RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences

Exploring the Anti-inflammatory Effects of Protopine Total Alkaloids of Macleaya Cordata (Willd.) R. Br

Macleaya cordata (Willd). R. Br. is a Chinese medicinal plant commonly used externally to treat inflammatory-related diseases such as arthritis, sores, and carbuncles. This study aimed to evaluate the anti-inflammatory activity of protopine total alkaloids (MPTAs) in Macleaya cordata (Willd.) R. Br. in vivo tests in rats with acute inflammation showed that MPTA (2.54 and 5.08 mg/kg) showed significant anti-inflammatory activity 6 h after carrageenan injection. Similarly, MPTA (3.67 and 7.33 mg/kg) showed significant anti-inflammatory activity in the mouse ear swelling test. In addition, the potential mechanisms of the anti-inflammatory effects of MPTA were explored based on network pharmacology and molecular docking. The two main active components of MPTA, protopine and allocryptopine, were identified, and the potential targets and signaling pathways of MPTA's anti-inflammatory effects were initially revealed using tools and databases (such as SwissTargetPrediction, GeneCards, and STRING) combined with molecular docking results. This study provides the basis for the application of MPTA as an anti-inflammatory agent.

Regular combined training and vitamins modulated the apoptosis process in diabetic rats: Bioinformatics analysis of heart failure's differential genes expression network correlated with anti-apoptotic process

The apoptosis process could impose significantly by hyperglycemia. According to in silico language processing and high throughput raw data analysis, we recognized hub molecular mechanisms involved in the pathogenesis of diabetic hearts and suggested a new pharmaceutical approach for declining myocardial programed cell death. Fifty male Sprague-Dawley rats were classified into five groups: healthy rats as control, diabetic rats, diabetic combined resistance/endurance training, diabetic rats which consumed supplementation vitamins E and C, and the combined supplementation and training. Here, we calculated changes in gene expression based on artificial intelligence methods and evaluated gene expression in apoptotic influencing combined training and antioxidants vitamins consumption in heart injured models by streptozotocin via Real-Time PCR. Moreover, we assessed the binding affinity of the 3D structure of small molecules on macromolecule SIRT3 to a new compound pharmaceutical suggesting the decline in cell death program. The computational intelligence surveys revealed that the apoptosis process was a remarkable pathomechanism in the abnormality function of heart tissue in diabetic conditions. Furthermore, we showed that synchronizing antioxidant vitamin consumption and regular combined training could significantly decrease irreversible myocardial cell death in diabetic myocardiopathy. Hence, levels of antiapoptotic mRNA were modified in the combined training/vitamin consumption group compared with other classifications. We found that regular combined exercise and vitamin consumption could reverse the apoptosis process to enhance the survival of cardiac muscle cells in diabetes conditions. PRACTICAL APPLICATIONS: Machine learning and system biology indicated that the apoptosis process is a vital pathomechanism of hyperglycemia-induced heart failure. Sirt3/Fas/Bcl-2/Cycs and Bax, as a critical network of apoptosis, play an essential role in heart failure induced by hyperglycemia. Moreover, Type 2 diabetes and obesity increase the risk of heart failure by increasing high blood sugar levels. We calculated the binding power of the vitamins E and C on SIRT3 protein based on the drug software. In addition, this study assessed that regular combined training and vitamin consumption had an antiapoptotic effect. Also, our data might improve the hyperglycemia state.

Immunoinformatic approach for the construction of multi-epitopes vaccine against omicron COVID-19 variant

The newly discovered SARS-CoV-2 Omicron variant B.1.1.529 is a Variant of Concern (VOC) announced by the World Health Organization (WHO). It's becoming increasingly difficult to keep these variants from spreading over the planet. The fifth wave has begun in several countries because of Omicron variant, and it is posing a threat to human civilization. As a result, we need effective vaccination that can tackle Omicron SARS-CoV-2 variants that are bound to emerge. Therefore, the current study is an initiative to design a peptide-based chimeric vaccine that may potentially battle SARS-CoV-2 Omicron variant. As a result, the most relevant epitopes present in the mutagenic areas of Omicron spike protein were identified using a set of computational tools and immunoinformatic techniques to uncover common MHC-1, MHC-II, and B cell epitopes that may have the ability to influence the host immune mechanism. A final of three epitopes from CD8+ T-cell, CD4+ T-cell epitopes, and B-cell were shortlisted from spike protein, and that are highly antigenic, IFN-γ inducer, as well as overlapping for the construction of twelve vaccine models. As a result, the antigenic epitopes were coupled with a flexible and stable peptide linker, and the adjuvant was added at the N-terminal end to create a unique vaccine candidate. The structure of a 3D vaccine candidate was refined, and its quality was assessed by using web servers. However, the applied immunoinformatic study along with the molecular docking and simulation of 12 modeled vaccines constructs against six distinct HLAs, and TLRs (TLR2, and TLR4) complexes revealed that the V1 construct was non-allergenic, non-toxic, highly immunogenic, antigenic, and most stable. The vaccine candidate's stability was confirmed by molecular dynamics investigations. Finally, we studied the expression of the suggested vaccination using codon optimization and in-silico cloning. The current study proposed V1 Multi-Epitope Vaccine (MEV) as a significant vaccine candidate that may help the scientific community to treat SARS-CoV-2 infections.

Virus-host interaction network analysis in Colorectal cancer identifies core virus network signature and small molecules

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Systematic analysis of virus-host networks identified key pathways in CRC.

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Core virus-CRC network revealed the growth pathway regulated by viruses.

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Short linear motif analysis identified druggable regions in virus proteins.

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Virtual screening revealed key anti-viral molecules against viral proteins.

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Molecular dynamics simulations showed the effect of anti-viral molecules.

Colorectal cancer (CRC) is a significant contributor to cancer-related deaths caused by an unhealthy lifestyle. Multiple studies reveal that viruses are involved in colorectal tumorigenesis. The viruses such as Human Cytomegalovirus (HCMV), Human papillomaviruses (HPV16 & HPV18), and John Cunningham virus (JCV) are known to cause colorectal cancer. The molecular mechanisms of cancer genesis and maintenance shared by these viruses remain unclear. We analysed the virus-host networks and connected them with colorectal cancer proteome datasets and extracted the core shared interactions in the virus-host CRC network. Our network topology analysis identified prominent virus proteins RL6 (HCMV), VE6 (HPV16 and HPV18), and Large T antigen (JCV). Sequence analysis uncovered short linear motifs (SLiMs) in each viral target. We used these targets to identify the antiviral drugs through a structure-based virtual screening approach. This analysis highlighted that temsavir, pimodivir, famotine, and bictegravir bind to each virus protein target, respectively. We also assessed the effect of drug binding using molecular dynamic simulations, which shed light on the modulatory effect of drug molecules on SLiM regions in viral targets. Hence, our systematic screening of virus-host networks revealed viral targets, which could be crucial for cancer therapy.

Computational studies with flavonoids and terpenoids as BRPF1 inhibitors: in silico biological activity prediction, molecular docking, molecular dynamics simulations, MM/PBSA calculations

The BRPF1 protein is encoded by the BRPF1 gene. In addition, the BRPF1 gene is known to be upregulated in leukaemia. Recent studies have shown that it is also overexpressed in hepatocellular carcinoma (HCC) as well. Therefore, BRPF1 is a significant target for anti-cancer drug development studies, especially on HCC. 40 terpenoids and flavonoids were chosen because of their anticancer properties given in the literature. In this study, the biological activity of molecules was also investigated with in silico structure-activity relationship analysis. In addition, interactions between a series of terpenoids and flavonoids and the BRPF1 protein were investigated by molecular docking and molecular dynamics simulations. The energy change caused by the interactions of BRPF1 with different compounds was also evaluated by MM/PBSA calculations. It has been revealed that compound 5 (-9.2 kcal/mol), a kind of secoclerodane type diterpenoid, has a higher affinity both compared to other flavonoids and terpenoids, and 9F9 (-7.9 kcal/mol), a selective BRPF1 inhibitor. The study presented in this article demonstrates that compound 5, as a natural product, could form a chemical scaffold for the development of selective BRPF1 bromodomain inhibitors.

