Loop-length-dependent SVM prediction of domain linkers for high-throughput structural proteomics

The prediction of structural domains in novel protein sequences is becoming of practical importance. One important area of application is the development of computer-aided techniques for identifying, at a low cost, novel protein domain targets for large-scale functional and structural proteomics. Here, we report a loop-length-dependent support vector machine (SVM) prediction of domain linkers, which are loops separating two structural domains. (DLP-SVM is freely available at: http://www.tuat.ac.jp/ approximately domserv/cgi-bin/DLP-SVM.cgi.) We constructed three loop-length-dependent SVM predictors of domain linkers (SVM-All, SVM-Long and SVM-Short), and also built SVM-Joint, which combines the results of SVM-Short and SVM-Long into a single consolidated prediction. The performances of SVM-Joint were, in most aspects, the highest, with a sensitivity of 59.7% and a specificity of 43.6%, which indicated that the specificity and the sensitivity were improved by over 2 and 3% respectively, when loop-length-dependent characteristics were taken into account. Furthermore, the sensitivity and specificity of SVM-Joint were, respectively, 37.6 and 17.4% higher than those of a random guess, and also superior to those of previously reported domain linker predictors. These results indicate that SVMs can be used to predict domain linkers, and that loop-length-dependent characteristics are useful for improving SVM prediction performances.