MP3 is publicly available at http://metagenomics.iiserb.ac.in/mp3/index.php.
The identification of virulent proteins in any de-novo sequenced genome is useful in estimating its pathogenic ability and understanding the mechanism of pathogenesis. Similarly, the identification of such proteins could be valuable in comparing the metagenome of healthy and diseased individuals and estimating the proportion of pathogenic species. However, the common challenge in both the above tasks is the identification of virulent proteins since a significant proportion of genomic and metagenomic proteins are novel and yet unannotated. The currently available tools which carry out the identification of virulent proteins provide limited accuracy and cannot be used on large datasets.
Therefore, we have developed an MP3 standalone tool and web server for
the prediction of pathogenic proteins in both genomic and metagenomic
datasets. MP3 is developed using an integrated Support Vector Machine
(SVM) and Hidden Markov Model (HMM) approach to carry out highly fast,
sensitive and accurate prediction of pathogenic proteins. It displayed
Sensitivity, Specificity, MCC and accuracy values of 92%, 100%, 0.92 and
96%, respectively, on blind dataset constructed using complete
proteins. On the two metagenomic blind datasets (Blind A: 51–100 amino
acids and Blind B: 30–50 amino acids), it displayed Sensitivity,
Specificity, MCC and accuracy values of 82.39%, 97.86%, 0.80 and 89.32%
for Blind A and 71.60%, 94.48%, 0.67 and 81.86% for Blind B,
respectively. In addition, the performance of MP3 was validated on
selected bacterial genomic and real metagenomic datasets.
To our
knowledge, MP3 is the only program that specializes in fast and accurate
identification of partial pathogenic proteins predicted from short
(100–150 bp) metagenomic reads and also performs exceptionally well on
complete protein sequences.
