RCSB Protein Data Bank: improved annotation, search and visualization of membrane protein structures archived in the PDB

Leveraging shape screening and molecular dynamics simulations to optimize PARP1-Specific chemo/radio-potentiators for antitumor drug design

PARP1 plays a pivotal role in DNA repair within the base excision pathway, making it a promising therapeutic target for cancers involving BRCA mutations. Current study is focused on the discovery of PARP inhibitors with enhanced selectivity for PARP1. Concurrent inhibition of PARP1 with PARP2 and PARP3 affects cellular functions, potentially causing DNA damage accumulation and disrupting immune responses. In step 1, a virtual library of 593 million compounds has been screened using a shape-based screening approach to narrow down the promising scaffolds. In step 2, hierarchical docking approach embedded in Schrödinger suite was employed to select compounds with good dock score, drug-likeness and MMGBSA score. Analysis supplemented with decomposition energy, molecular dynamics (MD) simulations and hydrogen bond frequency analysis, pinpointed that active site residues; H862, G863, R878, M890, Y896 and F897 are crucial for specific binding of ZINC001258189808 and ZINC000092332196 with PARP1 as compared to PARP2 and PARP3. The binding of ZINC000656130962, ZINC000762230673, ZINC001332491123, and ZINC000579446675 also revealed interaction involving two additional active site residues of PARP1, namely N767 and E988. Weaker or no interaction was observed for these residues with PARP2 and PARP3. This approach advances our understanding of PARP-1 specific inhibitors and their mechanisms of action, facilitating the development of targeted therapeutics.

DCTPep, the data of cancer therapy peptides

With the discovery of the therapeutic activity of peptides, they have emerged as a promising class of anti-cancer agents due to their specific targeting, low toxicity, and potential for high selectivity. In particular, as peptide-drug conjugates enter clinical, the coupling of targeted peptides with traditional chemotherapy drugs or cytotoxic agents will become a new direction in cancer treatment. To facilitate the drug development of cancer therapy peptides, we have constructed DCTPep, a novel, open, and comprehensive database for cancer therapy peptides. In addition to traditional anticancer peptides (ACPs), the peptide library also includes peptides related to cancer therapy. These data were collected manually from published research articles, patents, and other protein or peptide databases. Data on drug library include clinically investigated and/or approved peptide drugs related to cancer therapy, which mainly come from the portal websites of drug regulatory authorities and organisations in different countries and regions. DCTPep has a total of 6214 entries, we believe that DCTPep will contribute to the design and screening of future cancer therapy peptides.

Revealing the pharmacological mechanisms of nao-an dropping pill in preventing and treating ischemic stroke via the PI3K/Akt/eNOS and Nrf2/HO-1 pathways

Nao-an Dropping Pill (NADP) is a Chinese patent medicine which commonly used in clinic for ischemic stroke (IS). However, the material basis and mechanism of its prevention or treatment of IS are unclear, then we carried out this study. 52 incoming blood components were resolved by UHPLC-MS/MS from rat serum, including 45 prototype components. The potential active prototype components hydroxysafflor yellow A, ginsenoside F1, quercetin, ferulic acid and caffeic acid screened by network pharmacology showed strongly binding ability with PIK3CA, AKT1, NOS3, NFE2L2 and HMOX1 by molecular docking. In vitro oxygen-glucose deprivation/reperfusion (OGD/R) experimental results showed that NADP protected HA1800 cells from OGD/R-induced apoptosis by affecting the release of LDH, production of NO, and content of SOD and MDA. Meanwhile, NADP could improve behavioral of middle cerebral artery occlusion/reperfusion (MCAO/R) rats, reduce ischemic area of cerebral cortex, decrease brain water and glutamate (Glu) content, and improve oxidative stress response. Immunohistochemical results showed that NADP significantly regulated the expression of PI3K, Akt, p-Akt, eNOS, p-eNOS, Nrf2 and HO-1 in cerebral ischemic tissues. The results suggested that NADP protects brain tissues and ameliorates oxidative stress damage to brain tissues from IS by regulating PI3K/Akt/eNOS and Nrf2/HO-1 signaling pathways.

Network Pharmacology and Experimental Validation to Explore the Potential Mechanism of Nigella sativa for the Treatment of Breast Cancer

Breast cancer is a prevalent and potentially life-threatening disease that affects women worldwide. Natural products have gained attention as potential anticancer agents due to their fewer side effects, low toxicity, and cost effectiveness compared to traditional chemotherapy drugs. In the current study, the network pharmacology approach was used following a molecular docking study to evaluate the therapeutic potential of N. sativa-derived phytochemicals against breast cancer. Specifically, the study aimed to identify potential anticancer agents targeting key proteins implicated in breast cancer progression. Five proteins (i.e., EGFR, MAPK3, ESR1, MAPK1, and PTGS2) associated with breast cancer were selected as receptor proteins. Fourteen phytochemicals from N. sativa were prioritized based on drug-likeness (DL) and oral bioavailability (OB) parameters (with criteria set at DL > 0.18 and OB > 30%, respectively). Subsequent analysis of gene targets identified 283 overlapping genes primarily related to breast cancer pathogenesis. Ten hub genes were identified through topological analysis based on their significance in the KEGG pathway and GO annotations. Molecular docking revealed strong binding affinities between folic acid, betulinic acid, stigmasterol, and selected receptor proteins. These phytochemicals also demonstrated druggability potential. In vitro experiments in the MDA-MB-231 breast cancer cell line revealed that betulinic acid and stigmasterol significantly reduced cell viability after 24 h of treatment, confirming their anticancer activity. Furthermore, in vivo evaluation using a DMBA-induced rat model showed that betulinic acid and stigmasterol contributed to the significant recovery of cancer markers. This study aimed to explore the mechanisms underlying the anticancer potential of N. sativa phytochemicals against breast cancer, with the ultimate goal of identifying novel therapeutic candidates for future drug development. Overall, these results highlight betulinic acid and stigmasterol as promising candidates to develop novel anticancer agents against breast cancer. The comprehensive approach of this study, which integrates network pharmacology and molecular docking study and its experimental validation, strengthens the evidence supporting the therapeutic benefits of N. sativa-derived phytochemicals in breast cancer treatment, making them promising candidates for the development of novel anticancer agents against breast cancer.

