deepHPI: a comprehensive deep learning platform for accurate prediction and visualization of host-pathogen protein-protein interactions.

Host-pathogen protein interactions (HPPIs) play vital roles in many biological processes and are directly involved in infectious diseases. With the outbreak of more frequent pandemics in the last couple of decades, such as the recent outburst of Covid-19 causing millions of deaths, it has become more critical to develop advanced methods to accurately predict pathogen interactions with their respective hosts. During the last decade, experimental methods to identify HPIs have been used to decipher host-pathogen systems with the caveat that those techniques are labor-intensive, expensive and time-consuming. Alternatively, accurate prediction of HPIs can be performed by the use of data-driven machine learning. To provide a more robust and accurate solution for the HPI prediction problem, we have developed a deepHPI tool based on deep learning. The web server delivers four host-pathogen model types: plant-pathogen, human-bacteria, human-virus and animal-pathogen, leveraging its operability to a wide range of analyses and cases of use. The deepHPI web tool is the first to use convolutional neural network models for HPI prediction. These models have been selected based on a comprehensive evaluation of protein features and neural network architectures. The best prediction models have been tested on independent validation datasets, which achieved an overall Matthews correlation coefficient value of 0.87 for animal-pathogen using the combined pseudo-amino acid composition and conjoint triad (PAAC_CT) features, 0.75 for human-bacteria using the combined pseudo-amino acid composition, conjoint triad and normalized Moreau-Broto feature (PAAC_CT_NMBroto), 0.96 for human-virus using PAAC_CT_NMBroto and 0.94 values for plant-pathogen interactions using the combined pseudo-amino acid composition, composition and transition feature (PAAC_CTDC_CTDT). Our server running deepHPI is deployed on a high-performance computing cluster that enables large and multiple user requests, and it provides more information about interactions discovered. It presents an enriched visualization of the resulting host-pathogen networks that is augmented with external links to various protein annotation resources. We believe that the deepHPI web server will be very useful to researchers, particularly those working on infectious diseases. Additionally, many novel and known host-pathogen systems can be further investigated to significantly advance our understanding of complex disease-causing agents. The developed models are established on a web server, which is freely accessible at http://bioinfo.usu.edu/deepHPI/.

GreeningDB: A Database of Host-Pathogen Protein-Protein Interactions and Annotation Features of the Bacteria Causing Huanglongbing HLB Disease.

The Citrus genus comprises some of the most important and commonly cultivated fruit plants. Within the last decade, citrus greening disease (also known as huanglongbing or HLB) has emerged as the biggest threat for the citrus industry. This disease does not have a cure yet and, thus, many efforts have been made to find a solution to this devastating condition. There are challenges in the generation of high-yield resistant cultivars, in part due to the limited and sparse knowledge about the mechanisms that are used by the Liberibacter bacteria to proliferate the infection in Citrus plants. Here, we present GreeningDB, a database implemented to provide the annotation of Liberibacter proteomes, as well as the host-pathogen comparactomics tool, a novel platform to compare the predicted interactomes of two HLB host-pathogen systems. GreeningDB is built to deliver a user-friendly interface, including network visualization and links to other resources. We hope that by providing these characteristics, GreeningDB can become a central resource to retrieve HLB-related protein annotations, and thus, aid the community that is pursuing the development of molecular-based strategies to mitigate this disease's impact. The database is freely available at http://bioinfo.usu.edu/GreeningDB/ (accessed on 11 August 2021).

In silico prediction of host-pathogen protein interactions in melioidosis pathogen Burkholderia pseudomallei and human reveals novel virulence factors and their targets.

The aerobic, Gram-negative motile bacillus, Burkholderia pseudomallei is a facultative intracellular bacterium causing melioidosis, a critical disease of public health importance, which is widely endemic in the tropics and subtropical regions of the world. Melioidosis is associated with high case fatality rates in animals and humans; even with treatment, its mortality is 20-50%. It also infects plants and is designated as a biothreat agent. B. pseudomallei is pathogenic due to its ability to invade, resist factors in serum and survive intracellularly. Despite its importance, to date only a few effector proteins have been functionally characterized, and there is not much information regarding the host-pathogen protein-protein interactions (PPI) of this system, which are important to studying infection mechanisms and thereby develop prevention measures. We explored two computational approaches, the homology-based interolog and the domain-based method, to predict genome-scale host-pathogen interactions (HPIs) between two different strains of B. pseudomallei (prototypical, and highly virulent) and human. In total, 76 335 common HPIs (between the two strains) were predicted involving 8264 human and 1753 B. pseudomallei proteins. Among the unique PPIs, 14 131 non-redundant HPIs were found to be unique between the prototypical strain and human, compared to 3043 non-redundant HPIs between the highly virulent strain and human. The protein hubs analysis showed that most B. pseudomallei proteins formed a hub with human dnaK complex proteins associated with tuberculosis, a disease similar in symptoms to melioidosis. In addition, drug-binding and carbohydrate-binding mechanisms were found overrepresented within the host-pathogen network, and metabolic pathways were frequently activated according to the pathway enrichment. Subcellular localization analysis showed that most of the pathogen proteins are targeting human proteins inside cytoplasm and nucleus. We also discovered the host targets of the drug-related pathogen proteins and proteins that form T3SS and T6SS in B. pseudomallei. Additionally, a comparison between the unique PPI patterns present in the prototypical and highly virulent strains was performed. The current study is the first report on developing a genome-scale host-pathogen protein interaction networks between the human and B. pseudomallei, a critical biothreat agent. We have identified novel virulence factors and their interacting partners in the human proteome. These PPIs can be further validated by high-throughput experiments and may give new insights on how B. pseudomallei interacts with its host, which will help medical researchers in developing better prevention measures.

