A Deep Learning-Based Method for Identification of Bacteriophage-Host Interaction

Gut phages and their interactions with bacterial and mammalian hosts

The mammalian gut microbiome is a dense and diverse community of microorganisms that reside in the distal gastrointestinal tract. In recent decades, the bacterial members of the gut microbiome have been the subject of intense research. Less well studied is the large community of bacteriophages that reside in the gut, which number in the billions of viral particles per gram of feces, and consist of considerable unknown viral "dark matter." This community of gut-residing bacteriophages, called the gut "phageome," plays a central role in the gut microbiome through predation and transformation of native gut bacteria, and through interactions with their mammalian hosts. In this review, we will summarize what is known about the composition and origins of the gut phageome, as well as its role in microbiome homeostasis and host health. Furthermore, we will outline the interactions of gut phages with their bacterial and mammalian hosts, and plot a course for the mechanistic study of these systems.

Factors Affecting Phage-Bacteria Coevolution Dynamics

Bacteriophages (phages) have coevolved with their bacterial hosts for billions of years. With the rise of antibiotic resistance, the significance of using phages in therapy is increasing. Investigating the dynamics of phage evolution can provide valuable insights for pre-adapting phages to more challenging clones of their hosts that may arise during treatment. Two primary models describe interactions in phage-bacteria systems: arms race dynamics and fluctuating selection dynamics. Numerous factors influence which dynamics dominate the interactions between a phage and its host. These dynamics, in turn, affect the coexistence of phages and bacteria, ultimately determining which organism will adapt more effectively to the other, and whether a stable state will be reached. In this review, we summarize key findings from research on phage-bacteria coevolution, focusing on the different concepts that can describe these interactions, the factors that may contribute to the prevalence of one model over others, and the effects of various dynamics on both phages and bacteria.

Predicting phage-host interaction via hyperbolic Poincaré graph embedding and large-scale protein language technique

Bacteriophages (phages) are increasingly viewed as a promising alternative for the treatment of antibiotic-resistant bacterial infections. However, the diversity of host ranges complicates the identification of target phages. Existing computational tools often fail to accurately identify phages across different bacterial species. In this study, we present GE-PHI, a machine-learning-based model for predicting phage-host interactions (PHIs) by integrating knowledge graph embedding algorithm with a large-scale protein language model. First, a phage-host heterogeneous association network (PHAN) was constructed that incorporated phage-phage and host-host similarity networks. Then, the multi-relational Poincaré graph embedding (MuRP) was used to extract topological patterns. Additionally, we employed the ESM-2 protein language model to capture evolutionary information from phage tail proteins and host-receptor-binding proteins. GE-PHI achieved a cross-validation area under the curve (AUC) of up to 0.9453 in silico and maintains this performance in case studies. This study provides insights into machine-learning-guided phage therapeutics and diagnostics in microbial engineering.

PHIStruct: improving phage-host interaction prediction at low sequence similarity settings using structure-aware protein embeddings

MOTIVATION

Recent computational approaches for predicting phage-host interaction have explored the use of sequence-only protein language models to produce embeddings of phage proteins without manual feature engineering. However, these embeddings do not directly capture protein structure information and structure-informed signals related to host specificity.

RESULTS

We present PHIStruct, a multilayer perceptron that takes in structure-aware embeddings of receptor-binding proteins, generated via the structure-aware protein language model SaProt, and then predicts the host from among the ESKAPEE genera. Compared against recent tools, PHIStruct exhibits the best balance of precision and recall, with the highest and most stable F1 score across a wide range of confidence thresholds and sequence similarity settings. The margin in performance is most pronounced when the sequence similarity between the training and test sets drops below 40%, wherein, at a relatively high-confidence threshold of above 50%, PHIStruct presents a 7%-9% increase in class-averaged F1 over machine learning tools that do not directly incorporate structure information, as well as a 5%-6% increase over BLASTp.

AVAILABILITY AND IMPLEMENTATION

The data and source code for our experiments and analyses are available at https://github.com/bioinfodlsu/PHIStruct.

A novel framework for phage-host prediction via logical probability theory and network sparsification

Bacterial resistance has emerged as one of the greatest threats to human health, and phages have shown tremendous potential in addressing the issue of drug-resistant bacteria by lysing host. The identification of phage-host interactions (PHI) is crucial for addressing bacterial infections. Some existing computational methods for predicting PHI are suboptimal in terms of prediction efficiency due to the limited types of available information. Despite the emergence of some supporting information, the generalizability of models using this information is limited by the small scale of the databases. Additionally, most existing models overlook the sparsity of association data, which severely impacts their predictive performance as well. In this study, we propose a dual-view sparse network model (DSPHI) to predict PHI, which leverages logical probability theory and network sparsification. Specifically, we first constructed similarity networks using the sequences of phages and hosts respectively, and then sparsified these networks, enabling the model to focus more on key information during the learning process, thereby improving prediction efficiency. Next, we utilize logical probability theory to compute high-order logical information between phages (hosts), which is known as mutual information. Subsequently, we connect this information in node form to the sparse phage (host) similarity network, resulting in a phage (host) heterogeneous network that better integrates the two information views, thereby reducing the complexity of model computation and enhancing information aggregation capabilities. The hidden features of phages and hosts are explored through graph learning algorithms. Experimental results demonstrate that mutual information is effective information in predicting PHI, and the sparsification procedure of similarity networks significantly improves the model's predictive performance.

