Gene interaction network approach to elucidate the multidrug resistance mechanisms in the pathogenic bacterial strain Proteus mirabilis

Effects of antimicrobial peptides from dietary Hermetia illucens larvae on the growth, immunity, gene expression, intestinal microbiota and resistance to Aeromonas hydrophila of juvenile red claw crayfish (Cherax quadricarinatus)

Antimicrobial peptides (AMPs), which are widely present in animals and plants, have a broad distribution, strong broad-spectrum antibacterial activity, low likelihood of developing drug resistance, high thermal stability and antiviral properties. The present study investigated the effects of adding AMPs from Hermetia illucens larvae on the growth performance, muscle composition, antioxidant capacity, immune response, gene expression, antibacterial ability and intestinal microbiota of Cherax quadricarinatus (red claw crayfish). Five experimental diets were prepared by adding 50 (M1), 100 (M2), 150 (M3) and 200 (M4) mg/kg of crude AMP extract from H. illucens larvae to the basal diet feed, which was also used as the control (M0). After an eight-week feeding experiment, it was discovered that the addition of 100-150 mg/kg of H. illucens larvae AMPs to the feed significantly improved the weight gain rate and specific growth rate of C. quadricarinatus. Furthermore, the addition of H. illucens larvae AMPs to the feed had no significant effect on the moisture content, crude protein, crude fat and ash content of the C. quadricarinatus muscle. The addition of 100-150 mg/kg of H. illucens larvae AMPs in the feed also increased the antioxidant capacity, nonspecific immune enzyme activity and related gene expression levels in C. quadricarinatus, thereby enhancing their antioxidant capacity and immune function. The H. illucens larvae AMPs improved the structure and composition of the intestinal microbiota of C. quadricarinatus, increasing the microbial community diversity of the crayfish gut. Finally, the addition of 100-150 mg/kg of H. illucens larvae AMPs in the feed enhanced the resistance of C. quadricarinatus against Aeromonas hydrophila, improving the survival rate of the crayfish. Based on the aforementioned findings, it is recommended that H. illucens larvae AMPs be incorporated into the C. quadricarinatus feed at a concentration of 100-150 mg/kg.

Genome-Wide Investigation Reveals Potential Therapeutic Targets in Shigella spp

Shigella stands as a major contributor to bacterial dysentery worldwide scale, particularly in developing countries with inadequate sanitation and hygiene. The emergence of multidrug-resistant strains exacerbates the challenge of treating Shigella infections, particularly in regions where access to healthcare and alternative antibiotics is limited. Therefore, investigations on how bacteria evade antibiotics and eventually develop resistance could open new avenues for research to develop novel therapeutics. The aim of this study was to analyze whole genome sequence (WGS) of human pathogenic Shigella spp. to elucidate the antibiotic resistance genes (ARGs) and their mechanism of resistance, gene-drug interactions, protein-protein interactions, and functional pathways to screen potential therapeutic candidate(s). We comprehensively analyzed 45 WGS of Shigella, including S. flexneri (n = 17), S. dysenteriae (n = 14), S. boydii (n = 11), and S. sonnei (n = 13), through different bioinformatics tools. Evolutionary phylogenetic analysis showed three distinct clades among the circulating strains of Shigella worldwide, with less genomic diversity. In this study, 2,146 ARGs were predicted in 45 genomes (average 47.69 ARGs/genome), of which only 91 ARGs were found to be shared across the genomes. Majority of these ARGs conferred their resistance through antibiotic efflux pump (51.0%) followed by antibiotic target alteration (23%) and antibiotic target replacement (18%). We identified 13 hub proteins, of which four proteins (e.g., tolC, acrR, mdtA, and gyrA) were detected as potential hub proteins to be associated with antibiotic efflux pump and target alteration mechanisms. These hub proteins were significantly (p < 0.05) enriched in biological process, molecular function, and cellular components. Therefore, the finding of this study suggests that human pathogenic Shigella strains harbored a wide range of ARGs that confer resistance through antibiotic efflux pumps and antibiotic target modification mechanisms, which must be taken into account to devise and formulate treatment strategy against this pathogen. Moreover, the identified hub proteins could be exploited to design and develop novel therapeutics against MDR pathogens like Shigella.

