Protein-protein interactions: Methods, databases, and applications in virus-host study

PPI network identifies interacting pathogenic signaling pathways in Candida albicans

Candida albicans, an opportunistic and systemic infection causing fungus, causes skin, nail, and mucosal layer lesions in healthy individuals and hospital borne catheter-related and nosocomial infections. This particular fungus exists in two distinct stages in its life cycle: yeast and hyphae. In this study, 20 signaling pathways associated with 177 proteins from C. albicans were identified to construct a PPI network. The core part of the network consisted of 165 proteins. Network topology analyses revealed that the formed PPI network is biologically robust and scale-free, with significant interactions between proteins through 19 252 shortest pathways. In this network, the top 10 hub proteins (RAS1, CDC42, HOG1, CPH1, STE11, EFG1, CEK1, HSP90, TEC1 and CST20) were identified using network analysis, which seem to be the most important proteins involved in different pathways for the development of pathogenesis and virulence. Modular analysis of the network resulted in top six sub-networks, three of which shared eight hub proteins. Ontology and functional enrichment analyses revealed that the majority of the proteins were associated with regulation of transcription by RNA polymerase II, plasma membrane and nucleic acid binding in biological processes, and cellular components and molecular functions, respectively. Enrichment analysis indicated that the proteins were mostly involved in oxidative phosphorylation and purine metabolism signaling pathways. We determined the complex web of signaling pathway involving proteins via PPI network analysis to unravel and decipher protein interactions within C. albicans to understand the complex pathogenesis processes for targeted therapeutic interferences using novel bioinformatics strategies.

Prediction and Evaluation of Coronavirus and Human Protein-Protein Interactions Integrating Five Different Computational Methods

The high lethality and infectiousness of coronaviruses, particularly SARS-Cov-2, pose a significant threat to human society. Understanding coronaviruses, especially the interactions between these viruses and humans, is crucial for mitigating the coronavirus pandemic. In this study, we conducted a comprehensive comparison and evaluation of five prevalent computational methods: interolog mapping, domain-domain interaction methodology, domain-motif interaction methodology, structure-based approaches, and machine learning techniques. These methods were assessed using unbiased datasets that include C1, C2h, C2v, and C3 test sets. Ultimately, we integrated these five methodologies into a unified model for predicting protein-protein interactions (PPIs) between coronaviruses and human proteins. Our final model demonstrates relatively better performance, particularly with the C2v and C3 test sets, which are frequently used datasets in practical applications. Based on this model, we further established a high-confidence PPI network between coronaviruses and humans, consisting of 18,012 interactions between 3843 human proteins and 129 coronavirus proteins. The reliability of our predictions was further validated through the current knowledge framework and network analysis. This study is anticipated to enhance mechanistic understanding of the coronavirus-human relationship a while facilitating the rediscovery of antiviral drug targets. The source codes and datasets are accessible at https://github.com/covhppilab/CoVHPPI.

Adoption of an in-silico analysis approach to assess the functional and structural impacts of rpoB-encoded protein mutations on Chlamydia pneumoniae sensitivity to antibiotics

BACKGROUND

Antibiotics are frequently used to treat infections caused by Chlamydia pneumoniae; an obligate intracellular gram-negative bacterium commonly associated with respiratory diseases. However, improper or overuse of these drugs has raised concerns about the development of antibiotic resistance, which poses a significant global health challenge. Previous studies have revealed a link between mutations in the rpoB-encoded protein of C. pneumoniae and antibiotic resistance. This study assessed these mutations via various bioinformatics tools to predict their impact on function, structural stability, antibiotic binding, and, ultimately, their effect on bacterial sensitivity to antibiotics.