Predicting phage-host interactions via feature augmentation and regional graph convolution

Identifying phage-host interactions (PHIs) is a crucial step in developing phage therapy, which is the promising solution to addressing the issue of antibiotic resistance in superbugs. However, the lifestyle of phages, which strongly depends on their host for life activities, limits their cultivability, making the study of predicting PHIs time-consuming and labor-intensive for traditional wet lab experiments. Although many deep learning (DL) approaches have been applied to PHIs prediction, most DL methods are predominantly based on sequence information, failing to comprehensively model the intricate relationships within PHIs. Moreover, most existing approaches are limited for sub-optimal performance, due to the potential risk of overfitting induced by the highly data sparsity in the task of PHIs prediction. In this study, we propose a novel approach called MI-RGC, which introduces mutual information for feature augmentation and employs regional graph convolution to learn meaningful representations. Specifically, MI-RGC treats the presence status of phages in environmental samples as random variables, and derives the mutual information between these random variables as the dependency relationships among phages. Consequently, a mutual information-based heterogeneous network is construted as feature augmentation for sequence information of phages, which is utilized for building a sequence information-based heterogeneous network. By considering the different contributions of neighboring nodes at varying distances, a regional graph convolutional model is designed, in which the neighboring nodes are segmented into different regions and a regional-level attention mechanism is employed to derive node embeddings. Finally, the embeddings learned from these two networks are aggregated through an attention mechanism, on which the prediction of PHIs is condcuted accordingly. Experimental results on three benchmark datasets demonstrate that MI-RGC derives superior performance over other methods on the task of PHIs prediction.

PHPGAT: predicting phage hosts based on multimodal heterogeneous knowledge graph with graph attention network

Antibiotic resistance poses a significant threat to global health, making the development of alternative strategies to combat bacterial pathogens increasingly urgent. One such promising approach is the strategic use of bacteriophages (or phages) to specifically target and eradicate antibiotic-resistant bacteria. Phages, being among the most prevalent life forms on Earth, play a critical role in maintaining ecological balance by regulating bacterial communities and driving genetic diversity. Accurate prediction of phage hosts is essential for successfully applying phage therapy. However, existing prediction models may not fully encapsulate the complex dynamics of phage-host interactions in diverse microbial environments, indicating a need for improved accuracy through more sophisticated modeling techniques. In response to this challenge, this study introduces a novel phage-host prediction model, PHPGAT, which leverages a multimodal heterogeneous knowledge graph with the advanced GATv2 (Graph Attention Network v2) framework. The model first constructs a multimodal heterogeneous knowledge graph by integrating phage-phage, host-host, and phage-host interactions to capture the intricate connections between biological entities. GATv2 is then employed to extract deep node features and learn dynamic interdependencies, generating context-aware embeddings. Finally, an inner product decoder is designed to compute the likelihood of interaction between a phage and host pair based on the embedding vectors produced by GATv2. Evaluation results using two datasets demonstrate that PHPGAT achieves precise phage host predictions and outperforms other models. PHPGAT is available at https://github.com/ZhaoZMer/PHPGAT.

Comprehensive retrospect and future perspective on bacteriophage and cancer

BACKGROUND

Researchers gradually focus on the relationship between phage and cancer.

OBJECTIVE

To summarize the research hotspots and trends in the field of bacteriophage and cancer.

METHODS

The downloaded articles were searched from the Web of Science Core Collection database from January 2008 to June 2023. Bibliometric analysis was carried out through CiteSpace, including the analysis of cooperative networks (country/region, institution, and author), co-citations of references, and key words.Visual analysis of three topics, including gut phage, phage and bacteria, and phage and tumor, was conducted.

RESULTS

Overall, the United States and China have the most phage-related research. In terms of gut phage, the future research directions are "gut microbiome", "database" and "microbiota". The bursting citations explored the phage-dominated viral genome to discover its diversity and individual specificity and investigated associations among bacteriome, metabolome, and virome. In terms of phage and bacteria, "lipopolysaccharide" and "microbiota" are future research directions. Future research hotspots should mainly concentrate on the further exploration and application of phage properties. As for phages and tumors, the future research directions should be "colorectal cancer", "protein" and "phage therapy". Future directions are likely to focus on the research on phages in cancer mechanisms, cancer diagnosis, and cancer treatment combined with genetic engineering techniques.

