scholarly journals Computer-aided prediction of inhibitors against STAT3 for managing COVID-19 associate cytokine storm

2021 ◽  
Author(s):  
Anjali Dhall ◽  
Sumeet Patiyal ◽  
Neelam Sharma ◽  
Naorem Leimarembi Devi ◽  
Gajendra P. S. Raghava
Keyword(s):  
Prediction Models ◽  
Validation Dataset ◽  
Cytokine Storm ◽  
Hybrid Features ◽  
Stat3 Signaling ◽  
Stat3 Signaling Pathway ◽  
Wide Range ◽  
Proinflammatory Responses ◽  
Highly Correlated ◽  
Approved Drugs

Abstract It has been shown in the past that levels of cytokines, including interleukin 6 (IL6), is highly correlated with the disease severity of COVID-19 patients. IL6 mediated activation of STAT3 is responsible to proliferate proinflammatory responses that leads to promotion of cytokine storm. Thus, STAT3 inhibitors may play a crucial role in managing pathogenesis of COVID-19. This paper describes a method developed for predicting inhibitors against the IL6-mediated STAT3 signaling pathway. The dataset used for training, testing, and evaluation of models contains small-molecule based 1564 STAT3 inhibitors and 1671 non-inhibitors. Analysis of data indicates that rings and aromatic groups are significantly abundant in STAT3 inhibitors compared to non-inhibitors. In order to build models, we generate a wide range of descriptors for each chemical compound. Firstly, we developed models using 2-D and 3-D descriptors and achieved maximum AUC 0.84 and 0.73, respectively. Secondly, fingerprints (FP) are used to build prediction models and achieved 0.86 AUC and accuracy of 78.70% on validation dataset. Finally, models were developed using hybrid features or descriptors, achieve a maximum of 0.87 AUC on the validation dataset. We used our best model to identify STAT3 inhibitors in FDA-approved drugs and found few drugs (e.g., Tamoxifen, and Perindopril) that can be used to manage COVID-19 associated cytokine storm. A webserver “STAT3In” (https://webs.iiitd.edu.in/raghava/stat3in/ ) has been developed to predict and design STAT3 inhibitors.

scholarly journals Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19

2020 ◽  
Author(s):  
Anjali Dhall ◽  
Sumeet Patiyal ◽  
Neelam Sharma ◽  
Salman Sadullah Usmani ◽  
Gajendra P S Raghava
Keyword(s):  
Scientific Community ◽  
Prediction Models ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Validation Dataset ◽  
Independent Validation ◽  
Immune Epitope ◽  
Learning Techniques ◽  
Wide Range ◽  
Immune Epitope Database

Abstract Interleukin 6 (IL-6) is a pro-inflammatory cytokine that stimulates acute phase responses, hematopoiesis and specific immune reactions. Recently, it was found that the IL-6 plays a vital role in the progression of COVID-19, which is responsible for the high mortality rate. In order to facilitate the scientific community to fight against COVID-19, we have developed a method for predicting IL-6 inducing peptides/epitopes. The models were trained and tested on experimentally validated 365 IL-6 inducing and 2991 non-inducing peptides extracted from the immune epitope database. Initially, 9149 features of each peptide were computed using Pfeature, which were reduced to 186 features using the SVC-L1 technique. These features were ranked based on their classification ability, and the top 10 features were used for developing prediction models. A wide range of machine learning techniques has been deployed to develop models. Random Forest-based model achieves a maximum AUROC of 0.84 and 0.83 on training and independent validation dataset, respectively. We have also identified IL-6 inducing peptides in different proteins of SARS-CoV-2, using our best models to design vaccine against COVID-19. A web server named as IL-6Pred and a standalone package has been developed for predicting, designing and screening of IL-6 inducing peptides (https://webs.iiitd.edu.in/raghava/il6pred/).

scholarly journals The IκB Kinase Inhibitor ACHP Targets the STAT3 Signaling Pathway in Human Non-Small Cell Lung Carcinoma Cells

