A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms

• Published in: BMC bioinformatics 2013-11, Vol.14 (1), p.315-315
• Main Authors: Goodswen, Stephen J, Kennedy, Paul J, Ellis, John T
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; Antigens - chemistry; Antigens - immunology; Artificial Intelligence; Caenorhabditis elegans; Caenorhabditis elegans Proteins - chemistry; Caenorhabditis elegans Proteins - immunology; Candidates; Classification; Computational Biology - methods; Computer Simulation; Drug Discovery; Feasibility studies; Immune system; Machine learning; Methodology; Pathogens; Pipelines; Plasmodium; Proteins; Protozoan Proteins - chemistry; Protozoan Proteins - immunology; Sensitivity and Specificity; Strategy; Toxoplasma gondii; Training; Vaccines; Vaccines - chemistry; Vaccines - immunology
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-14-315

An in silico vaccine discovery pipeline for eukaryotic pathogens typically consists of several computational tools to predict protein characteristics. The aim of the in silico approach to discovering subunit vaccines is to use predicted characteristics to identify proteins which are worthy of laboratory investigation. A major challenge is that these predictions are inherent with hidden inaccuracies and contradictions. This study focuses on how to reduce the number of false candidates using machine learning algorithms rather than relying on expensive laboratory validation. Proteins from Toxoplasma gondii, Plasmodium sp., and Caenorhabditis elegans were used as training and test datasets. The results show that machine learning algorithms can effectively distinguish expected true from expected false vaccine candidates (with an average sensitivity and specificity of 0.97 and 0.98 respectively), for proteins observed to induce immune responses experimentally. Vaccine candidates from an in silico approach can only be truly validated in a laboratory. Given any in silico output and appropriate training data, the number of false candidates allocated for validation can be dramatically reduced using a pool of machine learning algorithms. This will ultimately save time and money in the laboratory.