GlyComb: A novel glycoconjugate data repository that bridges glycomics and proteomics.

The glycosylation of proteins and lipids is known to be closely related to the mechanisms of various diseases such as influenza, cancer, and muscular dystrophy. Therefore, it has become clear that the analysis of post-translational modifications of proteins, including glycosylation, is important to accurately understand the functions of each protein molecule and the interactions among them. In order to conduct large-scale analyses more efficiently, it is essential to promote the accumulation, sharing, and reuse of experimental and analytical data in accordance with the FAIR (Findability, Accessibility, Interoperability, and Re-usability) data principles. However, a FAIR data repository for storing and sharing glycoconjugate information, including glycopeptides and glycoproteins, in a standardized format did not exist. Therefore, we have developed GlyComb (https://glycomb.glycosmos.org) as a new standardized data repository for glycoconjugate data. Currently, GlyComb can assign a unique identifier to a set of glycosylation information associated with a specific peptide sequence or UniProt ID. By standardizing glycoconjugate data via GlyComb identifiers and coordinating with existing web resources such as GlyTouCan and GlycoPOST, a comprehensive system for data submission and data sharing among researchers can be established. Here we introduce how GlyComb is able to integrate the variety of glycoconjugate data already registered in existing data repositories to obtain a better understanding of the available glycopeptides and glycoproteins, and their glycosylation patterns. We also explain how this system can serve as a foundation for a better understanding of glycan function.

Anonymous Pattern Molecular Fingerprint and its Applications on Property Identification.

Molecular fingerprints are significant cheminformatics tools to map molecules into vectorial space according to their characteristics in diverse functional groups, atom sequences, and other topological structures. In this paper, we investigate a novel molecular fingerprint Anonymous-FP that possesses abundant perception about the underlying interactions shaped in small, medium, and large-scale atom chains. In detail, the possible atom chains from each molecule are sampled and extended as anonymous atom chains using an anonymous encoding manner. After that, the molecular fingerprint Anonymous-FP is embedded into vectorial space in virtue of the Natural Language Processing technique PV-DBOW. Anonymous-FP is studied on molecular property identification via molecule classification experiments on a series of molecule databases and has shown valuable advantages such as less dependence on prior knowledge, rich information content, full structural significance, and high experimental performance. During the experimental verification, the scale of the atom chain or its anonymous pattern is found significant to the overall representation ability of Anonymous-FP. Generally, the typical scale r = 8 could enhance the molecule classification performance, and specifically, Anonymous-FP gains the classification accuracy to above 93% on all NCI datasets.

Analysis of endoplasmic reticulum stress-related gene signature for the prognosis and pattern in diffuse large B cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoma in adults. This study aimed to determine the prognostic significance of endoplasmic reticulum (ER) stress-related genes in DLBCL. ER stress-related genes were obtained from the molecular signatures database. Gene expression data and clinical outcomes from the gene expression omnibus and TCGA datasets were collected, and differentially expressed genes (DEGs) were screened out. Gene ontology enrichment analysis, the kyoto encyclopaedia of genes and genomes pathway analysis, and geneset enrichment analysis were used to analyse the possible biological function of ER stress-related DEGs in DLBCL. Protein-protein interaction network construction using the STRING online and hub genes were identified by cytoHubba on Cytoscape software. The significant prognosis-related genes were screened, and the differential expression was validated. The immune microenvironment assessment of significant genes were evaluated. Next, the nomogram was built using univariate and multivariate Cox regression analysis. 26 ER stress-related DEGs were screened. Functional enrichment analysis showed them to be involved in the regulation of the endoplasmic reticulum mainly. NUPR1 and TRIB3 were identified as the most significant prognostic-related genes by comparison with the GSE10846, GSE11318, and TCGA datasets. NUPR1 was correlated with a good prognosis and immune infiltration in DLBCL; on the other hand, high expression of TRIB3 significantly correlated with a poor prognosis, which was an independent prognostic factor for DLBCL. In summary, we identified NUPR1 and TRIB3 as critical ER stress-related genes in DLBCL. NUPR1 might be involved in immune infiltration in DLBCL, and TRIB3 might serve as a potential therapeutic target and prognostic factor in DLBCL.