RESULTS

Eight mutations in the rpoB-encoded protein (R421S, F450S, L456I, S454F, D461E, S476F, L478S, and S519Y) are associated with resistance to rifampin and rifalazil. These mutations occur in conserved regions of the protein, leading to decreased stability and affecting essential functional sites of RNA polymerase, the target of these antibiotics. Although the structural differences between the native and mutant proteins are minimal, notable changes in local hydrogen bonding have been observed. Despite similar binding energies, variations in hydrogen bonds and hydrophobic interactions in certain mutants (for instance, D461E for rifalazil and S476F for rifampin) indicate that these changes may diminish ligand affinity and specificity. Furthermore, protein-protein network analysis demonstrated a strong correlation between wild-type rpoB and ten C. pneumoniae proteins, each fulfilling specific functional roles. Consequently, some of these mutations can reduce the bacterium's sensitivity to rifampin and rifalazil, thereby contributing to antibiotic resistance.

CONCLUSION

The findings of this study indicate that mutations in the rpoB gene, which encodes the beta subunit of RNA polymerase, are pivotal in the resistance of C. pneumoniae to rifampin and rifalazil. Some of these mutations may result in reduced protein stability and changes in the structure, function, and antibiotic binding. As a consequence, the efficacy of these drugs in inhibiting RNA polymerase is compromised, allowing the bacteria to persist in transcription and replication even in the presence of antibiotics. Overall, these insights enhance our understanding of the resistance mechanisms in C. pneumoniae and could guide the development of strategies to address this challenge.

CLINICAL TRIAL NUMBER

Not applicable.

Prediction of influenza A virus-human protein-protein interactions using XGBoost with continuous and discontinuous amino acids information

Influenza A virus (IAV) has the characteristics of high infectivity and high pathogenicity, which makes IAV infection a serious public health threat. Identifying protein-protein interactions (PPIs) between IAV and human proteins is beneficial for understanding the mechanism of viral infection and designing antiviral drugs. In this article, we developed a sequence-based machine learning method for predicting PPI. First, we applied a new negative sample construction method to establish a high-quality IAV-human PPI dataset. Then we used conjoint triad (CT) and Moran autocorrelation (Moran) to encode biologically relevant features. The joint consideration utilizing the complementary information between contiguous and discontinuous amino acids provides a more comprehensive description of PPI information. After comparing different machine learning models, the eXtreme Gradient Boosting (XGBoost) model was determined as the final model for the prediction. The model achieved an accuracy of 96.89%, precision of 98.79%, recall of 94.85%, F1-score of 96.78%. Finally, we successfully identified 3,269 potential target proteins. Gene ontology (GO) and pathway analysis showed that these genes were highly associated with IAV infection. The analysis of the PPI network further revealed that the predicted proteins were classified as core proteins within the human protein interaction network. This study may encourage the identification of potential targets for the discovery of more effective anti-influenza drugs. The source codes and datasets are available at https://github.com/HVPPIlab/IVA-Human-PPI/.

Deformed wing virus coopts the host arginine kinase to enhance its fitness in honey bees (Apis mellifera)

BACKGROUND

Deformed wing virus (DWV) is a major honey bee pathogen that is actively transmitted by the parasitic mite Varroa destructor and plays a primary role in Apis mellifera winter colony losses. Despite intense investigation on this pollinator, which has a unique environmental and economic importance, the mechanisms underlying the molecular interactions between DWV and honey bees are still poorly understood. Here, we report on a group of honey bee proteins, identified by mass spectrometry, that specifically co-immunoprecipitate with DWV virus particles.

RESULTS

Most of the proteins identified are involved in fundamental metabolic pathways. Among the co-immunoprecipitated proteins, one of the most interesting was arginine kinase (ArgK), a conserved protein playing multiple roles both in physiological and pathological processes and stress response in general. Here, we investigated in more detail the relationship between DWV and this protein. We found that argK RNA level positively correlates with DWV load in field-collected honey bee larvae and adults and significantly increases in adults upon DWV injection in controlled laboratory conditions, indicating that the argK gene was upregulated by DWV infection. Silencing argK gene expression in vitro, using RNAi, resulted in reduced DWV viral load, thus confirming that argK upregulation facilitates DWV infection, likely through interfering with the delicate balance between metabolism and immunity.