Artificial intelligence analysis to explore synchronize exercise, cobalamin, and magnesium as new actors to therapeutic of migraine symptoms: a randomized, placebo-controlled trial

INTRODUCTION

Migraine is recognized as a complex neurological disorder that has imposed a social burden. We assessed the signaling pathways and molecular mechanisms based on the in silico analysis and predicted drug candidates by the biomedicine approach. Moreover, we evaluated high-intensity interval training and vitamin B12 + magnesium on women's migraine attacks and inflammatory status.

METHODS

This study computed differential gene expression in migraine syndrome and the dimension network parameters visualized by software. Moreover, we proposed the functional mechanism and binding energy of essential micronutrients on macromolecules based on drug discovery. In this clinical trial, 60 cases were randomized to four groups, including applied high-intensity interval training (HIIT), cases consumed supplementation vitamin B12 and magnesium (Supp), cases applied high-intensity interval training, and consumed supplementation (HIIT + Supp), and migraine cases for 2 months. Serum levels of calcitonin gene-related peptide (CGRP) were measured at baseline and at the end of the study. In addition, migraine disability assessment score (MIDAS), frequency, intensity, and duration were recorded before and during interventions.

RESULTS

In silico study revealed the association between inflammation signaling pathways and pathogenesis of migraine attacks as a remarkable pathomechanism in this disorder. Furthermore, serum concentrations of CGRP were significantly declined in the HIIT + Supp compared with other groups. In addition, MIDAS, frequency, intensity, and duration were reduced in the HIIT + Supp group compared with the other groups.

CONCLUSION

We found that the synergistic effects of cobalamin and magnesium followed by regular exercise could silence the inflammation signaling pathway, and a combination of HIIT + Supp could ameliorate migraine pain.

TRIAL REGISTRATION

This study was registered in the Iranian Registry of Clinical Trials; IRCT code: IRCT20170510033909N12. Approval Data: 2021/06/02.

De novo design of discrete, stable 310-helix peptide assemblies

The α-helix is pre-eminent in structural biology¹ and widely exploited in protein folding², design³ and engineering⁴. Although other helical peptide conformations do exist near to the α-helical region of conformational space-namely, 3₁₀-helices and π-helices⁵-these occur much less frequently in protein structures. Less favourable internal energies and reduced tendencies to pack into higher-order structures mean that 3₁₀-helices rarely exceed six residues in length in natural proteins, and that they tend not to form normal supersecondary, tertiary or quaternary interactions. Here we show that despite their absence in nature, synthetic peptide assemblies can be built from 3₁₀-helices. We report the rational design, solution-phase characterization and an X-ray crystal structure for water-soluble bundles of 3₁₀-helices with consolidated hydrophobic cores. The design uses six-residue repeats informed by analysing 3₁₀-helical conformations in known protein structures, and incorporates α-aminoisobutyric acid residues. Design iterations reveal a tipping point between α-helical and 3₁₀-helical folding, and identify features required for stabilizing assemblies of 3₁₀-helices. This work provides principles and rules to open opportunities for designing into this hitherto unexplored region of protein-structure space.

A closed Candidatus Odinarchaeum chromosome exposes Asgard archaeal viruses

Asgard archaea have recently been identified as the closest archaeal relatives of eukaryotes. Their ecology, and particularly their virome, remain enigmatic. We reassembled and closed the chromosome of Candidatus Odinarchaeum yellowstonii LCB_4, through long-range PCR, revealing CRISPR spacers targeting viral contigs. We found related viruses in the genomes of diverse prokaryotes from geothermal environments, including other Asgard archaea. These viruses open research avenues into the ecology and evolution of Asgard archaea.

Identification of Putative Plant-Based ALR-2 Inhibitors to Treat Diabetic Peripheral Neuropathy

Diabetic peripheral neuropathy (DPN) is a common diabetes complication (DM). Aldose reductase -2 (ALR-2) is an oxidoreductase enzyme that is most extensively studied therapeutic target for diabetes-related complications that can be inhibited by epalrestat, which has severe adverse effects; hence the discovery of potent natural inhibitors is desired. In response, a pharmacophore model based on the properties of eplarestat was generated. The specified pharmacophore model searched the NuBBE_DB database of natural compounds for prospective lead candidates. To assess the drug-likeness and ADMET profile of the compounds, a series of in silico filtering procedures were applied. The compounds were then put through molecular docking and interaction analysis. In comparison to the reference drug, four compounds showed increased binding affinity and demonstrated critical residue interactions with greater stability and specificity. As a result, we have identified four potent inhibitors: ZINC000002895847, ZINC000002566593, ZINC000012447255, and ZINC000065074786, that could be used as pharmacological niches to develop novel ALR-2 inhibitors.

Discovery of Anticancer Activity of Amentoflavone on Esophageal Squamous Cell Carcinoma: Bioinformatics, Structure-Based Virtual Screening, and Biological Evaluation

Esophageal squamous cell carcinoma (ESCC) is the most common primary esophageal malignancy with poor prognosis. Here, due to the necessity for exploring potential therapies against ESCC, we obtained the gene expression data on ESCC from the TCGA and GEO databases. Venn diagram analysis was applied to identify common targets. The protein-protein interaction network was constructed by Cytoscape software, and the hub targets were extracted from the network via cytoHubba. The potential hub nodes as drug targets were found by pharmacophore-based virtual screening and molecular modeling, and the antitumor activity was evaluated through in vitro studies. A total of 364 differentially expressed genes (DEGs) in ESCC were identified. Pathway enrichment analyses suggested that most DEGs were mainly involved in the cell cycle. Three hub targets were retrieved, including CENPF, CCNA2 (cyclin A), and CCNB1 (cyclin B1), which were highly expressed in esophageal cancer and associated with prognosis. Moreover, amentoflavone, a promising drug candidate found by pharmacophore-based virtual screening, showed antiproliferative and proapoptotic effects and induced G1 in esophageal squamous carcinoma cells. Taken together, our findings suggested that amentoflavone could be a potential cell cycle inhibitor targeting cyclin B1, and is therefore expected to serve as a great therapeutic agent for treating esophageal squamous cell carcinoma.

LPI-CSFFR: Combining serial fusion with feature reuse for predicting LncRNA-protein interactions

Long non-coding RNAs (LncRNAs) play important roles in a series of life activities, and they function primarily with proteins. The wet experimental-based methods in lncRNA-protein interactions (lncRPIs) study are time-consuming and expensive. In this study, we propose for the first time a novel feature fusion method, the LPI-CSFFR, to train and predict LncRPIs based on a Convolutional Neural Network (CNN) with feature reuse and serial fusion in sequences, secondary structures, and physicochemical properties of proteins and lncRNAs. The experimental results indicate that LPI-CSFFR achieves excellent performance on the datasets RPI1460 and RPI1807 with an accuracy of 83.7 % and 98.1 %, respectively. We further compare LPI-CSFFR with the state-of-the-art existing methods on the same benchmark datasets to evaluate the performance. In addition, to test the generalization performance of the model, we independently test sample pairs of five model organisms, where Mus musculus are the highest prediction accuracy of 99.5 %, and we find multiple hotspot proteins after constructing an interaction network. Finally, we test the predictive power of the LPI-CSFFR for sample pairs with unknown interactions. The results indicate that LPI-CSFFR is promising for predicting potential LncRPIs. The relevant source code and the data used in this study are available at https://github.com/JianjunTan-Beijing/LPI-CSFFR.