CONCLUSION

Phage therapy would become a hot spot and research direction of tumor and phage research, and the relationship between phage and tumor, especially colorectal cancer (CRC), is expected to be further explored.

A game of resistance: War between bacteria and phages and how phage cocktails can be the solution

While phages hold promise as an antibiotic alternative, they encounter significant challenges in combating bacterial infections, primarily due to the emergence of phage-resistant bacteria. Bacterial defence mechanisms like superinfection exclusion, CRISPR, and restriction-modification systems can hinder phage effectiveness. Innovative strategies, such as combining different phages into cocktails, have been explored to address these challenges. This review delves into these defence mechanisms and their impact at each stage of the infection cycle, their challenges, and the strategies phages have developed to counteract them. Additionally, we examine the role of phage cocktails in the evolving landscape of antibacterial treatments and discuss recent studies that highlight the effectiveness of diverse phage cocktails in targeting essential bacterial receptors and combating resistant strains.

DeepPBI-KG: a deep learning method for the prediction of phage-bacteria interactions based on key genes

Phages, the natural predators of bacteria, were discovered more than 100 years ago. However, increasing antimicrobial resistance rates have revitalized phage research. Methods that are more time-consuming and efficient than wet-laboratory experiments are needed to help screen phages quickly for therapeutic use. Traditional computational methods usually ignore the fact that phage-bacteria interactions are achieved by key genes and proteins. Methods for intraspecific prediction are rare since almost all existing methods consider only interactions at the species and genus levels. Moreover, most strains in existing databases contain only partial genome information because whole-genome information for species is difficult to obtain. Here, we propose a new approach for interaction prediction by constructing new features from key genes and proteins via the application of K-means sampling to select high-quality negative samples for prediction. Finally, we develop DeepPBI-KG, a corresponding prediction tool based on feature selection and a deep neural network. The results show that the average area under the curve for prediction reached 0.93 for each strain, and the overall AUC and area under the precision-recall curve reached 0.89 and 0.92, respectively, on the independent test set; these values are greater than those of other existing prediction tools. The forward and reverse validation results indicate that key genes and key proteins regulate and influence the interaction, which supports the reliability of the model. In addition, intraspecific prediction experiments based on Klebsiella pneumoniae data demonstrate the potential applicability of DeepPBI-KG for intraspecific prediction. In summary, the feature engineering and interaction prediction approaches proposed in this study can effectively improve the robustness and stability of interaction prediction, can achieve high generalizability, and may provide new directions and insights for rapid phage screening for therapy.

Bacteriophage-Based Bioanalysis

Bacteriophages, which are viral predators of bacteria, have evolved to efficiently recognize, bind, infect, and lyse their host, resulting in the release of tens to hundreds of propagated viruses. These abilities have attracted biosensor developers who have developed new methods to detect bacteria. Recently, several comprehensive reviews have covered many of the advances made regarding the performance of phage-based biosensors. Therefore, in this review, we first describe the landscape of phage-based biosensors and then cover advances in other aspects of phage biology and engineering that can be used to make high-impact contributions to biosensor development. Many of these advances are in fields adjacent to analytical chemistry such as synthetic biology, machine learning, and genetic engineering and will allow those looking to develop phage-based biosensors to start taking alternative approaches, such as a bottom-up design and synthesis of custom phages with the singular task of detecting their host.

Advances in phage-host interaction prediction: in silico method enhances the development of phage therapies

Phages can specifically recognize and kill bacteria, which lead to important application value of bacteriophage in bacterial identification and typing, livestock aquaculture and treatment of human bacterial infection. Considering the variety of human-infected bacteria and the continuous discovery of numerous pathogenic bacteria, screening suitable therapeutic phages that are capable of infecting pathogens from massive phage databases has been a principal step in phage therapy design. Experimental methods to identify phage-host interaction (PHI) are time-consuming and expensive; high-throughput computational method to predict PHI is therefore a potential substitute. Here, we systemically review bioinformatic methods for predicting PHI, introduce reference databases and in silico models applied in these methods and highlight the strengths and challenges of current tools. Finally, we discuss the application scope and future research direction of computational prediction methods, which contribute to the performance improvement of prediction models and the development of personalized phage therapy.

Exploring the Bacteriophage Frontier: A Bibliometric Analysis of Clinical Trials Between 1965 and 2024

