SBIR Phase I:Developing NLP-based Platform Technology for Phage-Host Interaction Predictions

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to improve agricultural operations with respect to infection.Antibiotic resistance in crops is increasing. One of the most promising alternatives to antibiotics is bacteriophages, also known as phages. Phages are safe bacterial viruses already used on a small commercial scale in the United States. One major bottleneck for phage use in a larger number of diseases is the difficulty involved in selecting phages that will be effective against the target pathogen. This project addresses this challenge by aiming to develop a state-of-the-art computational approach to select phages against a range of diseases in a rapid and efficient manner. This technology will allow for widespread phage use as a safer and more efficient substitution for antibiotics. Additionally, the final deliverable of this project includes developing the first-ever phage-based, sustainable, and cost-effective antimicrobial solution against Huanglongbing, a devastating and completely incurable bacterial disease of citruses.The proposed project aims to remove a key bottleneck in the bacteriophage field by building the first-ever commercially viable platform technology to computationally predict phage-host interactions. Specifically, it is currently difficult and time-consuming to select bacteriophages against target pathogens as this manual process is inefficient.Moreover, phages cannot be isolated against uncultivable bacteria. The proposed project aims to use cutting-edge Natural Language Processing for bacteriophage use against an increasing number of pathogens. This proposal’s first objective is to significantly improve the already built models and show that their predictions hold in vitro using the gold-standard plaque assay experiments. This proposal’s second objective is to use this platform technology and wet-lab methods to produce and test the first-ever antibacterial (phage-based) solution that works against the currently incurable Huanglongbing disease caused by an uncultivable pathogen. This project will further help determine if Transformer-based models can excel at complex genome understanding tasks and produce in-vitro-viable viral-host interaction predictions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.