RCSB Protein Data Bank: Enabling biomedical research and drug discovery

Molecular mechanisms underlying Tao-Hong-Si-Wu decoction treating hyperpigmentation based on network pharmacology, Mendelian randomization analysis, and experimental verification

CONTEXT

Hyperpigmentation, a common skin condition marked by excessive melanin production, currently has limited effective treatment options.

OBJECTIVE

This study explores the effects of Tao-Hong-Si-Wu decoction (THSWD) on hyperpigmentation and to elucidate the underlying mechanisms.

MATERIALS AND METHODS

We employed network pharmacology, Mendelian randomization, and molecular docking to identify THSWD's hub targets and mechanisms against hyperpigmentation. The Cell Counting Kit-8 (CCK-8) assay determined suitable THSWD treatment concentrations for PIG1 cells. These cells were exposed to graded concentrations of THSWD-containing serum (2.5%, 5%, 10%, 15%, 20%, 30%, 40%, and 50%) and treated with α-MSH (100 nM) to induce an in vitro hyperpigmentation model. Assessments included melanin content, tyrosinase activity, and Western blotting.

RESULTS

ALB, IL6, and MAPK3 emerged as primary targets, while quercetin, apigenin, and luteolin were the core active ingredients. The CCK-8 assay indicated that concentrations between 2.5% and 20% were suitable for PIG1 cells, with a 50% cytotoxicity concentration (CC50) of 32.14%. THSWD treatment significantly reduced melanin content and tyrosinase activity in α-MSH-induced PIG1 cells, along with downregulating MC1R and MITF expression. THSWD increased ALB and p-MAPK3/MAPK3 levels and decreased IL6 expression in the model cells.

DISCUSSION AND CONCLUSION

THSWD mitigates hyperpigmentation by targeting ALB, IL6, and MAPK3. This study paves the way for clinical applications of THSWD as a novel treatment for hyperpigmentation and offers new targeted therapeutic strategies.

PDB NextGen Archive: centralizing access to integrated annotations and enriched structural information by the Worldwide Protein Data Bank

The Protein Data Bank (PDB) is the global repository for public-domain experimentally determined 3D biomolecular structural information. The archival nature of the PDB presents certain challenges pertaining to updating or adding associated annotations from trusted external biodata resources. While each Worldwide PDB (wwPDB) partner has made best efforts to provide up-to-date external annotations, accessing and integrating information from disparate wwPDB data centers can be an involved process. To address this issue, the wwPDB has established the PDB Next Generation (or NextGen) Archive, developed to centralize and streamline access to enriched structural annotations from wwPDB partners and trusted external sources. At present, the NextGen Archive provides mappings between experimentally determined 3D structures of proteins and UniProt amino acid sequences, domain annotations from Pfam, SCOP2 and CATH databases and intra-molecular connectivity information. Since launch, the PDB NextGen Archive has seen substantial user engagement with over 3.5 million data file downloads, ensuring researchers have access to accurate, up-to-date and easily accessible structural annotations. Database URL: http://www.wwpdb.org/ftp/pdb-nextgen-archive-site.

Study on therapeutic mechanism of total salvianolic acids against myocardial ischemia-reperfusion injury based on network pharmacology, molecular docking, and experimental study

ETHNOPHARMACOLOGICAL RELEVANCE

Radix Salviae miltiorrhizae, also known as Danshen in Chinese, effectively activates the blood and resolves stasis. Total salvianolic acids (SA) is the main active ingredient of Danshen, and related preparations, such as salvianolate injection are commonly used clinically to treat myocardial ischemia-reperfusion injury (MIRI). However, the potential targets and key active ingredients of SA have not been sufficiently investigated.

AIM OF THE STUDY

This study aimed to investigate the mechanism of action of SA in treating MIRI.

MATERIALS AND METHODS

Network pharmacology and molecular docking techniques were used to predict SA targets against MIRI. The key acting pathway of SA were validated by performing experiments in a rat MIRI model.

RESULTS

Twenty potential ingredients and 54 targets of SA in treating MIRI were identified. Ingredient-target-pathway network analysis revealed that salvianolic acid B and rosmarinic acid had the highest degree value. Pathway enrichment analysis showed that SA may regulate MIRI through the IL-17 signaling pathway, and this result was confirmed in the rat MIRI experiment.

CONCLUSION

The results of this study indicate that SA may protect MIRI by regulating the IL-17 pathway.

Structural and mechanistic insights into Quinolone Synthase to address its functional promiscuity

Quinolone synthase from Aegle marmelos (AmQNS) is a type III polyketide synthase that yields therapeutically effective quinolone and acridone compounds. Addressing the structural and molecular underpinnings of AmQNS and its substrate interaction in terms of its high selectivity and specificity can aid in the development of numerous novel compounds. This paper presents a high-resolution AmQNS crystal structure and explains its mechanistic role in synthetic selectivity. Additionally, we provide a model framework to comprehend structural constraints on ketide insertion and postulate that AmQNS's steric and electrostatic selectivity plays a role in its ability to bind to various core substrates, resulting in its synthetic diversity. AmQNS prefers quinolone synthesis and can accommodate large substrates because of its wide active site entrance. However, our research suggests that acridone is exclusively synthesized in the presence of high malonyl-CoA concentrations. Potential implications of functionally relevant residue mutations were also investigated, which will assist in harnessing the benefits of mutations for targeted polyketide production. The pharmaceutical industry stands to gain from these findings as they expand the pool of potential drug candidates, and these methodologies can also be applied to additional promising enzymes.

AlphaFold2 modeling and molecular dynamics simulations of an intrinsically disordered protein

We use AlphaFold2 (AF2) to model the monomer and dimer structures of an intrinsically disordered protein (IDP), Nvjp-1, assisted by molecular dynamics (MD) simulations. We observe relatively rigid dimeric structures of Nvjp-1 when compared with the monomer structures. We suggest that protein conformations from multiple AF2 models and those from MD trajectories exhibit a coherent trend: the conformations of an IDP are deviated from each other and the conformations of a well-folded protein are consistent with each other. We use a residue-residue interaction network (RIN) derived from the contact map which show that the residue-residue interactions in Nvjp-1 are mainly transient; however, those in a well-folded protein are mainly persistent. Despite the variation in 3D shapes, we show that the AF2 models of both disordered and ordered proteins exhibit highly consistent profiles of the pLDDT (predicted local distance difference test) scores. These results indicate a potential protocol to justify the IDPs based on multiple AF2 models and MD simulations.

Discovery of the small molecular inhibitors against sclerostin loop3 as potential anti-osteoporosis agents by structural based virtual screening and molecular design

Sclerostin is a secreted glycoprotein that expresses predominantly in osteocytes and inhibits bone formation by antagonizing the Wnt/β-catenin signaling pathway, and the loop3 region of sclerostin has recently discovered as a novel therapeutic target for bone anabolic treatment without increasing cardiovascular risk. Herein, we used a structural based virtual screening to search for small molecular inhibitors selectively targeting sclerostin loop3. A novel natural product hit ZINC4228235 (THFA) was identified as the sclerostin loop3-selective inhibitor with a Kd value of 42.43 nM against sclerostin loop3. The simplification and derivation of THFA using molecular modeling-guided modification allowed the discovery of an effective and loop3-selective small molecular inhibitor, compound (4-(3-acetamidoprop-1-yn-1-yl)benzoyl)glycine (AACA), with improved binding affinity (Kd = 15.4 nM) compared to the hit THFA. Further in-vitro experiment revealed that compound AACA could attenuate the suppressive effect of transfected sclerostin on Wnt signaling and bone formation. These results make AACA as a potential candidate for development of anti-osteoporosis agents without increasing cardiovascular risk.

Investigating the Protein-Based Therapeutic Relationship between Honey Protein SHP-60 and Bevacizumab on Angiogenesis: Exploring the Synergistic Effect through In Vitro and In Silico Analysis

Honey is a natural product produced by honeybees, which has been used not only as food but also as a medicine by humans for thousands of years. In this study, 60 kDa protein was purified from Pakistani Sidr honey named as SHP-60 (Sidr Honey Protein-60), and its antioxidant potential and the effect of Bevacizumab with purified protein on in vitro angiogenesis using human umbilical vein endothelial cells (HUVEC) were investigated. We further validated the molecular protein-protein (SHP-60 with Bevacizumab) interactions through in silico analysis. It showed very promising antioxidant activity by reducing 2,2-diphenyl-1-picrylhydrazyl free radicals with a maximum of 83% inhibition at 50 μM and an IC50 of 26.45 μM statistically significant (**p < 0.01). Angiogenesis is considered a hallmark of cancer, and without it, the tumor cannot grow or metastasize. Bevacizumab, SHP-60, and both in combination were used to treat HUVEC, and the MTT assay was used to assess cell viability. To demonstrate in vitro angiogenesis, HUVEC was grown on Geltrex, and the formation of endotubes was examined using a tube formation assay. HUVEC viability was dose-dependently decreased by Bevacizumab, SHP-60, and both together. Bevacizumab and SHP-60 both inhibited angiogenesis in vitro, and their combination displayed levels of inhibition even higher than those of Bevacizumab alone. We investigated the protein-protein molecular docking interactions and molecular dynamics simulation analysis of MRJP3 (major royal jelly protein 3) similar to SHP-60 in molecular weight with both the heavy chain (HC) and light chain (LC) of Bevacizumab. We found significant interactions between the LC and MRJP3, indicating that ASN468, GLN470, and ASN473 of MRJP3 interact with SER156, SER159, and GLU161 of LC of Bevacizumab. The integration of experimental data and computational techniques is believed to improve the reliability of the findings and aid in future drug design.