CONCLUSIONS

In summary, these data indicate that DWV modulates the host ArgK through transcriptional regulation and cooptation to enhance its fitness in honey bees. Our findings open novel perspectives on possible new therapies for DWV control by targeting specific host proteins.

Bioinformatic Resources for Exploring Human-virus Protein-protein Interactions Based on Binding Modes

Historically, there have been many outbreaks of viral diseases that have continued to claim millions of lives. Research on human-virus protein-protein interactions (PPIs) is vital to understanding the principles of human-virus relationships, providing an essential foundation for developing virus control strategies to combat diseases. The rapidly accumulating data on human-virus PPIs offer unprecedented opportunities for bioinformatics research around human-virus PPIs. However, available detailed analyses and summaries to help use these resources systematically and efficiently are lacking. Here, we comprehensively review the bioinformatic resources used in human-virus PPI research, and discuss and compare their functions, performance, and limitations. This review aims to provide researchers with a bioinformatic toolbox that will hopefully better facilitate the exploration of human-virus PPIs based on binding modes.

Studying protein-protein interactions: Latest and most popular approaches

PPIs, or protein-protein interactions, are essential for many biological processes. According to the findings, abnormal PPIs have been linked to several diseases, such as cancer and infectious and neurological disorders. Consequently, focusing on PPIs is a path toward disease treatment and a crucial tool for producing novel medications. Many methods exist to investigate PPIs, including low- and high-throughput studies. Since many PPIs have been discovered using in vitro and in vivo experimental approaches, the use of computational methods to predict PPIs has grown due to the expanding scale of PPI data and the intrinsic complexity of interacting mechanisms. Recognizing PPI networks offers a systematic means of predicting protein functions, and pathways that are included. These investigations can help uncover the underlying molecular mechanisms of complex phenotypes and clarify the biological processes related to health and diseases. Therefore, our goal in this study is to provide an overview of the latest and most popular approaches for investigating PPIs. We also overview some important clinical approaches based on the PPIs and how these interactions can be targeted.

Identification of Host Factors Interacting with Movement Proteins of the 30K Family in Nicotiana tabacum

The interaction of viral proteins with host factors represents a crucial aspect of the infection process in plants. In this work, we developed a strategy to identify host factors in Nicotiana tabacum that interact with movement proteins (MPs) of the 30K family, a group of viral proteins around 30 kDa related to the MP of tobacco mosaic virus, which enables virus movement between plant cells. Using the alfalfa mosaic virus (AMV) MP as a model, we incorporated tags into its coding sequence, without affecting its functionality, enabling the identification of 121 potential interactors through in vivo immunoprecipitation of the tagged MP. Further analysis of five selected candidates (histone 2B (H2B), actin, 14-3-3A protein, eukaryotic initiation factor 4A (elF4A), and a peroxidase-POX-) were conducted using bimolecular fluorescence complementation (BiFC). The interactions between these factors were also studied, revealing that some form part of protein complexes associated with AMV MP. Moreover, H2B, actin, 14-3-3, and eIF4A interacted with other MPs of the 30K family. This observation suggests that, beyond functional and structural features, 30K family MPs may share common interactors. Our results demonstrate that tagging 30K family MPs is an effective strategy to identify host factors associated with these proteins during viral infection.

System biology-based assessment of the molecular mechanism of IMPHY000797 in Parkinson's disease: a network pharmacology and in-silico evaluation