Bacteria and antimicrobial resistance profile during the composting process of wastes from animal production

Livestock waste is widely used in agriculture. Although they provide benefits to the soil, and consequently to plants, they have the potential to contaminate the environment, as they contain pathogenic microorganisms and determinants of antimicrobial resistance, if not properly managed. Therefore, this study aims to evaluate the effect of composting horse bedding and poultry litter in organic and conventional production systems on the occurrence of bacteria in the Enterobacteriales order and to identify their antimicrobial resistance profiles. Bacterial strains were isolated from Salmonella-Shigella and eosin methylene blue solid media from animal waste during the composting process that was conducted for 125 days. After isolation, the strains were identified by the MALDI-TOF technique; the disk diffusion test was then performed for phenotypic detection of antimicrobial resistance. A total of 158 bacterial strains were isolated during composting of three wastes. The Enterobacteriaceae family was the most abundant, whereas Proteus mirabilis and Escherichia coli were the species with the highest percentage in the wastes, which also exhibited a multi-resistance profile. Poultry litter showed a greater abundance of resistant bacteria than horse bedding did. Similarly, a greater number of resistant bacteria was detected in conventional poultry litter than in organic poultry litter. The results obtained reinforce that animal wastes are reservoirs of pathogenic bacteria that are resistant to antimicrobials and highlight the importance of developing management strategies that aim to reduce and/or eliminate these contaminants to guarantee their safe use in agriculture.

Unveiling the role of hub proteins in controlling quorum sensing regulated virulence through analogues in Pseudomonas aeruginosa PAO1: A functional protein-protein network biology approach

The protein-protein interaction (PPI) network analysis of specific genes identified for biofilm production and virulence/secretion system mediated by quorum sensing. The PPI depicted 13 hub proteins (namely rhlR, lasR, pscU, vfr, exsA, lasI, gacA, toxA, pilJ, pscC, fleQ, algR, and chpA) out of 160 nodes involving 627 edges. The PPI network analysis based on topographical features depicted pcrD with the highest degree value and vfr gene with the greatest betweenness centrality and closeness centrality (BC and CC) values. Based on in silico results, curcumin used as an Acyl homo-serine lactone (AHL) mimicker in P. aeruginosa, was also found effective in suppressing the quorum sensing regulated virulence factors such as elastase and pyocyanin. Based on in vitro experiment, curcumin suppressed biofilm formation at 62 μg/ml concentration. Host-pathogen interaction experiment showed that curcumin was also proved to be efficient in saving C. elegans from paralysis and killing effects of P. aeruginosa PAO1.

Protein Interaction Network Analysis to Investigate Stress Response, Virulence, and Antibiotic Resistance Mechanisms in Listeria monocytogenes

Listeria monocytogenes is a deadly and costly foodborne pathogen that has a high fatality rate in the elderly, pregnant women, and people with weakened immunity. It can survive under various stress conditions and is a significant concern for the food industry. In this work, a data analysis approach was developed with existing tools and databases and used to create individual and combined protein interaction networks to study stress response, virulence, and antimicrobial resistance and their interaction with L. monocytogenes. The networks were analyzed, and 28 key proteins were identified that may serve as potential targets for new strategies to combat L. monocytogenes. Five of the twenty-eight proteins (i.e., sigB, flaA, cheA, cheY, and lmo0693) represent the most promising targets because they are highly interconnected within the combined network. The results of this study provide a new set of targets for future work to identify new strategies to improve food preservation methods and treatments for L. monocytogenes.