In silico drug design strategies for discovering novel tuberculosis therapeutics

INTRODUCTION

Tuberculosis remains a significant concern in global public health due to its intricate biology and propensity for developing antibiotic resistance. Discovering new drugs is a protracted and expensive endeavor, often spanning over a decade and incurring costs in the billions. However, computer-aided drug design (CADD) has surfaced as a nimbler and more cost-effective alternative. CADD tools enable us to decipher the interactions between therapeutic targets and novel drugs, making them invaluable in the quest for new tuberculosis treatments.

AREAS COVERED

In this review, the authors explore recent advancements in tuberculosis drug discovery enabled by in silico tools. The main objectives of this review article are to highlight emerging drug candidates identified through in silico methods and to provide an update on the therapeutic targets associated with Mycobacterium tuberculosis.

EXPERT OPINION

These in silico methods have not only streamlined the drug discovery process but also opened up new horizons for finding novel drug candidates and repositioning existing ones. The continued advancements in these fields hold great promise for more efficient, ethical, and successful drug development in the future.

In silico investigation of potential phytoconstituents against ligand- and voltage-gated ion channels as antiepileptic agents

The most promising anticonvulsant phytocompounds were explored in this work using docking, molecular dynamic (MD) simulation, and Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) approaches. A total of 70 phytochemicals were screened against α-amino-3-hydroxyl-5-methyl-4-isoxazole propionic acid (AMPA), N-methyl-d-aspartate (NMDA), voltage-gated sodium ion channels (VGSC), and carbonic anhydrase enzyme II (CA II) receptors, and the docking results were compared to the reference drug phenytoin. Amentoflavone displayed the highest affinity for AMPA and VGSC receptors, with docking scores of - 10.4 and - 10.1 kcal/mol, respectively. Oliganthin H-NMDA and epigallocatechin-3-gallate-CA II complexes showed docking scores of - 10.9 and - 6.9 kcal/mol, respectively. All four complexes depicted a high dock score compared to the phenytoin complex at the binding site of the corresponding proteins. The MD simulation investigated the stabilities and favorable conformation of apoproteins and ligand/reference-bound complexes. The results revealed that proteins AMPA, VGSC, and CA II were more efficiently stabilized by lead phytochemicals than phenytoin binding. Additionally, principal component analysis and MM-PBSA results suggested that these lead phytocompounds have good compactness and strong binding free energy. Further, physicochemical and pharmacokinetic studies revealed that these final lead phytochemicals would be suitable for oral intake, have sufficient intestinal permeability, and have the ability to cross the blood-brain barrier (BBB). Comprehensively, this study predicted amentoflavone as the best lead phytochemical out of the 70 anticonvulsant phytocompounds that can be used to treat epilepsy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-03948-1.

Structural highlights of macromolecular complexes and assemblies

The structures of macromolecular assemblies have given us deep insights into cellular processes and have profoundly impacted biological research and drug discovery. We highlight the structures of macromolecular assemblies that have been modeled using integrative and computational methods and describe how open access to these structures from structural archives has empowered the research community. The arsenal of experimental and computational methods for structure determination ensures a future where whole organelles and cells can be modeled.

Exploring host epigenetic enzymes as targeted therapies for visceral leishmaniasis: in silico design and in vitro efficacy of KDM6B and ASH1L inhibitors

In order to combat various infectious diseases, the utilization of host-directed therapies as an alternative to chemotherapy has gained a lot of attention in the recent past, since it bypasses the existing limitations of conventional therapies. The use of host epigenetic enzymes like histone lysine methyltransferases and lysine demethylases as potential drug targets has successfully been employed for controlling various inflammatory diseases like rheumatoid arthritis and acute leukemia. In our earlier study, we have already shown that the functional knockdown of KDM6B and ASH1L in the experimental model of visceral leishmaniasis has resulted in a significant reduction of organ parasite burden. Herein, we performed a high throughput virtual screening against KDM6B and ASH1L using > 53,000 compounds that were obtained from the Maybridge library and PubChem Database, followed by molecular docking to evaluate their docking score/Glide Gscore. Based on their docking scores, the selected inhibitors were later assessed for their in vitro anti-leishmanial efficacy. Out of all inhibitors designed against KDM6B and ASH1L, HTS09796, GSK-J4 and AS-99 particularly showed promising in vitro activity with IC50 < 5 µM against both extracellular promastigote and intracellular amastigote forms of L. donovani. In vitro drug interaction studies of these inhibitors further demonstrated their synergistic interaction with amphotericin-B and miltefosine. However, GSK-J4 makes an exception by displaying an in different mode of interaction with miltefosine. Collectively, our in silico and in vitro studies acted as a platform to identify the applicability of these inhibitors targeted against KDM6B and ASH1L for anti-leishmanial therapy.

GPCR-BERT: Interpreting Sequential Design of G Protein-Coupled Receptors Using Protein Language Models

With the rise of transformers and large language models (LLMs) in chemistry and biology, new avenues for the design and understanding of therapeutics have been opened up to the scientific community. Protein sequences can be modeled as language and can take advantage of recent advances in LLMs, specifically with the abundance of our access to the protein sequence data sets. In this letter, we developed the GPCR-BERT model for understanding the sequential design of G protein-coupled receptors (GPCRs). GPCRs are the target of over one-third of Food and Drug Administration-approved pharmaceuticals. However, there is a lack of comprehensive understanding regarding the relationship among amino acid sequence, ligand selectivity, and conformational motifs (such as NPxxY, CWxP, and E/DRY). By utilizing the pretrained protein model (Prot-Bert) and fine-tuning with prediction tasks of variations in the motifs, we were able to shed light on several relationships between residues in the binding pocket and some of the conserved motifs. To achieve this, we took advantage of attention weights and hidden states of the model that are interpreted to extract the extent of contributions of amino acids in dictating the type of masked ones. The fine-tuned models demonstrated high accuracy in predicting hidden residues within the motifs. In addition, the analysis of embedding was performed over 3D structures to elucidate the higher-order interactions within the conformations of the receptors.

Jianpi Huayu Decoction suppresses cellular senescence in colorectal cancer via p53-p21-Rb pathway: Network pharmacology and in vivo validation

ETHNOPHARMACOLOGICAL RELEVANCE

Jianpi Huayu Decoction (JHD) is an herbal prescription in traditional Chinese medicine based on Sijunzi Decoction to treat patients with colorectal cancer (CRC). Its effects on the inhibition of CRC cell proliferation and tumor growth are promising; however, its multicomponent nature makes a complete understanding of its mechanism challenging.

AIM OF THE STUDY

To explore the therapeutic targets and underlying molecular pathways of JHD in CRC treatment using network pharmacology techniques and in vivo validation.

MATERIALS AND METHODS

The active ingredients and targets of JHD were identified, compound-target interactions were mapped, and enrichment analyses were conducted. We identified the hub targets of JHD-induced cellular senescence in CRC. The binding affinities between compounds and targets were evaluated through molecular docking. Subsequently, we conducted bioinformatic analyses to compare the expression of hub targets between colorectal tissue and normal tissue. Finally, in vivo experiments were carried out utilizing a xenograft model to assess the effects of JHD on cellular senescence biomarkers.

RESULTS

Network pharmacology revealed 129 active ingredients in JHD that were associated with 678 targets, leading to the construction of compound-target and target-pathway networks. Enrichment analyses highlighted key pathways including cellular senescence. Based on this, hub targets associated with cellular senescence were determined and validated. Molecular docking indicated favorable interactions between the active components and hub targets. Gene expression and survival analysis in CRC tissue were consistent with the potential roles of hub genes. Animal experiments showed that JHD triggered cellular senescence and suppressed the growth of CRC by regulating the p53-p21-Rb signaling pathway.

CONCLUSIONS

This research adopted network pharmacology, bioinformatics, and animal experiments to unveil that JHD induces cellular senescence in CRC by influencing the p53-p21-Rb pathway and senescence-associated secretory phenotypes, highlighting its potential as a CRC treatment.

