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Related Experiment Video

Updated: Oct 8, 2025

Sorting of Streptomyces Cell Pellets Using a Complex Object Parametric Analyzer and Sorter
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SortPred: The first machine learning based predictor to identify bacterial sortases and their classes using

Adeel Malik1, Sathiyamoorthy Subramaniyam2, Chang-Bae Kim3

  • 1Institute of Intelligence Informatics Technology, Sangmyung University, Seoul 03016, Republic of Korea.

Computational and Structural Biotechnology Journal
|January 3, 2022
PubMed
Summary
This summary is machine-generated.

Sortase enzymes are crucial for Gram-positive bacteria. This study introduces SortPred, a machine-learning tool that accurately identifies bacterial sortase sequences and their classes, aiding in inhibitor development.

Keywords:
BioinformaticsCysteine transpeptidaseHybrid featuresMachine learningRandom forestSortase

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Enzymology

Background:

  • Sortase enzymes are essential surface proteins in Gram-positive bacteria, mediating protein anchoring and interaction with the environment.
  • Pathogenic substrates of sortases highlight their role in disease, driving research into sortase inhibitors.
  • The rapid increase in sequenced bacterial genomes has led to a surge in potential sortase sequences, complicating experimental characterization.

Purpose of the Study:

  • To develop an efficient computational tool for the accurate identification and classification of bacterial sortase enzymes.
  • To address the challenges posed by the large number of unannotated sortase sequences in public databases.
  • To facilitate the discovery of novel sortase inhibitors and the exploration of sortase functions.

Main Methods:

  • Development of a two-layer machine-learning predictor, SortPred.
  • Construction of a novel benchmarking dataset for sortase sequences.
  • Investigation and evaluation of 31 feature descriptors using five feature encoding algorithms and a random forest classifier.
  • Validation of model performance using cross-validation and independent datasets.

Main Results:

  • SortPred achieved accurate prediction of sortase sequences in the first layer and their classification into known classes in the second layer.
  • The study identified robust feature descriptors and optimized machine-learning models for sortase prediction.
  • The developed predictor demonstrated consistent performance across cross-validation and independent evaluations.

Conclusions:

  • SortPred is presented as the first machine-learning-based tool for predicting bacterial sortases and their classes.
  • The tool is expected to significantly accelerate the identification of bacterial sortases, aiding in drug discovery and functional studies.
  • SortPred is made publicly available via a webserver and standalone version for broader scientific use.