Gene network interaction analysis to elucidate the antimicrobial resistance mechanisms in the Clostridiumdifficile

Antimicrobial resistance has caused chaos worldwide due to the depiction of multidrug-resistant (MDR) infective microorganisms. A thorough examination of antimicrobial resistance (AMR) genes and associated resistant mechanisms is vital to solving this problem. Clostridium difficile (C. difficile) is an opportunistic nosocomial bacterial strain that has acquired exogenous AMR genes that confer resistance to antimicrobials such as erythromycin, azithromycin, clarithromycin, rifampicin, moxifloxacin, fluoroquinolones, vancomycin, and others. A network of interactions, including 20 AMR genes, was created and analyzed. In functional enrichment analysis, Cellular components (CC), Molecular Functions (MF), and Biological Processes (BP) were discovered to have substantial involvement. Mutations in the rpl genes, which encode ribosomal proteins, confer resistance in Gram-positive bacteria. Full erythromycin and azithromycin cross-resistance can be conferred if more than one of the abovementioned genes is present. In the enriched BP, rps genes related to transcriptional regulation and biosynthesis were found. The genes belong to the rpoB gene family, which has previously been related to rifampicin resistance. The genes rpoB, gyrA, gyrB, rpoS, rpl genes, rps genes, and Van genes are thought to be the hub genes implicated in resistance in C. difficile. As a result, new medications could be developed using these genes. Overall, our observations provide a thorough understanding of C. difficile AMR mechanisms.

Elucidating the mechanism of antimicrobial resistance in Mycobacterium tuberculosis using gene interaction networks

Antimicrobial resistance (AMR) in microorganisms is an urgent global health threat. AMR of Mycobacterium tuberculosis is associated with significant morbidity and mortality. It is of great importance to underpin the resistance pathways involved in the mechanisms of AMR and identify the genes that are directly involved in AMR. The focus of the current study was the bacteria M. tuberculosis, which carries AMR genes that give resistance that lead to multidrug resistance. We, therefore, built a network of 43 genes and examined for potential gene-gene interactions. Then we performed a clustering analysis and identified three closely related clusters that could be involved in multidrug resistance mechanisms. Through the bioinformatics pipeline, we consistently identified six-hub genes (dnaN, polA, ftsZ, alr, ftsQ, and murC) that demonstrated the highest number of interactions within the clustering analysis. This study sheds light on the multidrug resistance of MTB and provides a protocol for discovering genes that might be involved in multidrug resistance, which will improve the treatment of resistant strains of TB.

Systems biology approach to understand the interplay between Bacillus anthracis and human host genes that leads to CVDs

Humans infected with invasive Bacillus anthracis (B. anthracis) have a very poor prognosis and are at high risk for developing cardiovascular diseases (CVDs) and shock. Several bacterial elements probably have significant pathogenic roles in this pathogenic process of anthrax. In our current work, we have analysed the molecular level interactions between B. anthracis and human genes to understand the interplay during anthrax that leads to the CVDs. Our results have shown dense interactions between the functional partners in both host and the B. anthracis Gene interaction network (GIN). The functional enrichment analysis indicated that the clusters in the host GIN had genes related to hypoxia and autophagy in response to the lethal toxin; and genes related to adherens junction and actin cytoskeleton in response to edema toxin play a significant role in multiple stages of the disease. The B. anthracis genes BA_0530, guaA, polA, rpoB, ribD, secDF, metS, dinG and human genes ACTB, EGFR, EP300, CTNNB1, ESR1 have shown more than 50 direct interactions with the functional partners and hence they can be considered as hub genes in the network and they are observed to have important roles in CVDs. The outcome of our study will help to understand the molecular pathogenesis of CVDs in anthrax. The hub genes reported in the study can be considered potential drug targets and they can be exploited for new drug discovery.