Compositional Bias of Intrinsically Disordered Proteins and Regions and Their Predictions

Intrinsically disordered regions (IDRs) carry out many cellular functions and vary in length and placement in protein sequences. This diversity leads to variations in the underlying compositional biases, which were demonstrated for the short vs. long IDRs. We analyze compositional biases across four classes of disorder: fully disordered proteins; short IDRs; long IDRs; and binding IDRs. We identify three distinct biases: for the fully disordered proteins, the short IDRs and the long and binding IDRs combined. We also investigate compositional bias for putative disorder produced by leading disorder predictors and find that it is similar to the bias of the native disorder. Interestingly, the accuracy of disorder predictions across different methods is correlated with the correctness of the compositional bias of their predictions highlighting the importance of the compositional bias. The predictive quality is relatively low for the disorder classes with compositional bias that is the most different from the “generic” disorder bias, while being much higher for the classes with the most similar bias. We discover that different predictors perform best across different classes of disorder. This suggests that no single predictor is universally best and motivates the development of new architectures that combine models that target specific disorder classes.

Seafood Paramyosins as Sources of Anti-Angiotensin-Converting-Enzyme and Anti-Dipeptidyl-Peptidase Peptides after Gastrointestinal Digestion: A Cheminformatic Investigation

Paramyosins, muscle proteins occurring exclusively in invertebrates, are abundant in seafoods. The potential of seafood paramyosins (SP) as sources of anti-angiotensin-converting-enzyme (ACE) and anti-dipeptidyl-peptidase (DPP-IV) peptides is underexplored. This in silico study investigated the release of anti-ACE and anti-DPP-IV peptides from SP after gastrointestinal (GI) digestion. We focused on SP of the common octopus, Humboldt squid, Japanese abalone, Japanese scallop, Mediterranean mussel, Pacific oyster, sea cucumber, and Whiteleg shrimp. SP protein sequences were digested on BIOPEP-UWM, followed by identification of known anti-ACE and anti-DPP-IV peptides liberated. Upon screening for high-GI-absorption, non-allergenicity, and non-toxicity, shortlisted peptides were analyzed via molecular docking and dynamic to elucidate mechanisms of interactions with ACE and DPP-IV. Potential novel anti-ACE and anti-DPP-IV peptides were predicted by SwissTargetPrediction. Physicochemical and pharmacokinetics of peptides were predicted with SwissADME. GI digestion liberated 2853 fragments from SP. This comprised 26 known anti-ACE and 53 anti-DPP-IV peptides exhibiting high-GI-absorption, non-allergenicity, and non-toxicity. SwissTargetPrediction predicted three putative anti-ACE (GIL, DL, AK) and one putative anti-DPP-IV (IAL) peptides. Molecular docking found most of the anti-ACE peptides may be non-competitive inhibitors, whereas all anti-DPP-IV peptides likely competitive inhibitors. Twenty-five nanoseconds molecular dynamics simulation suggests the stability of these screened peptides, including the three predicted anti-ACE and one predicted anti-DPP-IV peptides. Seven dipeptides resembling approved oral-bioavailable peptide drugs in physicochemical and pharmacokinetic properties were revealed: AY, CF, EF, TF, TY, VF, and VY. In conclusion, our study presented in silico evidence for SP being a promising source of bioavailable and safe anti-ACE and anti-DPP-IV peptides following GI digestions.

AlphaFold2 and RoseTTAFold predict posttranslational modifications. Chromophore formation in GFP-like proteins

AlphaFold2 and RoseTTAfold are able to predict, based solely on their sequence whether GFP-like proteins will post-translationally form a chromophore (the part of the protein responsible for fluorescence) or not. Their training has not only taught them protein structure and folding, but also chemistry. The structures of 21 sequences of GFP-like fluorescent proteins that will post-translationally form a chromophore and of 23 GFP-like non-fluorescent proteins that do not have the residues required to form a chromophore were determined by AlphaFold2 and RoseTTAfold. The resultant structures were mined for a series of geometric measurements that are crucial to chromophore formation. Statistical analysis of these measurements showed that both programs conclusively distinguished between chromophore forming and non-chromophore forming proteins. A clear distinction between sequences capable of forming a chromophore and those that do not have the residues required for chromophore formation can be obtained by examining a single measurement—the RMSD of the overlap of the central alpha helices of the crystal structure of S65T GFP and the AlphaFold2 determined structure. Only 10 of the 578 GFP-like proteins in the pdb have no chromophore, yet when AlphaFold2 and RoseTTAFold are presented with the sequences of 44 GFP-like proteins that are not in the pdb they fold the proteins in such a way that one can unequivocally distinguish between those that can and cannot form a chromophore.

PDBx/mmCIF Ecosystem: Foundational Semantic Tools for Structural Biology

PDBx/mmCIF, Protein Data Bank Exchange (PDBx) macromolecular Crystallographic Information Framework (mmCIF), has become the data standard for structural biology. With its early roots in the domain of small-molecule crystallography, PDBx/mmCIF provides an extensible data representation that is used for deposition, archiving, remediation, and public dissemination of experimentally determined three-dimensional (3D) structures of biological macromolecules by the Worldwide Protein Data Bank (wwPDB, wwpdb.org). Extensions of PDBx/mmCIF are similarly used for computed structure models by ModelArchive (modelarchive.org), integrative/hybrid structures by PDB-Dev (pdb-dev.wwpdb.org), small angle scattering data by Small Angle Scattering Biological Data Bank SASBDB (sasbdb.org), and for models computed generated with the AlphaFold 2.0 deep learning software suite (alphafold.ebi.ac.uk). Community-driven development of PDBx/mmCIF spans three decades, involving contributions from researchers, software and methods developers in structural sciences, data repository providers, scientific publishers, and professional societies. Having a semantically rich and extensible data framework for representing a wide range of structural biology experimental and computational results, combined with expertly curated 3D biostructure data sets in public repositories, accelerates the pace of scientific discovery. Herein, we describe the architecture of the PDBx/mmCIF data standard, tools used to maintain representations of the data standard, governance, and processes by which data content standards are extended, plus community tools/software libraries available for processing and checking the integrity of PDBx/mmCIF data. Use cases exemplify how the members of the Worldwide Protein Data Bank have used PDBx/mmCIF as the foundation for its pipeline for delivering Findable, Accessible, Interoperable, and Reusable (FAIR) data to many millions of users worldwide.

Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing

Revolutionary breakthroughs in artificial intelligence (AI) and machine learning (ML) have had a profound impact on a wide range of scientific disciplines, including the development of artificial cell factories for biomanufacturing. In this paper, we review the latest studies on the application of data-driven methods for the design of new proteins, pathways, and strains. We first briefly introduce the various types of data and databases relevant to industrial biomanufacturing, which are the basis for data-driven research. Different types of algorithms, including traditional ML and more recent deep learning methods, are also presented. We then demonstrate how these data-based approaches can be applied to address various issues in cell factory development using examples from recent studies, including the prediction of protein function, improvement of metabolic models, and estimation of missing kinetic parameters, design of non-natural biosynthesis pathways, and pathway optimization. In the last section, we discuss the current limitations of these data-driven approaches and propose that data-driven methods should be integrated with mechanistic models to complement each other and facilitate the development of synthetic strains for industrial biomanufacturing.

Venus: Elucidating the Impact of Amino Acid Variants on Protein Function Beyond Structure Destabilisation

Exploring the functional effect of a non-synonymous coding variant at the protein level requires multiple pieces of information to be interpreted appropriately. This is particularly important when embarking on the study of a potentially pathogenic variant linked to a rare or monogenic disease. Whereas accurate protein stability predictions alone are generally informative, other effects, such as disruption of post-translational modifications or weakened ligand binding, may also contribute to the disease phenotype. Furthermore, consideration of nearby variants that are found in the healthy population may strengthen or refute a given mechanistic hypothesis. Whilst there are several bioinformatics tools available that score a genetic variant in terms of deleteriousness, there is no single tool that assembles multiple effects of a variant on the encoded protein, beyond structural stability, and presents them on the structure for inspection. Venus is a web application which, given a protein substitution, rapidly estimates the predicted effect on protein stability of the variant, flags if the variant affects a post-translational modification site, a predicted linear motif or known annotation, and determines the effect on protein stability of variants which affect nearby residues and have been identified in healthy populations. Venus is built upon Michelanglo and the results can be exported to it, allowing them to be annotated and shared with other researchers. Venus is freely accessible at https://venus.cmd.ox.ac.uk and its source code is openly available at https://github.com/CMD-Oxford/Michelanglo-and-Venus.

