SPIN: Submitting Sequences Determined at Protein Level to UniProt

Evaluation of the Effect and Mechanism of Sanhuang Ointment on MRSA Infection in the Skin and Soft Tissue via Network Pharmacology

Introduction

Skin and soft tissue infection (SSTI) is a frequently encountered clinical disease, and Sanhuang ointment, a traditional Chinese medicine, is used to treat it. However, the pharmacological effect of Sanhuang ointment on SSTI and its underlying mechanism remains unclear. Here, we investigate the protective effect of Sanhuang ointment on Methicillin-resistant Staphylococcus aureus (MRSA) infection in the skin and soft tissues and the underlying mechanism by network pharmacological analysis, followed by in vivo experimental validation.

Methods

Via network pharmacology, the active components and disease targets of Sanhuang ointment were screened and intersected for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A rat model of skin and soft tissue infection was established, and pathological features were observed. Large, medium, and small-dose groups (1 g, 0.5 g, and 0.25 g/animal, with the total amount of Vaseline, dispensed 1 g/animal) of Sanhuang ointment were prepared and Mupirocin ointment was used as a positive control (0.5 g/animal, with the total amount of Vaseline, dispensed 1 g/animal). The expressions of key proteins of the IL-17/NF-κB signaling pathway and downstream inflammatory factors were analyzed by histomorphological analysis, enzyme-linked immunosorbent assay, polymerase chain reaction, and Western blotting.

Results

In all, 119 active components and 275 target genes of Sanhuang ointment were identified and intersected with MRSA infection-related genes via network pharmacology analysis, and 34 target genes of Sanhuang ointment were found to be involved in skin and soft tissue infections with MRSA. Sanhuang ointment (1 g/mouse) could effectively ameliorate histopathological changes and significantly inhibit the expression of key proteins involved in the IL-17/NF-κB signaling pathway and downstream inflammatory factors (p < 0.05).

Conclusion

Sanhuang ointment has a protective effect on MRSA infection and inhibits inflammation by inhibiting the IL-17/NF-κB signaling pathway. Our findings are important for the secondary development and new drug development of Sanhuang ointment.

Peptidomics

Peptides are biopolymers, typically consisting of 2-50 amino acids. They are biologically produced by the cellular ribosomal machinery or by non-ribosomal enzymes and, sometimes, other dedicated ligases. Peptides are arranged as linear chains or cycles, and include post-translational modifications, unusual amino acids and stabilizing motifs. Their structure and molecular size render them a unique chemical space, between small molecules and larger proteins. Peptides have important physiological functions as intrinsic signalling molecules, such as neuropeptides and peptide hormones, for cellular or interspecies communication, as toxins to catch prey or as defence molecules to fend off enemies and microorganisms. Clinically, they are gaining popularity as biomarkers or innovative therapeutics; to date there are more than 60 peptide drugs approved and more than 150 in clinical development. The emerging field of peptidomics comprises the comprehensive qualitative and quantitative analysis of the suite of peptides in a biological sample (endogenously produced, or exogenously administered as drugs). Peptidomics employs techniques of genomics, modern proteomics, state-of-the-art analytical chemistry and innovative computational biology, with a specialized set of tools. The complex biological matrices and often low abundance of analytes typically examined in peptidomics experiments require optimized sample preparation and isolation, including in silico analysis. This Primer covers the combination of techniques and workflows needed for peptide discovery and characterization and provides an overview of various biological and clinical applications of peptidomics.

Network pharmacology combined with molecular docking to explore the anti-osteoporosis mechanisms of β-ecdysone derived from medicinal plants

β-Ecdysone is a phytosteroid derived from multifarious medicinal plants, such as Achyranthes root (Achyranthes bidentata) and Tinospora cordifolia, possessing the potential anti-osteoporosis effect. However, the underlying mechanisms for β-ecdysone treating osteoporosis remain unclear. This study aims to explore the molecular mechanisms of β-ecdysone against osteoporosis by network pharmacology and molecular docking. First, the potential targets of β-ecdysone and osteoporosis were predicted by public databases. Protein interaction and functional enrichment analyses of potential targets were performed using the STRING and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway databases. Finally, hub targets were identified from network pharmacology, and their interaction with β-ecdysone was validated by molecular docking. Results showed that 47 potential targets were related to the mechanisms of β-ecdysone treating osteoporosis. Enrichment analyses revealed that the potential targets were mainly associated with steroid biosynthetic and metabolic processes, as well as HIF-1 and estrogen signaling pathways. By protein–protein interaction network analysis, top 10 hub targets were screened, including TNF, ALB, SRC, STAT3, MAPK3, ESR1, PPARG, CASP3, TLR4, and NR3C1. Molecular docking showed that β-ecdysone had good affinity with TLR4, TNF, and ESR1. Therefore, β-ecdysone might exert therapeutic effect on osteoporosis development via targeting TLR4, TNF, and ESR1 and regulating HIF-1 and estrogen pathways.