Functional insights of antibiotic resistance mechanism in Helicobacter pylori: Driven by gene interaction network and centrality-based nodes essentiality analysis

Increased antibiotic resistance in Helicobacter pylori (H. pylori), a major human pathogen, constitutes a substantial threat to human health. Understanding the pathophysiology and development of antibiotic resistance can aid our battle with the infections caused by H. pylori. The aim of this study is to discover the high-impact key regulatory mechanisms and genes involved in antimicrobial drug resistance (AMR). In this study, we constructed a functional gene interaction network by integrating multiple sources of data related to antibiotic resistant genes (number-77) from H. pylori. The gene interaction network was assortative, with a hierarchical, scale-free topology enriched in a variety of gene ontology (GO) categories and KEGG pathways. Using an iterative clustering methodology, we identified a number of communities in the AMR gene network that comprised nine genes (sodB, groEL, gyrA, recA, polA, tuf, infB, rpsJ, and gyrB) that were present at the deepest level and hence were key regulators of AMR. Further, an antibiotic-resistant gene network-based centrality analysis revealed superoxide dismutase (sodB) as a bottleneck node in the network. Our findings suggested that sodB is critically enriched in the cellular response to oxidative stress, removal of superoxide radicals, cellular oxidant detoxification processes, cellular component biogenesis, response to reactive oxygen species, urea metabolic process, nitrogen cycle metabolic process and reactive oxygen species metabolic process. We demonstrated how the sodB, which are involved in the response to reactive oxygen species, urea metabolic process, nitrogen cycle metabolic process, reactive oxygen species metabolic process, regulated by Fur gene/proteins, claim a major authority over regulation and signal propagation in the AMR.

Porphyromonas gingivalis resistance and virulence: An integrated functional network analysis

BACKGROUND

The gram-negative bacteria Porphyromonas gingivalis (PG) is the most prevalent cause of periodontal diseases and multidrug-resistant (MDR) infections. Periodontitis and MDR infections are severe due to PG's ability to efflux antimicrobial and virulence factors. This gives rise to colonisation, biofilm development, evasion, and modulation of the host defence system. Despite extensive studies on the MDR efflux pump in other pathogens, little is known about the efflux pump and its association with the virulence factor in PG. Prolonged infection of PG leads to complete loss of teeth and other systemic diseases. This necessitates the development of new therapeutic interventions to prevent and control MDR.

OBJECTIVE

The study aims to identify the most indispensable proteins that regulate both resistance and virulence in PG, which could therefore be used as a target to fight against the MDR threat to antibiotics.

METHODS

We have adopted a hierarchical network-based approach to construct a protein interaction network. Firstly, individual networks of four major efflux pump proteins and two virulence regulatory proteins were constructed, followed by integrating them into one. The relationship between proteins was investigated using a combination of centrality scores, k-core network decomposition, and functional annotation, to computationally identify the indispensable proteins.

RESULTS

Our study identified four topologically significant genes, PG_0538, PG_0539, PG_0285, and PG_1797, as potential pharmacological targets. PG_0539 and PG_1797 were identified to have significant associations between the efflux pump and virulence genes. This type of underpinning research may help in narrowing the drug spectrum used for treating periodontal diseases, and may also be exploited to look into antibiotic resistance and pathogenicity in bacteria other than PG.

In silico Methods for Identification of Potential Therapeutic Targets

At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.

Graphical abstract

Genome-scale protein interaction network construction and topology analysis of functional hypothetical proteins in Helicobacter pylori divulges novel therapeutic targets

The emergence and spread of multi-drug resistance among Helicobacter pylori (H. pylori) strain raise more stakes for genetic research for discovering new drugs. The quantity of uncharacterized hypothetical proteins in the genome may provide an opportunity to explore their property and promulgation could act as a platform for designing the drugs, making them an intriguing genetic target. In this context, the present study aims to identify the key hypothetical proteins (HPs) and their biological regulatory processes in H. pylori. This investigation could provide a foundation to establish the molecular connectivity among the pathways using topological analysis of the protein interaction networks (PINs). The giant network derived from the extended network has 374 nodes connected via 925 edges. A total of 43 proteins with high betweenness centrality (BC), 54 proteins with a large degree, and 23 proteins with high BC and large degrees have been identified. HP 1479, HP 0056, HP 1481, HP 1021, HP 0043, HP 1019, gmd, flgA, HP 0472, HP 1486, HP 1478, and HP 1473 are categorized as hub nodes because they have a higher number of direct connections and are potentially more important in understanding HP's molecular interactions. The pathway enrichment analysis of the network clusters revealed significant involvement of HPs in pathways such as flagellar assembly, bacterial chemotaxis and lipopolysaccharide biosynthesis. This comprehensive computational study revealed HP's functional role and its druggability characteristics, which could be useful in the development of drugs to combat H. pylori infections.

