Abstract
Knowledge of protein function is important for biological, medical and therapeutic studies, but many proteins are still unknown in function. There is a need for more improved functional prediction methods. Our SVM-Prot web-server employed a machine learning method for predicting protein functional families from protein sequences irrespective of similarity, which complemented those similarity-based and other methods in predicting diverse classes of proteins including the distantly-related proteins and homologous proteins of different functions. Since its publication in 2003, we made major improvements to SVM-Prot with (1) expanded coverage from 54 to 192 functional families, (2) more diverse protein descriptors protein representation, (3) improved predictive performances due to the use of more enriched training datasets and more variety of protein descriptors, (4) newly integrated BLAST analysis option for assessing proteins in the SVM-Prot predicted functional families that were similar in sequence to a query protein, and (5) newly added batch submission option for supporting the classification of multiple proteins. Moreover, 2 more machine learning approaches, K nearest neighbor and probabilistic neural networks, were added for facilitating collective assessment of protein functions by multiple methods. SVM-Prot can be accessed at http://bidd2.nus.edu.sg/cgi-bin/svmprot/svmprot.cgi.
Highlights
The knowledge of protein function is essential for studying biological processes [1], understanding disease mechanisms [2], and exploring novel therapeutic targets [3,4]
As part of the collective efforts in developing such prediction methods, we have developed a web-based software SVM-Prot that employs a machine learning method, support vector machines (SVM), for predicting protein functional families from protein sequences irrespective of sequence or structural similarity [12], which have shown good predictive performances [33,34,35,36,37,38,39,40] to complement other methods or as part of the integrated approaches in predicting the function of diverse classes of proteins including the distantlyrelated proteins and homologous proteins of different functions
SE, PR and SP of the SVM model were in the range of 50.00~99.99%, 5.31~99.99% and 82.06~99.99%, respectively
Summary
The knowledge of protein function is essential for studying biological processes [1], understanding disease mechanisms [2], and exploring novel therapeutic targets [3,4]. SVM-Prot was upgraded by using the enriched protein data and more diverse protein descriptors to train models for all 192 functional families and to improve the predictive performance of SVM-Prot. SVM-Prot is capable of predicting the functional families of novel proteins at comparable yield and reduced false hit rates with respect to FFPred.
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