IDOPS: in-silico mining of novel biopesticides

Overview

IDOPS is an advanced bioinformatics platform designed to accelerate the discovery of novel biopesticides through in-silico screening of microbial genomes. By combining hidden Markov models, machine learning, and comparative genomics, IDOPS addresses four critical needs in crop protection: discovery of new pesticidal proteins, characterization and optimization of pesticidal gene expression units, in-silico target prediction, and development of environmentally friendly biopesticides. The platform fills a significant gap, as no existing software implements this integrated approach for mining bacterial pesticidal proteins.

The solution supports the growing demand for sustainable alternatives to chemical pesticides, enabling researchers and industry partners to identify promising biopesticide candidates faster and more comprehensively than traditional methods.

Technical specifications

Key features and capabilities:

• High-quality collection of hidden Markov models for bacterial pesticidal proteins, validated against the latest BPPRC classification
• Comprehensive screening of all available genomes (over 415,000 from the GOLD database)
• Characterization of genomic contexts, including accessory elements, regulators, and co-located genes relevant for optimal toxin expression
• Machine learning-based prediction of potential targets for novel or uncharacterized toxins
• Benchmarking demonstrated superior performance: IDOPS identified all toxins from the BPPRC and uniquely enables genomic context characterization
• Discovery of previously unknown toxin cassettes, including the first chromosomal cry toxin cassette associated with a prophage region
• Planned development as a user-friendly web server accessible to the broader scientific community

Technology readiness level

IDOPS has been validated through benchmarking against existing tools (BtToxinDigger and CryProcessor), where it demonstrated superior sensitivity and unique genomic context analysis capabilities. The platform has already revealed novel toxin candidates from UniProtKB scans, many awaiting formal description. Future development includes expanding the HMM database, enabling large-scale screening, systematic study of accessory elements, and training/validation of target prediction algorithms. The team possesses the IT infrastructure, bioinformatics expertise, and professional network needed for continued development. A one-year collaboration with an industry partner would support design, discussion, and funding of key personnel to advance the platform toward broader deployment.