Biomolecules ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 875 ◽  
Author(s):  
Jong Hyun Lee ◽  
Chakrabhavi Dhananjaya Mohan ◽  
Salundi Basappa ◽  
Shobith Rangappa ◽  
Arunachalam Chinnathambi ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Cell Lines ◽  
Inhibitory Activity ◽  
Small Cell ◽  
Nsclc Cell ◽  
Small Cell Lung ◽  
Cell Lung Carcinoma ◽  
Stat3 Signaling ◽  
Stat3 Signaling Pathway ◽  
Wide Range

STAT3 is an oncogenic transcription factor that regulates the expression of genes which are involved in malignant transformation. Aberrant activation of STAT3 has been observed in a wide range of human malignancies and its role in negative prognosis is well-documented. In this report, we performed high-throughput virtual screening in search of STAT3 signaling inhibitors using a cheminformatics platform and identified 2-Amino-6-[2-(Cyclopropylmethoxy)-6-Hydroxyphenyl]-4-Piperidin-4-yl Nicotinonitrile (ACHP) as the inhibitor of the STAT3 signaling pathway. The predicted hit was evaluated in non-small cell lung cancer (NSCLC) cell lines for its STAT3 inhibitory activity. In vitro experiments suggested that ACHP decreased the cell viability and inhibited the phosphorylation of STAT3 on Tyr705 of NSCLC cells. In addition, ACHP imparted inhibitory activity on the constitutive activation of upstream protein tyrosine kinases, including JAK1, JAK2, and Src. ACHP decreased the nuclear translocation of STAT3 and downregulated its DNA binding ability. Apoptosis was evidenced by cleavage of caspase-3 and PARP with the subsequent decline in antiapoptotic proteins, including Bcl-2, Bcl-xl, and survivin. Overall, we report that ACHP can act as a potent STAT3 signaling inhibitor in NSCLC cell lines.

scholarly journals ChAlPred: A Web Server for Prediction of Allergenicity of Chemical Compounds

2021 ◽  
Author(s):  
Neelam Sharma ◽  
Sumeet Patiyal ◽  
Anjali Dhall ◽  
Leimarembi Devi Naorem ◽  
Gajendra P.S. Raghava
Keyword(s):  
Chemical Compound ◽  
Prediction Models ◽  
Web Server ◽  
Chemical Compounds ◽  
Validation Dataset ◽  
Fingerprint Analysis ◽  
Approved Drugs ◽  
3D Descriptors ◽  
User Friendly ◽  
Fda Approved Drugs

Allergy is the abrupt reaction of the immune system that may occur after the exposure with allergens like protein/peptide or chemical allergens. In past number of methods of have been developed for classifying the protein/peptide based allergen. To the best of our knowledge, there is no method to classify the allergenicity of chemical compound. Here, we have proposed a method named ChAlPred, which can be used to fill the gap for predicting the chemical compound that might cause allergy. In this study, we have obtained the dataset of 403 allergen and 1074 non-allergen chemical compounds and used 2D, 3D and FP descriptors to train, test and validate our prediction models. The fingerprint analysis of the dataset indicates that PubChemFP129 and GraphFP1014 are more frequent in the allergenic chemical compounds, whereas KRFP890 is highly present in non-allergenic chemical compounds. Our XGB based model achieved the AUC of 0.89 on validation dataset using 2D descriptors. RF based model has outperformed other classifiers using 3D descriptors (AUC = 0.85), FP descriptors (AUC = 0.92), combined descriptors (AUC = 0.93), and hybrid model (AUC = 0.92) on validation dataset. In addition, we have also reported some FDA-approved drugs like Cefuroxime, Spironolactone, and Tioconazole which can cause the allergic symptoms. A user user-friendly web server named ChAlPred has been developed to predict the chemical allergens. It can be easily accessed at https://webs.iiitd.edu.in/raghava/chalpred/.

scholarly journals Identification of Vaccinia Virus Inhibitors and Cellular Functions Necessary for Efficient Viral Replication by Screening Bioactives and FDA-Approved Drugs