An Integrated Mass Spectrometry-Based Glycomics-Driven Glycoproteomics Analytical Platform to Functionally Characterize Glycosylation Inhibitors

Cancer progression is linked to aberrant protein glycosylation due to the overexpression of several glycosylation enzymes. These enzymes are underexploited as potential anticancer drug targets and the development of rapid-screening methods and identification of glycosylation inhibitors are highly sought. An integrated bioinformatics and mass spectrometry-based glycomics-driven glycoproteomics analysis pipeline was performed to identify an N-glycan inhibitor against lung cancer cells. Combined network pharmacology and in silico screening approaches were used to identify a potential inhibitor, pictilisib, against several glycosylation-related proteins, such as Alpha1-6FucT, GlcNAcT-V, and Alpha2,6-ST-I. A glycomics assay of lung cancer cells treated with pictilisib showed a significant reduction in the fucosylation and sialylation of N-glycans, with an increase in high mannose-type glycans. Proteomics analysis and in vitro assays also showed significant upregulation of the proteins involved in apoptosis and cell adhesion, and the downregulation of proteins involved in cell cycle regulation, mRNA processing, and protein translation. Site-specific glycoproteomics analysis further showed that glycoproteins with reduced fucosylation and sialylation were involved in apoptosis, cell adhesion, DNA damage repair, and chemical response processes. To determine how the alterations in N-glycosylation impact glycoprotein dynamics, modeling of changes in glycan interactions of the ITGA5-ITGB1 (Integrin alpha 5-Integrin beta-1) complex revealed specific glycosites at the interface of these proteins that, when highly fucosylated and sialylated, such as in untreated A549 cells, form greater hydrogen bonding interactions compared to the high mannose-types in pictilisib-treated A549 cells. This study highlights the use of mass spectrometry to identify a potential glycosylation inhibitor and assessment of its impact on cell surface glycoprotein abundance and protein-protein interaction.

The functional mechanisms of synchronizing royal jelly consumption and physical activity on rat with multiple sclerosis-like behaviors hallmarks based on bioinformatics analysis, and experimental survey

Background

Natural nutrition and physical training have been defined as non-pharmacochemical complementary and alternative medicines to prevent and treat various pathogenesis. Royal jelly possesses various pharmacological properties and is an effective therapeutic supplement for halting neurodegeneration. Multiple sclerosis is a prevalent neurodegenerative disorder that manifests as a progressive neurological condition. Inflammation, hypoxia, and oxidative stress have been identified as significant hallmarks of multiple sclerosis pathology.

Results

In the present study, based on artificial intelligence and bioinformatics algorithms, we marked hub genes, molecular signaling pathways, and molecular regulators such as non-coding RNAs involved in multiple sclerosis. Also, microRNAs as regulators can affect gene expression in many processes. Numerous pathomechanisms, including immunodeficiency, hypoxia, oxidative stress, neuroinflammation, and mitochondrial dysfunction, can play a significant role in the MSc pathogenesis that results in demyelination. Furthermore, we computed the binding affinity of bioactive compounds presented in Royal Jelly on macromolecules surfaces. Also, we predicted the alignment score of bioactive compounds over the pharmacophore model of candidate protein as a novel therapeutic approach. Based on the q-RT-PCR analysis, the expression of the Dnajb1/Dnajb1/Foxp1/Tnfsf14 and Hspa4 networks as well as miR-34a-5p and miR155-3p were regulated by the interaction of exercise training and 100 mg/kg Royal Jelly (ET-100RJ). Interestingly, characteristics, motor function, a proinflammatory cytokine, and demyelination were ameliorated by ET-100RJ.

Discussion

Here, we indicated that interaction between exercise training and 100 mg/kg Royal jelly had a more effect on regulating the microRNA profiles and hub genes in rats with Multiple sclerosis.

Gene Differential Expression and Interaction Networks Illustrate the Biomarkers and Molecular Biological Mechanisms of Unsaponifiable Matter in Kanglaite Injection for Pancreatic Ductal Adenocarcinoma

Background

Kanglaite injection (KLTi) has shown good clinical efficacy in the treatment of pancreatic ductal adenocarcinoma (PDAC). While previous studies have demonstrated the antitumor effects of the oil compounds in KLTi, it is unclear whether the unsaponifiable matter (USM) also has antitumor effects. This study used network pharmacology, molecular docking, and database verification methods to investigate the molecular biological mechanisms of USM.

Methods

Compounds of USM were obtained from GC-MS, and targets from DrugBank. Next, the GEO database was searched for differentially expressed genes in cancerous tissues and healthy tissues of PDAC to identify targets. Subsequently, the protein-protein interaction of USM and PDAC targets was constructed by BisoGenet to extract candidate genes. The candidate genes were enriched using GO and KEGG by Metascape, and the gene-pathway network was constructed to screen the key genes. Molecular docking and molecular dynamic simulations of core compound targets were finally performed and to explore the diagnostic, survival, and prognosis value of targets.

Results

A total of 10 active compounds and 36 drug targets were screened for USM, 919 genes associated with PDAC, and 139 USM candidate genes against PDAC were excavated. The enrichment predicted USM by acting on RELA, NFKB1, IKBKG, JUN, MAPK1, TP53, and AKT1. Molecular docking and dynamic simulations confirmed the screened core targets had good affinity and stability with the corresponding compounds. In diagnostic ROC validation, the above targets have certain accuracy for diagnosing PDAC, and the combined diagnosis is more advantageous. As the most diagnostic value of RELA, it is equally significant in predicting disease-specific survival and progression-free interval.

Conclusions

USM in KLTi plays an anti-PDAC role by intervening in the cell cycle, inducing apoptosis, and downregulating the NF-κB, MAPK, and PI3K-Akt pathways. It might participate in the pancreatic cancer pathway, and core target groups have diagnostic, survival, and prognosis value biomarker significance.

Comprehensive characterization of posttranscriptional impairment-related 3'-UTR mutations in 2413 whole genomes of cancer patients

The 3' untranslated region (3'-UTR) is the vital element regulating gene expression, but most studies have focused on variations in RNA-binding proteins (RBPs), miRNAs, alternative polyadenylation (APA) and RNA modifications. To explore the posttranscriptional function of 3'-UTR somatic mutations in tumorigenesis, we collected whole-genome data from 2413 patients across 18 cancer types. Our updated algorithm, PIVar, revealed 25,216 3'-UTR posttranscriptional impairment-related SNVs (3'-UTR piSNVs) spanning 2930 genes; 24 related RBPs were significantly enriched. The somatic 3'-UTR piSNV ratio was markedly increased across all 18 cancer types, which was associated with worse survival for four cancer types. Several cancer-related genes appeared to facilitate tumorigenesis at the protein and posttranscriptional regulation levels, whereas some 3'-UTR piSNV-affected genes functioned mainly via posttranscriptional mechanisms. Moreover, we assessed immune cell and checkpoint characteristics between the high/low 3'-UTR piSNV ratio groups and predicted 80 compounds associated with the 3'-UTR piSNV-affected gene expression signature. In summary, our study revealed the prevalence and clinical relevance of 3'-UTR piSNVs in cancers, and also demonstrates that in addition to affecting miRNAs, 3'-UTR piSNVs perturb RBPs binding, APA and m6A RNA modification, which emphasized the importance of considering 3'-UTR piSNVs in cancer biology.