DIANA-miRPath v4.0: expanding target-based miRNA functional analysis in cell-type and tissue contexts.

DIANA-miRPath is an online miRNA analysis platform harnessing predicted or experimentally supported miRNA interactions towards the exploration of combined miRNA effects. In its latest version (v4.0, http://www.microrna.gr/miRPathv4), DIANA-miRPath breaks new ground by introducing the capacity to tailor its target-based miRNA functional analysis engine to specific biological and/or experimental contexts. Via a redesigned modular interface with rich interaction, annotation and parameterization options, users can now perform enrichment analysis on Gene Ontology (GO) terms, KEGG and REACTOME pathways, sets from Molecular Signatures Database (MSigDB) and PFAM. Included miRNA interaction sets are derived from state-of-the-art resources of experimentally supported (DIANA-TarBase v8.0, miRTarBase and microCLIP cell-type-specific interactions) or from in silico miRNA-target interactions (updated DIANA-microT-CDS and TargetScan predictions). Bulk and single-cell expression datasets from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx) and adult/fetal single-cell atlases are integrated and can be used to assess the expression of enriched term components across a wide range of states. A discrete module enabling enrichment analyses using CRISPR knock-out screen datasets enables the detection of selected miRNAs with potentially crucial roles within conditions under study. Notably, the option to upload custom interaction, term, expression and screen sets further expands the versatility of miRPath webserver.

Identification of prognostic immune-related lncRNA signature predicting the overall survival for colorectal cancer.

Long non-coding RNA (lncRNA) is an important regulator of gene expression and serves a fundamental role in immune regulation. The present study aimed to develop a novel immune-related lncRNA signature to assess the prognosis of patients with colorectal cancer (CRC). Transcriptome data and clinical information of patients with CRC were downloaded from The Cancer Genome Atlas (TCGA) and UCSC Xena platforms. Immune-related mRNAs were extracted from the Molecular Signatures Database (MSigDB), and the immune-related lncRNAs were identified based on correlation analysis. Then, univariate, Lasso and multivariate Cox regression were applied to construct an immune-related lncRNA signature, and CRC patients were divided into high- and low-risk groups according to the median risk score. Finally, we evaluated the signature from the perspectives of clinical outcome, clinicopathological parameters, tumor-infiltrating immune cells (TIICs), immune status, tumor mutation burden (TMB) and immunotherapy responsiveness. In total, 272 immune-related lncRNAs were identified, five of which were applied to construct an immune-related lncRNA signature. The signature divided patients with CRC into low- and high-risk groups, the prognosis of patients in the high-risk group were significantly poorer than those in low-risk group, and the results were further confirmed in external validation cohort. Furthermore, the high-risk group showed aggressive clinicopathological characteristics, specific TIIC and immune function status, and low sensitivity to immunotherapy. The immune-related lncRNA signature could be exploited as a promising biomarker for predicting the prognosis and immune status of patients with CRC.

The Cancer Epitope Database and Analysis Resource (CEDAR).

We established The Cancer Epitope Database and Analysis Resource (CEDAR) to catalog all epitope data in the context of cancer. The specific molecular targets of adaptive T cell and B cell immune responses are referred to as epitopes. Epitopes derived from cancer antigens are of high relevance as they are recognized by anti-cancer immune cells. Detailed knowledge of the molecular characteristic of cancer epitopes and associated metadata is relevant to understanding and planning prophylactic and therapeutic applications and accurately characterizing naturally occurring immune responses and cancer immunopathology. CEDAR provides a freely accessible, comprehensive collection of cancer epitope and receptor data curated from the literature and serves as a companion site to the Immune Epitope Database (IEDB), which is focused on infectious, autoimmune, and allergic diseases. CEDAR is freely accessible at https://cedar.iedb.org/.