Vaccines ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 401
Author(s):  
Chen Peng ◽  
Yanan Zhou ◽  
Shuai Cao ◽  
Anil Pant ◽  
Marlene L. Campos Guerrero ◽  
...  
Keyword(s):  
Vaccinia Virus ◽  
High Throughput Screening ◽  
Janus Kinase ◽  
Cellular Functions ◽  
Cellular Targets ◽  
Stat3 Signaling ◽  
Assay Procedure ◽  
Stat3 Signaling Pathway ◽  
Cellular Pathways ◽  
Approved Drugs

Four decades after the eradication of smallpox, poxviruses continue to threaten the health of humans and other animals. Vaccinia virus (VACV) was used as the vaccine that successfully eradicated smallpox and is a prototypic member of the poxvirus family. Many cellular pathways play critical roles in productive poxvirus replication. These pathways provide opportunities to expand the arsenal of poxvirus antiviral development by targeting the cellular functions required for efficient poxvirus replication. In this study, we developed and optimized a secreted Gaussia luciferase-based, simplified assay procedure suitable for high throughput screening. Using this procedure, we screened a customized compound library that contained over 3200 bioactives and FDA (Food and Drug Administration)-approved chemicals, most having known cellular targets, for their inhibitory effects on VACV replication. We identified over 140 compounds that suppressed VACV replication. Many of these hits target cellular pathways previously reported to be required for efficient VACV replication, validating the effectiveness of our screening. Importantly, we also identified hits that target cellular functions with previously unknown roles in the VACV replication cycle. Among those in the latter category, we verified the antiviral role of several compounds targeting the janus kinase/signal transducer and activator of transcription-3 (JAK/STAT3) signaling pathway by showing that STAT3 inhibitors reduced VACV replication. Our findings identify pathways that are candidates for use in the prevention and treatment of poxvirus infections and additionally provide a foundation to investigate diverse cellular pathways for their roles in poxvirus replications.

scholarly journals Repurposing dextromethorphan and metformin for treating nicotine-induced cancer by directly targeting CHRNA7 to inhibit JAK2/STAT3/SOX2 signaling

Oncogene ◽  
2021 ◽  
Author(s):  
Lu Wang ◽  
Du Liang ◽  
Xiao Xiong ◽  
Yusheng Lin ◽  
Jianlin Zhu ◽  
...  
Keyword(s):  
Cholinergic Receptor ◽  
Dna Hypomethylation ◽  
Stat3 Signaling ◽  
Stat3 Signaling Pathway ◽  
Related Risk ◽  
Cancer Types ◽  
Alpha 7 ◽  
Promoter Dna ◽  
Approved Drugs ◽  
Fda Approved Drugs

AbstractSmoking is one of the most impactful lifestyle-related risk factors in many cancer types including esophageal squamous cell carcinoma (ESCC). As the major component of tobacco and e-cigarettes, nicotine is not only responsible for addiction to smoking but also a carcinogen. Here we report that nicotine enhances ESCC cancer malignancy and tumor-initiating capacity by interacting with cholinergic receptor nicotinic alpha 7 subunit (CHRNA7) and subsequently activating the JAK2/STAT3 signaling pathway. We found that aberrant CHRNA7 expression can serve as an independent prognostic factor for ESCC patients. In multiple ESCC mouse models, dextromethorphan and metformin synergistically repressed nicotine-enhanced cancer-initiating cells (CIC) properties and inhibited ESCC progression. Mechanistically, dextromethorphan non-competitively inhibited nicotine binding to CHRNA7 while metformin downregulated CHRNA7 expression by antagonizing nicotine-induced promoter DNA hypomethylation of CHRNA7. Since dextromethorphan and metformin are two safe FDA-approved drugs with minimal undesirable side-effects, the combination of these drugs has a high potential as either a preventive and/or a therapeutic strategy against nicotine-promoted ESCC and perhaps other nicotine-sensitive cancer types as well.

