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Related Experiment Video

Updated: Jun 26, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

BlaPred: Predicting and classifying β-lactamase using a 3-tier prediction system via Chou's general PseAAC.

Abhishikha Srivastava¹, Ravindra Kumar¹, Manish Kumar¹

¹Department of Biophysics, University of Delhi South Campus, New Delhi 110021, India.

Journal of Theoretical Biology

|August 24, 2018

Summary

This study introduces BlaPred, a computational tool for identifying and classifying beta-lactamase enzymes using protein sequences. BlaPred accurately distinguishes beta-lactamases and predicts their Ambler classes, offering a faster alternative to experimental methods.

Keywords:

Antibiotic resistance Leave-one-out cross-validation Pseudo amino acid composition Support vector machine β-lactamase

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Last Updated: Jun 26, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

The Use of a β-lactamase-based Conductimetric Biosensor Assay to Detect Biomolecular Interactions

The Use of a β-lactamase-based Conductimetric Biosensor Assay to Detect Biomolecular Interactions

Published on: February 1, 2018

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

Biochemistry
Computational Biology
Drug Resistance

Background:

Beta-lactam antibiotics are widely used, but beta-lactamase enzymes confer bacterial resistance.
Existing methods for identifying and classifying beta-lactamases are time-consuming and resource-intensive.
The rapid generation of genomic data necessitates efficient computational tools for enzyme analysis.

Purpose of the Study:

To develop a fast and accurate computational system for predicting and classifying beta-lactamase enzymes from protein sequences.
To provide a tool that aids in understanding bacterial resistance mechanisms in the post-genomic era.

Main Methods:

Development of a three-tier prediction system, BlaPred, based on support vector machines.
Utilized amino acid composition and pseudo amino acid compositions as input features.
Employed leave-one-out cross-validation and independent datasets for performance evaluation.

Main Results:

BlaPred achieved high accuracy in discriminating beta-lactamases from non-beta-lactamases (93.57% in tier-I).
Accuracies for predicting Ambler classes A, B, C, and D were above 93%.
Subclass prediction accuracies for B1, B2, and B3 were 84.78%, 95.65%, and 89.13%, respectively, with overall accuracy on independent datasets exceeding 92% for classes and 87% for subclasses.

Conclusions:

BlaPred offers an efficient and accurate computational method for beta-lactamase identification and classification.
The system significantly outperforms existing methods on benchmark datasets.
BlaPred is available as a webserver and standalone software for broader accessibility.