Functional, structural and epitopic prediction of hypothetical proteins of Mycobacterium tuberculosis H37Rv: An in silico approach for prioritizing the targets

Challenges in Serologic Diagnostics of Neglected Human Systemic Mycoses: An Overview on Characterization of New Targets

Systemic mycoses have been viewed as neglected diseases and they are responsible for deaths and disabilities around the world. Rapid, low-cost, simple, highly-specific and sensitive diagnostic tests are critical components of patient care, disease control and active surveillance. However, the diagnosis of fungal infections represents a great challenge because of the decline in the expertise needed for identifying fungi, and a reduced number of instruments and assays specific to fungal identification. Unfortunately, time of diagnosis is one of the most important risk factors for mortality rates from many of the systemic mycoses. In addition, phenotypic and biochemical identification methods are often time-consuming, which has created an increasing demand for new methods of fungal identification. In this review, we discuss the current context of the diagnosis of the main systemic mycoses and propose alternative approaches for the identification of new targets for fungal pathogens, which can help in the development of new diagnostic tests.

Structural and functional characterization of a hypothetical protein in the RD7 region in clinical isolates of Mycobacterium tuberculosis - an in silico approach to candidate vaccines

Background

Mycobacterium tuberculosis has been ravaging humans by inflicting respiratory tuberculosis since centuries. Bacillus Calmette Guerine (BCG) is the only vaccine available for tuberculosis, and it is known to be poorly effective against adult tuberculosis. Proteins belonging to the ESAT-6 family and PE/PPE family show immune responses and are included in different vaccine trials. Herein, we study the functional and structural characterization of a 248 amino acid long putative protein novel hypothetical protein 1 (NHP1) present in the RD7 region of Mycobacterium tuberculosis (identified first by subtractive hybridization in the clinical isolate RGTB123) using bioinformatics tools.

Results

Physicochemical properties were studied using Expasy ProtParam and SMS software. We predicted different B-cell and T-cell epitopes by using the immune epitope database (IEDB) and also tested antigenicity, immunogenicity, and allergenicity. Secondary structure of the protein predicted 30% alpha helices, 20% beta strands, and 48% random coils. Tertiary structure of the protein was predicted using the Robetta server using the Mycobacterium smegmatis protein as the putative protein with homology. Structural evaluations were done with Ramachandran plot analysis, ProSA-web, and VERIFY3D, and with GalaxyWEB server, a more stable structure was validated with good stereo chemical properties.

Conclusion

The present study of a subtracted genomic locus using various bioinformatics tools indicated good immunological properties of the putative mycobacterial protein, NHP1. Evidence obtained from the analyses of NHP1 using structure prediction tools strongly point to the fact that NHP1 is an ancient protein having flavodoxin folding structure with ATP binding sites. Positive scores were obtained for antigenicity, immunogenicity, and virulence too, implying the possibility of NHP1 to be a potential vaccine candidate. Such computational studies might give clues for developing newer vaccines for tuberculosis, which is the need of the hour.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43141-022-00340-5.

Questing functions and structures of hypothetical proteins from Campylobacter jejuni: a computer-aided approach

Campylobacter jejuni (C. jejuni) is considered to be one of the most frequent causes of bacterial gastroenteritis globally, especially in young children. The genome of C. jejuni contains many proteins with unknown functions termed as hypothetical proteins (HPs). These proteins might have essential biological role to show the full spectrum of this bacterium. Hence, our study aimed to determine the functions of HPs, pertaining to the genome of C. jejuni. An in-silico work flow integrating various tools were performed for functional assignment, three-dimensional structure determination, domain architecture predictors, subcellular localization, physicochemical characterization, and protein-protein interactions (PPIs). Sequences of 267 HPs of C. jejuni were analyzed and successfully attributed the function of 49 HPs with higher confidence. Here, we found proteins with enzymatic activity, transporters, binding and regulatory proteins as well as proteins with biotechnological interest. Assessment of the performance of various tools used in this analysis revealed an accuracy of 95% using receiver operating characteristic (ROC) curve analysis. Functional and structural predictions and the results from ROC analyses provided the validity of in-silico tools used in the present study. The approach used for this analysis leads us to assign the function of unknown proteins and relate them with the functions that have already been described in previous literature.

