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Cited by 150Machine Learning in BioinformaticsGenomics and Phylogenetic Studiesvaccines and immunoinformatics approaches

MP3: A Software Tool for the Prediction of Pathogenic Proteins in Genomic and Metagenomic Data

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Gupta, Ankit, Kapil, Rohan, Dhakan, Darshan B., Sharma, Vineet K.·2014·PLoS ONE
DOI 10.1371/journal.pone.0093907PMC PMC3988012
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Abstract

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. MP3 is publicly available at http://metagenomics.iiserb.ac.in/mp3/index.php.

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  • Considering the fields of ecology and evolutionary biology, the average % of variance explained in an analysis is 3.6%.

    “Considering the fields of ecology and evolutionary biology, the average % of variance explained in an analysis is 3.6%123.”

    From A phylogenetic approach to comparative genomics (2025) · cites this paper as [123]

    The Results and Discussions sections report prediction accuracy, sensitivity, specificity, and MCC for pathogenic-protein classifiers, not variance explained across ecology and evolutionary biology analyses. Searches for the claimed 3.6% average and for variance explained in those fields found no passage addressing the claim; page numbers are unavailable in our copy. (LLM, conf=0.77)

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Cited by

Sorted by citations

Authors
Gupta, A., Kapil, R., Dhakan, D. B., & Sharma, V. K.
Affiliations
Indian Institute of Science Education and Research, Bhopal (IN)
Corresponding author
Sharma, Vineet K.
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2014-04-15
Volume
9
Issue
4
Pages
e93907
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0093907
PMC
PMC3988012
OpenAlex
W2039095343
Cited by
150
References
1
Citations by year
2026: 6 · 2025: 14 · 2024: 10 · 2023: 18 · 2022: 32 · 2021: 12 · 2020: 15 · 2019: 9 · 2018: 12 · 2017: 8 · 2016: 9 · 2015: 4 · 2014: 1
FWCI
2.77
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Indian Institute of Science Education and Research Bhopal
  • Indian Institute of Science Education and Research Mohali
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords
virulence factors, metagenomics, bacterial genomics, support vector machine, hidden Markov models, web server
MeSH terms
Humans, Markov Chains, Software, Reproducibility of Results, Internet, Genomics, Databases, Genetic, Proteomics, Metagenomics, Support Vector Machine, Web Browser, Datasets as Topic

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