in prediction
Rank Number of predicted regions for the result
Options Plot Detail Select Options


References

1: Ebina T, Toh H, Kuroda Y: Loop-length dependent SVM prediction of domain linkers for high-throughput structural proteomics. Biopolymers 2009;92(1):1-8.
2: Miyazaki S, Kuroda Y, Yokoyama S: Characterization and prediction of linker sequences of multi-domain proteins by a neural network. J Struct Funct Genomics 2002, 2(1):37-51.
3: Tanaka T, Yokoyama S, Kuroda Y: Improvement of domain linker prediction by incorporating loop-length-dependent characteristics. Biopolymers 2006, 84(2):161-168.
Last modification: 28 September 2009
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