An Approach for Identification of Novel Drug Targets in Streptococcus pyogenes SF370 Through Pathway Analysis

Potential Therapeutic Candidates against Chlamydia pneumonia Discovered and Developed In Silico Using Core Proteomics and Molecular Docking and Simulation-Based Approaches

Chlamydia pneumonia, a species of the family Chlamydiacea, is a leading cause of pneumonia. Failure to eradicate C. pneumoniae can lead to chronic infection, which is why it is also considered responsible for chronic inflammatory disorders such as asthma, arthritis, etc. There is an urgent need to tackle the major concerns arising due to persistent infections caused by C. pneumoniae as no FDA-approved drug is available against this chronic infection. In the present study, an approach named subtractive proteomics was employed to the core proteomes of five strains of C. pneumonia using various bioinformatic tools, servers, and software. However, 958 non-redundant proteins were predicted from the 4754 core proteins of the core proteome. BLASTp was used to analyze the non-redundant genes against the proteome of humans, and the number of potential genes was reduced to 681. Furthermore, based on subcellular localization prediction, 313 proteins with cytoplasmic localization were selected for metabolic pathway analysis. Upon subsequent analysis, only three cytoplasmic proteins, namely 30S ribosomal protein S4, 4-hydroxybenzoate decarboxylase subunit C, and oligopeptide binding protein, were identified, which have the potential to be novel drug target candidates. The Swiss Model server was used to predict the target proteins’ three-dimensional (3D) structure. The molecular docking technique was employed using MOE software for the virtual screening of a library of 15,000 phytochemicals against the interacting residues of the target proteins. Molecular docking experiments were also evaluated using molecular dynamics simulations and the widely used MM-GBSA and MM-PBSA binding free energy techniques. The findings revealed a promising candidate as a novel target against C. pneumonia infections.

Multiple Protein Subcellular Locations Prediction Based on Deep Convolutional Neural Networks with Self-Attention Mechanism

As an important research field in bioinformatics, protein subcellular location prediction is critical to reveal the protein functions and provide insightful information for disease diagnosis and drug development. Predicting protein subcellular locations remains a challenging task due to the difficulty of finding representative features and robust classifiers. Many feature fusion methods have been widely applied to tackle the above issues. However, they still suffer from accuracy loss due to feature redundancy. Furthermore, multiple protein subcellular locations prediction is more complicated since it is fundamentally a multi-label classification problem. The traditional binary classifiers or even multi-class classifiers cannot achieve satisfactory results. This paper proposes a novel method for protein subcellular location prediction with both single and multiple sites based on deep convolutional neural networks. Specifically, we first obtain the integrated features by simultaneously considering the pseudo amino acid, amino acid index distribution, and physicochemical property. We then adopt deep convolutional neural networks to extract high-dimensional features from the fused feature, removing the redundant preliminary features and gaining better representations of the raw sequences. Moreover, we use the self-attention mechanism and a customized loss function to ensure that the model is more inclined to positive data. In addition, we use random k-label sets to reduce the number of prediction labels. Meanwhile, we employ a hybrid strategy of over-sampling and under-sampling to tackle the data imbalance problem. We compare our model with three representative classification alternatives. The experiment results show that our model achieves the best performance in terms of accuracy, demonstrating the efficacy of the proposed model.

In Silico Core Proteomics and Molecular Docking Approaches for the Identification of Novel Inhibitors against Streptococcus pyogenes

Streptococcus pyogenes is a significant pathogen that causes skin and upper respiratory tract infections and non-suppurative complications, such as acute rheumatic fever and post-strep glomerulonephritis. Multidrug resistance has emerged in S. pyogenes strains, making them more dangerous and pathogenic. Hence, it is necessary to identify and develop therapeutic methods that would present novel approaches to S. pyogenes infections. In the current study, a subtractive proteomics approach was employed to core proteomes of four strains of S. pyogenes using several bioinformatic software tools and servers. The core proteome consists of 1324 proteins, and 302 essential proteins were predicted from them. These essential proteins were analyzed using BLASTp against human proteome, and the number of potential targets was reduced to 145. Based on subcellular localization prediction, 46 proteins with cytoplasmic localization were chosen for metabolic pathway analysis. Only two cytoplasmic proteins, i.e., chromosomal replication initiator protein DnaA and two-component response regulator (TCR), were discovered to have the potential to be novel drug target candidates. Three-dimensional (3D) structure prediction of target proteins was carried out via the Swiss Model server. Molecular docking approach was employed to screen the library of 1000 phytochemicals against the interacting residues of the target proteins through the MOE software. Further, the docking studies were validated by running molecular dynamics simulation and highly popular binding free energy approaches of MM-GBSA and MM-PBSA. The findings revealed a promising candidate as a novel target against S. pyogenes infections.

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

BACKGROUND

Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.

OBJECTIVE

The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.

METHODS

Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.

RESULTS

In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.

CONCLUSION

In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Computational advances of tumor marker selection and sample classification in cancer proteomics

Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.