Domain-wise differentiation of Mycobacterium tuberculosis H37 Rv hypothetical proteins: A roadmap to discover bacterial survival potentials

Proteomic information revealed approximately 3,923 proteins in Mycobacterium tuberculosis H₃₇ Rv genome of which around ∼25% of proteins are hypothetical proteins (HPs). The present work comprises computational approaches to identify and characterize the HPs of M. tuberculosis that symbolize the putative target for rationale development of a drug or antituberculosis strategy. Proteins were primarily classified based on motif and domain information, which were further analyzed for the presence of virulence factors (VFs), determination of localization, and signal peptide/enzymatic cleavage sites. 863 HPs were found, and 599 HPs were finalized based on motifs, that is, GTP (525), Trx (47), SAM (14), PE-PGRS (5), and CBD (8). 80 HPs contain virulence factor (VF), 24 HPs localized in membrane region, and 4 HPs contain signal peptide/enzymatic cleavage sites. The overall parametric study finalizes four HPs Rv0679c, Rv0906, Rv3627c, and Rv3811 that also comprise GTPase domain. Structure prediction, structure-based function prediction, molecular docking and mutation analysis of selected proteins were done. Docking studies revealed that GTP and GTPase inhibitor (mac0182344) were docked with all four proteins with high affinities. In silico point mutation studies showed that substitution of aspartate with glycine within a GTPase motif showed the largest decrease in stability and pH differentiation also affects protein's stability. This analysis thus fixes a roadmap in the direction of finding potential target of this bacterium for drug development and enlightens the efficacy of GTP as a major regulator of Mycobacterial cellular pathways.

Analysis of Putative Epitope Candidates of Mycobacterium tuberculosis Against Pakistani Human Leukocyte Antigen Background: An Immunoinformatic Study for the Development of Future Vaccine

Tuberculosis (TB), a chronic disease caused by Mycobacterium tuberculosis (Mtb), is a global health issue across the world. Pakistan ranks fifth among the countries, which are facing, a significantly great number of mortalities and morbidities due to TB. Unfortunately, all previously reported treatments are not successful for the eradication of TB. Here in this study, we report an emerging treatment option for this disease. We have applied immunoinformatics to predict highly conserved B and T-cell epitopes from Mtb, showing significant binding affinities to the frequent HLA alleles in the Pakistani population. A total of ten highly referenced and experimentally validated epitopes were selected from the Immune Epitope Database (IEDB), followed by their conservancy analysis using weblogos. The consensus sequences and variants derived from these sequences were examined, for their binding affinities, with prevalent HLA alleles of Pakistan. Moreover, the antigenic and allergenic natures of these peptides were also evaluated via Vaxijen and AllerTOP, respectively. Consequently, all potentially allergenic and non-antigenic, peptide fragments, were excluded from the analysis. Among all putative epitopes, three CD8 + T-cell epitopes were selected, as ideal vaccine candidates and, population coverage analysis revealed that the combination of these three peptides was covering, 67.28% Pakistani Asian and 57.15% mixed Pakistani populations. Likewise, eleven linear and six conformational or discontinuous B-cell epitopes were also marked as potential vaccine candidates based on their prediction score, non-allergenic nature, and antigenic properties. These epitopes, however, need the final validation via wet-lab studies. After their approval, these epitopes would be effective candidates for the future designing of epitope-based vaccines against Mtb infections in Pakistan.

Identification of novel therapeutic target and epitopes through proteome mining from essential hypothetical proteins in Salmonella strains: An In silico approach towards antivirulence therapy and vaccine development

Salmonella strains are responsible for a huge mortality rate through foodborne ailment in the world that necessitated the discovery of novel drugs and vaccines. Essential hypothetical proteins (EHPs), whose structures and functions were previously unknown, could serve as potential therapeutic and vaccine targets. Antivirulence therapy shall emerge as a superior therapeutic approach that uses virulence factors as drug targets. This study annotated the biological functions of 96 out of total 106 essential hypothetical proteins in five strains of Salmonella and classified into nine important protein categories. 34 virulence factors were predicted among the EHPs, out of which, 11 were identified to be pathogen specific potential drug targets for antivirulence therapy. These targets were non-homologous to both human and gut microbiota proteome to avoid cross-reactivity with them. Seven identified targets had druggable property, while the rest four targets were novel targets. Four identified targets (DEG10320148, DEG10110027, DEG10110040 and DEG10110142) had antigenic properties and were further classified as: two membrane-bound Lipid-binding transmembrane proteins, a Zinc-binding membrane protein and an extracellular glycosylase. These targets could be potentially used for the development of subunit vaccines. The study further identified 11 highly conserved and exposed epitope sequences from these 4 vaccine targets. The three-dimensional structures of the vaccine targets were also elucidated along with highlighting the conformation of the epitopes. This study identified potential therapeutic targets for antivirulence therapy against Salmonella. It would therefore instigate in novel drug designing as well as provide important leads to new Salmonella vaccine development.