scholarly journals Prediction of antibiotic resistant strains of bacteria from their beta-lactamases protein

2021 ◽  
Author(s):  
Lubna Maryam ◽  
Anjali Dhall ◽  
Sumeet Patiyal ◽  
Salman Sadullah Usmani ◽  
Neelam Sharma ◽  
...  
Keyword(s):  
Amino Acids ◽  
Antibiotic Resistance ◽  
Prediction Models ◽  
Training Dataset ◽  
Validation Dataset ◽  
Side Chains ◽  
Beta Lactamase ◽  
Beta Lactamases ◽  
Wide Range ◽  
Resistant Strains

Number of beta-lactamase variants have ability to deactivate ceftazidime antibiotic, which is the most commonly used antibiotic for treating infection by Gram-negative bacteria. In this study an attempt has been made to develop a method that can predict ceftazidime resistant strains of bacteria from amino acid sequence of beta-lactamases. We obtained beta-lactamases proteins from the β-lactamase database, corresponding to 87 ceftazidime-sensitive and 112 ceftazidime-resistant bacterial strains. All models developed in this study were trained, tested, and evaluated on a dataset of 199 beta-lactamases proteins. We generate 9149 features for beta-lactamases using Pfeature and select relevant features using different algorithms in scikit-learn package. A wide range of machine learning techniques (like KNN, DT, RF, GNB, LR, SVC, XGB) has been used to develop prediction models. Our random forest-based model achieved maximum performance with AUROC of 0.80 on training dataset and 0.79 on the validation dataset. The study also revealed that ceftazidime-resistant beta-lactamases have amino acids with non-polar side chains in abundance. In contrast, ceftazidime-sensitive beta-lactamases have amino acids with polar side chains and charged entities in abundance. Finally, we developed a webserver- ABCRpred, for the scientific community working in the era of antibiotic resistance to predict the antibiotic resistance/susceptibility of beta-lactamase protein sequences. The server is freely available at (http://webs.iiitd.edu.in/raghava/abcrpred/ ).

scholarly journals In silico model for predicting IL-2 inducing peptides in human

2021 ◽  
Author(s):  
Anjali Lathwal ◽  
Rajesh Kumar ◽  
Dilraj Kaur ◽  
Gajendra P.S. Raghava
Keyword(s):  
Amino Acid ◽  
High Efficiency ◽  
Prediction Models ◽  
Interleukin 2 ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Validation Dataset ◽  
Amino Acid Residues ◽  
Hybrid Features ◽  
C Terminus

Interleukin-2 (IL-2) based immunotherapy has been already approved to treat certain type of cancers as it plays vital role in immune system. Thus it is important to discover new peptides or epitopes that can induce IL-2 with high efficiency. We analyzed experimentally validated IL-2 inducing and non-inducing peptides and observed differ in average amino acid composition, motifs, length, and positional preference of amino acid residues at the N- and C-terminus. In this study, 2528 IL-2 inducing and 2104 non-IL-2 inducing peptides have been used for traning, testing, traing and validation of our models. A large number of machine learning techniques and around 10,000 peptide features have been used for developing prediction models. The Random Forest-based model using hybrid features achieved a maximum accuracy of 73.25%, with AUC of 0.73 on the training set; accuracy of 72.89% with AUC of 0.72 on validation dataset. A web-server IL2pred has been developed for predicting IL-2 inducing peptides, scanning IL-inducing regions in a protein and designing IL-2 specific epitopes by ranking peptide analogs ( https://webs.iiitd.edu.in/raghava/il2pred/ ).