The natural breakthrough: phytochemicals as potent therapeutic agents against spinocerebellar ataxia type 3

There is no FDA-approved drug for neurological disorders like spinocerebellar ataxia type 3. CAG repeats mutation in the ATXN3 gene, causing spinocerebellar ataxia type 3 disease. Symptoms include sleep cycle disturbance, neurophysiological abnormalities, autonomic dysfunctions, and depression. This research focuses on drug discovery against ATXN3 using phytochemicals of different plants. Three phytochemical compounds (flavonoids, diterpenoids, and alkaloids) were used as potential drug candidates and screened against the ATXN3 protein. The 3D structure of ATXN3 protein and phytochemicals were retrieved and validation of the protein was 98.1% Rama favored. The protein binding sites were identified for the interaction by CASTp. ADMET was utilized for the pre-clinical analysis, including solubility, permeability, drug likeliness and toxicity, and chamanetin passed all the ADMET properties to become a lead drug candidate. Boiled egg analysis attested that the ligand could cross the gastrointestinal tract. Pharmacophore analysis showed that chamanetin has many hydrogen acceptors and donors which can form interaction bonds with the receptor proteins. Chamanetin passed all the screening analyses, having good absorption, no violation of Lipinski's rule, nontoxic properties, and good pharmacophore properties. Chamanetin was one of the lead compounds with a - 7.2 kcal/mol binding affinity after screening the phytochemicals. The stimulation of ATXN3 showed stability after 20 ns of interaction in an overall 50 ns MD simulation. Chamanetin (Flavonoid) was predicted to be highly active against ATXN3 with good drug-like properties. In-silico active drug against ATXN3 from a plant source and good pharmacokinetics parameters would be excellent drug therapy for SC3, such as flavonoids (Chamanetin).

New perspectives on the potential of tetrandrine in the treatment of non-small cell lung cancer: bioinformatics, Mendelian randomization study and experimental investigation

BACKGROUND

Although there are numerous treatment methods for NSCLC, long-term survival remains a challenge for patients. The objective of this study is to investigate the role and causal relationship between the target of tetrandrine and non-small cell lung cancer (NSCLC) through transcriptome and single-cell sequencing data, summary-data-based Mendelian Randomization (SMR) and basic experiments. The aim is to provide a new perspective for the treatment of NSCLC.

METHODS

We obtained the drug target gene of tetrandrine through the drug database, and then used the GSE19188 data set to obtain the NSCLC pathogenic gene, established a drug-disease gene interaction network, screened out the hub drug-disease gene, and performed bioinformatics and tumor cell immune infiltration analysis. Single-cell sequencing data (GSE148071) to determine gene location, SMR to clarify causality and drug experiment verification.

RESULTS

10 drug-disease genes were obtained from 213 drug targets and 529 disease genes. DO/GO/KEGG analysis showed that the above genes were all related to the progression and invasion of NSCLC. Four drug-disease genes were identified from a drug-disease PPI network. These four genes were highly expressed in tumors and positively correlated with plasma cells, T cells, and macrophages. Subsequent single-cell sequencing data confirmed that these four genes were distributed in epithelial cells, and SMR analysis revealed the causal relationship between CCNA2 and CCNB1 and the development of NSCLC. The final molecular docking and drug experiments showed that CCNA2 and CCNB1 are key targets for tetrandrine in the treatment of NSCLC.

In-silico Investigations for the Identification of Novel Inhibitors Targeting Hepatitis C Virus RNA-dependent RNA Polymerase

BACKGROUND

Hepatitis C is an inflammatory condition of the liver caused by the hepatitis C virus, exhibiting acute and chronic manifestations with severity ranging from mild to severe and lifelong illnesses leading to liver cirrhosis and cancer. According to the World Health Organization's global estimates, a population of about 58 million have chronic hepatitis C virus infection, with around 1.5 million new infections occurring every year.

OBJECTIVE

The present study aimed to identify novel molecules targeting the Hepatitis C viral RNA Dependent RNA polymerases, which play a crucial role in genome replication, mRNA synthesis, etc. Methods: Structure-based virtual screening of chemical libraries of small molecules was done using AutoDock/Vina. The top-ranking pose for every ligand was complexed with the protein and used for further protein-ligand interaction analysis using the Protein-ligand interaction Profiler. Molecules from virtual screening were further assessed using the pkCSM web server. The proteinligand interactions were further subjected to molecular dynamics simulation studies to establish dynamic stability.

RESULTS

Molecular docking-based virtual screening of the database of small molecules, followed by screening based on pharmacokinetic and toxicity parameters, yielded eight probable RNA Dependent RNA polymerase inhibitors. The docking scores for the proposed candidates ranged from - 8.04 to -9.10 kcal/mol. The potential stability of the ligands bound to the target protein was demonstrated by molecular dynamics simulation studies.

CONCLUSION

Data from exhaustive computational studies proposed eight molecules as potential anti-viral candidates, targeting Hepatitis C viral RNA Dependent RNA polymerases, which can be further evaluated for their biological potential.

Biochemical and molecular docking-based strategies of Acalypha indica and Boerhavia diffusa extract by targeting bacterial strains and cancer proteins

Antibiotic-resistant microbes have emerged around the world, presenting a risk to health. Plant-derived drugs have become a potential source for the production of antibiotic-resistant drugs and cancer therapies. In this study, we investigated the antibacterial, cytotoxic and antioxidant properties of Acalypha indica and Boerhavia diffusa, and conducted in silico molecular docking experiments against EGFR and VEGFR-2 proteins. The metabolic extract of A. indica inhibited Streptococcus iniae and Staphylococcus sciuri with inhibition zones of 21.66 ± 0.57 mm and 20.33 ± 0.57 mm, respectively. The B. diffusa leaf extract produced inhibition zones of 20.3333 ± 0.5773 mm and 20.33 ± 0.57 mm against Streptococcus iniae and Edwardsiella anguillarum, respectively. A. indica and B. diffusa extracts had toxicities of 162.01 μg/ml and 175.6 μg/ml, respectively. Moreover, B. diffusa (IC50 =154.42 µg/ml) leaf extract exhibited moderately higher antioxidant activity compared with the A. indica (IC50 = 218.97 µg/ml) leaf extract. Multiple interactions were observed at Leu694, Met769 and Leu820 sites for EGFR and at Asp1046 and Cys1045 sites for VEGFR during the molecular docking study. CID-235030, CID-70825 and CID-156619353 had binding energies of -7.6 kJ/mol, -7.5 kJ/mol and -7.6 kJ/mol, respectively, with EGFR protein. VEGFR-2 protein had docking energies of -7.5 kJ/mol, -7.6 kJ/mol and -7.3 kJ/mol, respectively, for CID-6420353, CID-156619353 and CID-70825 compounds. The MD simulation trajectories revealed the hit compound; CID-235030 and EGFR complex, CID-6420353 and VEGFR-2 exhibit stable profile in the root mean square deviation (RMSD), radius of gyration (Rg), solvent accessible surface area (SASA), hydrogen bond and root mean square fluctuation (RMSF) and the binding free energy by MM-PBSA method. This study indicates that methanol extracts of A. indica and B. diffusa may play a crucial role in developing antibiotic-resistant and cancer drugs.Communicated by Ramaswamy H. Sarma.

Exploration of the effect and mechanism of Scutellaria barbata D. Don in the treatment of ovarian cancer based on network pharmacology and in vitro experimental verification

The mortality rate of ovarian cancer is the highest among gynecological cancers, posing a serious threat to women health and life. Scutellaria barbata D. Don (SBD) can effectively treat ovarian cancer. However, its mechanism of action is unclear. The aim of this study was to elucidate the mechanism of SBD in the treatment of ovarian cancer using network pharmacology, and to verify the experimental results using human ovarian cancer SKOV3 cells. The Herb and Disease Gene databases were searched to identify common targets of SBD and ovarian cancer. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and Protein-Protein Interaction (PPI) network analyses were performed to identify the potential molecular mechanisms behind SBD. Finally, the molecular docking and main possible pathways were verified by experimental studies. Cell proliferation, the mRNA expression level of key genes and signaling pathway were all investigated and evaluated in vitro. A total of 29 bioactive ingredients and 137 common targets in SBD were found to inhibit ovarian cancer development. The active ingredients identified include quercetin, luteolin, and wogonin. Analysis of the PPI network showed that AKT1, VEGFA, JUN, TNF, and Caspase-3 shared centrality among all target genes. The results of the KEGG pathway analysis indicated that the cancer pathway, PI3K-Akt signaling pathway, and MAPK signaling pathways mediated the effects of SBD against ovarian cancer progression. Cell experiments showed that quercetin, luteolin, and wogonin inhibited the proliferation and clone formation of SKOV3 cells and regulated mRNA expression of 5 key genes by inhibiting PI3K/Akt signaling pathway. Our results demonstrate that SBD exerted anti-ovarian cancer effects through its key components quercetin, luteolin and wogonin. Mechanistically, its anti-cancer effects were mediated by inhibition of the PI3K/Akt signaling pathways. Therefore, SBD might be a candidate drug for ovarian cancer treatment.