In Silico Drug Target Discovery Through Proteome Mining from M. tuberculosis: An Insight into Antivirulent Therapy

AIM AND OBJECTIVE

One of the challenges to conventional therapies against Mycobacterium tuberculosis is the development of multi-drug resistant pathogenic strains. This study was undertaken to explore new therapeutic targets for the revolutionary antivirulence therapy utilizing the pathogen's essential hypothetical proteins, serving as virulence factors, which is the essential first step in novel drug designing.

METHODS

Functional annotations of essential hypothetical proteins from Mycobacterium tuberculosis (H37Rv strain) were performed through domain annotation, Gene Ontology analysis, physicochemical characterization and prediction of subcellular localization. Virulence factors among the essential hypothetical proteins were predicted, among which pathogen-specific drug target candidates, non-homologous to human and gut microbiota, were identified. This was followed by druggability and spectrum analysis of the identified targets.

RESULTS AND CONCLUSION

The study successfully assigned functions of 83 essential hypothetical proteins of Mycobacterium tuberculosis, among which 25 were identified as virulence factors. Out of 25, 12 virulence factors were observed as potential pathogen-specific drug target candidates. Nine potential targets had druggable properties and rest three were considered as novel targets. Exploration of these targets will provide new insights into future drug development. Characterization of subcellular localizations revealed that most of the predicted targets were cytoplasmic which could be ideal for intracellular drugs, while two drug targets were membranebound, ideal for vaccines. Spectrum analysis identified one broad-spectrum and 11 narrowspectrum targets. This study would, therefore, instigate designing novel therapeutics for antivirulence therapy, which have the potential to serve as revolutionary treatment instead of conventional antibiotic therapies to overcome the lethality of antibiotic-resistant strains.

A deep learning ensemble for function prediction of hypothetical proteins from pathogenic bacterial species

Protein function prediction is a crucial task in the post-genomics era due to their diverse irreplaceable roles in a biological system. Traditional methods involved cost-intensive and time-consuming molecular biology techniques but they proved to be ineffective after the outburst of sequencing data through the advent of cost-effective and advanced sequencing techniques. To manage the pace of annotation with that of data generation, there is a shift to computational approaches which are based on homology, sequence and structure-based features, protein-protein interaction networks, phylogenetic profiles, and physicochemical properties, etc. A combination of these features has proven to be promising for protein function prediction in terms of improving prediction accuracy. In the present work, we have employed a combination of features based on sequence, physicochemical property, subsequence and annotation features with a total of 9890 features extracted and/or calculated for 171,212 reviewed prokaryotic proteins of 9 bacterial phyla from UniProtKB, to train a supervised deep learning ensemble model with the aim to categorize a bacterial hypothetical/unreviewed protein's function into 1739 GO terms as functional classes. The proposed system being fully dedicated to bacterial organisms is a novel attempt amongst various existing machine learning based protein function prediction systems based on mixed organisms. Experimental results demonstrate the success of the proposed deep learning ensemble model based on deep neural network method with F1 measure of 0.7912 on the prepared Test dataset 1 of reviewed proteins.

Investigating function roles of hypothetical proteins encoded by the Mycobacterium tuberculosis H37Rv genome

Background

Mycobacterium tuberculosis (MTB) is a common bacterium causing tuberculosis and remains a major pathogen for mortality. Although the MTB genome has been extensively explored for two decades, the functions of 27% (1051/3906) of encoded proteins have yet to be determined and these proteins are annotated as hypothetical proteins.

Methods

We assigned functions to these hypothetical proteins using SSEalign, a newly designed algorithm utilizing structural information. A set of rigorous criteria was applied to these annotations in order to examine whether they were supported by each parameter. Virulence factors and potential drug targets were also screened among the annotated proteins.

