IDOPS, a Profile HMM-Based Tool to Detect Pesticidal Sequences and Compare Their Genetic Context

Abstract

Biopesticide-based crop protection is constantly challenged by insect resistance. Thus, expansion of available biopesticides is crucial for sustainable agriculture. Although Bacillus thuringiensis is the major agent for pesticide bioprotection, the number of bacteria species synthesizing proteins with biopesticidal potential is much higher. The Bacterial Pesticidal Protein Resource Center (BPPRC) offers a database of sequences for the control of insect pests, grouped in structural classes. Here we present IDOPS, a tool that detects novel biopesticidal sequences and analyzes them within their genetic environment. The backbone of the IDOPS detection unit is a curated collection of high-quality hidden Markov models that is in accordance with the BPPRC nomenclature. IDOPS was positively benchmarked with BtToxin_Digger and Cry_Processor. In addition, a scan of the UniProtKB database using the IDOPS models returned an abundance of new pesticidal protein candidates distributed across all of the structural groups. Gene expression depends on the genomic environment, therefore, IDOPS provides a comparative genomics module to investigate the genetic regions surrounding pesticidal genes. This feature enables the investigation of accessory elements and evolutionary traits relevant for optimal toxin expression and functional diversification. IDOPS contributes and expands our current arsenal of pesticidal proteins used for crop protection.

Comparison of the performance of multiple whole-genome sequence-based tools for the identification of Bacillus cereus sensu stricto biovar Thuringiensis

“…All other IDOPS models (i.e., CryM1 to M4, CytM1, CytM2, and VipM1) correctly predicted the presence of Bt toxin-encoding genes. The CryM5 profile HMM targets Cry proteins with less conserved variants of the classical 3 domains, while CryM6 targets the C-terminal region of Cry pesticidal proteins ( 26 ). This could explain why the CryM5 and CryM6 models produced false positive predictions on non- Bt strains.…”

“…Thus, multiple whole-genome sequencing (WGS)-based bioinformatics tools have been developed not only to detect known Bt toxin gene sequences but also to predict new variants of these genes. These tools include BtToxin_Digger ( 25 ), IDOPS (identification of pesticidal sequences) ( 26 ), Cry_processor ( 27 ), and BTyper3 ( 13 ). Each tool has been developed using a different approach.…”

Comparison of the performance of multiple whole-genome sequence-based tools for the identification ofBacillus cereus sensu strictobiovar Thuringiensis

“…Bt toxin-encoding genes were detected in each assembled genome using each of the following bioinformatic pipelines: BTyper3 (v3.2.0) (13), BtToxin_Digger (v1.0.10) (25), IDOPS (v0.2.2) (26), and Cry_processor (Last update at August 2019; domain only [DO] and find domain [FD] were used) (27) ( Table 3 ). The four bioinformatic tools were applied to both short-read and hybrid assemblies.…”

“…Thus, multiple whole-genome sequencing (WGS)-based bioinformatics tools have been developed not only to detect known Bt toxin gene sequences, but also to predict new variants of these genes. These tools include BtToxin_Digger (25), IDOPS (26), Cry_processor (27), and BTyper3 (13). Despite the availability of these tools, their performance has not been compared by phenotypic assays.…”

Identification and functional analysis of novel stress-resistance genes from metagenomes of extreme environments

“…In this context, profiling methods gained sensitivity by incorporating position-specific information into the alignment process and quantifying variation between family members at each position (Madera & Gough, 2002;Skewes-Cox et al, 2014). Some software based on HMM profiles were shown to be useful for identifying sequences of interest, such as IDOP or CryProcessor, used for detecting toxin genes (Díaz-Valerio et al, 2021;Shikov et al, 2020). In the same way, deep learning has been shown to be a powerful machine learning approach in predicting DNA sequence affinities and identifying new genes of interest, such as resistance genes or transcription factors (Arango-Argoty et al, 2018;Oliveira Monteiro et al, 2022).…”