In recent years, the rise of antibiotic-resistant bacteria has posed a severe threat to global public health, necessitating innovative and alternative approaches to combat this escalating crisis. Bacteriophages, viruses that infect and replicate within bacteria, have emerged as promising candidates for therapeutic intervention against antibiotic-resistant pathogens. This study delves into the intricate landscape of bacteriophage research, unraveling the trends and impact of research in the field. The analysis considers the chronological evolution of research, identifying key contributors, collaborative networks, and thematic trends that have shaped the trajectory of this rapidly growing field. Out of 101717 search results in the PubMed database, 163 clinical trials were identified, revealing a dynamic landscape of research activity between 1965 and 2024. The annual scientific publication analysis unveiled fluctuations in the number of publications, indicating an overall increasing trend. Notably, 2011 emerged as a peak year, signifying heightened activity in bacteriophage research. Employing Lotka's law, the authors' productivity analysis illustrated an inherent imbalance in author contributions, with a majority contributing to a single clinical trial. Co-authorship analysis highlighted leading collaborators. Co-occurrence analysis of keywords unveiled thematic clusters, providing insights into the diverse aspects of bacteriophage research. A word cloud emphasized significant terms, while a thematic map categorized themes into various developmental stages. Antimicrobial Agents, Chemotherapy, and Poultry Science were the most relevant journals based on the number of publications. The analysis of countries' contributions revealed the United States as a leading contributor, with Switzerland and China following suit. Collaboration patterns suggested predominantly independent research, with potential for increased international partnerships in certain regions. Additionally, temporal analysis of authors, institutions, sources, and countries revealed productivity patterns, historical context, and research shifts. By scrutinizing a vast array of scientific literature, this investigation aims to provide a panoramic view of how the scientific community has explored the potential of bacteriophages in the context of antibiotic resistance.

Biological and bioinformatic tools for the discovery of unknown phage-host combinations

The field of microbial ecology has been transformed by metagenomics in recent decades and has culminated in vast datasets that facilitate the bioinformatic dissection of complex microbial communities. Recently, attention has turned from defining the microbiota composition to the interactions and relationships that occur between members of the microbiota. Within complex microbiota, the identification of bacteriophage-host combinations has been a major challenge. Recent developments in artificial intelligence tools to predict protein structure and function as well as the relationships between bacteria and their infecting bacteriophages allow a strategic approach to identifying and validating phage-host relationships. However, biological validation of these predictions remains essential and will serve to improve the existing predictive tools. In this review, I provide an overview of the most recent developments in both bioinformatic and experimental approaches to predicting and experimentally validating unknown phage-host combinations.

Identifying ARG-carrying bacteriophages in a lake replenished by reclaimed water using deep learning techniques

As important mobile genetic elements, phages support the spread of antibiotic resistance genes (ARGs). Previous analyses of metaviromes or metagenome-assembled genomes (MAGs) failed to assess the extent of ARGs transferred by phages, particularly in the generation of antibiotic pathogens. Therefore, we have developed a bioinformatic pipeline that utilizes deep learning techniques to identify ARG-carrying phages and predict their hosts, with a special focus on pathogens. Using this method, we discovered that the predominant types of ARGs carried by temperate phages in a typical landscape lake, which is fully replenished by reclaimed water, were related to multidrug resistance and β-lactam antibiotics. MAGs containing virulent factors (VFs) were predicted to serve as hosts for these ARG-carrying phages, which suggests that the phages may have the potential to transfer ARGs. In silico analysis showed a significant positive correlation between temperate phages and host pathogens (R = 0.503, p < 0.001), which was later confirmed by qPCR. Interestingly, these MAGs were found to be more abundant than those containing both ARGs and VFs, especially in December and March. Seasonal variations were observed in the abundance of phages harboring ARGs (from 5.62 % to 21.02 %) and chromosomes harboring ARGs (from 18.01 % to 30.94 %). In contrast, the abundance of plasmids harboring ARGs remained unchanged. In summary, this study leverages deep learning to analyze phage-transferred ARGs and demonstrates an alternative method to track the production of potential antibiotic-resistant pathogens by metagenomics that can be extended to microbiological risk assessment.

PTBGRP: predicting phage-bacteria interactions with graph representation learning on microbial heterogeneous information network

Identifying the potential bacteriophages (phage) candidate to treat bacterial infections plays an essential role in the research of human pathogens. Computational approaches are recognized as a valid way to predict bacteria and target phages. However, most of the current methods only utilize lower-order biological information without considering the higher-order connectivity patterns, which helps to improve the predictive accuracy. Therefore, we developed a novel microbial heterogeneous interaction network (MHIN)-based model called PTBGRP to predict new phages for bacterial hosts. Specifically, PTBGRP first constructs an MHIN by integrating phage-bacteria interaction (PBI) and six bacteria-bacteria interaction networks with their biological attributes. Then, different representation learning methods are deployed to extract higher-level biological features and lower-level topological features from MHIN. Finally, PTBGRP employs a deep neural network as the classifier to predict unknown PBI pairs based on the fused biological information. Experiment results demonstrated that PTBGRP achieves the best performance on the corresponding ESKAPE pathogens and PBI dataset when compared with state-of-art methods. In addition, case studies of Klebsiella pneumoniae and Staphylococcus aureus further indicate that the consideration of rich heterogeneous information enables PTBGRP to accurately predict PBI from a more comprehensive perspective. The webserver of the PTBGRP predictor is freely available at http://120.77.11.78/PTBGRP/.