Macroalgal deep genomics illuminate multiple paths to aquatic, photosynthetic multicellularity

Macroalgae are multicellular, aquatic autotrophs that play vital roles in global climate maintenance and have diverse applications in biotechnology and eco-engineering, which are directly linked to their multicellularity phenotypes. However, their genomic diversity and the evolutionary mechanisms underlying multicellularity in these organisms remain uncharacterized. In this study, we sequenced 110 macroalgal genomes from diverse climates and phyla, and identified key genomic features that distinguish them from their microalgal relatives. Genes for cell adhesion, extracellular matrix formation, cell polarity, transport, and cell differentiation distinguish macroalgae from microalgae across all three major phyla, constituting conserved and unique gene sets supporting multicellular processes. Adhesome genes show phylum- and climate-specific expansions that may facilitate niche adaptation. Collectively, our study reveals genetic determinants of convergent and divergent evolutionary trajectories that have shaped morphological diversity in macroalgae and provides genome-wide frameworks to understand photosynthetic multicellular evolution in aquatic environments.

An integrated network pharmacology approach to discover therapeutic mechanisms of Commiphora wightii for the treatment of Bell's palsy

Bell's palsy (BP) can result in facial paralysis. Inflammation or injury to the cranial nerves that regulate the facial muscles is primarily responsible for that disease. Commiphora wightii remains recognized as a cure for a few human ailments. This study focused on therapeutic phenomena of C. wightii for the treatment of Bell's palsy, utilizing the network drug discovery and molecular docking techniques. Active biological constituents of C. wightii were retrieved from literature and independent databases. Potential therapeutic targets (431) of 13 bioactive phytochemicals were fetched via SwissTargetPrediction tool. Putative intersecting targets (855) of Bell's palsy were computed through the DisGeNET and GeneCards datasets. Subsequently, by the analysis of potential shared targets (87) of C. wightii and Bell's palsy, a Venn diagram was drawn. DAVID database was used to evaluate gene functional annotations and enriched pathways that are involved in Bell's palsy. STRING database was used for generating the protein-protein relationship complex. Visual presentations of the interactions of potential targets to active chemical constituents were done by the Cytoscape. Whereas, the conformational research sorted out 10 key targets through the protein-protein interactions network. Moreover, the capacity of therapeutic ingredients to interact with a target inhibiting Bell's palsy was confirmed by molecular docking, which might ratify the findings of network pharmacology. In the molecular complex of AKT1-cholesterol, a 100-ns simulation unveiled a graceful stability, with a minimal 0.167 Å ligand shift and resilient hydrogen bonds (ASN54 and SER205). The final 20 ns showcased a P1 motif pirouette, gracefully forming aromatic bonds with H165 and W186, underscoring the complex's dynamic finesse. This study evaluated compound-target interactions and their impact on disease-related genes. It revealed that five genes (AKT1, TNF, MAPK3, EGFR and SRC) of C. wightii might be useful therapeutic targets for the treatment of Bell's palsy, as well as helping in lowering down the blood pressure.Communicated by Ramaswamy H. Sarma.

Accurate prediction of RNA secondary structure including pseudoknots through solving minimum-cost flow with learned potentials

Pseudoknots are key structure motifs of RNA and pseudoknotted RNAs play important roles in a variety of biological processes. Here, we present KnotFold, an accurate approach to the prediction of RNA secondary structure including pseudoknots. The key elements of KnotFold include a learned potential function and a minimum-cost flow algorithm to find the secondary structure with the lowest potential. KnotFold learns the potential from the RNAs with known structures using an attention-based neural network, thus avoiding the inaccuracy of hand-crafted energy functions. The specially designed minimum-cost flow algorithm used by KnotFold considers all possible combinations of base pairs and selects from them the optimal combination. The algorithm breaks the restriction of nested base pairs required by the widely used dynamic programming algorithms, thus enabling the identification of pseudoknots. Using 1,009 pseudoknotted RNAs as representatives, we demonstrate the successful application of KnotFold in predicting RNA secondary structures including pseudoknots with accuracy higher than the state-of-the-art approaches. We anticipate that KnotFold, with its superior accuracy, will greatly facilitate the understanding of RNA structures and functionalities.

Inhibition of IL-17 signaling in macrophages underlies the anti-arthritic effects of halofuginone hydrobromide: Network pharmacology, molecular docking, and experimental validation

BACKGROUND

Rheumatoid arthritis (RA) is a prevalent autoimmune disease marked by chronic synovitis as well as cartilage and bone destruction. Halofuginone hydrobromide (HF), a bioactive compound derived from the Chinese herbal plant Dichroa febrifuga Lour., has demonstrated substantial anti-arthritic effects in RA. Nevertheless, the molecular mechanisms responsible for the anti-RA effects of HF remain unclear.

METHODS

This study employed a combination of network pharmacology, molecular docking, and experimental validation to investigate potential targets of HF in RA.

RESULTS

Network pharmacology analyses identified 109 differentially expressed genes (DEGs) resulting from HF treatment in RA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses unveiled a robust association between these DEGs and the IL-17 signaling pathway. Subsequently, a protein-protein interaction (PPI) network analysis revealed 10 core DEGs, that is, EGFR, MMP9, TLR4, ESR1, MMP2, PPARG, MAPK1, JAK2, STAT1, and MAPK8. Among them, MMP9 displayed the greatest binding energy for HF. In an in vitro assay, HF significantly inhibited the activity of inflammatory macrophages, and regulated the IL-17 signaling pathway by decreasing the levels of IL-17 C, p-NF-κB, and MMP9.

CONCLUSION

In summary, these findings suggest that HF has the potential to inhibit the activation of inflammatory macrophages through its regulation of the IL-17 signaling pathway, underscoring its potential in the suppression of immune-mediated inflammation in RA.

The Omicron XBB.1 Variant and Its Descendants: Genomic Mutations, Rapid Dissemination and Notable Characteristics

The SARS-CoV-2 virus, which is a major threat to human health, has undergone many mutations during the replication process due to errors in the replication steps and modifications in the structure of viral proteins. The XBB variant was identified for the first time in Singapore in the fall of 2022. It was then detected in other countries, including the United States, Canada, and the United Kingdom. We study the impact of sequence changes on spike protein structure on the subvariants of XBB, with particular attention to the velocity of variant diffusion and virus activity with respect to its diffusion. We examine the structural and functional distinctions of the variants in three different conformations: (i) spike glycoprotein in complex with ACE2 (1-up state), (ii) spike glycoprotein (closed-1 state), and (iii) S protein (open-1 state). We also estimate the affinity binding between the spike protein and ACE2. The market binding affinity observed in specific variants raises questions about the efficacy of current vaccines in preparing the immune system for virus variant recognition. This work may be useful in devising strategies to manage the ongoing COVID-19 pandemic. To stay ahead of the virus evolution, further research and surveillance should be carried out to adjust public health measures accordingly.