Identification and Screening of Novel Anti-Cancer Compounds for Aurora Kinase-A from Chemical Database.

Aurora kinase is a group of enzymes that belongs to a serine-threonine family and plays a critical role in cellular division. Aurora Kinase A is overexpressed and distributed beyond the nucleus and is involved in tumorigenesis. Flavones are a class of flavonoids that are present in plants that show anticancer activity. Similar compounds of 2'Fluoroflavones are retrieved from the PubChem database. Then drug-like filters viz. REOS and PAINS were applied to remove toxic compounds using Canvas software, resulting in 3882 compounds being subjected to Glide docking with Aurora kinase A. The lead compounds were selected on the merit of hydrogen bonding, salt bridge, as well as pi-pi interactions, 4-(6-Fluoro-4-oxychromen-2yl) benzoic acid, has been found one of the best molecules from docking studies. The binding mode of the lead compound with AURKA reveals that the amino acid residues viz, Lys162, Ala213, and His280 are more important for binding with the binding affinity of -11.760 kcal/mol. The molecular dynamics simulations of 100 ns were done, which shows the mean RMSD value of 1.77 Å for all 3 complexes of the protein and Fluoroflavone and its analogs. This shows that Fluoroflavone and its 2 best analogs are tightly attached to the active sites and thus have conformational stability. Our finding suggests that 4-(6-fluoro-4-oxochromen-2-yl)benzoic acid and 4-(4-Oxochromen-2-yl)benzoate can be further used in vitro and in vivo experiments and can probably serve as a novel drug for cancer treatment.

A zinc metabolism-related gene signature for predicting prognosis and characteristics of breast cancer.

Background: Breast cancer is one of the most serious and prevalent malignancies. Zinc is commonly known to play a crucial role in the development and progression of breast cancer; however, the detailed mechanisms underlying this role are not well understood. This study aimed to develop a zinc metabolism-related gene (ZMRG) signature based on a multi-database study to predict patient prognosis and investigate the relationship between drug therapy response and immune enrichment. Methods: Data for breast cancer samples from The Cancer Genome Atlas and Gene Expression Omnibus databases were screened for zinc metabolism-related genes using the Molecular Signature Database. Cox and Least Absolute Shrinkage and Selection Operator regressions were performed to construct a ZMRG signature. To assess the predictive performance of the gene signature, Kaplan-Meier analysis and receiver operating characteristic curves were used. Additionally, we utilised single-sample gene set enrichment analysis, the Tumour Immune Estimation Resource, the Genomics of Drug Sensitivity in Cancer database, and the Cancer Therapeutics Response Portal to investigate the association between the tumour microenvironment and drug sensitivity. Quantitative PCR was used to assess the expression of each gene in the signature in breast cancer cell lines and patient samples. Results: Five ZMRGs were identified (ATP7B, BGLAP, P2RX4, SLC39A11, and TH) and a risk profile was constructed for each. Two risk groups, high- and low-risk, were identified in this way, and the high-risk score subgroups were found to have worse prognosis. This risk profile was validated using the GSE42568 dataset. Tumour microenvironment and drug sensitivity analyses showed that the expression of these five ZMRGs was significantly associated with immune response. The high-risk group showed substantial immune cell infiltration and enrichment of immune pathways, and patients were more sensitive to drugs commonly used in breast cancer. Conclusion: The ZMRG signature represents a new prognostic predictor for patients with breast cancer, and may also provide new insights into individualised treatment of breast cancer.

A database of anti-coronavirus peptides.