Protein embeddings improve phage-host interaction prediction

With the growing interest in using phages to combat antimicrobial resistance, computational methods for predicting phage-host interactions have been explored to help shortlist candidate phages. Most existing models consider entire proteomes and rely on manual feature engineering, which poses difficulty in selecting the most informative sequence properties to serve as input to the model. In this paper, we framed phage-host interaction prediction as a multiclass classification problem that takes as input the embeddings of a phage's receptor-binding proteins, which are known to be the key machinery for host recognition, and predicts the host genus. We explored different protein language models to automatically encode these protein sequences into dense embeddings without the need for additional alignment or structural information. We show that the use of embeddings of receptor-binding proteins presents improvements over handcrafted genomic and protein sequence features. The highest performance was obtained using the transformer-based protein language model ProtT5, resulting in a 3% to 4% increase in weighted F1 and recall scores across different prediction confidence thresholds, compared to using selected handcrafted sequence features.

GSPHI: A novel deep learning model for predicting phage-host interactions via multiple biological information

Emerging evidence suggests that due to the misuse of antibiotics, bacteriophage (phage) therapy has been recognized as one of the most promising strategies for treating human diseases infected by antibiotic-resistant bacteria. Identification of phage-host interactions (PHIs) can help to explore the mechanisms of bacterial response to phages and provide new insights into effective therapeutic approaches. Compared to conventional wet-lab experiments, computational models for predicting PHIs can not only save time and cost, but also be more efficient and economical. In this study, we developed a deep learning predictive framework called GSPHI to identify potential phage and target bacterium pairs through DNA and protein sequence information. More specifically, GSPHI first initialized the node representations of phages and target bacterial hosts via a natural language processing algorithm. Then a graph embedding algorithm structural deep network embedding (SDNE) was utilized to extract local and global information from the interaction network, and finally, a deep neural network (DNN) was applied to accurately detect the interactions between phages and their bacterial hosts. In the drug-resistant bacteria dataset ESKAPE, GSPHI achieved a prediction accuracy of 86.65 % and AUC of 0.9208 under the 5-fold cross-validation technique, significantly better than other methods. In addition, case studies in Gram-positive and negative bacterial species demonstrated that GSPHI is competent in detecting potential Phage-host interactions. Taken together, these results indicate that GSPHI can provide reasonable candidate sensitive bacteria to phages for biological experiments. The webserver of the GSPHI predictor is freely available at http://120.77.11.78/GSPHI/.

Nanoparticle-mediated stimulus-responsive antibacterial therapy

The limitations associated with conventional antibacterial therapies and the subsequent amplification of multidrug-resistant (MDR) microorganisms have increased, necessitating the urgent development of innovative antibacterial techniques. Accordingly, nanoparticle-mediated therapeutics have emerged as potential candidates for antibacterial treatment due to their suitable dimensions, penetration capacity, and high efficiency in targeted drug delivery. However, although nanoparticle-based drug delivery systems have been demonstrated to be effective, they are limited by their overuse and unwanted side effects. Thus, to overcome these drawbacks, stimulus-responsive antibiotic delivery has been extended as a promising strategy for site-specific restricted drug exemption. Nano-formulations that are triggered by various stimuli, such as intrinsic, extrinsic, and bacterial stimuli, have been developed. Thus, by harnessing the physicochemical properties of various nanoparticles, the selective release of therapeutic cargoes can be achieved through the application of a variety of local stimuli such as light, sound, irradiation, pH, and magnetic field. In this review, we also highlight the progress and perspectives of stimulus-responsive combination therapy, with special emphasis on the eradication of MDR strains and biofilms. Hence, this review addresses the advancement and challenges in the applications of stimulus-responsive nanoparticles together with the various future prospects of this technique.

Advances in the field of phage-based therapy with special emphasis on computational resources

In the current era, one of the major challenges is to manage the treatment of drug/antibiotic-resistant strains of bacteria. Phage therapy, a century-old technique, may serve as an alternative to antibiotics in treating bacterial infections caused by drug-resistant strains of bacteria. In this review, a systematic attempt has been made to summarize phage-based therapy in depth. This review has been divided into the following two sections: general information and computer-aided phage therapy (CAPT). In the case of general information, we cover the history of phage therapy, the mechanism of action, the status of phage-based products (approved and clinical trials) and the challenges. This review emphasizes CAPT, where we have covered primary phage-associated resources, phage prediction methods and pipelines. This review covers a wide range of databases and resources, including viral genomes and proteins, phage receptors, host genomes of phages, phage-host interactions and lytic proteins. In the post-genomic era, identifying the most suitable phage for lysing a drug-resistant strain of bacterium is crucial for developing alternate treatments for drug-resistant bacteria and this remains a challenging problem. Thus, we compile all phage-associated prediction methods that include the prediction of phages for a bacterial strain, the host for a phage and the identification of interacting phage-host pairs. Most of these methods have been developed using machine learning and deep learning techniques. This review also discussed recent advances in the field of CAPT, where we briefly describe computational tools available for predicting phage virions, the life cycle of phages and prophage identification. Finally, we describe phage-based therapy's advantages, challenges and opportunities.