Identification of docosahexaenoic and eicosapentaenoic acids multiple targets facing periodontopathogens

The eicosapentaenoic (EPA) and docosahexaenoic acid (DHA) play a substantial role in Periodontal Disease (PD) due to their antimicrobial and immunomodulatory actions. However, their antimicrobial mechanism of action against bacteria involved in PD remains unclear. We aimed to estimate the probable targets of EPA and DHA against the seven periodontopathogens. Through in silico analyses, the protein-acids interactions, protein characterization, and molecular docking were performed. We identified 165 proteins from periodontopathogens that may interact with EPA and DHA. Fusobacterium nucleatum has the highest number of predicted proteins among analyzed bacteria (n = 43, 26.06%). The EPA shows more interactions than DHA. The EPA and DHA interact mainly with proteins involved in the metabolism (n = 69, 41.81%). Also, the EPA and DHA interact with proteins located in any subcellular location. The affinities between acids and pathogenic proteins were moderate (binding energy was lower than -4.0 kcal/mol). The interactions between EPA and DHA and periodontopathogens occur in multiples proteins. There is not a predilection about the functional class of pathogenic proteins targeting EPA and DHA. However, there are moderate binding affinities between EPA or DHA and essential pathogenic proteins (TolC, CRISPR, FusA).

Genome sequencing and molecular characterisation of XDR Acinetobacter baumannii reveal complexities in resistance: Novel combination of sulbactam-durlobactam holds promise for therapeutic intervention

Emerging nosocomial strains of Acinetobacter baumannii are of recent concern as they are expressing extensive drug resistance (XDR). Using whole-genome sequencing and molecular characterisation analysis, the current study reveals the presence of carbapenemase genes in 92.86% of studied Indian isolates. These included bla_OXA-51 , bla_OXA-23 , bla_OXA-58 , and bla_NDM genes, with over a third expressing dual carbapenemase genes. As per the MLST scheme, IC2^Oxf /CC2^Pas was the predominant clone, with 57.14% isolates belonging to this lineage. The presence of these carbapenemase genes resulted in sulbactam (SUL) resistance (MIC: 16-256 µg/ml) in all of the studied isolates. The efficacy of durlobactam (DUR), a novel β-lactamase inhibitor that also inhibits PBP2 was assessed through in silico intermolecular interaction analysis. Several nonsynonymous single nucleotide polymorphisms were identified in PBP2 (G264S, I108V, S259T) and PBP3 (A515V, T526S) sequences. Minimal variations were recorded in the protein backbone dynamics in active-site motifs of wild-type and mutants, which correlated with negligible binding energy fluctuations for the PBP3-SUL (-5.85 ± 0.04 kcal/mol) and PBP2-DUR (-5.16 ± 0.66 kcal/mol) complexes. Furthermore, higher binding affinities and low inhibition constants were noted in OXA23-DUR (-7.36 kcal/mol; 4.01 µM), OXA58-DUR (-6.44 kcal/mol; 19.07 µM), and NDM-DUR (-6.82 kcal/mol; 10.01 µM) complexes when compared with the conventional drugs avibactam and aztreonam. Stable interaction profiles of DUR with carbapenemases can possibly restore SUL activity against both PBP3_WT and PBP3_MTs . The study establishes the efficacy of the novel SUL-DUR combination as a successful treatment strategy in combating emerging XDR strains of A. baumannii.