New aspects of DNA recognition by group II WRKY transcription factor revealed by structural and functional study of AtWRKY18 DNA binding domain

WRKY transcription factors (TFs) constitute one of the largest families of plant TFs. Based on the organization of domains and motifs, WRKY TFs are divided into three Groups (I-III). The WRKY subgroup IIa includes three representatives in A. thaliana, AtWRKY18, AtWRKY40, and AtWRKY60, that participate in biotic and abiotic stress responses. Here we present crystal structures of the DNA binding domain (DBD) of AtWRKY18 alone and in the complex with a DNA duplex containing the WRKY-recognition sequence, W-box. Subgroup IIa WRKY TFs are known to form homo and heterodimers. Our data suggest that the dimerization interface of the full-length AtWRKY18 involves contacts between the DBD subunits. DNA binding experiments and structural analysis point out novel aspects of DNA recognition by WRKY TFs. In particular, AtWRKY18-DBD preferentially binds an overlapping tandem of W-boxes accompanied by a quasi-W-box motif. The binding of DNA deforms the B-type double helix, which suggests that the DNA fragment must be prone to form a specific structure. This can explain why despite the short W-box consensus, WRKY TFs can precisely control gene expression. Finally, this first experimental structure of a Group II WRKY TF allowed us to compare Group I-III representatives.

SLiMAn: An Integrative Web Server for Exploring Short Linear Motif-Mediated Interactions in Interactomes

Cells express thousands of macromolecules, and their functioning relies on multiple networks of intermolecular interactions. These interactions can be experimentally determined at different spatial and temporal resolutions. But, physical interfaces are not often delineated directly, especially in high-throughput experiments. A large fraction of protein-protein interactions involves domain and so-called SLiMs (for Short Linear Motifs). Often, SLiMs lie in disordered regions or loops. Their small size, limited sequence conservation, and loosely folded nature prevent straightforward detection. SLiMAn (Short Linear Motif Analysis), a new web server, is provided to help thorough analysis of interactomics data. From a list of putative interactants (e.g., output from an interactomics study), SLiMs (from ELM) and SLiM-recognition domains (from Pfam) are extracted, and putative pairings are displayed. Predicted results can be filtered using motif E-values, IUPred2 scores, or BioGRID interaction matches. When structural templates are available, a given SLiM and its recognition domain can be modeled using SCWRL. We illustrate here the use of SLiMAn on distinct examples, including one real-case study. We oversee wide-range applications for SLiMAn in the context of the massive analysis of protein-protein interactions. This new web server is made freely available at https://sliman.cbs.cnrs.fr.

AlphaFold illuminates half of the dark human proteins

We investigate the use of confidence scores to evaluate the accuracy of a given AlphaFold (AF2) protein model for drug discovery. Prediction of accuracy is improved by not considering confidence scores below 80 due to the effects of disorder. On a set of recent crystal structures, 95% are likely to have accurate folds. Conformational discordance in the training set has a much more significant effect on accuracy than sequence divergence. We propose criteria for models and residues that are possibly useful for virtual screening. Based on these criteria, AF2 provides models for half of understudied (dark) human proteins and two-thirds of residues in those models.

O-methylated flavonol as a multi-kinase inhibitor of leukemogenic kinases exhibits a potential treatment for acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is a heterogeneous disease with poor overall survival characterized by various genetic changes. The continuous activation of oncogenic pathways leads to the development of drug resistance and limits current therapeutic efficacy. Therefore, a multi-targeting inhibitor may overcome drug resistance observed in AML treatment. Recently, groups of flavonoids, such as flavones and flavonols, have been shown to inhibit a variety of kinase activities, which provides potential opportunities for further anticancer applications.

PURPOSE

In this study, we evaluated the anticancer effects of flavonoid compounds collected from our in-house library and investigated their potential anticancer mechanisms by targeting multiple kinases for inhibition in AML cells.

METHODS

The cytotoxic effect of the compounds was detected by cell viability assays. The kinase inhibitory activity of the selected compound was detected by kinase-based and cell-based assays. The binding conformation and interactions were investigated by molecular docking analysis. Flow cytometry was used to evaluate the cell cycle distribution and cell apoptosis. The protein and gene expression were estimated by western blotting and qPCR, respectively.

RESULTS

In this study, an O-methylated flavonol (compound 11) was found to possess remarkable cytotoxic activity against AML cells compared to treatment in other cancer cell lines. The compound was demonstrated to act against multiple kinases, which play critical roles in survival signaling in AML, including FLT3, MNK2, RSK, DYRK2 and JAK2 with IC₅₀ values of 1 - 2 μM. Compared to our previous flavonoid compounds, which only showed inhibitions against MNKs or FLT3, compound 11 exhibited multiple kinase inhibitory abilities. Moreover, compound 11 showed effectiveness in inhibiting internal tandem duplications of FLT3 (FLT3-ITDs), which accounts for 25% of AML cases. The interactions between compound 11 and targeted kinases were investigated by molecular docking analysis. Mechanically, compound 11 caused dose-dependent accumulation of leukemic cells at the G₀/G₁ phase and followed by the cells undergoing apoptosis.

CONCLUSION

O-methylated flavonol, compound 11, can target multiple kinases, which may provide potential opportunities for the development of novel therapeutics for drug-resistant AMLs. This work provides a good starting point for further compound optimization.

From systems to structure - using genetic data to model protein structures

Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical-genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.

Synchronized resistance training and bioactive herbal compounds of Tribulus Terrestris reverse the disruptive influence of Stanozolol

Androgenic-Anabolic Steroids (AAS) consumption may have irreversible effects on athletes' hearts. The beneficial effects of Tribulus Terrestris (TT) have been shown to reduce cardiovascular risks through disruption in apoptosome complex construction. Therefore, this study aimed to investigate the effect of eight weeks of resistance training (RT) with TT consumption in the heart tissue of rats exposed to Stanozolol. Thirty-five male rats were divided into seven groups, Control group, Stanozolol (ST), ST + 100 mg/kg TT, ST + 50 mg/kg TT, RT + ST, RT + ST + 100 mg/kg TT, and RT + ST + 50 mg/kg TT. Differential genes expression was measured by q-RT-PCR. Artificial intelligence highlighted apoptosis pathways as a vital process in cardiovascular risks. Hence, we estimated the binding affinity of chemical and bioactive molecules on the cut point hub gene by pharmacophore modeling and molecular docking. Moreover, ST increased IL-6, Cat, Aif-1, and Caspase-9. 100 mg/kg TT has a more favorable effect than 50 mg/kg T. Also, RT with TT had interactive effects on reducing IL-6, Cat, Aif-1, and Caspase-9. RT and TT consumption seemed to synergistically reduce the apoptotic pathway markers in the heart tissue of rats exposed to the supra-physiologic dose of ST. Moreover, TT could be added to supplements and sports drink to increase an athlete's performance.

Point-specific interactions of isovitexin with the neighboring amino acid residues of the hACE2 receptor as a targeted therapeutic agent in suppressing the SARS-CoV-2 influx mechanism

Objective:

Despite the development of several vaccines against severe acute respiratory syndrome coronavirus-2, the need for an additional prophylactic agent is evident. In recent in silico studies, isovitexin exhibited a higher binding affinity against the human angiotensin converting-enzyme 2 (hACE2) receptor than existing antiviral drugs. The research aimed to find out the point specificity of isovitexin for the hACE2 receptor and to assess its therapeutic potential, depending on the stability of the isovitexin–hACE2 complex.

Materials and Methods:

The pharmacokinetic profile of isovitexin was analyzed. The crystal structure of the hACE2 receptor and the ligand isovitexin were docked to form a ligand–protein complex following molecular optimization. To determine the isovitexin–hACE2 complex stability, their binding affinity, hydrogen bonding, and hydrophobic interactions were studied. Lastly, the root mean square deviation (RMSD), root mean square fluctuation, solvent accessible surface area, molecular surface area, radius of gyration (Rg), polar surface area, and principal component analysis values were found by simulating the complex with molecular dynamic (MD).