Bioinformatics approaches for unveiling virus-host interactions

The coronavirus disease-2019 (COVID-19) pandemic has elucidated major limitations in the capacity of medical and research institutions to appropriately manage emerging infectious diseases. We can improve our understanding of infectious diseases by unveiling virus-host interactions through host range prediction and protein-protein interaction prediction. Although many algorithms have been developed to predict virus-host interactions, numerous issues remain to be solved, and the entire network remains veiled. In this review, we comprehensively surveyed algorithms used to predict virus-host interactions. We also discuss the current challenges, such as dataset biases toward highly pathogenic viruses, and the potential solutions. The complete prediction of virus-host interactions remains difficult; however, bioinformatics can contribute to progress in research on infectious diseases and human health.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

An ensemble method for prediction of phage-based therapy against bacterial infections

Phage therapy is a viable alternative to antibiotics for treating microbial infections, particularly managing drug-resistant strains of bacteria. One of the major challenges in designing phage-based therapy is to identify the most appropriate potential phage candidate to treat bacterial infections. In this study, an attempt has been made to predict phage-host interactions with high accuracy to identify the potential bacteriophage that can be used for treating a bacterial infection. The developed models have been created using a training dataset containing 826 phage- host interactions, and have been evaluated on a validation dataset comprising 1,201 phage-host interactions. Firstly, alignment-based models have been developed using similarity between phage-phage (BLASTPhage), host-host (BLASTHost) and phage-CRISPR (CRISPRPred), where we achieved accuracy between 42.4-66.2% for BLASTPhage, 55-78.4% for BLASTHost, and 43.7-80.2% for CRISPRPred across five taxonomic levels. Secondly, alignment free models have been developed using machine learning techniques. Thirdly, hybrid models have been developed by integrating the alignment-free models and the similarity-scores where we achieved maximum performance of (60.6-93.5%). Finally, an ensemble model has been developed that combines the hybrid and alignment-based models. Our ensemble model achieved highest accuracy of 67.9, 80.6, 85.5, 90, and 93.5% at Genus, Family, Order, Class, and Phylum levels on validation dataset. In order to serve the scientific community, we have also developed a webserver named PhageTB and provided a standalone software package (https://webs.iiitd.edu.in/raghava/phagetb/) for the same.

Bacteriophage Genetic Edition Using LSTM

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.

Advances and challenges in cataloging the human gut virome

The human gut virome, which is often referred to as the "dark matter" of the gut microbiome, remains understudied. A better understanding of the composition and variations of the gut virome across populations is critical for exploring its impact on diseases and health. A series of advances in the characterization of human gut virome have unveiled high genetic diversity and various functional potentials of gut viruses. Here, we summarize the recently available human gut virome databases and discuss their features, procedures, and challenges with the intention to provide a reference to researchers to use while choosing a profiling database. We also propose a "best practice" for cataloging the viral population.

Deploying Viruses against Phytobacteria: Potential Use of Phage Cocktails as a Multifaceted Approach to Combat Resistant Bacterial Plant Pathogens

Plants in nature are under the persistent intimidation of severe microbial diseases, threatening a sustainable food production system. Plant-bacterial pathogens are a major concern in the contemporary era, resulting in reduced plant growth and productivity. Plant antibiotics and chemical-based bactericides have been extensively used to evade plant bacterial diseases. To counteract this pressure, bacteria have evolved an array of resistance mechanisms, including innate and adaptive immune systems. The emergence of resistant bacteria and detrimental consequences of antimicrobial compounds on the environment and human health, accentuates the development of an alternative disease evacuation strategy. The phage cocktail therapy is a multidimensional approach effectively employed for the biocontrol of diverse resistant bacterial infections without affecting the fauna and flora. Phages engage a diverse set of counter defense strategies to undermine wide-ranging anti-phage defense mechanisms of bacterial pathogens. Microbial ecology, evolution, and dynamics of the interactions between phage and plant-bacterial pathogens lead to the engineering of robust phage cocktail therapeutics for the mitigation of devastating phytobacterial diseases. In this review, we highlight the concrete and fundamental determinants in the development and application of phage cocktails and their underlying mechanism, combating resistant plant-bacterial pathogens. Additionally, we provide recent advances in the use of phage cocktail therapy against phytobacteria for the biocontrol of devastating plant diseases.

Computational Prediction of Bacteriophage Host Ranges

Increased antibiotic resistance has prompted the development of bacteriophage agents for a multitude of applications in agriculture, biotechnology, and medicine. A key factor in the choice of agents for these applications is the host range of a bacteriophage, i.e., the bacterial genera, species, and strains a bacteriophage is able to infect. Although experimental explorations of host ranges remain the gold standard, such investigations are inherently limited to a small number of viruses and bacteria amendable to cultivation. Here, we review recently developed bioinformatic tools that offer a promising and high-throughput alternative by computationally predicting the putative host ranges of bacteriophages, including those challenging to grow in laboratory environments.