Metal and Metal Oxide Nanoparticle as a Novel Antibiotic Carrier for the Direct Delivery of Antibiotics

In addition to the benefits, increasing the constant need for antibiotics has resulted in the development of antibiotic bacterial resistance over time. Antibiotic tolerance mainly evolves in these bacteria through efflux pumps and biofilms. Leading to its modern and profitable uses, emerging nanotechnology is a significant field of research that is considered as the most important scientific breakthrough in recent years. Metal nanoparticles as nanocarriers are currently attracting a lot of interest from scientists, because of their wide range of applications and higher compatibility with bioactive components. As a consequence of their ability to inhibit the growth of bacteria, nanoparticles have been shown to have significant antibacterial, antifungal, antiviral, and antiparasitic efficacy in the battle against antibiotic resistance in microorganisms. As a result, this study covers bacterial tolerance to antibiotics, the antibacterial properties of various metal nanoparticles, their mechanisms, and the use of various metal and metal oxide nanoparticles as novel antibiotic carriers for direct antibiotic delivery.

Protein Integrated Network Analysis to Reveal Potential Drug Targets Against Extended Drug-Resistant Mycobacterium tuberculosis XDR1219

The reconstruction and analysis of the protein-protein interaction (PPI) network is a powerful approach to understand the complex biological and molecular functions in normal and disease states of the cell. The interactome of most organisms is largely unidentified except some model organisms. The current study focused on the construction of PPI network for the human pathogen Mycobacterium tuberculosis (MTB)-resistant strain XDR1219 using computational methods. In this work, a bioinformatics approach was employed to reveal potential drug targets. The pipeline adopted the combination of an extensive integrated network analysis that led to identify 22 key proteins involved in drug resistance, resistant metabolic pathways, virulence, pathogenesis and persistency of the infection. The MTB XDR1219 interactome consists of 11,383 non-redundant PPIs among 1499 proteins covering 38% of the entire MTB XDR1219 proteome. The overall quality of the network was assessed and topological parameters of the PPI were calculated. The predicted interactions were functionally annotated and their relevance was assessed with the functional similarity. The study attempts to present the interactome of previously unidentified MTB XDR1219 and revealed potential drug targets that can be further explored by scientific community.

Network topology analysis of essential genes interactome of Helicobacter pylori to explore novel therapeutic targets

The Helicobacter pylori chronic colonization produces a wide range of gastric diseases in the gastric mucosa by abetting inflammation. Amidst coevolution and reorganization of its metabolism with humans, it has become difficult still imperative to understand and prevent its growth. This study focus to explore functional insights into identification of hub proteins/genes by aggregating the behavior of genes connected in a protein-protein interaction (PPI) network. We have constructed a PPI network of 123 essential genes along with 1213 interactions in H. pylori 26695. The degree and other centrality measures analysis assist in identifying the important hub nodes, which are top-ranked proteins. A total of nine proteins (recA, guaA, dnaK, rpsB, rplQ, rpmA, rpmC, rpmF, and rpsE) were obtained with high degree (k), betweenness centrality (BC) value. Gene ontology analysis reveals 8, 5 and 3 GO terms correspond to biological processes, cellular components and molecular function respectively. Gene complexes of hypothetical proteins (HPs) were related to aminoacyl-tRNA biosynthesis, biosynthesis of secondary metabolites, bacterial secretion system and protein export. The MCODE analysis revealed that protein from module M1, M3 and M6 include the proteins which have highest degree and BC values. It is noteworthy to mention that the bifunctional GMP synthase/glutamine amidotransferase protein (guaA), molecular chaperon (dnaK), recombinase A (recA) constitute as hub proteins. As a result, these genes are considered as network hub nodes that might be used as therapeutic targets. Our analysis affords a detailed understanding of the molecular process and pathways regulated by the essential genes in H. pylori 26695.