Results:

The predicted Lethal dose₅₀ for isovitexin was 2.56 mol/kg, with an acceptable maximum tolerated dose and no hepatotoxicity or AMES toxicity. Interactions with the amino acid residues Thr371, Asp367, Glu406, Pro346, His345, Phe274, Tyr515, Glu375, Thr347, Glu402, and His374 of the hACE2 protein were required for the high binding affinity and specificity of isovitexin. Based on what was learned from the MD simulation, the hACE2 receptor-blocking properties of isovitexin were looked at.

Conclusions:

Isovitexin is a phytochemical with a reasonable bioactivity and safety profile for use in humans, and it can potentially be used as a hACE2-specific therapeutic to inhibit COVID-19 infection.

Folding and Evolution of a Repeat Protein on the Ribosome

Life on earth is the result of the work of proteins, the cellular nanomachines that fold into elaborated 3D structures to perform their functions. The ribosome synthesizes all the proteins of the biosphere, and many of them begin to fold during translation in a process known as cotranslational folding. In this work we discuss current advances of this field and provide computational and experimental data that highlight the role of ribosome in the evolution of protein structures. First, we used the sequence of the Ankyrin domain from the Drosophila Notch receptor to launch a deep sequence-based search. With this strategy, we found a conserved 33-residue motif shared by different protein folds. Then, to see how the vectorial addition of the motif would generate a full structure we measured the folding on the ribosome of the Ankyrin repeat protein. Not only the on-ribosome folding data is in full agreement with classical in vitro biophysical measurements but also it provides experimental evidence on how folded proteins could have evolved by duplication and fusion of smaller fragments in the RNA world. Overall, we discuss how the ribosomal exit tunnel could be conceptualized as an active site that is under evolutionary pressure to influence protein folding.

Promising Acinetobacter baumannii Vaccine Candidates and Drug Targets in Recent Years

In parallel to the uncontrolled use of antibiotics, the emergence of multidrug-resistant bacteria, like Acinetobacter baumannii, has posed a severe threat. A. baumannii predominates in the nosocomial setting due to its ability to persist in hospitals and survive antibiotic treatment, thereby eventually leading to an increasing prevalence and mortality due to its infection. With the increasing spectra of drug resistance and the incessant collapse of newly discovered antibiotics, new therapeutic countermeasures have been in high demand. Hence, recent research has shown favouritism towards the long-term solution of designing vaccines. Therefore, being a realistic alternative strategy to combat this pathogen, anti-A. Baumannii vaccines research has continued unearthing various antigens with variable results over the last decade. Again, other approaches, including pan-genomics, subtractive proteomics, and reverse vaccination strategies, have shown promise for identifying promiscuous core vaccine candidates that resulted in chimeric vaccine constructs. In addition, the integration of basic knowledge of the pathobiology of this drug-resistant bacteria has also facilitated the development of effective multiantigen vaccines. As opposed to the conventional trial-and-error approach, incorporating the in silico methods in recent studies, particularly network analysis, has manifested a great promise in unearthing novel vaccine candidates from the A. baumannii proteome. Some studies have used multiple A. baumannii data sources to build the co-functional networks and analyze them by k-shell decomposition. Additionally, Whole Genomic Protein Interactome (GPIN) analysis has utilized a rational approach for identifying essential proteins and presenting them as vaccines effective enough to combat the deadly pathogenic threats posed by A. baumannii. Others have identified multiple immune nodes using network-based centrality measurements for synergistic antigen combinations for different vaccination strategies. Protein-protein interactions have also been inferenced utilizing structural approaches, such as molecular docking and molecular dynamics simulation. Similar workflows and technologies were employed to unveil novel A. baumannii drug targets, with a similar trend in the increasing influx of in silico techniques. This review integrates the latest knowledge on the development of A. baumannii vaccines while highlighting the in silico methods as the future of such exploratory research. In parallel, we also briefly summarize recent advancements in A. baumannii drug target research.

GrAfSS: a webserver for substructure similarity searching and comparisons in the structures of proteins and RNA

The GrAfSS (Graph theoretical Applications for Substructure Searching) webserver is a platform to search for three-dimensional substructures of: (i) amino acid side chains in protein structures; and (ii) base arrangements in RNA structures. The webserver interfaces the functions of five different graph theoretical algorithms – ASSAM, SPRITE, IMAAAGINE, NASSAM and COGNAC – into a single substructure searching suite. Users will be able to identify whether a three-dimensional (3D) arrangement of interest, such as a ligand binding site or 3D motif, observed in a protein or RNA structure can be found in other structures available in the Protein Data Bank (PDB). The webserver also allows users to determine whether a protein or RNA structure of interest contains substructural arrangements that are similar to known motifs or 3D arrangements. These capabilities allow for the functional annotation of new structures that were either experimentally determined or computationally generated (such as the coordinates generated by AlphaFold2) and can provide further insights into the diversity or conservation of functional mechanisms of structures in the PDB. The computed substructural superpositions are visualized using integrated NGL viewers. The GrAfSS server is available at http://mfrlab.org/grafss/.

Mechanisms of Cardiorenal Protection With SGLT2 Inhibitors in Patients With T2DM Based on Network Pharmacology

Purpose

Sodium-glucose cotransporter 2 (SGLT2) inhibitors have cardiorenal protective effects regardless of whether they are combined with type 2 diabetes mellitus, but their specific pharmacological mechanisms remain undetermined.

Materials and Methods

We used databases to obtain information on the disease targets of “Chronic Kidney Disease,” “Heart Failure,” and “Type 2 Diabetes Mellitus” as well as the targets of SGLT2 inhibitors. After screening the common targets, we used Cytoscape 3.8.2 software to construct SGLT2 inhibitors' regulatory network and protein-protein interaction network. The clusterProfiler R package was used to perform gene ontology functional analysis and Kyoto encyclopedia of genes and genomes pathway enrichment analyses on the target genes. Molecular docking was utilized to verify the relationship between SGLT2 inhibitors and core targets.

Results

Seven different SGLT2 inhibitors were found to have cardiorenal protective effects on 146 targets. The main mechanisms of action may be associated with lipid and atherosclerosis, MAPK signaling pathway, Rap1 signaling pathway, endocrine resistance, fluid shear stress, atherosclerosis, TNF signaling pathway, relaxin signaling pathway, neurotrophin signaling pathway, and AGEs-RAGE signaling pathway in diabetic complications were related. Docking of SGLT2 inhibitors with key targets such as GAPDH, MAPK3, MMP9, MAPK1, and NRAS revealed that these compounds bind to proteins spontaneously.

Conclusion

Based on pharmacological networks, this study elucidates the potential mechanisms of action of SGLT2 inhibitors from a systemic and holistic perspective. These key targets and pathways will provide new ideas for future studies on the pharmacological mechanisms of cardiorenal protection by SGLT2 inhibitors.

Drug Design by Pharmacophore and Virtual Screening Approach

Computer-aided drug discovery techniques reduce the time and the costs needed to develop novel drugs. Their relevance becomes more and more evident with the needs due to health emergencies as well as to the diffusion of personalized medicine. Pharmacophore approaches represent one of the most interesting tools developed, by defining the molecular functional features needed for the binding of a molecule to a given receptor, and then directing the virtual screening of large collections of compounds for the selection of optimal candidates. Computational tools to create the pharmacophore model and to perform virtual screening are available and generated successful studies. This article describes the procedure of pharmacophore modelling followed by virtual screening, the most used software, possible limitations of the approach, and some applications reported in the literature.

Role of genomics in combating COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) quickly swept over the world, becoming one of the most devastating outbreaks in human history. Being the first pandemic in the post-genomic era, advancements in genomics contributed significantly to scientific understanding and public health response to COVID-19. Genomic technologies have been employed by researchers all over the world to better understand the biology of SARS-CoV-2 and its origin, genomic diversity, and evolution. Worldwide genomic resources have greatly aided in the investigation of the COVID-19 pandemic. The pandemic has ushered in a new era of genomic surveillance, wherein scientists are tracking the changes of the SARS-CoV-2 genome in real-time at the international and national levels. Availability of genomic and proteomic information enables the rapid development of molecular diagnostics and therapeutics. The advent of high-throughput sequencing and genome editing technologies led to the development of modern vaccines. We briefly discuss the impact of genomics in the ongoing COVID-19 pandemic in this review.