Inhibition of respiratory syncytial virus by Daclatasvir and its derivatives: synthesis of computational derivatives as a new drug development

The most common cause of respiratory tract illness in newborns and young children is the respiratory syncytial virus (RSV). There is no approved vaccination or specific antiviral medication for RSV infections. Here, an attempt has been made to explore the potential of currently marketed drugs as well as their probable derivatives to improve the possibility of developing stronger medications against RSV. From the 100 synthetic drug compounds library, the best drug molecule was identified through drug-likeness properties, toxicity, molecular docking and molecular dynamics simulations. Molecular Mechanics Generalized Born Surface Area (MM-GBSA) was also a method that was applied in this study. Daclatasvir showed the highest binding energy and appeared as the best drug to inhibit matrix protein and a fusion protein of RSV. Based on Daclatasvir, 40 computational derivatives were made. D28, D34 and D40 showed far better results than the actual drug. Changes in lipophilicity character increase the binding energy of derivatives. Molecular dynamic simulations showed their non-deviated, non-fluctuated and stable complex formation with target proteins. The high number of amino acid contacts throughout the trajectory increases the stability and effectiveness of derivatives. The key to producing a novel medicine to eradicate RSV is provided by derivatives. Daclatasvir will be employed as a potential RSV inhibitor up until that point.Communicated by Ramaswamy H. Sarma.

UniTmp: unified resources for transmembrane proteins

The UNIfied database of TransMembrane Proteins (UniTmp) is a comprehensive and freely accessible resource of transmembrane protein structural information at different levels, from localization of protein segments, through the topology of the protein to the membrane-embedded 3D structure. We not only annotated tens of thousands of new structures and experiments, but we also developed a new system that can serve these resources in parallel. UniTmp is a unified platform that merges TOPDB (Topology Data Bank of Transmembrane Proteins), TOPDOM (database of conservatively located domains and motifs in proteins), PDBTM (Protein Data Bank of Transmembrane Proteins) and HTP (Human Transmembrane Proteome) databases and provides interoperability between the incorporated resources and an easy way to keep them regularly updated. The current update contains 9235 membrane-embedded structures, 9088 sequences with 536 035 topology-annotated segments and 8692 conservatively localized protein domains or motifs as well as 5466 annotated human transmembrane proteins. The UniTmp database can be accessed at https://www.unitmp.org.

ATLAS: protein flexibility description from atomistic molecular dynamics simulations

Dynamical behaviour is one of the most crucial protein characteristics. Despite the advances in the field of protein structure resolution and prediction, analysis and prediction of protein dynamic properties remains a major challenge, mostly due to the low accessibility of data and its diversity and heterogeneity. To address this issue, we present ATLAS, a database of standardised all-atom molecular dynamics simulations, accompanied by their analysis in the form of interactive diagrams and trajectory visualisation. ATLAS offers a large-scale view and valuable insights on protein dynamics for a large and representative set of proteins, by combining data obtained through molecular dynamics simulations with information extracted from experimental structures. Users can easily analyse dynamic properties of functional protein regions, such as domain limits (hinge positions) and residues involved in interaction with other biological molecules. Additionally, the database enables exploration of proteins with uncommon dynamic properties conditioned by their environment such as chameleon subsequences and Dual Personality Fragments. The ATLAS database is freely available at https://www.dsimb.inserm.fr/ATLAS.

Outer membrane β-barrel structure prediction through the lens of AlphaFold2

Most proteins found in the outer membrane of gram-negative bacteria share a common domain: the transmembrane β-barrel. These outer membrane β-barrels (OMBBs) occur in multiple sizes and different families with a wide range of functions evolved independently by amplification from a pool of homologous ancestral ββ-hairpins. This is part of the reason why predicting their three-dimensional (3D) structure, especially by homology modeling, is a major challenge. Recently, DeepMind's AlphaFold v2 (AF2) became the first structure prediction method to reach close-to-experimental atomic accuracy in CASP even for difficult targets. However, membrane proteins, especially OMBBs, were not abundant during their training, raising the question of how accurate the predictions are for these families. In this study, we assessed the performance of AF2 in the prediction of OMBBs and OMBB-like folds of various topologies using an in-house-developed tool for the analysis of OMBB 3D structures, and barrOs. In agreement with previous studies on other membrane protein classes, our results indicate that AF2 predicts transmembrane β-barrel structures at high accuracy independently of the use of templates, even for novel topologies absent from the training set. These results provide confidence on the models generated by AF2 and open the door to the structural elucidation of novel transmembrane β-barrel topologies identified in high-throughput OMBB annotation studies or designed de novo.

CC+ : A searchable database of validated coiled coils in PDB structures and AlphaFold2 models

α-Helical coiled coils are common tertiary and quaternary elements of protein structure. In coiled coils, two or more α helices wrap around each other to form bundles. This apparently simple structural motif can generate many architectures and topologies. Coiled coil-forming sequences can be predicted from heptad repeats of hydrophobic and polar residues, hpphppp, although this is not always reliable. Alternatively, coiled-coil structures can be identified using the program SOCKET, which finds knobs-into-holes (KIH) packing between side chains of neighboring helices. SOCKET also classifies coiled-coil architecture and topology, thus allowing sequence-to-structure relationships to be garnered. In 2009, we used SOCKET to create a relational database of coiled-coil structures, CC+ , from the RCSB Protein Data Bank (PDB). Here, we report an update of CC+ following an update of SOCKET (to Socket2) and the recent explosion of structural data and the success of AlphaFold2 in predicting protein structures from genome sequences. With the most-stringent SOCKET parameters, CC+ contains ≈12,000 coiled-coil assemblies from experimentally determined structures, and ≈120,000 potential coiled-coil structures within single-chain models predicted by AlphaFold2 across 48 proteomes. CC+ allows these and other less-stringently defined coiled coils to be searched at various levels of structure, sequence, and side-chain interactions. The identified coiled coils can be viewed directly from CC+ using the Socket2 application, and their associated data can be downloaded for further analyses. CC+ is available freely at http://coiledcoils.chm.bris.ac.uk/CCPlus/Home.html. It will be updated automatically. We envisage that CC+ could be used to understand coiled-coil assemblies and their sequence-to-structure relationships, and to aid protein design and engineering.