Statins Induce Locomotion and Muscular Phenotypes in Drosophila melanogaster That Are Reminiscent of Human Myopathy: Evidence for the Role of the Chloride Channel Inhibition in the Muscular Phenotypes

The underlying mechanisms for statin-induced myopathy (SIM) are still equivocal. In this study, we employ Drosophila melanogaster to dissect possible underlying mechanisms for SIM. We observe that chronic fluvastatin treatment causes reduced general locomotion activity and climbing ability. In addition, transmission microscopy of dissected skeletal muscles of fluvastatin-treated flies reveals strong myofibrillar damage, including increased sarcomere lengths and Z-line streaming, which are reminiscent of myopathy, along with fragmented mitochondria of larger sizes, most of which are round-like shapes. Furthermore, chronic fluvastatin treatment is associated with impaired lipid metabolism and insulin signalling. Mechanistically, knockdown of the statin-target Hmgcr in the skeletal muscles recapitulates fluvastatin-induced mitochondrial phenotypes and lowered general locomotion activity; however, it was not sufficient to alter sarcomere length or elicit myofibrillar damage compared to controls or fluvastatin treatment. Moreover, we found that fluvastatin treatment was associated with reduced expression of the skeletal muscle chloride channel, ClC-a (Drosophila homolog of CLCN1), while selective knockdown of skeletal muscle ClC-a also recapitulated fluvastatin-induced myofibril damage and increased sarcomere lengths. Surprisingly, exercising fluvastatin-treated flies restored ClC-a expression and normalized sarcomere lengths, suggesting that fluvastatin-induced myofibrillar phenotypes could be linked to lowered ClC-a expression. Taken together, these results may indicate the potential role of ClC-a inhibition in statin-associated muscular phenotypes. This study underlines the importance of Drosophila melanogaster as a powerful model system for elucidating the locomotion and muscular phenotypes, promoting a better understanding of the molecular mechanisms underlying SIM.

The regulatory role of AP-2β in monoaminergic neurotransmitter systems: insights on its signalling pathway, linked disorders and theragnostic potential

Monoaminergic neurotransmitter systems play a central role in neuronal function and behaviour. Dysregulation of these systems gives rise to neuropsychiatric and neurodegenerative disorders with high prevalence and societal burden, collectively termed monoamine neurotransmitter disorders (MNDs). Despite extensive research, the transcriptional regulation of monoaminergic neurotransmitter systems is not fully explored. Interestingly, certain drugs that act on these systems have been shown to modulate central levels of the transcription factor AP-2 beta (AP-2β, gene: TFAP2Β). AP-2β regulates multiple key genes within these systems and thereby its levels correlate with monoamine neurotransmitters measures; yet, its signalling pathways are not well understood. Moreover, although dysregulation of TFAP2Β has been associated with MNDs, the underlying mechanisms for these associations remain elusive. In this context, this review addresses AP-2β, considering its basic structural aspects, regulation and signalling pathways in the controlling of monoaminergic neurotransmitter systems, and possible mechanisms underpinning associated MNDS. It also underscores the significance of AP-2β as a potential diagnostic biomarker and its potential and limitations as a therapeutic target for specific MNDs as well as possible pharmaceutical interventions for targeting it. In essence, this review emphasizes the role of AP-2β as a key regulator of the monoaminergic neurotransmitter systems and its importance for understanding the pathogenesis and improving the management of MNDs.

Network pharmacology-based investigation of potential targets of astragalus membranaceous-angelica sinensis compound acting on diabetic nephropathy

To explore the mechanism of the Astragalus membranaceous (AM)-Angelica sinensis (AS) compound in the treatment of diabetic nephropathy (DN) we used network pharmacology and molecular docking. Screen the components and targets of the AM-AS compound in the TCMSP and the BATMAN-TCM, and establish a component-target interaction network by Cytoscape 3.7.2. After searching relevant targets of DN in related databases, the common targets of the AM-AS compound and DN were obtained by comparison. Gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analysis were performed through David database. Molecular docking was performed by PyMoL2.3.0 and AutoDock Vina software. After screening, 142 main targets of the AM-AS compound in the treatment of DN have been identified. Target network was established and the topology of PPI network was analyzed. KEGG pathway enrichment analysis shows that these targets are related to apoptosis, oxidative stress, inflammation, insulin resistance, etc. Molecular docking shows that the target proteins have good combinations with the main active components of the AM-AS compound. AM-AS compound may treat DN by acting on VEGFA, TP53, IL-6, TNF, MARK1, etc., and regulate apoptosis, oxidative stress, inflammation, glucose, and lipid metabolism processes. The in vivo study results suggest that AM-AS compound can significantly reduce the FBG level of diabetic rats, increase the level of INS, improve renal functions, reduce urinary proteins, inhibit glycogen deposition, granulocyte infiltration and collagen fiber proliferation in renal tissue, and restrain the progress of DN. In vivo study combined with network pharmacology and molecular docking methods provides new ideas for the pathogenesis and treatments of DN.

