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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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GPApred: The first computational predictor for identifying proteins with LPXTG-like motif using sequence-based

Adeel Malik¹, Watshara Shoombuatong², Chang-Bae Kim³

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

International Journal of Biological Macromolecules

|January 3, 2023

Summary

We developed GPApred, a machine learning tool to identify LPXTG-like proteins in gram-positive bacteria. This predictor aids in understanding bacterial infections and developing new drugs or vaccines.

Keywords:

Cell wall sorting signal Feature selection Machine learning Sortase Support vector machine Surface proteins

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Identification of Antibacterial Immunity Proteins in Escherichia coli using MALDI-TOF-TOF-MS/MS and Top-Down Proteomic Analysis

Identification of Antibacterial Immunity Proteins in Escherichia coli using MALDI-TOF-TOF-MS/MS and Top-Down Proteomic Analysis

Published on: May 23, 2021

Area of Science:

Microbiology
Bioinformatics
Computational Biology

Background:

Cell surface proteins in gram-positive bacteria are crucial for host cell infection and are potential drug/vaccine targets.
LPXTG-like proteins, a major class of these surface proteins, possess a conserved C-terminal cell wall sorting signal.
Increased bacterial genome sequencing has led to many unannotated LPXTG-like protein sequences, posing identification challenges.

Purpose of the Study:

To develop the first machine learning-based predictor, GPApred, for identifying LPXTG-like proteins from primary amino acid sequences.
To address the challenge of annotating LPXTG-like proteins in the absence of experimental characterization.

Main Methods:

Development of GPApred using a newly constructed benchmark dataset of LPXTG-like proteins.
Exploration and optimization of various classifiers, feature encodings, and their hybrids.
Utilizing recursive feature elimination for optimal feature selection and support vector machine for training.

Main Results:

GPApred was developed as a machine learning predictor for LPXTG-like proteins.
The predictor was trained and validated using rigorous cross-validation and independent datasets.
The final GPApred model demonstrated consistent performance in identifying LPXTG-like sequences.

Conclusions:

GPApred is an effective tool for accurately predicting LPXTG-like protein sequences.
The predictor can facilitate functional characterization and aid in the development of novel drug or vaccine targets.
GPApred is publicly available at https://procarb.org/gpapred/.