Mechanism of Valeriana Jatamansi Jones for the treatment of spinal cord injury based on network pharmacology and molecular docking

Spinal cord injury (SCI) is characterized by high rates of disability and death. Valeriana jatamansi Jones (VJJ), a Chinese herbal medicine, has been identified to improve motor function recovery in rats with SCI. The study aimed to analyze the potential molecular mechanisms of action of VJJ in the treatment of SCI. The main ingredients of VJJ were obtained from the literature and the SwissADME platform was used to screen the active ingredients. The Swiss TargetPrediction platform was used to predict the targets of VJJ, and the targets of SCI were obtained from the GeneCards and OMIM databases. The intersecting genes were considered potential targets of VJJ in SCI. The protein-protein interaction network was constructed using the STRING database and the hub genes of VJJ for SCI treatment were screened according to their degree values. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed using the Metascape database. Cytoscape software was used to construct the "herb-ingredient-target-pathway" network. Preliminary validation was performed using molecular docking via Auto Dock Vina software. A total of 56 active ingredients of VJJ, mainly iridoids, were identified. There were 1493 GO items (P < .01) and 173 signaling pathways (P < .01) obtained from GO and Kyoto Encyclopedia of Genes and Genomes enrichment, including the phosphoinositide-3-kinase (PI3K)-protein kinase B (Akt) signaling pathway, hypoxia-inducible factor 1 signaling pathway, and tumor necrosis factor signaling pathway. Molecular docking revealed that 12 hub genes enriched in the PI3K/Akt signaling pathway had a high binding affinity for the active ingredient of VJJ. VJJ may exert its therapeutic effects on SCI through the iridoid fraction, acting on signal transducer and activator of transcription 3, CASP3, AKT1, tumor necrosis factor, mammalian target of rapamycin, interleukin 6, and other hub genes, which may be related to the PI3K/Akt signaling pathway.

PDBminer to Find and Annotate Protein Structures for Computational Analysis

Computational methods relying on protein structure strongly depend on the structure selected for investigation. Typical sources of protein structures include experimental structures available at the Protein Data Bank (PDB) and high-quality in silico model structures, such as those available at the AlphaFold Protein Structure Database. Either option has significant advantages and drawbacks, and exploring the wealth of available structures to identify the most suitable ones for specific applications can be a daunting task. We provide an open-source software package, PDBminer, with the purpose of making structure identification and selection easier, faster, and less error prone. PDBminer searches the AlphaFold Database and the PDB for available structures of interest and provides an up-to-date, quality-ranked table of structures applicable for further use. PDBminer provides an overview of the available protein structures to one or more input proteins, parallelizing the runs if multiple cores are specified. The output table reports the coverage of the protein structures aligned to the UniProt sequence, overcoming numbering differences in PDB structures and providing information regarding model quality, protein complexes, ligands, and nucleic acid chain binding. The PDBminer2coverage and PDBminer2network tools assist in visualizing the results. PDBminer can be applied to overcome the tedious task of choosing a PDB structure without losing the wealth of additional information available in the PDB. Here, we showcase the main functionalities of the package on the p53 tumor suppressor protein. The package is available at http://github.com/ELELAB/PDBminer.

PDBe CCDUtils: an RDKit-based toolkit for handling and analysing small molecules in the Protein Data Bank

While the Protein Data Bank (PDB) contains a wealth of structural information on ligands bound to macromolecules, their analysis can be challenging due to the large amount and diversity of data. Here, we present PDBe CCDUtils, a versatile toolkit for processing and analysing small molecules from the PDB in PDBx/mmCIF format. PDBe CCDUtils provides streamlined access to all the metadata for small molecules in the PDB and offers a set of convenient methods to compute various properties using RDKit, such as 2D depictions, 3D conformers, physicochemical properties, scaffolds, common fragments, and cross-references to small molecule databases using UniChem. The toolkit also provides methods for identifying all the covalently attached chemical components in a macromolecular structure and calculating similarity among small molecules. By providing a broad range of functionality, PDBe CCDUtils caters to the needs of researchers in cheminformatics, structural biology, bioinformatics and computational chemistry.

A New Drug Discovery Platform: Application to DNA Polymerase Eta and Apurinic/Apyrimidinic Endonuclease 1

The ability to quickly discover reliable hits from screening and rapidly convert them into lead compounds, which can be verified in functional assays, is central to drug discovery. The expedited validation of novel targets and the identification of modulators to advance to preclinical studies can significantly increase drug development success. Our SaXPyTM ("SAR by X-ray Poses Quickly") platform, which is applicable to any X-ray crystallography-enabled drug target, couples the established methods of protein X-ray crystallography and fragment-based drug discovery (FBDD) with advanced computational and medicinal chemistry to deliver small molecule modulators or targeted protein degradation ligands in a short timeframe. Our approach, especially for elusive or "undruggable" targets, allows for (i) hit generation; (ii) the mapping of protein-ligand interactions; (iii) the assessment of target ligandability; (iv) the discovery of novel and potential allosteric binding sites; and (v) hit-to-lead execution. These advances inform chemical tractability and downstream biology and generate novel intellectual property. We describe here the application of SaXPy in the discovery and development of DNA damage response inhibitors against DNA polymerase eta (Pol η or POLH) and apurinic/apyrimidinic endonuclease 1 (APE1 or APEX1). Notably, our SaXPy platform allowed us to solve the first crystal structures of these proteins bound to small molecules and to discover novel binding sites for each target.

Integrating Metabolomics and Network Pharmacology to Explore the Mechanism of Xiao-Yao-San in the Treatment of Inflammatory Response in CUMS Mice

Depression can trigger an inflammatory response that affects the immune system, leading to the development of other diseases related to inflammation. Xiao-Yao-San (XYS) is a commonly used formula in clinical practice for treating depression. However, it remains unclear whether XYS has a modulating effect on the inflammatory response associated with depression. The objective of this study was to examine the role and mechanism of XYS in regulating the anti-inflammatory response in depression. A chronic unpredictable mild stress (CUMS) mouse model was established to evaluate the antidepressant inflammatory effects of XYS. Metabolomic assays and network pharmacology were utilized to analyze the pathways and targets associated with XYS in its antidepressant inflammatory effects. In addition, molecular docking, immunohistochemistry, Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR), and Western Blot were performed to verify the expression of relevant core targets. The results showed that XYS significantly improved depressive behavior and attenuated the inflammatory response in CUMS mice. Metabolomic analysis revealed the reversible modulation of 21 differential metabolites by XYS in treating depression-related inflammation. Through the combination of liquid chromatography and network pharmacology, we identified seven active ingredients and seven key genes. Furthermore, integrating the predictions from network pharmacology and the findings from metabolomic analysis, Vascular Endothelial Growth Factor A (VEGFA) and Peroxisome Proliferator-Activated Receptor-γ (PPARG) were identified as the core targets. Molecular docking and related molecular experiments confirmed these results. The present study employed metabolomics and network pharmacology analyses to provide evidence that XYS has the ability to alleviate the inflammatory response in depression through the modulation of multiple metabolic pathways and targets.

Genetic characteristics of common variable immunodeficiency patients with autoimmunity

Background: The pathogenesis of common variable immunodeficiency disorder (CVID) is complex, especially when combined with autoimmunity. Genetic factors may be potential explanations for this complex situation, and whole genome sequencing (WGS) provide the basis for this potential. Methods: Genetic information of patients with CVID with autoimmunity, together with their first-degree relatives, was collected through WGS. The association between genetic factors and clinical phenotypes was studied using genetic analysis strategies such as sporadic and pedigree. Results: We collected 42 blood samples for WGS (16 CVID patients and 26 first-degree relatives of healthy controls). Through pedigree, sporadic screening strategies and low-frequency deleterious screening of rare diseases, we obtained 9,148 mutation sites, including 8,171 single-nucleotide variants (SNVs) and 977 Insertion-deletions (InDels). Finally, we obtained a total of 28 candidate genes (32 loci), of which the most common mutant was LRBA. The most common autoimmunity in the 16 patients was systematic lupus erythematosis. Through KEGG pathway enrichment, we identified the top ten signaling pathways, including "primary immunodeficiency", "JAK-STAT signaling pathway", and "T-cell receptor signaling pathway". We used PyMOL to predict and analyse the three-dimensional protein structures of the NFKB1, RAG1, TIRAP, NCF2, and MYB genes. In addition, we constructed a PPI network by combining candidate mutants with genes associated with CVID in the OMIM database via the STRING database. Conclusion: The genetic background of CVID includes not only monogenic origins but also oligogenic effects. Our study showed that immunodeficiency and autoimmunity may overlap in genetic backgrounds. Clinical Trial Registration: identifier ChiCTR2100044035.