Computational design of phage cocktails based on phage-bacteria infection networks

The misuse and overuse of antibiotics have boosted the proliferation of multidrug-resistant (MDR) bacteria, which are considered a major public health issue in the twenty-first century. Phage therapy may be a promising way in the treatment of infections caused by MDR pathogens, without the side effects of the current available antimicrobials. Phage therapy is based on phage cocktails, that is, combinations of phages able to lyse the target bacteria. In this work, we present and explain in detail two innovative computational methods to design phage cocktails taking into account a given phage-bacteria infection network. One of the methods (Exhaustive Search) always generates the best possible phage cocktail, while the other method (Network Metrics) always keeps a very reduced runtime (a few milliseconds). Both methods have been included in a Cytoscape application that is available for any user. A complete experimental study has been performed, evaluating and comparing the biological quality, runtime, and the impact when additional phages are included in the cocktail.

Digital phagograms: predicting phage infectivity through a multilayer machine learning approach

Machine learning has been broadly implemented to investigate biological systems. In this regard, the field of phage biology has embraced machine learning to elucidate and predict phage-host interactions, based on receptor-binding proteins, (anti-)defense systems, prophage detection, and life cycle recognition. Here, we highlight the enormous potential of integrating information from omics data with insights from systems biology to better understand phage-host interactions. We conceptualize and discuss the potential of a multilayer model that mirrors the phage infection process, integrating adsorption, bacterial pan-immune components and hijacking of the bacterial metabolism to predict phage infectivity. In the future, this model can offer insights into the underlying mechanisms of the infection process, and digital phagograms can support phage cocktail design and phage engineering.

Identifying multi-functional bioactive peptide functions using multi-label deep learning

The bioactive peptide has wide functions, such as lowering blood glucose levels and reducing inflammation. Meanwhile, computational methods such as machine learning are becoming more and more important for peptide functions prediction. Most of the previous studies concentrate on the single-functional bioactive peptides prediction. However, the number of multi-functional peptides is on the increase; therefore, novel computational methods are needed. In this study, we develop a method MLBP (Multi-Label deep learning approach for determining the multi-functionalities of Bioactive Peptides), which can predict multiple functions including anti-cancer, anti-diabetic, anti-hypertensive, anti-inflammatory and anti-microbial simultaneously. MLBP model takes the peptide sequence vector as input to replace the biological and physiochemical features used in other peptides predictors. Using the embedding layer, the dense continuous feature vector is learnt from the sequence vector. Then, we extract convolution features from the feature vector through the convolutional neural network layer and combine with the bidirectional gated recurrent unit layer to improve the prediction performance. The 5-fold cross-validation experiments are conducted on the training dataset, and the results show that Accuracy and Absolute true are 0.695 and 0.685, respectively. On the test dataset, Accuracy and Absolute true of MLBP are 0.709 and 0.697, with 5.0 and 4.7% higher than those of the suboptimum method, respectively. The results indicate MLBP has superior prediction performance on the multi-functional peptides identification. MLBP is available at https://github.com/xialab-ahu/MLBP and http://bioinfo.ahu.edu.cn/MLBP/.

PHIAF: prediction of phage-host interactions with GAN-based data augmentation and sequence-based feature fusion

Phage therapy has become one of the most promising alternatives to antibiotics in the treatment of bacterial diseases, and identifying phage-host interactions (PHIs) helps to understand the possible mechanism through which a phage infects bacteria to guide the development of phage therapy. Compared with wet experiments, computational methods of identifying PHIs can reduce costs and save time and are more effective and economic. In this paper, we propose a PHI prediction method with a generative adversarial network (GAN)-based data augmentation and sequence-based feature fusion (PHIAF). First, PHIAF applies a GAN-based data augmentation module, which generates pseudo PHIs to alleviate the data scarcity. Second, PHIAF fuses the features originated from DNA and protein sequences for better performance. Third, PHIAF utilizes an attention mechanism to consider different contributions of DNA/protein sequence-derived features, which also provides interpretability of the prediction model. In computational experiments, PHIAF outperforms other state-of-the-art PHI prediction methods when evaluated via 5-fold cross-validation (AUC and AUPR are 0.88 and 0.86, respectively). An ablation study shows that data augmentation, feature fusion and an attention mechanism are all beneficial to improve the prediction performance of PHIAF. Additionally, four new PHIs with the highest PHIAF score in the case study were verified by recent literature. In conclusion, PHIAF is a promising tool to accelerate the exploration of phage therapy.

