Bacteriophage Genetic Edition Using LSTM

Abstract

Bacteriophages are gaining increasing interest as antimicrobial tools, largely due to the emergence of multi-antibiotic–resistant bacteria. Although their huge diversity and virulence make them particularly attractive for targeting a wide range of bacterial pathogens, it is difficult to select suitable phages due to their high specificity which limits their host range. In addition, other challenges remain such as structural fragility under certain environmental conditions, immunogenicity of phage therapy, or development of bacterial resistance. The use of genetically engineered phages may reduce characteristics that hinder prophylactic and therapeutic applications of phages. Nowadays, there is no systematic method to modify a given phage genome conferring its sought characteristics. We explore the use of artificial intelligence for this purpose as it has the potential to both guide and accelerate genome modification to generate phage variants with unique properties that overcome the limitations of natural phages. We propose an original architecture composed of two deep learning–driven components: a phage–bacterium interaction predictor and a phage genome-sequence generator. The former is a multi-branch 1-D convolutional neural network (1D-CNN) that analyses phage and bacterial genomes to predict interactions. The latter is a recurrent neural network, more particularly a long short-term memory (LSTM), that performs genomic modifications to a phage to offer substantial host range improvement. For this component, we developed two different architectures composed of one or two stacked LSTM layers with 256 neurons each. These generators are used to modify, more precisely to rewrite, the genome sequence of 42 selected phages, while the predictor is used to estimate the host range of the modified bacteriophages across 46 strains of Pseudomonas aeruginosa. The proposed generators, trained with an average accuracy of 96.1%, are able to improve the host range for an average of 18 phages among the 42 under study, increasing both their average host range, by 73.0 and 103.7%, and the maximum host ranges from 21 to 24 and 29, respectively. These promising results showed that the use of deep learning methodologies allows genetic modification of phages to extend, for instance, their host range, confirming the potential of these approaches to guide bacteriophage engineering.

Understanding Bacteriophage Tail Fiber Interaction with Host Surface Receptor: The Key “Blueprint” for Reprogramming Phage Host Range

“…To systemically engineer the phage genome, deep learning (a subset of machine learning) can also be used. Ataee et al proposed a two-component deep learning model: the first component is the PERFHECT model that predicts the interaction between bacteria and phages using a 1-D convolutional neural network (CNN); and the second component is the PERPHECT generator that alters the existing phage genome to enhance host range prediction ( Figure 5 B) [ 109 ]. In the predictor model, they used genomic information from both phages and bacteria to predict interactions.…”

“…Next, the features are fitted into different machine learning models, which are evaluated to predict the best result. ( B ) Representation of PERPHECT model; phage and bacterial genetic information are used by the PERPHECT model and PERPHECT generator to provide guidance for genomic modification of the existing phage [ 109 ]. Created by .…”