Since 2019, the novel coronavirus (SARS-COV-2) disease (COVID-19) has caused a worldwide epidemic. Anti-coronavirus peptides (ACovPs), a type of antimicrobial peptides (AMPs), have demonstrated excellent inhibitory effects on coronaviruses. However, state-of-the-art AMP databases contain only a small number of ACovPs. Additionally, the fields of these databases are not uniform, and the units or evaluation standards of the same field are inconsistent. Most of these databases have not included the target domains of ACovPs and description of in vitro and in vivo assays to measure the inhibitory effects of ACovPs. Here, we present a database focused on ACovPs (ACovPepDB), which contains comprehensive and precise ACovPs information of 518 entries with 214 unique ACovPs manually collected from public databases and published peer-reviewed articles. We believe that ACovPepDB is of great significance for facilitating the development of new peptides and improving treatment for coronavirus infection. The database will become a portal for ACovPs and guide and help researchers perform further studies. The ACovPepDB is available at http://i.uestc.edu.cn/ACovPepDB/ .

Virtual Screening of Natural Chemical Databases to Search for Potential ACE2 Inhibitors.

The angiotensin-converting enzyme II (ACE2) is a multifunctional protein in both health and disease conditions, which serves as a counterregulatory component of RAS function in a cardioprotective role. ACE2 modulation may also have relevance to ovarian cancer, diabetes, acute lung injury, fibrotic diseases, etc. Furthermore, since the outbreak of the coronavirus disease in 2019 (COVID-19), ACE2 has been recognized as the host receptor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The receptor binding domain of the SARS-CoV-2 S-protein has a strong interaction with ACE2, so ACE2 may be a potent drug target to prevent the virus from invading host cells for anti-COVID-19 drug discovery. In this study, structure- and property-based virtual screening methods were combined to filter natural product databases from ChemDiv, TargetMol, and InterBioScreen to find potential ACE2 inhibitors. The binding affinity between protein and ligands was predicted using both Glide SP and XP scoring functions and the MM-GBSA method. ADME properties were also calculated to evaluate chemical drug-likeness. Then, molecular dynamics (MD) simulations were performed to further explore the binding modes between the highest-potential compounds and ACE2. Results showed that the compounds 154-23-4 and STOCK1N-07141 possess potential ACE2 inhibition activities and deserve further study.

A gene expression biomarker for predictive toxicology to identify chemical modulators of NF-κB.

The nuclear factor-kappa B (NF-κB) is a transcription factor with important roles in inflammation, immune response, and oncogenesis. Dysregulation of NF-κB signaling is associated with inflammation and certain cancers. We developed a gene expression biomarker predictive of NF-κB modulation and used the biomarker to screen a large compendia of gene expression data. The biomarker consists of 108 genes responsive to tumor necrosis factor α in the absence but not the presence of IκB, an inhibitor of NF-κB. Using a set of 450 profiles from cells treated with immunomodulatory factors with known NF-κB activity, the balanced accuracy for prediction of NF-κB activation was > 90%. The biomarker was used to screen a microarray compendium consisting of 12,061 microarray comparisons from human cells exposed to 2,672 individual chemicals to identify chemicals that could cause toxic effects through NF-κB. There were 215 and 49 chemicals that were identified as putative or known NF-κB activators or suppressors, respectively. NF-κB activators were also identified using two high-throughput screening assays; 165 out of the ~3,800 chemicals (ToxCast assay) and 55 out of ~7,500 unique compounds (Tox21 assay) were identified as potential activators. A set of 32 chemicals not previously associated with NF-κB activation and which partially overlapped between the different screens were selected for validation in wild-type and NFKB1-null HeLa cells. Using RT-qPCR and targeted RNA-Seq, 31 of the 32 chemicals were confirmed to be NF-κB activators. These results comprehensively identify a set of chemicals that could cause toxic effects through NF-κB.

The Chemical Property Position of Bedaquiline Construed by a Chemical Global Positioning System-Natural Product.