Unravelling the Molecular Mechanisms of a Quercetin Nanocrystal for Treating Potential Parkinson's Disease in a Rotenone Model: Supporting Evidence of Network Pharmacology and In Silico Data Analysis

The prevalence of Parkinson's disease places a significant burden on society; therefore, there is an urgent need to develop more effective drugs. However, the development of these drugs is both expensive and risky. Quercetin (QUE) has potent pharmacological effects on neurodegenerative diseases, but its low solubility in water and poor bioavailability limit its use in pharmaceutical applications. In this study, Quercetin nanocrystals (QNC) were synthesized and compared to standard QUE. A network-pharmacology-based methodology was applied, including target prediction, network construction, a gene ontology (GO) analysis, a KEGG pathway enrichment analysis, and molecular docking. This study aimed to identify the targets of QUE relevant to the treatment of Parkinson's disease and investigate the associated pharmacological mechanisms. Most of the predicted targets are involved in dopamine uptake during synaptic transmission. QUE regulates the key targets DRD2 and DRD4, which significantly affect dopaminergic synapses. The molecular docking results showed that QUE had a better binding affinity than the standard drug l-Dopa. From these experiments, it can be concluded that QNC effectively reduced the adverse effects caused by rotenone-induced oxidative stress in biochemical, neurochemical, and histopathological alterations. Therefore, QNC can potentially treat Parkinson's disease, and its effectiveness should be assessed in future clinical trials.

Identification of potential diagnostic biomarkers and therapeutic targets for endometriosis based on bioinformatics and machine learning analysis

PURPOSE

Endometriosis (EMs) is a major gynecological condition in women. Due to the absence of definitive symptoms, its early detection is very challenging; thus, it is crucial to find biomarkers to ease its diagnosis and therapy. Here, we aimed to identify potential diagnostic and therapeutic targets for EMs by constructing a regulatory network and using machine learning approaches.

METHODS

Three Gene Expression Omnibus (GEO) datasets were merged, and differentially expressed genes (DEGS) were identified after preprocessing steps. Using the DEGs, a transcription factor (TF)-mRNA-miRNA regulatory network was constructed, and hub genes were detected based on four different algorithms in CytoHubba. The hub genes were used to build a GaussianNB diagnostic model and also in docking analysis that were performed using Discovery Studio and AutoDock Vina software.

RESULTS

A total of 119 DEGs were identified between EMs and non-EMs samples. A regulatory network consisting of 52 mRNAs, 249 miRNAs, and 37 TFs was then constructed. The diagnostic model was introduced using the hub genes selected from the network (GATA6, HMOX1, HS3ST1, NFASC, and PTGIS) that its area under the curve (AUC) was 0.98 and 0.92 in the training and validation cohorts, respectively. Based on docking analysis, two chemical compounds, rofecoxib and retinoic acid, had potential therapeutic effects on EMs.

CONCLUSION

In conclusion, this study identified potential diagnostic and therapeutic targets for EMs which demand more experimental confirmations.

17-DMAG ameliorates neuroinflammation and BBB disruption via SOX5 mediated PI3K/Akt pathway after intracerebral hemorrhage in rats

Intracerebral hemorrhage (ICH) can result in secondary brain injury due to inflammation and breakdown of the blood-brain barrier (BBB), which are closely associated with patient prognosis. The potential of the heat shock protein 90 (Hsp90) inhibitor 17-DMAG in promoting neuroprotection has been observed in certain vascular diseases. However, the precise role of 17-DMAG treatment in ICH is not yet fully understood. In this study, we found that treatment with 17-DMAG (5 mg/kg) effectively reduced hematoma expansion and resulted in improved neurological outcomes. Meanwhile, the injection of 17-DMAG had a positive effect on reducing BBB disruption in rats with ICH. This effect was achieved by increasing the levels of BBB tight junction proteins (TJPs) such as zo-1, claudin-5, and occludin. As a result, the leakage of EB extravasation, brain edema and IgG in the peri-hematoma tissue were reduced. Furthermore, the injection of 17-DMAG decreased the infiltration of neutrophils into the brain tissues surrounding the hematoma in ICH rats and also reduced the production of proinflammatory cytokines IL-6 and TNF-α. Next, we used integrative mass spectrometry (MS) and molecular docking analysis to confirm that sex determining region Y-box protein 5 (SOX5) is a potential direct target of 17-DMAG in ICH. SOX5 encodes a positive regulator of the PI3K/Akt axis, and treatment with 17-DMAG resulted in a noticeable increase in SOX5 accumulation. To further investigate the role of SOX5, we employed virus-regulated SOX5 silencing and found that suppressing SOX5 blocked the ability of 17-DMAG to suppress neutrophil trafficking. Additionally, silencing SOX5 blocked the protective effects of 17-DMAG on the BBB by inhibiting PI3K, p-Akt, and BBB TJPs levels, which led to an increase in EB and IgG leakage in the peri-hematoma tissue after ICH. Similarly, when SOX5 was knocked down, the protective effects of 17-DMAG were lost. Overall, the results of our study indicate that the injection of 17-DMAG has the potential to mitigate neuroinflammation and prevent the disruption of the BBB caused by ICH, resulting in improved neurological outcomes in rats. These positive effects are attributed to the regulation of SOX5 and activation of the PI3K/Akt pathway. These findings highlight the possibility of targeting SOX5 and the PI3K/Akt pathway as a novel therapeutic approach for ICH.

An In Silico Design of Peptides Targeting the S1/S2 Cleavage Site of the SARS-CoV-2 Spike Protein

SARS-CoV-2, responsible for the COVID-19 pandemic, invades host cells via its spike protein, which includes critical binding regions, such as the receptor-binding domain (RBD), the S1/S2 cleavage site, the S2 cleavage site, and heptad-repeat (HR) sections. Peptides targeting the RBD and HR1 inhibit binding to host ACE2 receptors and the formation of the fusion core. Other peptides target proteases, such as TMPRSS2 and cathepsin L, to prevent the cleavage of the S protein. However, research has largely ignored peptides targeting the S1/S2 cleavage site. In this study, bioinformatics was used to investigate the binding of the S1/S2 cleavage site to host proteases, including furin, trypsin, TMPRSS2, matriptase, cathepsin B, and cathepsin L. Peptides targeting the S1/S2 site were designed by identifying binding residues. Peptides were docked to the S1/S2 site using HADDOCK (High-Ambiguity-Driven protein-protein DOCKing). Nine peptides with the lowest HADDOCK scores and strong binding affinities were selected, which was followed by molecular dynamics simulations (MDSs) for further investigation. Among these peptides, BR582 and BR599 stand out. They exhibited relatively high interaction energies with the S protein at -1004.769 ± 21.2 kJ/mol and -1040.334 ± 24.1 kJ/mol, respectively. It is noteworthy that the binding of these peptides to the S protein remained stable during the MDSs. In conclusion, this research highlights the potential of peptides targeting the S1/S2 cleavage site as a means to prevent SARS-CoV-2 from entering cells, and contributes to the development of therapeutic interventions against COVID-19.

AI-driven pan-proteome analyses reveal insights into the biohydrometallurgical properties of Acidithiobacillia

Microorganism-mediated biohydrometallurgy, a sustainable approach for metal recovery from ores, relies on the metabolic activity of acidophilic bacteria. Acidithiobacillia with sulfur/iron-oxidizing capacities are extensively studied and applied in biohydrometallurgy-related processes. However, only 14 distinct proteins from Acidithiobacillia have experimentally determined structures currently available. This significantly hampers in-depth investigations of Acidithiobacillia's structure-based biological mechanisms pertaining to its relevant biohydrometallurgical processes. To address this issue, we employed a state-of-the-art artificial intelligence (AI)-driven approach, with a median model confidence of 0.80, to perform high-quality full-chain structure predictions on the pan-proteome (10,458 proteins) of the type strain Acidithiobacillia. Additionally, we conducted various case studies on de novo protein structural prediction, including sulfate transporter and iron oxidase, to demonstrate how accurate structure predictions and gene co-occurrence networks can contribute to the development of mechanistic insights and hypotheses regarding sulfur and iron utilization proteins. Furthermore, for the unannotated proteins that constitute 35.8% of the Acidithiobacillia proteome, we employed the deep-learning algorithm DeepFRI to make structure-based functional predictions. As a result, we successfully obtained gene ontology (GO) terms for 93.6% of these previously unknown proteins. This study has a significant impact on improving protein structure and function predictions, as well as developing state-of-the-art techniques for high-throughput analysis of large proteomic data.

Model-Dependent Solvation of the K-18 Domain of the Intrinsically Disordered Protein Tau

A known imbalance between intra-protein and protein-water interactions in many empirical force fields results in collapsed conformational ensembles of intrinsically disordered proteins in explicit solvent simulations that disagree with experiments. Multiple strategies have been introduced in the literature to modify protein-water interactions, which improve agreement between experiments and simulations. In this work, we combine simulations with standard and modified force fields with a spatially resolved analysis of solvation free energy contributions and compare the consequences of each strategy. We find that enhanced Lennard-Jones (LJ) interactions between protein atoms and water oxygens primarily improve the solvation of nonpolar functional groups of the protein. In contrast, modified electrostatics in the water model or strengthened LJ interactions between the protein and water hydrogens mainly affect the hydration of polar functional groups. Modified electrostatics further impact the average orientation of water molecules in the hydration shell. As a result, protein-water interactions with the first hydration layers are strengthened, while interactions with water molecules in higher hydration shells are weakened. Hence, distinct strategies to balance intra-protein and protein-water interactions in simulations have qualitatively different effects on protein solvation. These differences are not necessarily captured by comparisons to experiments that report on global parameters describing protein conformational ensembles, e.g., the radius of gyration, but will influence the tendency of a protein to form aggregates or phase-separated droplets.