Lead identification against Mycobacterium tuberculosis using highly enriched active molecules against pantothenate synthetase

The Pantothenate synthetase (PS) from the Mycobacterium tuberculosis (Mtb) holds a crucial role in the survival and robust proliferation of bacteria through its catalysis of coenzyme A and acyl carrier protein synthesis. The present study undertook the PS drug target in complex with a co-crystallized ligand and subjected it to docking and virtual screening approaches. The experimental design encompassed three discrete datasets: an active dataset featuring 136 compounds, an inactive dataset comprising 56 compounds, and a decoys dataset curated from the zinc library, comprising an extensive compilation of approximately 53,000 compounds. The compounds' binding energies were observed to be in the range of -5 to ∼-14 kcal/mol. Additionally, binding energy results were further refined through Enrichment Factor analysis (EF). EF is a new statistical approach which uses the scores obtained from docking-based virtual screening and predicts the precision of the scoring function. Remarkably, the Enrichment Factor (EF) analysis produced exceptionally favorable outcomes, attaining an EF of approximately 49% within the uppermost 1% fraction of the compound distribution. Finally, a total of eight compounds, evenly distributed between the active dataset and the decoys dataset, emerged as potent inhibitors of the Pantothenate synthetase (PS) enzyme. The analysis of inhibition constants and binding energy revealed a notable correlation, with an r-squared value (r2) of 0.912 between the two parameters. Furthermore, the shortlisted compounds were subjected to 100 ns MD simulation to determine their stability and dynamics behavior. The decoy compounds that have been identified, exhibiting properties comparable to the active compounds, are postulated as potential candidates for targeting the Pantothenate synthetase (PS) enzyme to treat Mtb infection. Nevertheless, in the pursuit of a comprehensive investigation, it is advisable to undertake additional experimental validation as a component of the subsequent study.Communicated by Ramaswamy H. Sarma.

Underlying Mechanisms of Bergenia spp. to Treat Hepatocellular Carcinoma Using an Integrated Network Pharmacology and Molecular Docking Approach

Hepatocellular carcinoma (HCC) is the fifth most common and fatal cancer reported, representing 72.5% of malignancies around the world. The majority of HCC incidents have been associated with infections caused by hepatitis B and C viruses. Many first- and second-line conventional drugs, e.g., sorafenib, cabozantinib, or ramucirumab, have been used for the management of HCC. Despite different combinational therapies, there are still no defined biomarkers for an early stage diagnosis of HCC. The current study evaluated the potential of Bergenia stracheyi, Bergenia ciliata, Bergenia pacumbis, and Bergenia purpurascens, which belong to the family Saxifragaceae, to treat HCC using an integrated network pharmacology and molecular docking approach. Four active phytochemicals were selected based on oral bioavailability (OB) and drug likeness (DL) parameters. The criteria of phytochemical selection were set to OB > 30% and DL > 0.18. Similarly, the gene targets related to Bergenia spp. and the genes related to HCC were retrieved from different databases. The integration of these genes revealed 98 most common overlapping genes, which were mainly interrelated with HCC pathogenesis. Ultimately, the 98 Bergenia-HCC associated genes were used for protein-protein interaction (PPI), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Gene Ontology (GO) enrichment analyses. Finally, the topological analysis revealed the top ten hub genes with maximum degree rank. From the top ten genes, STAT3, MAPK3, and SRC were selected due to their involvement in GO annotation and KEGG pathway. To confirm the network pharmacology results, molecular docking analysis was performed to target STAT3, MAPK3, and SRC receptor proteins. The phytochemical (+)-catechin 3-gallate exhibited a maximum binding score and strong residue interactions with the active amino acids of MAPK3-binding pockets (S-score: -10.2 kcal/mol), SRC (S-score: -8.9 kcal/mol), and STAT3 (S-score: -8.9 kcal/mol) as receptor proteins. (+)-Catechin 3-gallate and β-sitosterol induced a significant reduction in cell viability in HepG2 after 24 h of treatment in a dose-dependent manner. The results of this study explore the potential of (+)-catechin 3-gallate and β-sitosterol, which can be used in the future as potential drug candidates to suppress HCC.

Exploring the inhibition mechanism of interleukin-1-beta in gouty arthritis by polygonum cuspidatum using network pharmacology and molecular docking: A review

Polygonum cuspidatum (Huzhang, HZ) is one of the commonly used traditional Chinese medicines for treating gouty arthritis (GA), but the specific mechanism is not clear. This study employed network pharmacology and molecular docking techniques to examine the molecular mechanisms underlying the therapeutic effects of HZ on GA. The network pharmacology approach, including active ingredient and target screening, drug-compound-target-disease network construction, protein-protein interaction (PPI) networks, enrichment analysis, and molecular docking, was used to explore the mechanism of HZ against GA. Ten active ingredients of HZ were predicted to interact with 191 targets, 14 of which interact with GA targets. Network pharmacology showed that quercetin, physovenine, luteolin, and beta-sitosterol are the core components of HZ, and IL (interleukin)-1β, IL-6, and tumor necrosis factor (TNF) are the core therapeutic targets. The mechanism of HZ in GA treatment was shown to be related to the IL-17 signaling pathway, NOD-like receptor signaling pathway, and Toll-like receptor signaling pathway, and is involved in the inflammatory response, positive regulation of gene expression, cellular response to lipopolysaccharide, and other biological processes. Molecular docking showed that all four core compounds had good binding properties to IL-1β, with luteolin and beta-sitosterol showing better docking results than anakinra, suggesting that they could be used as natural IL-1β inhibitors in further experimental studies. The mechanism of action of HZ against GA has multi-target and multi-pathway characteristics, which provides an important theoretical basis for the study of the active ingredients of HZ as natural IL-1β inhibitors.

CroMaSt: a workflow for assessing protein domain classification by cross-mapping of structural instances between domain databases and structural alignment

Motivation

Protein domains can be viewed as building blocks, essential for understanding structure-function relationships in proteins. However, each domain database classifies protein domains using its own methodology. Thus, in many cases, domain models and boundaries differ from one domain database to the other, raising the question of domain definition and enumeration of true domain instances.

Results

We propose an automated iterative workflow to assess protein domain classification by cross-mapping domain structural instances between domain databases and by evaluating structural alignments. CroMaSt (for Cross-Mapper of domain Structural instances) will classify all experimental structural instances of a given domain type into four different categories ('Core', 'True', 'Domain-like' and 'Failed'). CroMast is developed in Common Workflow Language and takes advantage of two well-known domain databases with wide coverage: Pfam and CATH. It uses the Kpax structural alignment tool with expert-adjusted parameters. CroMaSt was tested with the RNA Recognition Motif domain type and identifies 962 'True' and 541 'Domain-like' structural instances for this domain type. This method solves a crucial issue in domain-centric research and can generate essential information that could be used for synthetic biology and machine-learning approaches of protein domain engineering.