Purification and characterization of anti-tubercular and anticancer protein from Staphylococcus hominis strain MANF2: In silico structural and functional insight of peptide

The present context was investigated to purify and characterize anti-tubercular as well as anticancer protein from fermented food associated Staphylococcus hominis strain MANF2. Initially, the anti-tubercular potency of strain MANF2 was assessed against Mycobacterium tuberculosis H37Rv using luciferase reporter phase assay which revealed pronounced relative light unit (RLU) reduction of 92.5 ± 1.2%. The anticancer property of strain MANF2 was demonstrated against lung cancer (A549) and colon cancer (HT-29) cell lines using MTT assay which showed reduced viabilities. Anti-tubercular activities of the purified protein were observed to be increased significantly (P < 0.05) ranging from 34.6 ± 0.3 to 71.4 ± 0.4% of RLU reduction. Likewise, the purified protein showed significantly (P < 0.05) reduced viabilities of A549 and HT-29 cancer cells with IC₅₀ values of 46.6 and 48.9 µg/mL, respectively. The nominal mass of the purified protein was found to be 7712.3 Da as obtained from MALDI-TOF MS/MS spectrum. The protein showed the sequence homology with 1–336 amino acids of Glyceraldehyde-3-phosphate dehydrogenase from Staphylococcus sp., thus, categorizing as a new class of Glyceraldehyde-3-phosphate dehydrogenase-like protein. The amino acid sequence of the most abundant peptide (m/z = 1922.12) in the purified protein was obtained as ‘KAIGLVIPEIDGKLDGGAQRV’ and it was identified as peptide NMANF2. In silico tools predicted significant stereo-chemical, physiochemical, and functional characteristics of peptide NMANF2. In a nutshell, protein purified from strain MANF2 can certainly be used as an ideal therapeutic agent against tuberculosis and cancer (lung and colon).

Subtractive Genomics, Molecular Docking and Molecular Dynamics Simulation Revealed LpxC as a Potential Drug Target Against Multi-Drug Resistant Klebsiella pneumoniae

The emergence and dissemination of pan drug resistant clones of Klebsiella pneumoniae are great threat to public health. In this regard new therapeutic targets must be highlighted to pave the path for novel drug discovery and development. Subtractive proteomic pipeline brought forth UDP-3-O-[3-hydroxymyristoyl] N-acetylglucosamine deacetylase (LpxC), a Zn+2 dependent cytoplasmic metalloprotein and catalyze the rate limiting deacetylation step of lipid A biosynthesis pathway. Primary sequence analysis followed by 3-dimensional (3-D) structure elucidation of the protein led to the detection of K. pneumoniae LpxC (KpLpxC) topology distinct from its orthologous counterparts in other bacterial species. Molecular docking study of the protein recognized receptor antagonist compound 106, a uridine-based LpxC inhibitory compound, as a ligand best able to fit the binding pocket with a Gold Score of 67.53. Molecular dynamics simulation of docked KpLpxC revealed an alternate binding pattern of ligand in the active site. The ligand tail exhibited preferred binding to the domain I residues as opposed to the substrate binding hydrophobic channel of subdomain II, usually targeted by inhibitory compounds. Comparison with the undocked KpLpxC system demonstrated ligand induced high conformational changes in the hydrophobic channel of subdomain II in KpLpxC. Hence, ligand exerted its inhibitory potential by rendering the channel unstable for substrate binding.

Identification of novel drug targets in bovine respiratory disease: an essential step in applying biotechnologic techniques to develop more effective therapeutic treatments

BACKGROUND

Bovine Respiratory Disease (BRD) is a major problem in cattle production which causes substantial economic loss. BRD has multifactorial aetiologies, is multi-microbial, and several of the causative pathogens are unknown. Consequently, primary management practices such as metaphylactic antimicrobial injections for BRD prevention are used to reduce the incidence of BRD in feedlot cattle. However, this poses a serious threat in the form of development of antimicrobial resistance and demands an urgent need to find novel interventions that could reduce the effects of BRD drastically and also delay/prevent bacterial resistance.

MATERIALS AND METHODS

We have employed a subtractive genomics approach that helps delineate essential, host-specific, and druggable targets in pathogens responsible for BRD. We also proposed antimicrobials from FDA green and orange book that could be repositioned for BRD.

RESULTS

We have identified 107 putative targets that are essential, selective and druggable. We have also confirmed the susceptibility of two BRD pathogens to one of the proposed antimicrobials - oxytetracycline.

CONCLUSION

This approach allows for repositioning drugs known for other infections to BRD, predicting novel druggable targets for BRD infection, and providing a new direction in developing more effective therapeutic treatments for BRD.

Computational Systems Biology of Metabolism in Infection

A systems approach to elucidate the effect of infection on cell metabolism provides several opportunities from a better understanding of molecular mechanisms to the identification of potential biomarkers and drug targets. This is obvious from the fact that we have witnessed the accelerated use of computational systems biology in the last five years to study metabolic changes in pathogen and/or host cells in response to infection. In this chapter, we aim to present a comprehensive review of the recent research by focusing on genome-scale metabolic network models of pathogen-host systems and genome-wide metabolomics and fluxomics analysis of infected cells.

Comprehensive Mass Spectrometric Survey of Streptococcus pyogenes Subcellular Proteomes

Streptococcus pyogenes is a major global health burden causing a wide variety of diseases. Because a vaccine against this bacterium is still lacking, vaccine candidates or antimicrobial therapies are urgently needed. Here we use an invasive and clinically relevant streptococcal M1 serotype to characterize the bacterial proteome in-depth. An elaborate fractionation technique is employed to separate the different cell fractions, followed by shotgun mass-spectrometry analysis, allowing us to confirm the expression of nearly two-thirds (1022) of the 1690 open reading frames predicted for the streptococcal M1 reference proteome. In contrast with other studies, we present the entire isolated membrane proteome, which opens up a whole new source for drug targets. We show both the unique and most prevalent proteins for each cellular fraction and analyze the presence of predicted cell-wall-anchored proteins and lipoproteins. With our approach, we also identify a variety of novel proteins whose presence has not been reported in previous proteome studies. Proteins of interest, potential virulence factors, and drug or vaccine targets are discussed for each cellular fraction. Overall, the results of this work represent the so-far widest proteomic approach to characterize the protein composition and localization in S. pyogenes.