A semiempirical method optimized for modeling proteins

CONTEXT

In recent years, semiempirical methods such as PM6, PM6-D3H4, and PM7 have been increasingly used for modeling proteins, in particular enzymes. These methods were designed for more general use, and consequently were not optimized for studying proteins. Because of this, various specific errors have been found that could potentially cast doubt on the validity of these methods for modeling phenomena of biochemical interest such as enzyme catalytic mechanisms and protein-ligand interactions. To correct these and other errors, a new method specifically designed for use in organic and biochemical modeling has been developed.

METHODS

Two alterations were made to the procedures used in developing the earlier PMx methods. A minor change was made to the theoretical framework, which affected only the non-quantum theory interatomic interaction function, while the major change involved changing the training set for optimizing parameters, moving the focus to systems of biochemical significance. This involved both the selection of reference data and the weighting factors, i.e., the relative importance that the various data were given. As a result of this change of focus, the accuracy in prediction of heats of formation, hydrogen bonding, and geometric quantities relating to non-covalent interactions in proteins was improved significantly.

Systematic Analysis of Covalent and Allosteric Protein Kinase Inhibitors

In drug discovery, protein kinase inhibitors (PKIs) are intensely investigated as drug candidates in different therapeutic areas. While ATP site-directed, non-covalent PKIs have long been a focal point in protein kinase (PK) drug discovery, in recent years, there has been increasing interest in allosteric PKIs (APKIs), which are expected to have high kinase selectivity. In addition, as compounds acting by covalent mechanisms experience a renaissance in drug discovery, there is also increasing interest in covalent PKIs (CPKIs). There are various reasons for this increasing interest such as the anticipated high potency, prolonged residence times compared to non-competitive PKIs, and other favorable pharmacokinetic properties. Due to the popularity of PKIs for therapeutic intervention, large numbers of PKIs and large volumes of activity data have accumulated in the public domain, providing a basis for large-scale computational analysis. We have systematically searched for CPKIs containing different reactive groups (warheads) and investigated their potency and promiscuity (multi-PK activity) on the basis of carefully curated activity data. For seven different warheads, sufficiently large numbers of CPKIs were available for detailed follow-up analysis. For only three warheads, the median potency of corresponding CPKIs was significantly higher than of non-covalent PKIs. However, for CKPIs with five of seven warheads, there was a significant increase in the median potency of at least 100-fold compared to PKI analogues without warheads. However, in the analysis of multi-PK activity, there was no general increase in the promiscuity of CPKIs compared to non-covalent PKIs. In addition, we have identified 29 new APKIs in X-ray structures of PK-PKI complexes. Among structurally characterized APKIs, 13 covalent APKIs in complexes with five PKs are currently available, enabling structure-based investigation of PK inhibition by covalent-allosteric mechanisms.

Exploring the multifunctional roles of quantum dots for unlocking the future of biology and medicine

With recent advancements in nanomedicines and their associated research with biological fields, their translation into clinically-applicable products is still below promises. Quantum dots (QDs) have received immense research attention and investment in the four decades since their discovery. We explored the extensive biomedical applications of QDs, viz. Bio-imaging, drug research, drug delivery, immune assays, biosensors, gene therapy, diagnostics, their toxic effects, and bio-compatibility. We unravelled the possibility of using emerging data-driven methodologies (bigdata, artificial intelligence, machine learning, high-throughput experimentation, computational automation) as excellent sources for time, space, and complexity optimization. We also discussed ongoing clinical trials, related challenges, and the technical aspects that should be considered to improve the clinical fate of QDs and promising future research directions.

Neuropharmacological and antiproliferative activity of Tetrastigma leucostaphyllum (Dennst.) Alston: Evidence from in-vivo, in-vitro and in-silico approaches

As the incidence of neurodegeneration and cancer fatalities remains high, researchers are focusing their efforts on discovering and developing effective medications, especially plant-based drugs, against these diseases. Hence, this research aimed to investigate the neuropharmacological potentials of aerial parts of Tetrastigma leucostaphyllum, employing some behavioral models, while the antiproliferative effect was explored against a panel of cancer cell lines (MGC-803, A549, U-251, HeLa and MCF-7) using a colorimetric assay. In addition, active extracts were analyzed by GC-MS technique to identify the active compounds, where some selective compounds were docked with the particular pure proteins to check their binding affinity. Results from neuropharmacological research indicated that the total extract and its fractions may be effective (p = 0.05, 0.01, and 0.001, respectively) at doses of 100, 200, and 400 mg/kg of animal body weight. The greatest antidepressant and anxiolytic effects were found in the n-hexane fraction. The n-haxane fraction also exhibited the highest cytotoxicity against the U-251 cell line (IC5014.3 μg/mL), followed by the A549, MG-803, HeLa, and MCF-7 cell lines, respectively. From the n-hexane fraction, ten chemicals were detected using the GC-MS method. Additionally, the in-silico research revealed interactions between the n-hexane fractions' identified compounds and the antidepressant, anxiolytic, and cytotoxic receptors. The molecules showed binding affinities that ranged from 4.6 kcal/mol to 6.8 kcal/mol, which indicates the likelihood that they would make good drug candidates. This study highlighted the plant's neuropharmacological and cytotoxic properties, however, more research is needed to determine the etymological origin of these effects.

Nephroprotective mechanisms of Rhizoma Chuanxiong and Radix et Rhizoma Rhei against acute renal injury and renal fibrosis based on network pharmacology and experimental validation

The molecular mechanisms of Rhizoma Chuanxiong (Chuanxiong, CX) and Rhei Radix et Rhizoma (Dahuang, DH) in treating acute kidney injury (AKI) and subsequent renal fibrosis (RF) were investigated in this study by applying network pharmacology and experimental validation. The results showed that aloe-emodin, (-)-catechin, beta-sitosterol, and folic acid were the core active ingredients, and TP53, AKT1, CSF1R, and TGFBR1 were the core target genes. Enrichment analyses showed that the key signaling pathways were the MAPK and IL-17 signaling pathways. In vivo experiments confirmed that Chuanxiong and Dahuang pretreatments significantly inhibited the levels of SCr, BUN, UNAG, and UGGT in contrast media-induced acute kidney injury (CIAKI) rats (p < 0.001). The results of Western blotting showed that compared with the control group, the protein levels of p-p38/p38 MAPK, p53, and Bax in the contrast media-induced acute kidney injury group were significantly increased, and the levels of Bcl-2 were significantly reduced (p < 0.001). Chuanxiong and Dahuang interventions significantly reversed the expression levels of these proteins (p < 0.01). The localization and quantification of p-p53 expression in immunohistochemistry technology also support the aforementioned results. In conclusion, our data also suggest that Chuanxiong and Dahuang may inhibit tubular epithelial cell apoptosis and improve acute kidney injury and renal fibrosis by inhibiting p38 MAPK/p53 signaling.

Network pharmacology integrated with experimental validation revealed potential molecular mechanisms of Camellia nitidissima C. W. Chi in the treatment of lung cancer

ETHNOPHARMACOLOGICAL RELEVANCE

Camellia nitidissima C.W.Chi (CNC), an ethnomedicine mainly distributed in Southern China's Guangxi Zhuang Autonomous Region, is known as "Panda in plants" and "Camellias Queen" due to its golden blossom. CNC has been applied as a traditional folk medicine in cancer therapy.

AIM OF THE STUDY

This study utilized network pharmacology analysis combined with experimental validation to identify the substance basis and potential molecular mechanism of CNC against lung cancer.

MATERIALS AND METHODS

The active ingredients of CNC were identified based on published literature. The associated potential targets of CNC in lung cancer treatment were predicted using integrated network pharmacology analysis and molecular docking. The underlying molecular mechanism of CNC in lung cancer were validated in human lung cancer cell lines.

RESULTS

A total of 30 active ingredients and 53 targets of CNC were screened. An enrichment analysis of Gene Ontology (GO) revealed that the effects of CNC in lung cancer mainly involve protein binding, regulation of cell proliferation and apoptosis, and signal transduction. KEGG pathways analysis suggested that CNC might exert cancer suppression effects mainly through pathways in cancer, PI3K/AKT signaling pathway. Molecular docking revealed that CNC has high affinity for binding of EGFR, SRC, AKT1, and CCND1 to the key active ingredients including luteolin, kaempferol, quercetin, eriodictyol and 3'4-O-dimethylcedrusin. In in vitro experiments, CNC played the inhibitory roles in lung cancer cells by inducing cell apoptosis, causing G0/G1 and S cell cycle arrest, increasing intracellular ROS levels, and promoting the apoptotic proteins Bax and Caspase-3. Meanwhile, CNC also regulated the expression of core proteins EGFR, SRC, and AKT.