AlgPred 2.0: an improved method for predicting allergenic proteins and mapping of IgE epitopes

2020 ◽  
Author(s):  
Neelam Sharma ◽  
Sumeet Patiyal ◽  
Anjali Dhall ◽  
Akshara Pande ◽  
Chakit Arora ◽  
...  
Keyword(s):  
Prediction Models ◽  
Immunoglobulin E ◽  
Web Server ◽  
Training Dataset ◽  
Validation Dataset ◽  
Local Alignment ◽  
Blast Search ◽  
Wide Range ◽  
Allergenic Proteins ◽  
Search Tool

Abstract AlgPred 2.0 is a web server developed for predicting allergenic proteins and allergenic regions in a protein. It is an updated version of AlgPred developed in 2006. The dataset used for training, testing and validation consists of 10 075 allergens and 10 075 non-allergens. In addition, 10 451 experimentally validated immunoglobulin E (IgE) epitopes were used to identify antigenic regions in a protein. All models were trained on 80% of data called training dataset, and the performance of models was evaluated using 5-fold cross-validation technique. The performance of the final model trained on the training dataset was evaluated on 20% of data called validation dataset; no two proteins in any two sets have more than 40% similarity. First, a Basic Local Alignment Search Tool (BLAST) search has been performed against the dataset, and allergens were predicted based on the level of similarity with known allergens. Second, IgE epitopes obtained from the IEDB database were searched in the dataset to predict allergens based on their presence in a protein. Third, motif-based approaches like multiple EM for motif elicitation/motif alignment and search tool have been used to predict allergens. Fourth, allergen prediction models have been developed using a wide range of machine learning techniques. Finally, the ensemble approach has been used for predicting allergenic protein by combining prediction scores of different approaches. Our best model achieved maximum performance in terms of area under receiver operating characteristic curve 0.98 with Matthew’s correlation coefficient 0.85 on the validation dataset. A web server AlgPred 2.0 has been developed that allows the prediction of allergens, mapping of IgE epitope, motif search and BLAST search (https://webs.iiitd.edu.in/raghava/algpred2/).

scholarly journals Functional Group-Dependent Induction of Astrocytogenesis and Neurogenesis by Flavone Derivatives

Biomolecules ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 812 ◽  
Author(s):  
Ha-Rim Lee ◽  
Jin Mi Kang ◽  
Young Min Kim ◽  
Sagang Kim ◽  
Jihyae Ann ◽  
...  
Keyword(s):  
Methoxy Group ◽  
Structural Changes ◽  
Cell Types ◽  
Stat3 Signaling ◽  
Stat3 Signaling Pathway ◽  
Wide Range ◽  
Methoxy Flavone ◽  
Astrocytic Differentiation ◽  
Multiple Cell ◽  
Flavone Derivatives

Neural stem cells (NSCs) differentiate into multiple cell types, including neurons, astrocytes, and oligodendrocytes, and provide an excellent platform to screen drugs against neurodegenerative diseases. Flavonoids exert a wide range of biological functions on several cell types and affect the fate of NSCs. In the present study, we investigated whether the structure-activity relationships of flavone derivatives influence NSC differentiation. As previously reported, we observed that PD98059 (2′-amino-3′-methoxy-flavone), compound 2 (3′-methoxy-flavone) induced astrocytogenesis. In the present study, we showed that compound 3 (2′-hydroxy-3′-methoxy-flavone), containing a 3′-methoxy group, and a non-bulky group at C2′ and C4′, induced astrocytogenesis through JAK-STAT3 signaling pathway. However, compound 1 and 7–12 without the methoxy group did not show such effects. Interestingly, the compounds 4 (2′,3′-dimethoxyflavone), 5 (2′-N-phenylacetamido-3′-methoxy-flavone), and 6 (3′,4′-dimethoxyflavone) containing 3′-methoxy could not promote astrocytic differentiation, suggesting that both the methoxy groups at C3′ and non-bulky group at C2′ and C4′ are required for the induction of astrocytogenesis. Notably, compound 6 promoted neuronal differentiation, whereas its 4′-demethoxylated analog, compound 2, repressed neurogenesis, suggesting an essential role of the methoxy group at C4′ in neurogenesis. These findings revealed that subtle structural changes of flavone derivatives have pronounced effects on NSC differentiation and can guide to design and develop novel flavone chemicals targeting NSCs fate regulation.

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