Availability and implementation

The workflow and the Results archive for the CroMaSt runs presented in this article are available from WorkflowHub (doi: 10.48546/workflowhub.workflow.390.2).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Telomere-to-Telomere genome assemblies of human-infecting Encephalitozoon species

BACKGROUND

Microsporidia are diverse spore forming, fungal-related obligate intracellular pathogens infecting a wide range of hosts. This diversity is reflected at the genome level with sizes varying by an order of magnitude, ranging from less than 3 Mb in Encephalitozoon species (the smallest known in eukaryotes) to more than 50 Mb in Edhazardia spp. As a paradigm of genome reduction in eukaryotes, the small Encephalitozoon genomes have attracted much attention with investigations revealing gene dense, repeat- and intron-poor genomes characterized by a thorough pruning of molecular functions no longer relevant to their obligate intracellular lifestyle. However, because no Encephalitozoon genome has been sequenced from telomere-to-telomere and since no methylation data is available for these species, our understanding of their overall genetic and epigenetic architectures is incomplete.

METHODS

In this study, we sequenced the complete genomes from telomere-to-telomere of three human-infecting Encephalitozoon spp. -E. intestinalis ATCC 50506, E. hellem ATCC 50604 and E. cuniculi ATCC 50602- using short and long read platforms and leveraged the data generated as part of the sequencing process to investigate the presence of epigenetic markers in these genomes. We also used a mixture of sequence- and structure-based computational approaches, including protein structure prediction, to help identify which Encephalitozoon proteins are involved in telomere maintenance, epigenetic regulation, and heterochromatin formation.

RESULTS

The Encephalitozoon chromosomes were found capped by TTAGG 5-mer telomeric repeats followed by telomere associated repeat elements (TAREs) flanking hypermethylated ribosomal RNA (rRNA) gene loci featuring 5-methylcytosines (5mC) and 5-hemimethylcytosines (5hmC), themselves followed by lesser methylated subtelomeres and hypomethylated chromosome cores. Strong nucleotide biases were identified between the telomeres/subtelomeres and chromosome cores with significant changes in GC/AT, GT/AC and GA/CT contents. The presence of several genes coding for proteins essential to telomere maintenance, epigenetic regulation, and heterochromatin formation was further confirmed in the Encephalitozoon genomes.

CONCLUSION

Altogether, our results strongly support the subtelomeres as sites of heterochromatin formation in Encephalitozoon genomes and further suggest that these species might shutdown their energy-consuming ribosomal machinery while dormant as spores by silencing of the rRNA genes using both 5mC/5hmC methylation and facultative heterochromatin formation at these loci.

Unified access to up-to-date residue-level annotations from UniProtKB and other biological databases for PDB data

More than 61,000 proteins have up-to-date correspondence between their amino acid sequence (UniProtKB) and their 3D structures (PDB), enabled by the Structure Integration with Function, Taxonomy and Sequences (SIFTS) resource. SIFTS incorporates residue-level annotations from many other biological resources. SIFTS data is available in various formats like XML, CSV and TSV format or also accessible via the PDBe REST API but always maintained separately from the structure data (PDBx/mmCIF file) in the PDB archive. Here, we extended the wwPDB PDBx/mmCIF data dictionary with additional categories to accommodate SIFTS data and added the UniProtKB, Pfam, SCOP2, and CATH residue-level annotations directly into the PDBx/mmCIF files from the PDB archive. With the integrated UniProtKB annotations, these files now provide consistent numbering of residues in different PDB entries allowing easy comparison of structure models. The extended dictionary yields a more consistent, standardised metadata description without altering the core PDB information. This development enables up-to-date cross-reference information at the residue level resulting in better data interoperability, supporting improved data analysis and visualisation.

Cryo-EM-based structural insights into supramolecular assemblies of γ-hemolysin from S. aureus reveal the pore formation mechanism

γ-Hemolysin (γ-HL) is a hemolytic and leukotoxic bicomponent β-pore-forming toxin (β-PFT), a potent virulence factor from the Staphylococcus aureus Newman strain. In this study, we performed single-particle cryoelectron microscopy (cryo-EM) of γ-HL in a lipid environment. We observed clustering and square lattice packing of octameric HlgAB pores on the membrane bilayer and an octahedral superassembly of octameric pore complexes that we resolved at resolution of 3.5 Å. Our atomic model further demonstrated the key residues involved in hydrophobic zipping between the rim domains of adjacent octameric complexes, providing additional structural stability in PFTs post oligomerization. We also observed extra densities at the octahedral and octameric interfaces, providing insights into the plausible lipid-binding residues involved for HlgA and HlgB components. Furthermore, the hitherto elusive N-terminal region of HlgA was also resolved in our cryo-EM map, and an overall mechanism of pore formation for bicomponent β-PFTs is proposed.

Genome-wide identification and expression analysis of the U-box E3 ubiquitin ligase gene family related to salt tolerance in sorghum (Sorghum bicolor L.)

Plant U-box (PUB) E3 ubiquitin ligases play essential roles in many biological processes and stress responses, but little is known about their functions in sorghum (Sorghum bicolor L.). In the present study, 59 SbPUB genes were identified in the sorghum genome. Based on the phylogenetic analysis, the 59 SbPUB genes were clustered into five groups, which were also supported by the conserved motifs and structures of these genes. SbPUB genes were found to be unevenly distributed on the 10 chromosomes of sorghum. Most PUB genes (16) were found on chromosome 4, but there were no PUB genes on chromosome 5. Analysis of cis-acting elements showed that SbPUB genes were involved in many important biological processes, particularly in response to salt stress. From proteomic and transcriptomic data, we found that several SbPUB genes had diverse expressions under different salt treatments. To verify the expression of SbPUBs, qRT-PCR analyses also were conducted under salt stress, and the result was consistent with the expression analysis. Furthermore, 12 SbPUB genes were found to contain MYB-related elements, which are important regulators of flavonoid biosynthesis. These results, which were consistent with our previous multi-omics analysis of sorghum salt stress, laid a solid foundation for further mechanistic study of salt tolerance in sorghum. Our study showed that PUB genes play a crucial role in regulating salt stress, and might serve as promising targets for the breeding of salt-tolerant sorghum in the future.

A machine learning-integrated stepwise method to discover novel anti-obesity phytochemicals that antagonize the glucocorticoid receptor

As a type of stress hormone, glucocorticoids (GCs) affect numerous physiological pathways by binding to the glucocorticoid receptor (GR) and regulating the transcription of various genes. However, when GCs are dysregulated, the resulting hypercortisolism may contribute to various metabolic disorders, including obesity. Thus, attempts have been made to discover potent GR antagonists that can reverse excess-GC-related metabolic diseases. Phytochemicals are a collection of valuable bioactive compounds that are known for their wide variety of chemotypes. Recently, various computational methods have been developed to obtain active phytochemicals that can modulate desired target proteins. In this study, we developed a workflow comprising two consecutive quantitative structure-activity relationship-based machine learning models to discover novel GR-antagonizing phytochemicals. These two models collectively identified 65 phytochemicals that bind to and antagonize GR. Of these, nine commercially available phytochemicals were validated for GR-antagonist and anti-obesity activities. In particular, we confirmed that demethylzeylasteral, a phytochemical of the Tripterygium wilfordii Radix, exhibits potent anti-obesity activity in vitro through GR antagonism.