Characterization of putative transcriptional regulator (PH0140) and its distal homologue

In this study, a phylogenetic tree was constructed using 1854 sequences of various Lrp/AnsC (FFRPs) and ArsR proteins from pathogenic and non-pathogenic organisms. Despite having sequence similarities, FFRPs and ArsR proteins functioning differently as a transcriptional regulator and de-repressor in the presence of exogenous amino acids and metal ions, respectively. To understand these functional differences, the structures of various FFRPs and ArsR proteins (134 sequences) were modeled. Several ArsR proteins exhibited high similarity to the FFRPs while in few proteins, unusual structural folds were observed. However, the Helix-turn-Helix (HTH) domains are common among them and the ligand-binding domains are structurally dissimilar suggest the differences in their binding preferences. Despite low sequence conservation, most of these proteins revealed negatively charged surfaces in the active site pockets. Representative structures (PH0140 and TtArsR protein) from FFRPs and ArsR protein families were considered and evaluated for their functional differences using molecular modeling studies. Our earlier study has explained the binding preference of exogenous Tryptophan and the related transcriptional regulatory mechanism of PH0140 protein. In this study, a Cu²⁺ ion-induced de-repression mechanism of the TtArsR-DNA complex was characterized through docking and molecular dynamics. Further, the proteins were purified and their efficiency for sensing Tryptophan and Cu²⁺ ions were analyzed using cyclic voltammetry. Overall, the study explores the structural evolution and functional difference of FFRPs and ArsR proteins that present the possibilities of PH0140 and TtArsR as potential bio-sensory molecules.

Pan-genomic analyses of 47 complete genomes of the Rickettsia genus and prediction of new vaccine targets and virulence factors of the species

The genus Rickettsia belongs to the Proteobacteria phylum and these bacteria infect animals and humans causing a range of diseases worldwide. The genus is divided into 4 groups and despite the public health threat and the knowledge accumulated so far, the mandatory intracellular bacteria behaviour and limitation for in vitro culture makes it difficult to create new vaccines and drug targets to these bacteria. In an attempt to overcome these limitations, pan-genomic approaches has used 47 genomes of the genus Rickettsia, in order to describe species similarities and genomics islands. Moreover, we conducted reverse vaccinology and docking analysis aiming the identification of proteins that have great potential to become vaccine and drug targets. We found out that the bacteria of the four Rickettsia groups have a high similarity with each other, with about 90 to 100% of identity. A pathogenicity island and a resistance island were predicted. In addition, 8 proteins were also predicted as strong candidates for vaccine and 9 as candidates for drug targets. The prediction of the proteins leads us to believe in a possibility of prospecting potential drugs or creating a polyvalent vaccine, which could reach most strains of this large group of bacteria.Communicated by Ramaswamy H. Sarma.

Amalgamation of 3D structure and sequence information for protein-protein interaction prediction

Protein is the primary building block of living organisms. It interacts with other proteins and is then involved in various biological processes. Protein-protein interactions (PPIs) help in predicting and hence help in understanding the functionality of the proteins, causes and growth of diseases, and designing new drugs. However, there is a vast gap between the available protein sequences and the identification of protein-protein interactions. To bridge this gap, researchers proposed several computational methods to reveal the interactions between proteins. These methods merely depend on sequence-based information of proteins. With the advancement of technology, different types of information related to proteins are available such as 3D structure information. Nowadays, deep learning techniques are adopted successfully in various domains, including bioinformatics. So, current work focuses on the utilization of different modalities, such as 3D structures and sequence-based information of proteins, and deep learning algorithms to predict PPIs. The proposed approach is divided into several phases. We first get several illustrations of proteins using their 3D coordinates information, and three attributes, such as hydropathy index, isoelectric point, and charge of amino acids. Amino acids are the building blocks of proteins. A pre-trained ResNet50 model, a subclass of a convolutional neural network, is utilized to extract features from these representations of proteins. Autocovariance and conjoint triad are two widely used sequence-based methods to encode proteins, which are used here as another modality of protein sequences. A stacked autoencoder is utilized to get the compact form of sequence-based information. Finally, the features obtained from different modalities are concatenated in pairs and fed into the classifier to predict labels for protein pairs. We have experimented on the human PPIs dataset and Saccharomyces cerevisiae PPIs dataset and compared our results with the state-of-the-art deep-learning-based classifiers. The results achieved by the proposed method are superior to those obtained by the existing methods. Extensive experimentations on different datasets indicate that our approach to learning and combining features from two different modalities is useful in PPI prediction.