IMPHY000797 derivatives have been well known for their efficacy in various diseases. Moreover, IMPHY000797 derivatives have been found to modulate such genes involved in multiple neurological disorders. Hence, this study seeks to identify such genes and the probable molecular mechanism that could be involved in the pathogenesis of Parkinson's disease. The study utilized various biological tools such as DisGeNET, STRING, Swiss target predictor, Cytoscape, AutoDock 4.2, Schrodinger suite, ClueGo, and GUSAR. All the reported genes were obtained using DisGeNET, and further, the common genes were incorporated into the STRING to get the KEGG pathway, and all the data was converted to a protein/pathway network via Cytoscape. The clustering of the genes was performed for the gene-enriched data using two-sided hypergeometrics (p-value). The binding affinity of the IMPHY000797 was verified with the highest regulated 25 proteins via utilizing the "Monte Carlo iterated search technique" and the "Emodel and Glide score" function. Three thousand five hundred eighty-three genes were identified for Parkinson's disease and 31 genes for IMPHY000797 compound, among which 25 common genes were identified. Further, the "FOXO-signaling pathway" was identified to be a modulated pathway. Among the 25 proteins, the highest modulated genes and highest binding affinity were exhibited by SIRT3, FOXO1, and PPARGC1A with the compound IMPHY000797. Further, rat toxicity analysis provided the efficacy and safety of the compound. The study was required to identify the probable molecular mechanism, which needs more confirmation from other studies, which is still a significant hit-back.

Intelligence model on sequence-based prediction of PPI using AISSO deep concept with hyperparameter tuning process

Protein-protein interaction (PPI) prediction is vital for interpreting biological activities. Even though many diverse sorts of data and machine learning approaches have been employed in PPI prediction, performance still has to be enhanced. As a result, we adopted an Aquilla Influenced Shark Smell (AISSO)-based hybrid prediction technique to construct a sequence-dependent PPI prediction model. This model has two stages of operation: feature extraction and prediction. Along with sequence-based and Gene Ontology features, unique features were produced in the feature extraction stage utilizing the improved semantic similarity technique, which may deliver reliable findings. These collected characteristics were then sent to the prediction step, and hybrid neural networks, such as the Improved Recurrent Neural Network and Deep Belief Networks, were used to predict the PPI using modified score level fusion. These neural networks' weight variables were adjusted utilizing a unique optimal methodology called Aquila Influenced Shark Smell (AISSO), and the outcomes showed that the developed model had attained an accuracy of around 88%, which is much better than the traditional methods; this model AISSO-based PPI prediction can provide precise and effective predictions.

Polypeptide N-Acetylgalactosaminyl transferase 14 is a novel mediator in pancreatic β-cell function and growth

Polypeptide N-Acetylgalactosaminyl transferase 14 (GALNT14) plays important roles in cancer progression and chemotherapy response. Here, we show that GALNT14 is highly expressed in pancreatic β cells and regulates β cell function and growth. We found that the expression level of Ganlt14 was significantly decreased in the primary islets from three rodent type-2 diabetic models. Single-Cell sequencing defined that Galnt14 was mainly expressed in β cells of mouse islets. Galnt14 knockout (G14KO) INS-1 cell line, constructed by using CRISPR/Cas9 technology were growth normal, but showed blunt shape, and increased basal insulin secretion. Combined proteomics and glycoproteomics demonstrated that G14KO altered cell-to-cell junctions, communication, and adhesion. Insulin receptor (IR) and IGF1-1R were indirectly confirmed for GALNT14 substrates, contributed to diminished IGF1-induced p-AKT levels and cell growth in G14KO cells. Overall, this study uncovers that GALNT14 is a novel modulator in regulating β cells biology, providing a missing link of β cells O-glycosylation to diabetes development.

Unlocking protein-protein interactions in plants: a comprehensive review of established and emerging techniques

Protein-protein interactions orchestrate plant development and serve as crucial elements for cellular and environmental communication. Understanding these interactions offers a gateway to unravel complex protein networks that will allow a better understanding of nature. Methods for the characterization of protein-protein interactions have been around over 30 years, yet the complexity of some of these interactions has fueled the development of new techniques that provide a better understanding of the underlying dynamics. In many cases, the application of these techniques is limited by the nature of the available sample. While some methods require an in vivo set-up, others solely depend on protein sequences to study protein-protein interactions via an in silico set-up. The vast number of techniques available to date calls for a way to select the appropriate tools for the study of specific interactions. Here, we classify widely spread tools and new emerging techniques for the characterization of protein-protein interactions based on sample requirements while providing insights into the information that they can potentially deliver. We provide a comprehensive overview of commonly used techniques and elaborate on the most recent developments, showcasing their implementation in plant research.