PredHPI: an integrated web server platform for the detection and visualization of host-pathogen interactions using sequence-based methods.

MOTIVATION: Understanding the mechanisms underlying infectious diseases is fundamental to develop prevention strategies. Host-pathogen interactions (HPIs) are actively studied worldwide to find potential genomic targets for the development of novel drugs, vaccines and other therapeutics. Determining which proteins are involved in the interaction system behind an infectious process is the first step to develop an efficient disease control strategy. Very few computational methods have been implemented as web services to infer novel HPIs, and there is not a single framework which combines several of those approaches to produce and visualize a comprehensive analysis of HPIs. RESULTS: Here, we introduce PredHPI, a powerful framework that integrates both the detection and visualization of interaction networks in a single web service, facilitating the apprehension of model and non-model host-pathogen systems to aid the biologists in building hypotheses and designing appropriate experiments. PredHPI is built on high-performance computing resources on the backend capable of handling proteome-scale sequence data from both the host as well as pathogen. Data are displayed in an information-rich and interactive visualization, which can be further customized with user-defined layouts. We believe PredHPI will serve as an invaluable resource to diverse experimental biologists and will help advance the research in the understanding of complex infectious diseases. AVAILABILITY AND IMPLEMENTATION: PredHPI tool is freely available at http://bioinfo.usu.edu/PredHPI/. SUPPLEMENTARY INFORMATION: Sup plementary data are available at Bioinformatics online.

citSATdb: Genome-Wide Simple Sequence Repeat (SSR) Marker Database of Citrus Species for Germplasm Characterization and Crop Improvement.

Microsatellites or simple sequence repeats (SSRs) are popular co-dominant markers that play an important role in crop improvement. To enhance genomic resources in general horticulture, we identified SSRs in the genomes of eight citrus species and characterized their frequency and distribution in different genomic regions. Citrus is the world's most widely cultivated fruit crop. We have implemented a microsatellite database, citSATdb, having the highest number (~1,296,500) of putative SSR markers from the genus Citrus, represented by eight species. The database is based on a three-tier approach using MySQL, PHP, and Apache. The markers can be searched using multiple search parameters including chromosome/scaffold number(s), motif types, repeat nucleotides (1-6), SSR length, patterns of repeat motifs and chromosome/scaffold location. The cross-species transferability of selected markers can be checked using e-PCR. Further, the markers can be visualized using the Jbrowse feature. These markers can be used for distinctness, uniformity, and stability (DUS) tests of variety identification, marker-assisted selection (MAS), gene discovery, QTL mapping, and germplasm characterization. citSATdb represents a comprehensive source of markers for developing/implementing new approaches for molecular breeding, required to enhance Citrus productivity. The potential polymorphic SSR markers identified by cross-species transferability could be used for genetic diversity and population distinction in other species.

Plant-mSubP: a computational framework for the prediction of single- and multi-target protein subcellular localization using integrated machine-learning approaches.

The subcellular localization of proteins is very important for characterizing its function in a cell. Accurate prediction of the subcellular locations in computational paradigm has been an active area of interest. Most of the work has been focused on single localization prediction. Only few studies have discussed the multi-target localization, but have not achieved good accuracy so far; in plant sciences, very limited work has been done. Here we report the development of a novel tool Plant-mSubP, which is based on integrated machine learning approaches to efficiently predict the subcellular localizations in plant proteomes. The proposed approach predicts with high accuracy 11 single localizations and three dual locations of plant cell. Several hybrid features based on composition and physicochemical properties of a protein such as amino acid composition, pseudo amino acid composition, auto-correlation descriptors, quasi-sequence-order descriptors and hybrid features are used to represent the protein. The performance of the proposed method has been assessed through a training set as well as an independent test set. Using the hybrid feature of the pseudo amino acid composition, N-Center-C terminal amino acid composition and the dipeptide composition (PseAAC-NCC-DIPEP), an overall accuracy of 81.97 %, 84.75 % and 87.88 % is achieved on the training data set of proteins containing the single-label, single- and dual-label combined, and dual-label proteins, respectively. When tested on the independent data, an accuracy of 64.36 %, 64.84 % and 81.08 % is achieved on the single-label, single- and dual-label, and dual-label proteins, respectively. The prediction models have been implemented on a web server available at http://bioinfo.usu.edu/Plant-mSubP/. The results indicate that the proposed approach is comparable to the existing methods in single localization prediction and outperforms all other existing tools when compared for dual-label proteins. The prediction tool will be a useful resource for better annotation of various plant proteomes.

NGSEP3: accurate variant calling across species and sequencing protocols.

MOTIVATION: Accurate detection, genotyping and downstream analysis of genomic variants from high-throughput sequencing data are fundamental features in modern production pipelines for genetic-based diagnosis in medicine or genomic selection in plant and animal breeding. Our research group maintains the Next-Generation Sequencing Experience Platform (NGSEP) as a precise, efficient and easy-to-use software solution for these features. RESULTS: Understanding that incorrect alignments around short tandem repeats are an important source of genotyping errors, we implemented in NGSEP new algorithms for realignment and haplotype clustering of reads spanning indels and short tandem repeats. We performed extensive benchmark experiments comparing NGSEP to state-of-the-art software using real data from three sequencing protocols and four species with different distributions of repetitive elements. NGSEP consistently shows comparative accuracy and better efficiency compared to the existing solutions. We expect that this work will contribute to the continuous improvement of quality in variant calling needed for modern applications in medicine and agriculture. AVAILABILITY AND IMPLEMENTATION: NGSEP is available as open source software at http://ngsep.sf.net. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.