The Bacteriophage pEp_SNUABM_08 Is a Novel Singleton Siphovirus with High Host Specificity for Erwinia pyrifoliae

Species belonging to the genus Erwinia are predominantly plant pathogens. A number of bacteriophages capable of infecting Erwinia have been used for the control of plant diseases such as fire blight. Public repositories provide the complete genome information for such phages, which includes genomes ranging from 30 kb to 350 kb in size. However, limited information is available regarding bacteriophages belonging to the family Siphoviridae. A novel lytic siphophage, pEp_SNUABM_08, which specifically infects Erwinia pyrifoliae, was isolated from the soil of an affected apple orchard in South Korea. A comprehensive genome analysis was performed using the Erwinia-infecting siphophage. The whole genome of pEp_SNUABM_08 comprised 62,784 bp (GC content, 57.24%) with 79 open reading frames. The genomic characteristics confirmed that pEp_SNUABM_08 is a singleton lytic bacteriophage belonging to the family Siphoviridae, and no closely related phages have been reported thus far. Our study not only characterized a unique phage, but also provides insight into the genetic diversity of Erwinia bacteriophages.

Sequence-Based Prediction of Transmembrane Protein Crystallization Propensity

Transmembrane proteins play a vital role in cell life activities. There are several techniques to determine transmembrane protein structures and X-ray crystallography is the primary methodology. However, due to the special properties of transmembrane proteins, it is still hard to determine their structures by X-ray crystallography technique. To reduce experimental consumption and improve experimental efficiency, it is of great significance to develop computational methods for predicting the crystallization propensity of transmembrane proteins. In this work, we proposed a sequence-based machine learning method, namely Prediction of TransMembrane protein Crystallization propensity (PTMC), to predict the propensity of transmembrane protein crystallization. First, we obtained several general sequence features and the specific encoded features of relative solvent accessibility and hydrophobicity. Second, feature selection was employed to filter out redundant and irrelevant features, and the optimal feature subset is composed of hydrophobicity, amino acid composition and relative solvent accessibility. Finally, we chose extreme gradient boosting by comparing with other several machine learning methods. Comparative results on the independent test set indicate that PTMC outperforms state-of-the-art sequence-based methods in terms of sensitivity, specificity, accuracy, Matthew's Correlation Coefficient (MCC) and Area Under the receiver operating characteristic Curve (AUC). In comparison with two competitors, Bcrystal and TMCrys, PTMC achieves an improvement by 0.132 and 0.179 for sensitivity, 0.014 and 0.127 for specificity, 0.037 and 0.192 for accuracy, 0.128 and 0.362 for MCC, and 0.027 and 0.125 for AUC, respectively.

Global overview and major challenges of host prediction methods for uncultivated phages

Bacterial communities play critical roles across all of Earth's biomes, affecting human health and global ecosystem functioning. They do so under strong constraints exerted by viruses, that is, bacteriophages or 'phages'. Phages can reshape bacterial communities' structure, influence long-term evolution of bacterial populations, and alter host cell metabolism during infection. Metagenomics approaches, that is, shotgun sequencing of environmental DNA or RNA, recently enabled large-scale exploration of phage genomic diversity, yielding several millions of phage genomes now to be further analyzed and characterized. One major challenge however is the lack of direct host information for these phages. Several methods and tools have been proposed to bioinformatically predict the potential host(s) of uncultivated phages based only on genome sequence information. Here we review these different approaches and highlight their distinct strengths and limitations. We also outline complementary experimental assays which are being proposed to validate and refine these bioinformatic predictions.

Viral Host Range database, an online tool for recording, analyzing and disseminating virus-host interactions

Motivation

Viruses are ubiquitous in the living world, and their ability to infect more than one host defines their host range. However, information about which virus infects which host, and about which host is infected by which virus, is not readily available.

Results

We developed a web-based tool called the Viral Host Range database to record, analyze and disseminate experimental host range data for viruses infecting archaea, bacteria and eukaryotes.

Availability and implementation

The ViralHostRangeDB application is available from https://viralhostrangedb.pasteur.cloud. Its source code is freely available from the Gitlab instance of Institut Pasteur (https://gitlab.pasteur.fr/hub/viralhostrangedb).

LMI-DForest: A deep forest model towards the prediction of lncRNA-miRNA interactions

The interactions between miRNAs and long non-coding RNAs (lncRNAs) are subject to intensive recent studies due to its critical role in gene regulations. Computational prediction of lncRNA-miRNA interactions has become a popular alternative strategy to the experimental methods for identification of underlying interactions. It is desirable to develop the machine learning-based models for prediction of lncRNA-miRNA based on the experimentally validated interactions between lncRNAs and miRNAs. The accuracy and robustness of existing models based on machine learning techniques are subject to further improvement. Considering that the attributes of lncRNA and miRNA contribute key importance in the interaction between these two RNAs, a deep learning model, named LMI-DForest, is proposed here by combining the deep forest and autoencoder strategies. Systematic comparison on the experiment validated datasets for lncRNA-miRNA interaction datasets demonstrates that the proposed method consistently shows superior performance over the other machine learning models in the lncRNA-miRNA interaction prediction.