Identification of hub genes and potential networks by centrality network analysis of PCR amplified Fusarium oxysporum f. sp. lycopersici EF1α gene

BACKGROUND

Fusarium wilt is a devastating soil-borne fungal disease of tomato across the world. Conventional method of disease prevention including usage of common pesticides and methods like soil solarisation are usually ineffective in the treatment of this disease. Therefore, there is an urgent need to identify virulence related genes in the pathogen which can be targeted for fungicide development.

RESULTS

Pathogenicity testing and phylogenetic classification of the pathogen used in this study confirmed it as Fusarium oxysporum f. sp. lycopersici (Fol) strain. A recent discovery indicates that EF1α, a protein with conserved structural similarity across several fungal genera, has a role in the pathogenicity of Magnaporthe oryzae, the rice blast fungus. Therefore, in this study we have done structural and functional classification of EF1α to understand its role in pathogenicity of Fol. The protein model of Fol EF1α was created using the template crystal structure of the yeast elongation factor complex EEF1A:EEF1BA which showed maximum similarity with the target protein. Using the STRING online database, the interactive information among the hub genes of EF1α was identified and the protein-protein interaction network was recognized using the Cytoscape software. On combining the results of functional analysis, MCODE, CytoNCA and CytoHubba 4 hub genes including Fol EF1α were selected for further investigation. The three interactors of Fol EF1α showed maximum similarity with homologous proteins found in Neurospora crassa complexed with the known fungicide, cycloheximide. Through the sequence similarity and PDB database analysis, homologs of Fol EF1α were found: EEF1A:EEF1BA in complex with GDPNP in yeast and EF1α in complex with GDP in Sulfolobus solfataricus. The STITCH database analysis suggested that EF1α and its other interacting partners interact with guanosine diphosphate (GDPNP) and guanosine triphosphate (GTP).

CONCLUSIONS

Our study offers a framework for recognition of several hub genes network in Fusarium wilt that can be used as novel targets for fungicide development. The involvement of EF1α in nucleocytoplasmic transport pathway suggests that it plays role in GTP binding and thus apart from its use as a biomarker, it may be further exploited as an effective target for fungicide development. Since, the three other proteins that were found to be tightly associated Fol EF1α have shown maximum similarity with homologous proteins of Neurospora crassa that form complex with fungicide- Cycloheximide. Therefore, we suggest that cycloheximide can also be used against Fusarium wilt disease in tomato. The active site cavity of Fol EF1α can also be determined for computational screening of fungicides using the homologous proteins observed in yeast and Sulfolobus solfataricus. On this basis, we also suggest that the other closely associated genes that have been identified through STITCH analysis, they can also be targeted for fungicide development.

The Applications of Artificial Intelligence (AI)-Driven Tools in Virus-Like Particles (VLPs) Research

Viral-like particles (VLPs) represent versatile nanoscale structures mimicking the morphology and antigenic characteristics of viruses, devoid of genetic material, making them promising candidates for various biomedical applications. The integration of artificial intelligence (AI) into VLP research has catalyzed significant advancements in understanding, production, and therapeutic applications of these nanostructures. This comprehensive review explores the collaborative utilization of AI tools, computational methodologies, and state-of-the-art technologies within the VLP domain. AI's involvement in bioinformatics facilitates sequencing and structure prediction, unraveling genetic intricacies and three-dimensional configurations of VLPs. Furthermore, AI-enabled drug discovery enables virtual screening, demonstrating promise in identifying compounds to inhibit VLP activity. In VLP production, AI optimizes processes by providing strategies for culture conditions, nutrient concentrations, and growth kinetics. AI's utilization in image analysis and electron microscopy expedites VLP recognition and quantification. Moreover, network analysis of protein-protein interactions through AI tools offers an understanding of VLP interactions. The integration of multi-omics data via AI analytics provides a comprehensive view of VLP behavior. Predictive modeling utilizing machine learning algorithms aids in forecasting VLP stability, guiding optimization efforts. Literature mining facilitated by text mining algorithms assists in summarizing information from the VLP knowledge corpus. Additionally, AI's role in laboratory automation enhances experimental efficiency. Addressing data security concerns, AI ensures the protection of sensitive information in the digital era of VLP research. This review serves as a roadmap, providing insights into AI's current and future applications in VLP research, thereby guiding innovative directions in medicine and beyond.