Results

For 78% (823/1051) of the hypothetical proteins, we could identify homologs in Escherichia coli and Salmonella typhimurium by using SSEalign. Functional classification analysis indicated that 62.2% (512/823) of these annotated proteins were enzymes with catalytic activities and most of these annotations were supported by at least two other independent parameters. A relatively high proportion of transporter was identified in MTB genome, indicating the potential frequent transportation of frequent absorbing essential metabolites and excreting toxic materials in MTB. Twelve virulence factors and ten vaccine candidates were identified within these MTB hypothetical proteins, including two genes (rpoS and pspA) related to stress response to the host immune system. Furthermore, we have identified six novel drug target candidates among our annotated proteins, including Rv0817 and Rv2927c, which could be used for treating MTB infection.

Conclusions

Our annotation of the MTB hypothetical proteins will probably serve as a useful dataset for future MTB studies.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5746-6) contains supplementary material, which is available to authorized users.

Functional Prediction of Hypothetical Proteins from Shigella flexneri and Validation of the Predicted Models by Using ROC Curve Analysis

Shigella spp. constitutes some of the key pathogens responsible for the global burden of diarrhoeal disease. With over 164 million reported cases per annum, shigellosis accounts for 1.1 million deaths each year. Majority of these cases occur among the children of the developing nations and the emergence of multi-drug resistance Shigella strains in clinical isolates demands the development of better/new drugs against this pathogen. The genome of Shigella flexneri was extensively analyzed and found 4,362 proteins among which the functions of 674 proteins, termed as hypothetical proteins (HPs) had not been previously elucidated. Amino acid sequences of all these 674 HPs were studied and the functions of a total of 39 HPs have been assigned with high level of confidence. Here we have utilized a combination of the latest versions of databases to assign the precise function of HPs for which no experimental information is available. These HPs were found to belong to various classes of proteins such as enzymes, binding proteins, signal transducers, lipoprotein, transporters, virulence and other proteins. Evaluation of the performance of the various computational tools conducted using receiver operating characteristic curve analysis and a resoundingly high average accuracy of 93.6% were obtained. Our comprehensive analysis will help to gain greater understanding for the development of many novel potential therapeutic interventions to defeat Shigella infection.

Structural and functional annotation of hypothetical proteins of human adenovirus: prioritizing the novel drug targets

OBJECTIVE

Human adenoviruses are small double stranded DNA viruses that provoke vast array of human diseases. Next generation sequencing techniques increase genomic data of HAdV rapidly, which increase their serotypes. The complete genome sequence of human adenovirus shows that it contains large amount of proteins with unknown cellular or biochemical function, known as hypothetical proteins. Hence, it is indispensable to functionally and structurally annotate these proteins to get better understanding of the novel drug targets. The purpose was the characterization of 38 randomly retrieved hypothetical proteins through determination of their physiochemical properties, subcellular localization, function, structure and ligand binding sites using various sequence and structure based bioinformatics tools.

RESULTS

Function of six hypothetical proteins P03269, P03261, P03263, Q83127, Q1L4D7 and I6LEV1 were predicted confidently and then used further for structure analysis. We found that these proteins may act as DNA terminal protein, DNA polymerase, DNA binding protein, adenovirus E3 region protein CR1 and adenoviral protein L1. Functional and structural annotation leading to detection of binding sites by means of docking analysis can indicate potential target for therapeutics to defeat adenoviral infection.

Genome analysis of Chlamydia trachomatis for functional characterization of hypothetical proteins to discover novel drug targets

C. trachomatis is a Gram-negative bacterium that causes trachoma and sexually transmitted disease (STD) Chlamydia in humans. Chlamydial genital infections are the most frequent among all communicable diseases. The D/UW-3/Cx strain of C. trachomatis contains 935 genes and three pseudogenes. Out of these genes, 887 genes code for proteins while six for rRNA, 37 tRNA, and three genes translate into other RNAs. The proteome of C. trachomatis made of 887 proteins contains 269 Hypothetical proteins (HPs) that are subjected to functional characterization. This study suggests some known methods of functional characterization of such HPs. All of these methods are explicitly used to assign functions to the HPs with the accuracy of more than 90%. After extensive analysis of all the HPs, we have successfully assigned functions to 89 HPs with high precision. In the newly assigned HPs, there are enzymes, transporters, binding proteins, proteins involved in biosynthesis and regulatory processes and proteins with miscellaneous functions. The study suggests that the functionally annotated HPs may play a vital role in the growth and pathogenesis of this organism. Therefore, they can be considered potential drug targets.