RCSB Protein Data Bank: Efficient Searching and Simultaneous Access to One Million Computed Structure Models Alongside the PDB Structures Enabled by Architectural Advances

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) provides open access to experimentally-determined three-dimensional (3D) structures of biomolecules. The RCSB PDB RCSB.org research-focused web portal is used annually by many millions of users around the world. They access biostructure information, run complex queries utilizing various search services (e.g., full-text, structural and chemical attribute, chemical, sequence, and structure similarity searches), and visualize macromolecules in 3D, all at no charge and with no limitations on data usage. Notwithstanding more than 24,000-fold growth of the PDB over the past five decades, experimentally-determined structures are only available for a small subset of the millions of proteins of known sequence. Recently developed machine learning software tools can predict 3D structures of proteins at accuracies comparable to lower-resolution experimental methods. The RCSB PDB now provides access to ∼1,000,000 Computed Structure Models (CSMs) of proteins coming from AlphaFold DB and the ModelArchive alongside ∼200,000 experimentally-determined PDB structures. Both CSMs and PDB structures are available on RCSB.org and via well-established RCSB PDB Data, Search, and 1D-Coordinates application programming interfaces (APIs). Simultaneous delivery of PDB data and CSMs provides users with access to complementary structural information across the human proteome and those of model organisms and selected pathogens. API enhancements are backwards-compatible and programmatic users can "opt in" to access CSMs with minimal effort. Herein, we describe modifications to RCSB PDB cyberinfrastructure required to support sixfold scaling of 3D biostructure data delivery and lay the groundwork for scaling to accommodate hundreds of millions of CSMs.

Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications

Membrane proteins mediate a wide spectrum of biological processes, such as signal transduction and cell communication. Due to the arduous and costly nature inherent to the experimental process, membrane proteins have long been devoid of well-resolved atomic-level tertiary structures and, consequently, the understanding of their functional roles underlying a multitude of life activities has been hampered. Currently, computational tools dedicated to furthering the structure-function understanding are primarily focused on utilizing intelligent algorithms to address a variety of site-wise prediction problems (e.g., topology and interaction sites), but are scattered across different computing sources. Moreover, the recent advent of deep learning techniques has immensely expedited the development of computational tools for membrane protein-related prediction problems. Given the growing number of applications optimized particularly by manifold deep neural networks, we herein provide a review on the current status of computational strategies mainly in membrane protein type classification, topology identification, interaction site detection, and pathogenic effect prediction. Meanwhile, we provide an overview of how the entire prediction process proceeds, including database collection, data pre-processing, feature extraction, and method selection. This review is expected to be useful for developing more extendable computational tools specific to membrane proteins.

Mechanism of Tao Hong Decoction in the treatment of atherosclerosis based on network pharmacology and experimental validation

Background

Atherosclerosis (AS) has long been recognized as a cardiovascular disease and stroke risk factor. A well-known traditional Chinese medicine prescription, Tao Hong decoction (THD), has been proven effective in treating AS, but its mechanism of action is still unclear.

Objective

To assess the effects, explore THD's primary mechanism for treating AS, and provide a basis for rational interpretation of its prescription compatibility.

Methods

Based on network pharmacology, we evaluated the mechanism of THD on AS by data analysis, target prediction, the construction of PPI networks, and GO and KEGG analysis. AutoDockTools software to conduct Molecular docking. Then UPLC-Q-TOF-MS was used to identify significant constituents of THD. Furthermore, an AS mice model was constructed and intervened with THD. Immunofluorescence, RT-qPCR, and Western blot were used to verify the critical targets in animal experiments.

Results

The network pharmacology results indicate that eight core targets and seven core active ingredients play an essential role in this process. The GO and KEGG analysis results suggested that the mechanism is mainly involved in Fluid shear stress and atherosclerosis and Lipid and atherosclerosis. The molecular docking results indicate a generally strong affinity. The animal experiment showed that THD reduced plaque area, increased plaque stability, and decreased the levels of inflammatory cytokines (NF-κB, IL-1α, TNF-α, IL-6, IL-18, IL-1β) in high-fat diet -induced ApoE-/-mice. Decreased levels of PTGS2, HIF-1α, VEGFA, VEGFC, FLT-4, and the phosphorylation of PI3K, AKT, and p38 were detected in the THD-treated group.

Conclusion

THD plays a vital role in treating AS with multiple targets and pathways. Angiogenesis regulation, oxidative stress regulation, and immunity regulation consist of the crucial regulation cores in the mechanism. This study identified essential genes and pathways associated with the prognosis and pathogenesis of AS from new insights, demonstrating a feasible method for researching THD's chemical basis and pharmacology.

Bile acid metabolism involved into the therapeutic action of Xiaojianzhong Tang via gut microbiota to treat chronic atrophic gastritis in rats

BACKGROUND

As a classical traditional Chinese medicine (TCM), Xiaojianzhong Tang (XJZ) is effective in treating chronic atrophic gastritis (CAG). However, the pharmacological mechanism of XJZ has not been fully explained.

PURPOSE

The purpose of this study was to investigate the mechanism of XJZ against CAG rats via gut microbiome using a multi-omics approach.

METHODS

The rat cecal contents were analyzed through the integration of an untargeted metabolomic approach based on ultra-high performance liquid chromatography coupled with the quadrupole-time of flight mass spectrometry (UHPLC-QTOF-MS) and 16S rRNA gene sequencing. Finally, the interaction of differential metabolites with bile acid (BA)-related targets was verified by molecular docking.

RESULTS

A new strategy was adopted to screen out the differential metabolites based on the comprehensive evaluation of VIP, |log2(FC)|, -ln(p-value) and ǀp(corr)ǀ. As results, XJZ showed favor regulations on the screened metabolites, cholic acid, deoxycholic acid, glycoursodeoxycholic acid, taurochenodesoxycholic acid, docosahexaenoic acid and L-isoleucine. The 16S rRNA gene sequencing analysis showed that XJZ could regulate gut microbiota disturbances in CAG rats, especially bile acid (BA) metabolism-related bacteria (Butyricimonas, Desulfovibrio, Bacteroides, Parabacteroides, Acetobacter and Alistipes). Molecular docking further showed that the differential metabolites regulated by XJZ had a good docking effect on BA-related targets.