High throughput methods to study protein-protein interactions during host-pathogen interactions

The ability of a pathogen to survive and cause an infection is often determined by specific interactions between the host and pathogen proteins. Such interactions can be both intra- and extracellular and may define the outcome of an infection. There are a range of innovative biochemical, biophysical and bioinformatic techniques currently available to identify protein-protein interactions (PPI) between the host and the pathogen. However, the complexity and the diversity of host-pathogen PPIs has led to the development of several high throughput (HT) techniques that enable the study of multiple interactions at once and/or screen multiple samples at the same time, in an unbiased manner. We review here the major HT laboratory-based technologies employed for host-bacterial interaction studies.

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets

Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.

Unleashing the power of artificial intelligence for diagnosing and treating infectious diseases: A comprehensive review

Infectious diseases present a global challenge, requiring accurate diagnosis, effective treatments, and preventive measures. Artificial intelligence (AI) has emerged as a promising tool for analysing complex molecular data and improving the diagnosis, treatment, and prevention of infectious diseases. Computer-aided detection (CAD) using convolutional neural networks (CNN) has gained prominence for diagnosing tuberculosis (TB) and other infectious diseases such as COVID-19, HIV, and viral pneumonia. The review discusses the challenges and limitations associated with AI in this field and explores various machine-learning models and AI-based approaches. Artificial neural networks (ANN), recurrent neural networks (RNN), support vector machines (SVM), multilayer neural networks (MLNN), CNN, long short-term memory (LSTM), and random forests (RF) are among the models discussed. The review emphasizes the potential of AI to enhance the accuracy and efficiency of diagnosis, treatment, and prevention of infectious diseases, highlighting the need for further research and development in this area.

Profiling disease-selective drug targets: From proteomics to ligandomics

Despite advancements in omics technologies, including proteomics and transcriptomics, identification of therapeutic targets remains challenging. Ligandomics recently emerged as a unique technology of functional proteomics for global profiling of cell-binding protein ligands. When applied to diseased versus healthy vasculatures, comparative ligandomics systematically maps novel disease-restricted ligands that allow selective targeting of pathological but not physiological pathways, providing high efficacy with intrinsic safety. In this review, we discuss the potential of cellular ligands as therapeutic targets and summarize the development of ligandomics. We further compare the advantages and limitations of different omics technologies for drug target discovery and discuss target selection criteria to improve drug R&D success rates.

Computational models for prediction of protein-protein interaction in rice and Magnaporthe grisea

INTRODUCTION

Plant-microbe interactions play a vital role in the development of strategies to manage pathogen-induced destructive diseases that cause enormous crop losses every year. Rice blast is one of the severe diseases to rice Oryza sativa (O. sativa) due to Magnaporthe grisea (M. grisea) fungus. Protein-protein interaction (PPI) between rice and fungus plays a key role in causing rice blast disease.

METHODS

In this paper, four genomic information-based models such as (i) the interolog, (ii) the domain, (iii) the gene ontology, and (iv) the phylogenetic-based model are developed for predicting the interaction between O. sativa and M. grisea in a whole-genome scale.