The Search for Cryptic L-Rhamnosyltransferases on the Sporothrix schenckii Genome

The fungal cell wall is an attractive structure to look for new antifungal drug targets and for understanding the host-fungus interaction. Sporothrix schenckii is one of the main causative agents of both human and animal sporotrichosis and currently is the species most studied of the Sporothrix genus. The cell wall of this organism has been previously analyzed, and rhamnoconjugates are signature molecules found on the surface of both mycelia and yeast-like cells. Similar to other reactions where sugars are covalently linked to other sugars, lipids, or proteins, the rhamnosylation process in this organism is expected to involve glycosyltransferases with the ability to transfer rhamnose from a sugar donor to the acceptor molecule, i.e., rhamnosyltransferases. However, no obvious rhamnosyltransferase has thus far been identified within the S. schenckii proteome or genome. Here, using a Hidden Markov Model profile strategy, we found within the S. schenckii genome five putative genes encoding for rhamnosyltransferases. Expression analyses indicated that only two of them, named RHT1 and RHT2, were significantly expressed in yeast-like cells and during interaction with the host. These two genes were heterologously expressed in Escherichia coli, and the purified recombinant proteins showed rhamnosyltransferase activity, dependent on the presence of UDP-rhamnose as a sugar donor. To the best of our knowledge, this is the first report about rhamnosyltransferases in S. schenckii.

Physicochemical Heuristics for Identifying High Fidelity, Near-Native Structural Models of Peptide/MHC Complexes

There is long-standing interest in accurately modeling the structural features of peptides bound and presented by class I MHC proteins. This interest has grown with the advent of rapid genome sequencing and the prospect of personalized, peptide-based cancer vaccines, as well as the development of molecular and cellular therapeutics based on T cell receptor recognition of peptide-MHC. However, while the speed and accessibility of peptide-MHC modeling has improved substantially over the years, improvements in accuracy have been modest. Accuracy is crucial in peptide-MHC modeling, as T cell receptors are highly sensitive to peptide conformation and capturing fine details is therefore necessary for useful models. Studying nonameric peptides presented by the common class I MHC protein HLA-A*02:01, here we addressed a key question common to modern modeling efforts: from a set of models (or decoys) generated through conformational sampling, which is best? We found that the common strategy of decoy selection by lowest energy can lead to substantial errors in predicted structures. We therefore adopted a data-driven approach and trained functions capable of predicting near native decoys with exceptionally high accuracy. Although our implementation is limited to nonamer/HLA-A*02:01 complexes, our results serve as an important proof of concept from which improvements can be made and, given the significance of HLA-A*02:01 and its preference for nonameric peptides, should have immediate utility in select immunotherapeutic and other efforts for which structural information would be advantageous.

TeachOpenCADD 2022: open source and FAIR Python pipelines to assist in structural bioinformatics and cheminformatics research

Computational pipelines have become a crucial part of modern drug discovery campaigns. Setting up and maintaining such pipelines, however, can be challenging and time-consuming—especially for novice scientists in this domain. TeachOpenCADD is a platform that aims to teach domain-specific skills and to provide pipeline templates as starting points for research projects. We offer Python-based solutions for common tasks in cheminformatics and structural bioinformatics in the form of Jupyter notebooks, based on open source resources only. Including the 12 newly released additions, TeachOpenCADD now contains 22 notebooks that cover both theoretical background as well as hands-on programming. To promote reproducible and reusable research, we apply software best practices to our notebooks such as testing with automated continuous integration and adhering to the idiomatic Python style. The new TeachOpenCADD website is available at https://projects.volkamerlab.org/teachopencadd and all code is deposited on GitHub.

Mechanism of Ba Zhen Tang Delaying Skin Photoaging Based on Network Pharmacology and Molecular Docking

Purpose

To study the efficacy of Ba Zhen Tang in delaying skin photoaging and its potential mechanism based on network pharmacology and molecular docking.

Methods

First, we screened the active components and targets of Ba Zhen Tang by Traditional Chinese Medicine Database and Analysis Platform (TCMSP) and The Universal Protein Resource (UniProt). The target genes of skin photoaging were obtained from GeneCards and GeneMap database. Then, we analyzed the protein–protein interaction (PPI) by STRING database. The network map was constructed by Cytoscape. Finally, we performed Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis by Metascape database. The molecular docking via Autodock Vina and Pymol. Furthermore, skin photoaging cellular models were established, and the effects of Ba Zhen Tang on ameliorating skin photoaging were investigated.

Results

A total of 160 active ingredients in Ba Zhen Tang and 60 targets of Ba Zhen Tang for delaying skin photoaging were identified. By GO enrichment analysis, 1153 biological process entries, 45 cellular component entries and 89 molecular functional entries were obtained. A total of 155 signal pathways were obtained by KEGG analysis. Ba Zhen Tang is related to MAPK signaling pathway, TNF signaling pathway and AGE-RAGE signaling pathway in diabetic complications, etc., which directly affect the key nodes of photoaging. The molecular docking results showed that there was a certain affinity between the main compounds (kaempferol, quercetin, β-sitosterol, naringenin) and core target genes (PTGS2, CASP3, MAPK1, MAPK3, TP53). Ba Zhen Tang-treated mouse serum inhibited the senescence and p16^INK4a expression of human immortalized keratinocyte (HaCaT) cells irradiated by ultraviolet-B (UVB).

Conclusion

Our study elucidated the potential pharmacological mechanism of Ba Zhen Tang in the treatment of photoaging through multiple targets and pathways. The therapeutic effects of Ba Zhen Tang on skin photoaging were validated in cellular models.

The Gamification of XFEL Education Using XFEL Crystal Blaster

Novel groundbreaking techniques, such as serial femtosecond crystallography (SFX), which utilizes X-ray free-electron lasers (XFELs), have led to impressive advances in the field of structural biology. However, educating the next generation of scientists on this complex, advanced, and continuously evolving field can be challenging. Gamification has been shown to be an effective strategy for engaging new learners and has a positive influence on knowledge acquisition, student satisfaction, and motivation. Here, we present an educational game, XFEL Crystal Blaster, aimed at increasing middle and high school students’ exposure to advanced topics in crystallography. This simple and accessible game is available on multiple platforms, is intuitive for gamers, and requires no prior knowledge of the game’s content. The assessment of students’ experiences with the game suggests that the XFEL Crystal Blaster game is likely to develop some introductory knowledge of XFELs and X-ray crystallography and increase interest in learning more about X-ray crystallography. Both of these outcomes are key to engaging students in the exploration of emerging scientific fields that are potential career pathways.

Application of molecular dynamics simulation in biomedicine

Molecular dynamics (MD) simulation has been widely used in the field of biomedicine to study the conformational transition of proteins caused by mutation or ligand binding/unbinding. It provides some perspectives those are difficult to find in traditional biochemical or pathological experiments, for example, detailed effects of mutations on protein structure and protein-protein/ligand interaction at the atomic level. In this review, a broad overview on conformation changes and drug discovery by MD simulation is given. We first discuss the preparation of protein structure for MD simulation, which is a key step that determines the accuracy of the simulation. Then, we summarize the applications of commonly used force fields and MD simulations in scientific research. Finally, enhanced sampling methods and common applications of these methods are introduced. In brief, MD simulation is a powerful tool and it can be used to guide experimental study. The combination of MD simulation and experimental techniques is an a priori means to solve the biomedical problems and give a deep understanding on the relationship between protein structure and function.

