DREM: integrating resistance and domain probabilities to prioritize plant immune receptors
- 1University of Naples "FedericoII"
Abstract
Plant resistance (R) genes, key players in the immune system, have traditionally been identified with HMM-based tools relying on sequence homology, whose sensitivity decreases for divergent or non-canonical resistance proteins. Here, DREM (Deep Resistance gene Evaluator Model) is presented as a deep learning framework that learns the compositional properties of resistance proteins from sequence alone rather than searching for predefined motifs. The framework combines two bidirectional long short-term memory networks with attention: a binary resistance classifier and a multi-label classifier of resistance domains. Validation on experimentally characterized proteins from RefPlantNLR recovered 99.8% of sequences, and on 15,000 held-out proteins DREM outperformed the deep learning predictor PRGminer (Matthews correlation coefficient 0.938 vs 0.820), with about two-thirds fewer classification errors. Across five plant proteomes, DREM identified 4,046 High Confidence or Candidate proteins; canonical domain architecture was confirmed in 75.5%, whereas 992 carried strong DREM signals without detectable Pfam domains, and 54 of 55 characterized nucleotide-binding leucine-rich repeat (NLR) loci ranked within the top 1.7% of their proteomes. Unlike domain-gated pipelines such as R-Predictor, DREM also recovered experimentally validated atypical NLRs, RPW8 proteins and non-receptor resistance genes. Attention did not concentrate on canonical ATP-binding motifs, consistent with the model drawing on sequence information beyond conserved domain signatures. Lightweight and applicable to complete proteomes, DREM complements domain-based annotation with a calibrated ranking. It links genome-wide prediction to the selection of resistance genes for functional validation, including atypical receptors missed by profile-based tools.
Related
- Molecular Insights into the Collagen-binding Specificity of Discoidin Domain Receptors
- A dual-host regulatory hub integrating biofilm formation and innate immunity resistance
- Engineering an Exo70 integrated domain of a barley NLR for improved blast resistance
- Language-guided domain adaptation improves protein sequence rep-resentations for antimicrobial resistance analysis
- Effects of Sulfur Dioxide on Disease Resistance and Storage Quality of Durian (Durio zibethinus L.): A Comprehensive Analysis Integrating GC-IMS, HTS, Physiological and Biochemical Parameters.
BibTeX
@misc{dublino2026drem,
title = {{DREM: integrating resistance and domain probabilities to prioritize plant immune receptors}},
author = {R. Dublino and V. Carandente and G. Sperli and M. Ercolano},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.21.760626},
url = {https://www.biorxiv.org/content/10.64898/2026.10.21.760626v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - DREM: integrating resistance and domain probabilities to prioritize plant immune receptors
AU - R. Dublino
AU - V. Carandente
AU - G. Sperli
AU - M. Ercolano
PY - 2026
DA - 2026/10/21
PB - bioRxiv
DO - 10.64898/2026.10.21.760626
UR - https://www.biorxiv.org/content/10.64898/2026.10.21.760626v1
AB - Plant resistance (R) genes, key players in the immune system, have traditionally been identified with HMM-based tools relying on sequence homology, whose sensitivity decreases for divergent or non-canonical resistance proteins. Here, DREM (Deep Resistance gene Evaluator Model) is presented as a deep learning framework that learns the compositional properties of resistance proteins from sequence alone rather than searching for predefined motifs. The framework combines two bidirectional long short-term memory networks with attention: a binary resistance classifier and a multi-label classifier of resistance domains. Validation on experimentally characterized proteins from RefPlantNLR recovered 99.8% of sequences, and on 15,000 held-out proteins DREM outperformed the deep learning predictor PRGminer (Matthews correlation coefficient 0.938 vs 0.820), with about two-thirds fewer classification errors. Across five plant proteomes, DREM identified 4,046 High Confidence or Candidate proteins; canonical domain architecture was confirmed in 75.5%, whereas 992 carried strong DREM signals without detectable Pfam domains, and 54 of 55 characterized nucleotide-binding leucine-rich repeat (NLR) loci ranked within the top 1.7% of their proteomes. Unlike domain-gated pipelines such as R-Predictor, DREM also recovered experimentally validated atypical NLRs, RPW8 proteins and non-receptor resistance genes. Attention did not concentrate on canonical ATP-binding motifs, consistent with the model drawing on sequence information beyond conserved domain signatures. Lightweight and applicable to complete proteomes, DREM complements domain-based annotation with a calibrated ranking. It links genome-wide prediction to the selection of resistance genes for functional validation, including atypical receptors missed by profile-based tools.
ER -