RESULTS AND DISCUSSION

A total of 59,430 interacting pairs between 1,801 rice proteins and 135 blast fungus proteins are obtained from the four models. Furthermore, a machine learning model is developed to assess the predicted interactions. Using composition-based amino acid composition (AAC) and conjoint triad (CT) features, an accuracy of 88% and 89% is achieved, respectively. When tested on the experimental dataset, the CT feature provides the highest accuracy of 95%. Furthermore, the specificity of the model is verified with other pathogen-host datasets where less accuracy is obtained, which confirmed that the model is specific to O. sativa and M. grisea. Understanding the molecular processes behind rice resistance to blast fungus begins with the identification of PPIs, and these predicted PPIs will be useful for drug design in the plant science community.

DeepHomo2.0: improved protein-protein contact prediction of homodimers by transformer-enhanced deep learning

Protein-protein interactions play an important role in many biological processes. However, although structure prediction for monomer proteins has achieved great progress with the advent of advanced deep learning algorithms like AlphaFold, the structure prediction for protein-protein complexes remains an open question. Taking advantage of the Transformer model of ESM-MSA, we have developed a deep learning-based model, named DeepHomo2.0, to predict protein-protein interactions of homodimeric complexes by leveraging the direct-coupling analysis (DCA) and Transformer features of sequences and the structure features of monomers. DeepHomo2.0 was extensively evaluated on diverse test sets and compared with eight state-of-the-art methods including protein language model-based, DCA-based and machine learning-based methods. It was shown that DeepHomo2.0 achieved a high precision of >70% with experimental monomer structures and >60% with predicted monomer structures for the top 10 predicted contacts on the test sets and outperformed the other eight methods. Moreover, even the version without using structure information, named DeepHomoSeq, still achieved a good precision of >55% for the top 10 predicted contacts. Integrating the predicted contacts into protein docking significantly improved the structure prediction of realistic Critical Assessment of Protein Structure Prediction homodimeric complexes. DeepHomo2.0 and DeepHomoSeq are available at http://huanglab.phys.hust.edu.cn/DeepHomo2/.

Machine Learning Methods for Virus-Host Protein-Protein Interaction Prediction

The attachment of a virion to a respective cellular receptor on the host organism occurring through the virus-host protein-protein interactions (PPIs) is a decisive step for viral pathogenicity and infectivity. Therefore, a vast number of wet-lab experimental techniques are used to study virus-host PPIs. Taking the great number and enormous variety of virus-host PPIs and the cost as well as labor of laboratory work, however, computational approaches toward analyzing the available interaction data and predicting previously unidentified interactions have been on the rise. Among them, machine-learning-based models are getting increasingly more attention with a great body of resources and tools proposed recently.In this chapter, we first provide the methodology with major steps toward the development of a virus-host PPI prediction tool. Next, we discuss the challenges involved and evaluate several existing machine-learning-based virus-host PPI prediction tools. Finally, we describe our experience with several ensemble techniques as utilized on available prediction results retrieved from individual PPI prediction tools. Overall, based on our experience, we recognize there is still room for the development of new individual and/or ensemble virus-host PPI prediction tools that leverage existing tools.

A new framework for host-pathogen interaction research

COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed.

Whole-Genome Profile of Greek Patients with Teratozοοspermia: Identification of Candidate Variants and Genes

Male infertility is a global health problem that affects a large number of couples worldwide. It can be categorized into specific subtypes, including teratozoospermia. The present study aimed to identify new variants associated with teratozoospermia in the Greek population and to explore the role of genes on which these were identified. For this reason, whole-genome sequencing (WGS) was performed on normozoospermic and teratozoospermic individuals, and after selecting only variants found in teratozoospermic men, these were further prioritized using a wide range of tools, functional and predictive algorithms, etc. An average of 600,000 variants were identified, and of them, 61 were characterized as high impact and 153 as moderate impact. Many of these are mapped in genes previously associated with male infertility, yet others are related for the first time to teratozoospermia. Furthermore, pathway enrichment analysis and Gene ontology (GO) analyses revealed the important role of the extracellular matrix in teratozoospermia. Therefore, the present study confirms the contribution of genes studied in the past to male infertility and sheds light on new molecular mechanisms by providing a list of variants and candidate genes associated with teratozoospermia in the Greek population.