CONCLUSION

These results comprehensively clarified the associated substance basis and underlying molecular mechanism of CNC against lung cancer, which would be contributed to develop promising anti-cancer pharmaceuticals or therapeutic approaches for lung cancer therapy.

Lenalidomide Stabilizes Protein-Protein Complexes by Turning Labile Intermolecular H-Bonds into Robust Interactions

Targeted protein degradation is a promising therapeutic strategy, spearheaded by the anti-myeloma drugs lenalidomide and pomalidomide. These drugs stabilize very efficiently the complex between the E3 ligase Cereblon (CRBN) and several non-native client proteins (neo-substrates), including the transcription factors Ikaros and Aiolos and the enzyme Caseine Kinase 1α (CK1α,), resulting in their degradation. Although the structures for these complexes have been determined, there are no evident interactions that can account for the high efficiency of formation of the ternary complex. We show that lenalidomide's stabilization of the CRBN-CK1α complex is largely due to hydrophobic shielding of intermolecular hydrogen bonds. We also find a quantitative relationship between hydrogen bond robustness and binding affinities of the ternary complexes. These results pave the way to further understand cooperativity effects in drug-induced protein-protein complexes and could help in the design of improved molecular glues and more efficient protein degraders.

Exploration of the anti-liver injury active components of Shaoyao Gancao decoction by network pharmacology and experiments in vivo

BACKGROUND

Shaoyao Gancao decoction (SGD), a classic traditional Chinese herbal formula, has been widely used to treat febrile diseases in the clinic for centuries. In recent years, a growing number of studies have found that SGD has a favorable anti-liver injury effect.

PURPOSE

In this study, we want to know the potential active components of SGD treatment in liver injury.

STUDY DESIGN

A novel method combining computer simulation and in vivo experiment was established for the first time and used to investigate this problem.

METHODS

A network pharmacology was used to explore the active components of SGD treatment in liver injury, and preliminarily verified the results of network pharmacology through molecular docking. To further understand the active compounds of SGD in the treatment of liver injury, we compared the prototypes and metabolites of SGD in healthy rats and rats with liver injury after oral administration. In addition, a UPLC-MS/MS method was developed and successfully applied to investigate the pharmacokinetics of 9 compounds of SGD in healthy and liver injury rats.

RESULTS

It showed that SGD exerted protective effects against liver injury by the active components of liquiritin and albiflorin, etc. The values of the AUC_0-t, AUC_0-∞, t_1/2, T_max were significantly different after oral administration of SGD in healthy and liver injury rats. This indicates that the pharmacokinetic study in the pathological state of liver injury can provide more valuable information for guiding clinical medication.

CONCLUSION

In this study, the integration of network pharmacology and experiments in vivo provides a novel strategy to explore active components of TCMs to treat diseases.

Accurate prediction by AlphaFold2 for ligand binding in a reductive dehalogenase and implications for PFAS (per- and polyfluoroalkyl substance) biodegradation

Despite the success of AlphaFold2 (AF2), it is unclear how AF2 models accommodate for ligand binding. Here, we start with a protein sequence from Acidimicrobiaceae TMED77 (T7RdhA) with potential for catalyzing the degradation of per- and polyfluoroalkyl substances (PFASs). AF2 models and experiments identified T7RdhA as a corrinoid iron-sulfur protein (CoFeSP) which uses a norpseudo-cobalamin (BVQ) cofactor and two Fe₄S₄ iron-sulfur clusters for catalysis. Docking and molecular dynamics simulations suggest that T7RdhA uses perfluorooctanoic acetate (PFOA) as a substrate, supporting the reported defluorination activity of its homolog, A6RdhA. We showed that AF2 provides processual (dynamic) predictions for the binding pockets of ligands (cofactors and/or substrates). Because the pLDDT scores provided by AF2 reflect the protein native states in complex with ligands as the evolutionary constraints, the Evoformer network of AF2 predicts protein structures and residue flexibility in complex with the ligands, i.e., in their native states. Therefore, an apo-protein predicted by AF2 is actually a holo-protein awaiting ligands.

Pomegranate Peel in the Amelioration of High-Altitude Disease: A Network Pharmacology and Molecular Docking Study of Underlying Mechanisms

High-altitude disease (HAD) describes the failure to adapt to the lack of oxygen found at high altitudes and therapeutic antioxidant effects have been attributed to pomegranate peel (PP) extract. Network pharmacology, molecular docking, and experimental validation were used to study mechanisms responsible for the alleviation of HAD by PP. The aim was to establish a reference for future research and aid technological development, particularly in clinical settings. Network pharmacology analysis showed that PP affected many targets in HAD via the active ingredients, luteolin 7-O-glycoside, punicalagin, and ellagic acid. HNRNPA1, HSPA1B, HSPA1A, CUL4B, CLTC, PPP1CA, PARP1, RACK1, NEDD8, and MAP3K1 were all targets, responsible for effects on ribosomes, apoptosis, cell cycle, mRNA surveillance pathway, and the MAPK signaling pathway. PP had an antiapoptosis effect on H9c2 cells damaged by hypoxia, as shown by annexinV-FITC/PI double staining. Practical Applications. HAD comprises a group of diseases caused by failure to adapt to a low-oxygen environment. PP extract has previously been shown to have antioxidant effects. PP attenuated damage to H9c2 cells and reduced the apoptosis rate. The current results lay the foundation for further experimental investigations.

Smart de novo Macromolecular Structure Modeling from Cryo-EM Maps

The study of macromolecular structures has expanded our understanding of the amazing cell machinery and such knowledge has changed how the pharmaceutical industry develops new vaccines in recent years. Traditionally, X-ray crystallography has been the main method for structure determination, however, cryogenic electron microscopy (cryo-EM) has increasingly become more popular due to recent advancements in hardware and software. The number of cryo-EM maps deposited in the EMDataResource (formerly EMDatabase) since 2002 has been dramatically increasing and it continues to do so. De novo macromolecular complex modeling is a labor-intensive process, therefore, it is highly desirable to develop software that can automate this process. Here we discuss our automated, data-driven, and artificial intelligence approaches including map processing, feature extraction, modeling building, and target identification. Recently, we have enabled DNA/RNA modeling in our deep learning-based prediction tool, DeepTracer. We have also developed DeepTracer-ID, a tool that can identify proteins solely based on the cryo-EM map. In this paper, we will present our accumulated experiences in developing deep learning-based methods surrounding macromolecule modeling applications.

Cellular mechanism of action of forsythiaside for the treatment of diabetic kidney disease

Background: Diabetic kidney disease (DKD) becomes the leading cause of death for end-stage renal disease, whereas the potential mechanism is unclear and effective therapy is still rare. Our study was designed to investigate the cellular mechanism of Forsythiaside against DKD. Materials and Methods: The targets of Forsythiaside and the DKD-related targets were obtained from databases. The overlapping targets in these two sets were regarded as potential targets for alleviation of DKD by Forsythiaside. The targets of diabetic podocytopathy and tubulopathy were also detected to clarify the mechanism of Forsythiaside ameliorating DKD from the cellular level. Results: Our results explored that PRKCA and RHOA were regarded as key therapeutic targets of Forsythiaside with excellent binding affinity for treating DKD podocytopathy. Enrichment analysis suggested the underlying mechanism was mainly focused on the oxidative stress and mTOR signaling pathway. The alleviated effects of Forsythiaside on the reactive oxidative species accumulation and PRKCA and RHOA proteins upregulation in podocytes were also confirmed. Conclusion: The present study elucidates that Forsythiaside exerts potential treatment against DKD which may act directly RHOA and PRKCA target by suppressing the oxidative stress pathway in podocytes. And Forsythiaside could be regarded as one of the candidate drugs dealing with DKD in future experimental or clinical researches.

RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), founding member of the Worldwide Protein Data Bank (wwPDB), is the US data center for the open-access PDB archive. As wwPDB-designated Archive Keeper, RCSB PDB is also responsible for PDB data security. Annually, RCSB PDB serves >10 000 depositors of three-dimensional (3D) biostructures working on all permanently inhabited continents. RCSB PDB delivers data from its research-focused RCSB.org web portal to many millions of PDB data consumers based in virtually every United Nations-recognized country, territory, etc. This Database Issue contribution describes upgrades to the research-focused RCSB.org web portal that created a one-stop-shop for open access to ∼200 000 experimentally-determined PDB structures of biological macromolecules alongside >1 000 000 incorporated Computed Structure Models (CSMs) predicted using artificial intelligence/machine learning methods. RCSB.org is a 'living data resource.' Every PDB structure and CSM is integrated weekly with related functional annotations from external biodata resources, providing up-to-date information for the entire corpus of 3D biostructure data freely available from RCSB.org with no usage limitations. Within RCSB.org, PDB structures and the CSMs are clearly identified as to their provenance and reliability. Both are fully searchable, and can be analyzed and visualized using the full complement of RCSB.org web portal capabilities.