Bedaquiline is a novel adenosine triphosphate synthase inhibitor anti-tuberculosis drug. Bedaquiline belongs to the class of diarylquinolines, which are antituberculosis drugs that are quite different mechanistically from quinolines and flouroquinolines. The fact that relatively similar chemical drugs produce different mechanisms of action is still not widely understood. To enhance discrimination in favor of bedaquiline, a new approach using eight-score principal component analysis (PCA), provided by a ChemGPS-NP model, is proposed. PCA scores were calculated based on 35 + 1 different physicochemical properties and demonstrated clear differences when compared with other quinolines. The ChemGPS-NP model provided an exceptional 100 compounds nearest to bedaquiline from antituberculosis screening sets (with a cumulative Euclidian distance of 196.83), compared with the different 2Dsimilarity provided by Tanimoto methods (extended connective fingerprints and the Molecular ACCess System, showing 30% and 182% increases in cumulative Euclidian distance, respectively). Potentially similar compounds from publicly available antituberculosis compounds and Maybridge sets, based on bedaquiline's eight-dimensional similarity and different filtrations, were identified too.

Network Pharmacology Prediction: The Possible Mechanisms of Cinobufotalin against Osteosarcoma.

OBJECTIVE: To explore the active compounds and targets of cinobufotalin (huachansu) compared with the osteosarcoma genes to obtain the potential therapeutic targets and pharmacological mechanisms of action of cinobufotalin on osteosarcoma through network pharmacology. METHODS: The composition of cinobufotalin was searched by literature retrieval, and the target was selected from the CTD and TCMSP databases. The osteosarcoma genes, found from the GeneCards, OMIM, and other databases, were compared with the cinobufotalin targets to obtain potential therapeutic targets. The protein-protein interaction (PPI) network of potential therapeutic targets, constructed through the STRING database, was inputted into Cytoscape software to calculate the hub genes, using the NetworkAnalyzer. The hub genes were inputted into the Kaplan-Meier Plotter online database for exploring the survival curve. Functional enrichment analysis was identified using the DAVID database. RESULTS: 28 main active compounds of cinobufotalin were explored, including bufalin, adenosine, oleic acid, and cinobufagin. 128 potential therapeutic targets on osteosarcoma are confirmed among 184 therapeutic targets form cinobufotalin. The hub genes included TP53, ACTB, AKT1, MYC, CASP3, JUN, TNF, VEGFA, HSP90AA1, and STAT3. Among the hub genes, TP53, ACTB, MYC, TNF, VEGFA, and STAT3 affect the patient survival prognosis of sarcoma. Through function enrichment analysis, it is found that the main mechanisms of cinobufotalin on osteosarcoma include promoting sarcoma apoptosis, regulating the cell cycle, and inhibiting proliferation and differentiation. CONCLUSION: The possible mechanisms of cinobufotalin against osteosarcoma are preliminarily predicted through network pharmacology, and further experiments are needed to prove these predictions.

Building quantum mechanics quality force fields of proteins with the generalized energy-based fragmentation approach and machine learning.

We combined our generalized energy-based fragmentation (GEBF) approach and machine learning (ML) technique to construct quantum mechanics (QM) quality force fields for proteins. In our scheme, the training sets for a protein are only constructed from its small subsystems, which capture all short-range interactions in the target system. The energy of a given protein is expressed as the summation of atomic contributions from QM calculations of various subsystems, corrected by long-range Coulomb and van der Waals interactions. With the Gaussian approximation potential (GAP) method, our protocol can automatically generate training sets with high efficiency. To facilitate the construction of training sets for proteins, we store all trained subsystem data in a library. If subsystems in the library are detected in a new protein, corresponding datasets can be directly reused as a part of the training set on this new protein. With two polypeptides, 4ZNN and 1XQ8 segment, as examples, the energies and forces predicted by GEBF-GAP are in good agreement with those from conventional QM calculations, and dihedral angle distributions from GEBF-GAP molecular dynamics (MD) simulations can also well reproduce those from ab initio MD simulations. In addition, with the training set generated from GEBF-GAP, we also demonstrate that GEBF-ML force fields constructed by neural network (NN) methods can also show QM quality. Therefore, the present work provides an efficient and systematic way to build QM quality force fields for biological systems.