CONCLUSION

The current work indicated that XJZ's therapeutic action was strongly linked to BA-related microorganisms and metabolic processes. These findings provided new insights into the effects of XJZ for the treatment of CAG.

Study of Therapeutic Mechanisms of Puerarin against Sepsis-Induced Myocardial Injury by Integrating Network Pharmacology, Bioinformatics Analysis, and Experimental Validation

Myocardial injury is the most prevalent and serious complication of sepsis. The potential of puerarin (Pue) to treat sepsis-induced myocardial injury (SIMI) has been recently reported. Nevertheless, the specific anti-SIMI mechanisms of Pue remain largely unclear. Integrating network pharmacology, bioinformatics analysis, and experimental validation, we aimed to clarify the anti-SIMI mechanisms of Pue, thereby furnishing novel therapeutic targets. Pue-associated targets were collected from HIT, GeneCards, SwissTargetPrediction, SuperPred, and CTD databases. SIMI-associated targets were acquired from GeneCards and DisGeNET. Differentially expressed genes (DEGs) were identified from GEO database. Potential anti-SIMI targets of Pue were determined using VennDiagram. ClusterProfiler was employed for GO and KEGG analyses. STRING database and Cytoscape were used for protein-protein interaction (PPI) network construction, and cytoHubba was used for hub target screening. PyMOL and AutoDock were utilized for molecular docking. An in vitro SIMI model was built to further verify the therapeutic mechanisms of Pue. Seventy-three Pue-SIMI-DEG intersecting target genes were obtained. GO and KEGG analyses revealed that the targets were principally concentrated in cellular response to chemical stress, response to oxidative stress (OS), and insulin and neurotrophin signaling pathways. Through PPI analysis and molecular docking, AKT1, CASP3, TP53, and MAPK3 were identified as the pivotal targets. In vivo experiments indicated that Pue promoted cell proliferation, downregulated AKT1, CASP3, TP53, and MAPK3, and inhibited inflammation, myocardial injury, OS, and apoptosis in the cell model. Pue might inhibit inflammation, myocardial injury, OS, and apoptosis to treat SIMI by reducing AKT1, CASP3, TP53, and MAPK3.

Structural analysis of SARS-CoV-2 Spike protein variants through graph embedding

Since December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected almost all countries. The unprecedented spreading of this virus has led to the insurgence of many variants that impact protein sequence and structure that need continuous monitoring and analysis of the sequences to understand the genetic evolution and to prevent possible dangerous outcomes. Some variants causing the modification of the structure of the proteins, such as the Spike protein S, need to be monitored. Protein contact networks (PCNs) have been recently proposed as a modelling framework for protein structures. In such a framework, the protein structure is represented as an unweighted graph whose nodes are the central atoms of the backbones (C- α ), and edges connect two atoms falling in the spatial distance between 4 and 7 Å. PCN may also be a data-rich representation since we may add to each node/atom biological and topological information. Such formalism enables the possibility of using algorithms from graph theory to analyze the graph. In particular, we refer to graph embedding methods enabling the analysis of such graphs with deep learning methods. In this work, we explore the possibility of embedding PCN using Graph Neural Networks and then analyze in the embedded space each residue to distinguish mutated residues from non-mutated ones. In particular, we analyzed the structure of the Spike protein of the coronavirus. First, we obtained the PCNs of the Spike protein for the wild-type, α , β , and δ variants. Then we used the GraphSage embedding algorithm to obtain an unsupervised embedding. Then we analyzed the point of mutation in the embedded space. Results show the characteristics of the mutation point in the embedding space.

Integrative Multiomics and Regulatory Network Analyses Uncovers the Role of OAS3, TRAFD1, miR-222-3p, and miR-125b-5p in Hepatitis E Virus Infection

The hepatitis E virus (HEV) is a long-ignored virus that has spread globally with time. It ranked 6th among the top risk-ranking viruses with high zoonotic spillover potential; thus, considering its viral threats is a pressing priority. The molecular pathophysiology of HEV infection or the underlying cause is limited. Therefore, we incorporated an unbiased, systematic methodology to get insights into the biological heterogeneity associated with the HEV. Our study fetched 93 and 2016 differentially expressed genes (DEGs) from chronic HEV (CHEV) infection in kidney-transplant patients, followed by hub module selection from a weighted gene co-expression network (WGCN). Most of the hub genes identified in this study were associated with interferon (IFN) signaling pathways. Amongst the genes induced by IFNs, the 2'-5'-oligoadenylate synthase 3 (OAS3) protein was upregulated. Protein-protein interaction (PPI) modular, functional enrichment, and feed-forward loop (FFL) analyses led to the identification of two key miRNAs, i.e., miR-222-3p and miR-125b-5p, which showed a strong association with the OAS3 gene and TRAF-type zinc finger domain containing 1 (TRAFD1) transcription factor (TF) based on essential centrality measures. Further experimental studies are required to substantiate the significance of these FFL-associated genes and miRNAs with their respective functions in CHEV. To our knowledge, it is the first time that miR-222-3p has been described as a reference miRNA for use in CHEV sample analyses. In conclusion, our study has enlightened a few budding targets of HEV, which might help us understand the cellular and molecular pathways dysregulated in HEV through various factors. Thus, providing a novel insight into its pathophysiology and progression dynamics.

PPI-Miner: A Structure and Sequence Motif Co-Driven Protein-Protein Interaction Mining and Modeling Computational Method

Protein-protein interactions (PPIs) play important roles in biological processes of life, and predicting PPIs becomes a critical scientific issue of concern. Most PPIs occur through small domains or motifs (fragments), which are challenging and laborious to map by standard biochemical approaches because they generally require the cloning of several truncation mutants. Here, we present a computational method, named as PPI-Miner, to fish potential protein interacting partners utilizing protein motifs as queries. In brief, this work first developed a motif-matching algorithm designed to identify the proteins that contain sequential or structural similar motifs with the given query motif. Being aligned to the query motif, the binding mode of the discovered motif and its receptor protein will be initially determined to be used to build PPI complexes accordingly. Eventually, a PPI complex structure could be built and optimized with a designed automatic protocol. Besides discovering PPIs, PPI-Miner can also be applied to other areas, i.e., the rational design of molecular glues and protein vaccines. In this work, PPI-Miner was employed to mine the potential cereblon (CRBN) substrates from human proteome. As a result, 1,739 candidates were predicted, and 16 of them have been experimentally validated in previous studies. The source code of PPI-Miner can be obtained from the GitHub repository (https://github.com/Wang-Lin-boop/PPI-Miner), the webserver is freely available for users (https://bailab.siais.shanghaitech.edu.cn/services/ppi-miner), and the database of predicted CRBN substrates is accessible at https://bailab.siais.shanghaitech.edu.cn/services/crbn-subslib.