Bioinformatics study of the potential therapeutic effects of ginsenoside Rf in reversing nonalcoholic fatty liver disease

OBJECTIVE

Ginsenoside Rf, a tetracyclic triterpenoid only present in Panax ginseng, has been proven to relieve lipid metabolism and inflammatory reactions, which can be a potential treatment for nonalcoholic fatty liver disease (NAFLD). Therefore, this study aimed to reveal the underlying mechanisms of ginsenoside Rf in the treatment of early-stage NAFLD (NAFL) by using a bioinformatics method and biological experiments.

METHODS

Target genes associated with NAFL were screened from the Gene Expression Omnibus (GEO) database, a database repository of high-throughput gene expression data and hybridization arrays, chips, and microarrays. Subsequently, gene set enrichment analysis was performed by using Gene Ontology enrichment analysis tool. Then, the binding capacity between ginsenoside Rf and NAFL-related targets was evaluated by molecular docking. Finally, the FFA-induced HepG2 cell model treated with ginsenoside Rf was adopted to verify the effect of ginsenoside Rf and the related mechanisms.

RESULTS

There were 41 common differentially expressed genes in the GEO dataset. Gene Ontology and Reactome pathway enrichment analysis of the differentially expressed genes showed that many pathways could be related to the pathogenesis of NAFL, including those participating in the cytokine-mediated signaling pathway, G protein-coupled receptor signaling pathway, and response to lipopolysaccharide. Finally, the qRT-PCR analysis results indicated that ginsenoside Rf therapy could ameliorate the transcription of ANXA2, BAZ1A, DNMT3L and MMP9.

CONCLUSION

Our research discovered the relevant mechanisms and basic pharmacological effects of ginsenoside Rf in the treatment of NAFL. These results might facilitate the development of ginsenoside Rf as an alternative medication for NAFL.

Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first 6 months of the COVID-19 pandemic

Understanding the molecular evolution of the SARS-CoV-2 virus as it continues to spread in communities around the globe is important for mitigation and future pandemic preparedness. Three-dimensional structures of SARS-CoV-2 proteins and those of other coronavirusess archived in the Protein Data Bank were used to analyze viral proteome evolution during the first 6 months of the COVID-19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48 000 viral isolates revealed how each one of 29 viral proteins have undergone amino acid changes. Catalytic residues in active sites and binding residues in protein-protein interfaces showed modest, but significant, numbers of substitutions, highlighting the mutational robustness of the viral proteome. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi-Gaussian distribution. Detailed results are presented for potential drug discovery targets and the four structural proteins that comprise the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and protein-protein and protein-nucleic acid interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure-based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.

MrParse: finding homologues in the PDB and the EBI AlphaFold database for molecular replacement and more

Crystallographers have an array of search-model options for structure solution by molecular replacement (MR). The well established options of homologous experimental structures and regular secondary-structure elements or motifs are increasingly supplemented by computational modelling. Such modelling may be carried out locally or may use pre-calculated predictions retrieved from databases such as the EBI AlphaFold database. MrParse is a new pipeline to help to streamline the decision process in MR by consolidating bioinformatic predictions in one place. When reflection data are provided, MrParse can rank any experimental homologues found using eLLG, which indicates the likelihood that a given search model will work in MR. Inbuilt displays of predicted secondary structure, coiled-coil and transmembrane regions further inform the choice of MR protocol. MrParse can also identify and rank homologues in the EBI AlphaFold database, a function that will also interest other structural biologists and bioinformaticians.

The structural context of posttranslational modifications at a proteome-wide scale

The recent revolution in computational protein structure prediction provides folding models for entire proteomes, which can now be integrated with large-scale experimental data. Mass spectrometry (MS)-based proteomics has identified and quantified tens of thousands of posttranslational modifications (PTMs), most of them of uncertain functional relevance. In this study, we determine the structural context of these PTMs and investigate how this information can be leveraged to pinpoint potential regulatory sites. Our analysis uncovers global patterns of PTM occurrence across folded and intrinsically disordered regions. We found that this information can help to distinguish regulatory PTMs from those marking improperly folded proteins. Interestingly, the human proteome contains thousands of proteins that have large folded domains linked by short, disordered regions that are strongly enriched in regulatory phosphosites. These include well-known kinase activation loops that induce protein conformational changes upon phosphorylation. This regulatory mechanism appears to be widespread in kinases but also occurs in other protein families such as solute carriers. It is not limited to phosphorylation but includes ubiquitination and acetylation sites as well. Furthermore, we performed three-dimensional proximity analysis, which revealed examples of spatial coregulation of different PTM types and potential PTM crosstalk. To enable the community to build upon these first analyses, we provide tools for 3D visualization of proteomics data and PTMs as well as python libraries for data accession and processing.

Icariin: A Potential Molecule for Treatment of Knee Osteoarthritis

Background: Knee osteoarthritis (KOA) is a degenerative disease that develops over time. Icariin (ICA) has a positive effect on KOA, although the mechanism is unknown. To investigate drug-disease connections and processes, network pharmacology is commonly used. The molecular mechanisms of ICA for the treatment of KOA were investigated using network pharmacology, molecular docking and literature research approaches in this study.

Methods: We gathered KOA-related genes using the DisGeNET database, the OMIM database, and GEO microarray data. TCMSP database, Pubchem database, TTD database, SwissTargetPrediction database, and Pharmmapper database were used to gather ICA-related data. Following that, a protein-protein interaction (PPI) network was created. Using the Metascape database, we performed GO and KEGG enrichment analyses. After that, we built a targets-pathways network. Furthermore, molecular docking confirms the prediction. Finally, we looked back over the last 5 years of literature on icariin for knee osteoarthritis to see if the findings of this study were accurate.

Results: core targets relevant to KOA treatment include TNF, IGF1, MMP9, PTGS2, ESR1, MMP2 and so on. The main biological process involved regulation of inflammatory response, collagen catabolic process, extracellular matrix disassembly and so on. The most likely pathways involved were the IL-17 signaling pathway, TNF signaling pathway, Estrogen signaling pathway.

Conclusion: ICA may alleviate KOA by inhibiting inflammation, cartilage breakdown and extracellular matrix degradation. Our study reveals the molecular mechanism of ICA for the treatment of KOA, demonstrating its potential value for further research and as a new drug.

Integration of machine learning with computational structural biology of plants

Computational structural biology of proteins has developed rapidly in recent decades with the development of new computational tools and the advancement of computing hardware. However, while these techniques have widely been used to make advancements in human medicine, these methods have seen less utilization in the plant sciences. In the last several years, machine learning methods have gained popularity in computational structural biology. These methods have enabled the development of new tools which are able to address the major challenges that have hampered the wide adoption of the computational structural biology of plants. This perspective examines the remaining challenges in computational structural biology and how the development of machine learning techniques enables more in-depth computational structural biology of plants.

Application of Coarse-Grained (CG) Models to Explore Conformational Pathway of Large-Scale Protein Machines

Protein machines are clusters of protein assemblies that function in order to control the transfer of matter and energy in cells. For a specific protein machine, its working mechanisms are not only determined by the static crystal structures, but also related to the conformational transition dynamics and the corresponding energy profiles. With the rapid development of crystallographic techniques, the spatial scale of resolved structures is reaching up to thousands of residues, and the concomitant conformational changes become more and more complicated, posing a great challenge for computational biology research. Previously, a coarse-grained (CG) model aiming at conformational free energy evaluation was developed and showed excellent ability to reproduce the energy profiles by accurate electrostatic interaction calculations. In this study, we extended the application of the CG model to a series of large-scale protein machine systems. The spike protein trimer of SARS-CoV-2, ATP citrate lyase (ACLY) tetramer, and P4-ATPases systems were carefully studied and discussed as examples. It is indicated that the CG model is effective to depict the energy profiles of the conformational pathway between two endpoint structures, especially for large-scale systems. Both the energy change and energy barrier between endpoint structures provide reasonable mechanism explanations for the associated biological processes, including the opening of receptor binding domain (RBD) of spike protein, the phospholipid transportation of P4-ATPase, and the loop translocation of ACLY. Taken together, the CG model provides a suitable alternative in mechanistic studies related to conformational change in large-scale protein machines.