Natural anthraquinone compound emodin as a novel inhibitor of aurora A kinase: A pilot study.

Aurora kinase A (AURKA) carries out an essential role in proliferation and involves in cisplatin resistance in various cancer cells. Overexpression of AURKA is associated with the poor prognosis of cancer patients. Thus, AURKA has been considered as a target for cancer therapy. Developing AURKA inhibitors became an important issue in cancer therapy. A natural compound emodin mainly extracted from rhubarbs possesses anti-cancer properties. However, the effect of emodin on AURKA has never been investigated. In the present study, molecular docking analysis indicated that emodin interacts with AURKA protein active site. We also found nine emodin analogues from Key Organic database by using ChemBioFinder software. Among that, one analogue 8L-902 showed a similar anti-cancer effect as emodin. The bindings of emodin and 8L-902 on AURKA protein were confirmed by cellular thermal shift assay. Furthermore, emodin inhibited the AURKA kinase activity in vitro and enhanced the cisplatin-DNA adduct level in a resistant ovarian cancer cell line. It seems that emodin may have the potential to inhibit cancer cell growth and enhance cisplatin therapy in cancer with resistance. Collectively, our finding reveals a novel AURKA inhibitor, emodin, which may be vulnerable to ovarian cancer therapy in the future.

Identification of novel topoisomerase II alpha inhibitors by virtual screening, molecular docking, and bioassay.

Breast cancer is one of the most common tumors, and its treatment still leaves room for improvement. Topoisomerase II alpha is a potential target for the treatment of human diseases such as breast cancer. In this article, we attempted to discover a novel anticancer drug. We have used the topoisomerase II alpha protein-Homo sapiens (Human) to hierarchically screen the Maybridge database. Based on their docking score, the top hit compounds have been assayed for inhibition in a topoisomerase II pBR322 DNA relaxation assay in vitro. Candidate compound 6 (CP6) was found to have the best inhibitory effect for topoisomerase II among the 20 tested compounds. In addition, CP6 had potent cytotoxicity against eight tested tumor cell lines. At the same time, CP6 was shown to have potential anti-multidrug resistance capabilities. This study identifies CP6, which can contribute to the development of new topoisomerase II inhibitors as anticancer agents.

Discovery of microtubule stabilizers with novel scaffold structures based on virtual screening, biological evaluation, and molecular dynamics simulation.

Disrupting the dynamics and structures of microtubules can perturb mitotic spindle formation, cause cell cycle arrest in G2/M phase, and subsequently lead to cellular death via apoptosis. In this investigation, the structure-based virtual screening methods, including molecular docking and rescoring, and similarity analysis of interaction molecular fingerprints, were developed to discover novel tubulin inhibitors from ChemDiv database with 1,601,806 compounds. The screened compounds were further filtered by PAINS, ADME/T, Toxscore, SAscore, and Drug-likeness analysis. Finally, 17 hit compounds were selected, and then submitted to the biologic evaluation. Among these hits, the P2 exhibited the strongest antiproliferative activity against four tumor cells including HeLa, HepG2, MCF-7, and A549. The in vitro tubulin polymerization assay revealed P2 could promote tubulin polymerization in a dose dependent manner. Finally, in order to analyze the interaction modes of complexes, the molecular dynamics simulation was performed to investigate the interactions between P2 and tubulin. The molecular dynamics simulation analysis showed that P2 could stably bind to taxane site, induced H6-H7, B9-B10, and M-loop regions changes. The ΔGbind energies of tubulin-P2 and tubulin-paclitaxel were -68.25 ± 12.98 and -146.05 ± 16.17 kJ mol-1, respectively, which were in line with the results of the experimental test. Therefore, P2 has been well characterized as lead compounds for developing new tubulin inhibitors with potential anticancer activity.