Evolution of sequence traits of prion-like proteins linked to amyotrophic lateral sclerosis (ALS)

Prions are proteinaceous particles that can propagate an alternative conformation to further copies of the same protein. They have been described in mammals, fungi, bacteria and archaea. Furthermore, across diverse organisms from bacteria to eukaryotes, prion-like proteins that have similar sequence characters are evident. Such prion-like proteins have been linked to pathomechanisms of amyotrophic lateral sclerosis (ALS) in humans, in particular TDP43, FUS, TAF15, EWSR1 and hnRNPA2. Because of the desire to study human disease-linked proteins in model organisms, and to gain insights into the functionally important parts of these proteins and how they have changed across hundreds of millions of years of evolution, we analyzed how the sequence traits of these five proteins have evolved across eukaryotes, including plants and metazoa. We discover that the RNA-binding domain architecture of these proteins is deeply conserved since their emergence. Prion-like regions are also deeply and widely conserved since the origination of the protein families for FUS, TAF15 and EWSR1, and since the last common ancestor of metazoa for TDP43 and hnRNPA2. Prion-like composition is uncommon or weak in any plant orthologs observed, however in TDP43 many plant proteins have equivalent regions rich in other amino acids (namely glycine and tyrosine and/or serine) that may be linked to stress granule recruitment. Deeply conserved low-complexity domains are identified that likely have functional significance.

Virtual Screening and Network Pharmacology-Based Study to Explore the Pharmacological Mechanism of Clerodendrum Species for Anticancer Treatment

BACKGROUND

Cancer is a second leading cause of death in the world, killing approximately 3500 per million people each year. Therefore, the drugs with multitarget pharmacology based on biological networks are crucial to investigate the molecular mechanisms of cancer drugs and repurpose the existing drugs to reduce adverse effects. Clerodendrum is a diversified genus with a wide range of economic and pharmacological properties. Limited studies were conducted on the genus's putative anticancer properties and the mechanisms of action based on biological networks remains unknown. This study was aimed to construct the possible compound/target/pathway biological networks for anticancer effect of Clerodendrum sp. using docking weighted network pharmacological approach and to investigate its potential mechanism of action.

METHODS

A total of 194 natural Clerodendrum sp. Compounds were retrieved from public databases and screened using eight molecular descriptors. The cancer-associated gene targets were retrieved from databases and the function of the target genes with related pathways were examined. Cytoscape v3.7.2 was used to build three major networks: compound-target network, target-target pathway network, and compound-target-pathway network.

RESULTS

Our finding indicates that the anticancer activity of Clerodendrum sp. involves 6 compounds, 9 targets, and 63 signaling pathways, resulting in multicompounds, multitargets, and multipathways networks. Additionally, molecular dynamics (MD) simulations were used to estimate the binding affinity of the best hit protein-ligand complexes. Conclusion. This study suggests the potential anticancer activity of Clerodendrum sp. which could further contribute to scavenger novel compounds for the development of new alternative anticancer drugs.

Molecular Mechanisms of Cassia fistula against Epithelial Ovarian Cancer Using Network Pharmacology and Molecular Docking Approaches

Epithelial ovarian cancer (EOC) is one of the deadliest reproductive tract malignancies that form on the external tissue covering of an ovary. Cassia fistula is popular for its anti-inflammatory and anticarcinogenic properties in conventional medications. Nevertheless, its molecular mechanisms are still unclear. The current study evaluated the potential of C. fistula for the treatment of EOC using network pharmacology approach integrated with molecular docking. Eight active constituents of C. fistula were obtained from two independent databases and the literature, and their targets were retrieved from the SwissTargetPrediction. In total, 1077 EOC associated genes were retrieved from DisGeNET and GeneCardsSuite databases, and 800 potential targets of eight active constituents of C. fistula were mapped to the 1077 EOC targets and intersected targets from two databases. Ultimately, 98 potential targets were found from C. fistula for EOC. Finally, the protein–protein interaction network (PPI) topological interpretation revealed AKT1, CTNNB1, ESR1, and CASP3 as key targets. This is the first time four genes have been found against EOC from C. fistula. The major enriched pathways of these candidate genes were established by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) investigations. To confirm the network pharmacology findings, the molecular docking approach demonstrated that active molecules have higher affinity for binding to putative targets for EOC suppression. More pharmacological and clinical research is required for the development of a drug to treat EOC.

Protein-protein interaction prediction with deep learning: A comprehensive review

Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein-protein interaction and protein-ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein-protein interaction and their sites, protein-ligand binding, and protein design.

Therapeutic material basis and underling mechanisms of Shaoyao Decoction-exerted alleviation effects of colitis based on GPX4-regulated ferroptosis in epithelial cells

Background

Shaoyao Decoction (SYD) is a canonical herbal medicine prescription formulated by Liu Wan-Su in AD 1186. SYD has been widely used to treat inflammatory bowel disease by clearing heat and damp, removing stasis toxin in the intestine; however, the precise mechanisms and therapeutic material basis remain largely unclear. In the present study, we measured the effects of SYD on colitis symptom, epithelial barrier function, epithelial ferroptosis, colonic protein and mRNA expression of glutathione peroxidase 4 (GPX4) in colitis model, and determined whether SYD restored barrier loss in colitis by modulation of GPX4-regulated ferroptosis pathway.

Methods

Colitis was established by infusion with 1 mL 2,4,6-trinitrobenzene sulfonic acid (TNBS) dissolved in ethanol (40% v/v) in rats at a 125 mg/kg dose. Ferroptosis in epithelial cells was determined by flow cytometer. GPX4 promoter-firefly luciferase fusion construct was transfected to Caco-2 cell to determine GPX4 transcription. MS analysis was used to identified ingredients in SYD.

Results

Different doses of SYD significantly alleviated colitis, decreased ferroptosis in epithelial cells, knockout of GPX4 significantly reversed SYD-induced alleviation effects on colitis, restoration of epithelial barrier function, and epithelial ferroptosis. Wogonoside, wogonin, palmatine, paeoniflorin and liquiritin were identified as active ingredients of SYD-exerted alleviation effects of colitis based on GPX4 agonistic transcription.

Conclusion

SYD alleviated chemically induced colitis by activation of GPX4, inhibition of ferroptosis in epithelial cells and further restoration of barrier function. Wogonoside, wogonin, palmatine, paeoniflorin and liquiritin were identified as the key therapeutic material basis of SYD-exerted anti-colitis effects. The findings provide a scientific basis for the therapeutic effect of SYD on colitis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13020-022-00652-1.