Protonation states at different pH, conformational changes and impact of glycosylation in synapsin Ia

Synapsin I (SynI) is the most abundant brain phosphoprotein present at presynaptic terminals that regulates neurotransmitter release, clustering of synaptic vesicles (SVs) at active zones, and stimulates synaptogenesis and neurite outgrowth. Earlier studies have established that SynI displays pH-dependent tethering of SVs to actin filaments and exhibits a maximum binding around neutral pH, however, the effect of pH shift from acidic to basic on the conformational stability of SynI has not been explored yet. Another important aspect of SynIa is its O-GlcNAcylation (O-GlcNac) at the Thr87 position, which is responsible for the positive regulation of synaptic plasticity linked to learning and memory in mice. Furthermore, reduced levels of O-GlcNAc have been observed in Alzheimer's disease, suggesting a possible link to deficits in synaptic plasticity. In this study, the effect of pH and glycosylation on the structure and functional stability of SynIa is determined through molecular dynamics (MD) simulation approach. The 3D structure of SynIa was established via threading-based homology modeling methods. It was observed that the structure of SynIa adopts extended conformational changes as the pH shifts from acidic to basic, resulting in a compact conformation at pH 8.0. Moreover, the results obtained by comparing the glycosylated and unglycosylated protein indicated that the glycan moiety imparts stability to the protein by forming intramolecular hydrogen bond interactions with the protein residues. The results indicate that although O-GlcNAc moieties do not induce a significant change in SynIa structure they minimize protein dynamics, likely leading to enhanced protein stability.

In silico analysis of bacterial translation factors reveal distinct translation event specific pI values

Background

Protein synthesis is a cellular process that takes place through the successive translation events within the ribosome by the event-specific protein factors, namely, initiation, elongation, release, and recycling factors. In this regard, we asked the question about how similar are those translation factors to each other from a wide variety of bacteria? Hence, we did a thorough in silico study of the translation factors from 495 bacterial sp., and 4262 amino acid sequences by theoretically measuring their pI and MW values that are two determining factors for distinguishing individual proteins in 2D gel electrophoresis in experimental procedures. Then we analyzed the output from various angles.

Results

Our study revealed the fact that it’s not all same, or all random, but there are distinct orders and the pI values of translation factors are translation event specific. We found that the translation initiation factors are mainly basic, whereas, elongation and release factors that interact with the inter-subunit space of the intact 70S ribosome during translation are strictly acidic across bacterial sp. These acidic elongation factors and release factors contain higher frequencies of glutamic acids. However, among all the translation factors, the translation initiation factor 2 (IF2) and ribosome recycling factor (RRF) showed variable pI values that are linked to the order of phylogeny.

Conclusions

From the results of our study, we conclude that among all the bacterial translation factors, elongation and release factors are more conserved in terms of their pI values in comparison to initiation and recycling factors. Acidic properties of these factors are independent of habitat, nature, and phylogeny of the bacterial species. Furthermore, irrespective of the different shapes, sizes, and functions of the elongation and release factors, possession of the strictly acidic pI values of these translation factors all over the domain Bacteria indicates that the acidic nature of these factors is a necessary criterion, perhaps to interact into the partially enclosed rRNA rich inter-subunit space of the translating 70S ribosome.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07472-x.

An improved deep learning method for predicting DNA-binding proteins based on contextual features in amino acid sequences

As the number of known proteins has expanded, how to accurately identify DNA binding proteins has become a significant biological challenge. At present, various computational methods have been proposed to recognize DNA-binding proteins from only amino acid sequences, such as SVM, DNABP and CNN-RNN. However, these methods do not consider the context in amino acid sequences, which makes it difficult for them to adequately capture sequence features. In this study, a new method that coordinates a bidirectional long-term memory recurrent neural network and a convolutional neural network, called CNN-BiLSTM, is proposed to identify DNA binding proteins. The CNN-BiLSTM model can explore the potential contextual relationships of amino acid sequences and obtain more features than can traditional models. The experimental results show that the CNN-BiLSTM achieves a validation set prediction accuracy of 96.5%—7.8% higher than that of SVM, 9.6% higher than that of DNABP and 3.7% higher than that of CNN-RNN. After testing on 20,000 independent samples provided by UniProt that were not involved in model training, the accuracy of CNN-BiLSTM reached 94.5%—12% higher than that of SVM, 4.9% higher than that of DNABP and 4% higher than that of CNN-RNN. We visualized and compared the model training process of CNN-BiLSTM with that of CNN-RNN and found that the former is capable of better generalization from the training dataset, showing that CNN-BiLSTM has a wider range of adaptations to protein sequences. On the test set, CNN-BiLSTM has better credibility because its predicted scores are closer to the sample labels than are those of CNN-RNN. Therefore, the proposed CNN-BiLSTM is a more powerful method for identifying DNA-binding proteins.