SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity

PLoS One. 2016 Aug 15;11(8):e0155290. doi: 10.1371/journal.pone.0155290. eCollection 2016.

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.

MeSH terms

  • Amino Acid Sequence
  • Computational Biology / methods*
  • Databases, Protein
  • Internet*
  • Proteins / chemistry*
  • Proteins / metabolism*
  • Sequence Alignment
  • Support Vector Machine*

Substances

  • Proteins

Grant support

FZ is supported by grants from the Fundamental Research Funds for the Central Universities (CDJZR14468801, CDJKXB14011, 2015CDJXY); Ministry of Science and Technology, 863 Hi-Tech Program (2007AA02Z160); Key Special Project Grant 2009ZX09501-004 China; and Singapore Academic Research Fund grant R-148-000-208-112. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.