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Leaky Scanning

Leaky Scanning

During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA. Marilyn Kozak discovered that the sequence RCCAUGG (where R...

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Related Experiment Video

Updated: May 24, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Leveraging protein language models for robust antimicrobial peptide detection.

Lichao Zhang¹, Shuwen Xiong², Lei Xu³

¹School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen 518172, China.

Methods (San Diego, Calif.)

|March 6, 2025

Summary

This study introduces PLAPD, a new deep learning framework for predicting antimicrobial peptides (AMPs). PLAPD offers a faster, more accurate method for discovering novel AMPs to combat antibiotic resistance.

Keywords:

Antimicrobial peptides Deep learning Protein language model Transformer

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Area of Science:

Biochemistry
Computational Biology
Drug Discovery

Background:

Antimicrobial peptides (AMPs) show promise in combating antibiotic resistance.
Traditional AMP identification is slow and resource-intensive.
Deep learning, particularly protein language models (PLMs), offers new possibilities for AMP prediction.

Purpose of the Study:

To develop and evaluate PLAPD, a novel framework for accurate and high-throughput antimicrobial peptide (AMP) classification.
To leverage pre-trained protein language models (PLMs) for enhanced AMP prediction.

Main Methods:

Utilized a pre-trained ESM2 protein language model within the PLAPD framework.
Integrated local feature extraction using convolutional layers.
Incorporated global feature extraction via a residual Transformer module.
Benchmarked PLAPD against existing state-of-the-art AMP prediction models on an 8,268-sequence dataset.

Main Results:

PLAPD demonstrated superior performance compared to existing models.
Achieved high Accuracy (0.87), Precision (0.9359), Specificity (0.9456), Matthews Correlation Coefficient (MCC) (0.7486), and Area Under the Curve (AUC) (0.9225).

Conclusions:

PLAPD represents a significant advancement in AMP discovery.
The framework shows potential as a high-throughput and accurate tool for identifying novel antimicrobial peptides.
Highlights the efficacy of integrating PLMs with deep learning architectures for biological sequence analysis.