AlphaFold2 protein structure prediction: Implications for drug discovery

The drug discovery process involves designing compounds to selectively interact with their targets. The majority of therapeutic targets for low molecular weight (small molecule) drugs are proteins. The outstanding accuracy with which recent artificial intelligence methods compile the three-dimensional structure of proteins has made protein targets more accessible to the drug design process. Here, we present our perspective of the significance of accurate protein structure prediction on various stages of the small molecule drug discovery life cycle focusing on current capabilities and assessing how further evolution of such predictive procedures can have a more decisive impact in the discovery of new medicines.

Homology Modeling of Transporter Proteins

Membrane transporter proteins are divided into channels/pores and carriers and constitute protein families of physiological and pharmacological importance. Several presently used therapeutic compounds elucidate their effects by targeting membrane transporter proteins, including anti-arrhythmic, anesthetic, antidepressant, anxiolytic and diuretic drugs. The lack of three-dimensional structures of human transporters hampers experimental studies and drug discovery. In this chapter, the use of homology modeling for generating structural models of membrane transporter proteins is reviewed. The increasing number of atomic resolution structures available as templates, together with improvements in methods and algorithms for sequence alignments, secondary structure predictions, and model generation, in addition to the increase in computational power have increased the applicability of homology modeling for generating structural models of transporter proteins. Different pitfalls and hints for template selection, multiple-sequence alignments, generation and optimization, validation of the models, and the use of transporter homology models for structure-based virtual ligand screening are discussed.

Mechanistic Study of Schisandra chinensis Fruit Mixture Based on Network Pharmacology, Molecular Docking and Experimental Validation to Improve the Inflammatory Response of DKD Through AGEs/RAGE Signaling Pathway

Background

Diabetic kidney disease (DKD) is a major cause of end-stage renal disease (ESRD), and inflammation is the main causative mechanism. Schisandra chinensis fruit Mixture (SM) is an herbal formulation that has been used for a long time to treat DKD. However, its pharmacological and molecular mechanisms have not been clearly elucidated. The aim of this study was to investigate the potential mechanisms of SM for the treatment of DKD through network pharmacology, molecular docking and experimental validation.

Methods

The chemical components in SM were comprehensively identified and collected using liquid chromatography-tandem mass spectrometry (LC-MS) and database mining. The mechanisms were investigated using a network pharmacology, including obtaining SM-DKD intersection targets, completing protein-protein interactions (PPI) by Cytoscape to obtain key potential targets, and then revealing potential mechanisms of SM for DKD by GO and KEGG pathway enrichment analysis. The important pathways and phenotypes screened by the network analysis were validated experimentally in vivo. Finally, the core active ingredients were screened by molecular docking.

Results

A total of 53 active ingredients of SM were retrieved by database and LC-MS, and 143 common targets of DKD and SM were identified; KEGG and PPI showed that SM most likely exerted anti-DKD effects by regulating the expression of AGEs/RAGE signaling pathway-related inflammatory factors. In addition, our experimental validation results showed that SM improved renal function and pathological changes in DKD rats, down-regulated AGEs/RAGE signaling pathway, and further down-regulated the expression of TNF-α, IL-1β, IL-6, and up-regulated IL-10. Molecular docking confirmed the tight binding properties between (+)-aristolone, a core component of SM, and key targets.

Conclusion

This study reveals that SM improves the inflammatory response of DKD through AGEs/RAGE signaling pathway, thus providing a novel idea for the clinical treatment of DKD.

Preparing a database of corrected protein structures important in cell signaling pathways

Background and purpose

Precise structures of macromolecules are important for structure-based drug design. Due to the limited resolution of some structures obtained from X-ray diffraction crystallography, differentiation between the NH and O atoms can be difficult. Sometimes a number of amino acids are missing from the protein structure. In this research, we intend to introduce a small database that we have prepared for providing the corrected 3D structure files of proteins frequently used in structure-based drug design protocols.

Experimental approach

3454 soluble proteins belonging to the cancer signaling pathways were collected from the PDB database from which a dataset of 1001 was obtained. All were subjected to corrections in the protein preparation step. 896 protein structures out of 1001 were corrected successfully and the decision on the remained 105 proposed twelve for homology modeling to correct the missing residues. Three of them were subjected to molecular dynamics simulation for 30 ns.

Findings / Results

896 corrected proteins were perfect and homology modeling on 12 proteins with missing residues in the backbone resulted in acceptable models according to Ramachandran, z-score, and DOPE energy plots. RMSD, RMSF, and Rg values verified the stability of the models after 30 ns molecular dynamics simulation.

Conclusion and implication

A collection of 1001 proteins were modified for some defects such as adjustment of the bond orders and formal charges, and addition of missing side chains of residues. Homology modeling corrected the amino missing backbone residues. This database will be completed for quite a lot of water-soluble proteins to be uploaded to the internet.

Pan-kinome of Legionella expanded by a bioinformatics survey

The pathogenic Legionella bacteria are notorious for delivering numerous effector proteins into the host cell with the aim of disturbing and hijacking cellular processes for their benefit. Despite intensive studies, many effectors remain uncharacterized. Motivated by the richness of Legionella effector repertoires and their oftentimes atypical biochemistry, also by several known atypical Legionella effector kinases and pseudokinases discovered recently, we undertook an in silico survey and exploration of the pan-kinome of the Legionella genus, i.e., the union of the kinomes of individual species. In this study, we discovered 13 novel (pseudo)kinase families (all are potential effectors) with the use of non-standard bioinformatic approaches. Together with 16 known families, we present a catalog of effector and non-effector protein kinase-like families within Legionella, available at http://bioinfo.sggw.edu.pl/kintaro/ . We analyze and discuss the likely functional roles of the novel predicted kinases. Notably, some of the kinase families are also present in other bacterial taxa, including other pathogens, often phylogenetically very distant from Legionella. This work highlights Nature's ingeniousness in the pathogen-host arms race and offers a useful resource for the study of infection mechanisms.

Machine learning models exploring characteristic single-nucleotide signatures in yellow fever virus

Yellow fever virus (YFV) is the agent of the most severe mosquito-borne disease in the tropics. Recently, Brazil suffered major YFV outbreaks with a high fatality rate affecting areas where the virus has not been reported for decades, consisting of urban areas where a large number of unvaccinated people live. We developed a machine learning framework combining three different algorithms (XGBoost, random forest and regularized logistic regression) to analyze YFV genomic sequences. This method was applied to 56 YFV sequences from human infections and 27 from non-human primate (NHPs) infections to investigate the presence of genetic signatures possibly related to disease severity (in human related sequences) and differences in PCR cycle threshold (Ct) values (in NHP related sequences). Our analyses reveal four non-synonymous single nucleotide variations (SNVs) on sequences from human infections, in proteins NS3 (E614D), NS4a (I69V), NS5 (R727G, V643A) and six non-synonymous SNVs on NHP sequences, in proteins E (L385F), NS1 (A171V), NS3 (I184V) and NS5 (N11S, I374V, E641D). We performed comparative protein structural analysis on these SNVs, describing possible impacts on protein function. Despite the fact that the dataset is limited in size and that this study does not consider virus-host interactions, our work highlights the use of machine learning as a versatile and fast initial approach to genomic data exploration.

In silico structure-based designers of therapeutic targets for diabetes mellitus or obesity: A protocol for systematic review

Obesity is a significant risk factor for several chronic non-communicable diseases, being closely related to Diabetes Mellitus. Computer modeling techniques favor the understanding of interaction mechanisms between specific targets and substances of interest, optimizing drug development. In this article, the protocol of two protocols of systematic reviews are described for identifying therapeutic targets and models for treating obesity or diabetes mellitus investigated in silico. The protocol is by the guidelines from the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes Protocols (PRISMA-P) and was published in the International Prospective Register of Systematic Reviews database (PROSPERO: CRD42022353808). Search strategies will be developed based on the combination of descriptors and executed in the following databases: PubMed; ScienceDirect; Scopus; Web of Science; Virtual Health Library; EMBASE. Only original in silico studies with molecular dynamics, molecular docking, or both will be inserted. Two trained researchers will independently select the articles, extract the data, and assess the risk of bias. The quality will be assessed through an adapted version of the Strengthening the Reporting of Empirical Simulation Studies (STRESS) and the risk of bias using a checklist obtained from separate literature sources. The implementation of this protocol will result in the elaboration of two systematic reviews identifying the therapeutic targets for treating obesity (review 1) or diabetes mellitus (review 2) used in computer simulation studies and their models. The systematization of knowledge about these treatment targets and their in silico structures is fundamental, primarily because computer simulation contributes to more accurate planning of future either in vitro or in vivo studies. Therefore, the reviews developed from this protocol will guide decision-making regarding the choice of targets/models in future research focused on therapeutics of obesity or Diabetes Mellitus contributing to mitigate of factors such as costs, time, and necessity of in vitro and/or in vivo assays.