UNCOVERING THE ANTIMICROBIAL RESISTANCE PROTEINS WORLD WITH NO SEQUENCE ALIGNMENT: A DEEP LEARNING APPROACH

Tiago Cabral Borelli; Alexandre Paschoal; Ricardo Roberto da Silva

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Submission

Paper Title

UNCOVERING THE ANTIMICROBIAL RESISTANCE PROTEINS WORLD WITH NO SEQUENCE ALIGNMENT: A DEEP LEARNING APPROACH

Authors

Tiago Cabral Borelli
Alexandre Paschoal
Ricardo Roberto da Silva

Modality

Poster

Subject area

Proteins and Proteomics

Publishing Date

21/11/2024

Country of Publishing

Brazil | Brasil

Language of Publishing

Inglês

Paper Page

https://www.even3.com.br/anais/xmeeting-2024/832181-uncovering-the-antimicrobial-resistance-proteins-world-with-no-sequence-alignment--a-deep-learning-approach

ISBN

978-65-272-0843-3

Keywords

Deep learning, antimicrobial resistance, protein alignment

Summary

In the past few years surveying antimicrobial resistance (AMR) has been essential to track the evolution and spread of resistant genes/proteins since AMR is a major cause of death, being responsible for more deaths than HIV and malaria combined. Alignment-based annotation tools use strict similarity (>70%) cutoffs to distinguish between potential and AMR real sequences and only annotate proteins similar to those in their databases. DeepARG and AMRFinderPlus use artificial neural networks (ANN) and Hidden Markov Models (HMM) to annotate AMR proteins with remote homology. However, DeepARG needs a pre-processing step that aligns the query data and selects the most probable proteins, although the filtering uses looser cutoffs. HMMs also depend on multi-sequence alignment (MSA) and are focused on a single AMR class. In this work, we present DeepSEA, an alignment-free tool fitted on antimicrobial resistant proteins (APR) and non-resistant proteins (NRP) aligned and unaligned to ARP. We achieved high generalization performance even with limited and unbalanced datasets for training Our benchmarking results show that DeepSEA outperforms the current multi-class AMR classifiers. Furthermore, DeepSEA’s model can cluster AMR by resistant mechanisms besides being fitted on AMR class data, showing that the model's latent variables successfully captured distinguishing features of antibiotic resistance. Despite being trained on 10 AMR classes DeepSEA models annotated functionally validated tetracycline destructases (TDases) and confirmed the identification of a novel TDase previously found by HMM. Finally, after fine-tuning, DeepSEA provided evidence that deep learning models can achieve a high resolution and annotate AMR at the gene level instead of only classes.

Title of the Event: 20º Congresso Brasileiro de Bioinformática: X-Meeting 2024
City of the Event: Salvador
Title of the Proceedings of the event: X-Meeting presentations
Name of the Publisher: Even3
Means of Dissemination: Meio Digital

How to cite

BORELLI, Tiago Cabral; PASCHOAL, Alexandre; SILVA, Ricardo Roberto da. UNCOVERING THE ANTIMICROBIAL RESISTANCE PROTEINS WORLD WITH NO SEQUENCE ALIGNMENT: A DEEP LEARNING APPROACH.. In: X-Meeting presentations. Anais...Salvador(BA) Hotel Deville Prime, 2024. Available in: https//www.even3.com.br/anais/xmeeting-2024/832181-UNCOVERING-THE-ANTIMICROBIAL-RESISTANCE-PROTEINS-WORLD-WITH-NO-SEQUENCE-ALIGNMENT--A-DEEP-LEARNING-APPROACH. Access in: 27/04/2025