AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria.

Most of the existing methods developed for predicting antibacterial peptides (ABPs) are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we describe a method that allows us to predict ABPs against gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify ABPs and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict ABPs and obtained high precision with low sensitivity. To address the issue of poor sensitivity, we developed alignment-free methods for predicting ABPs using machine/deep learning techniques. In the case of alignment-free methods, we utilized a wide range of peptide features that include different types of composition, binary profiles of terminal residues, and fastText word embedding. In this study, a five-fold cross-validation technique has been used to build machine/deep learning models on training datasets. These models were evaluated on an independent dataset with no common peptide between training and independent datasets. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98, and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, on an independent dataset. Our method performs better than existing methods when compared with existing approaches on an independent dataset. A user-friendly web server, standalone package and pip package have been developed to facilitate peptide-based therapeutics.

SIRT1 Prevents R-Loops during Chronological Aging by Modulating DNA Replication at rDNA Loci.

In metazoans, the largest sirtuin, SIRT1, is a nuclear protein implicated in epigenetic modifications, circadian signaling, DNA recombination, replication, and repair. Our previous studies have demonstrated that SIRT1 binds replication origins and inhibits replication initiation from a group of potential initiation sites (dormant origins). We studied the effects of aging and SIRT1 activity on replication origin usage and the incidence of transcription-replication collisions (creating R-loop structures) in adult human cells obtained at different time points during chronological aging and in cancer cells. In primary, untransformed cells, SIRT1 activity declined and the prevalence of R-loops rose with chronological aging. Both the reduction in SIRT1 activity and the increased abundance of R-loops were also observed during the passage of primary cells in culture. All cells, regardless of donor age or transformation status, reacted to the short-term, acute chemical inhibition of SIRT1 with the activation of excessive replication initiation events coincident with an increased prevalence of R-loops. However, cancer cells activated dormant replication origins, genome-wide, during long-term proliferation with mutated or depleted SIRT1, whereas, in primary cells, the aging-associated SIRT1-mediated activation of dormant origins was restricted to rDNA loci. These observations suggest that chronological aging and the associated decline in SIRT1 activity relax the regulatory networks that protect cells against excess replication and that the mechanisms protecting from replication-transcription collisions at the rDNA loci manifest as differentially enhanced sensitivities to SIRT1 decline and chronological aging.

Risk assessment of cancer patients based on HLA-I alleles, neobinders and expression of cytokines.

Advancements in cancer immunotherapy have shown significant outcomes in treating cancers. To design effective immunotherapy, it's important to understand immune response of a patient based on its genomic profile. However, analyses to do that requires proficiency in the bioinformatic methods. Swiftly growing sequencing technologies and statistical methods create a blockage for the scientists who want to find the biomarkers for different cancers but don't have detailed knowledge of coding or tool. Here, we are providing a web-based resource that gives scientists with no bioinformatics expertise, the ability to obtain the prognostic biomarkers for different cancer types at different levels. We computed prognostic biomarkers from 8346 cancer patients for twenty cancer types. These biomarkers were computed based on i) presence of 352 Human leukocyte antigen class-I, ii) 660959 tumor-specific HLA1 neobinders, and iii) expression profile of 153 cytokines. It was observed that survival risk of cancer patients depends on presence of certain type of HLA-I alleles; for example, liver hepatocellular carcinoma patients with HLA-A*03:01 are at lower risk. Our analysis indicates that neobinders of HLA-I alleles have high correlation with overall survival of certain type of cancer patients. For example, HLA-B*07:02 binders have 0.49 correlation with survival of lung squamous cell carcinoma and -0.77 with kidney chromophobe patients. Additionally, we computed prognostic biomarkers based on cytokine expressions. Higher expression of few cytokines is survival favorable like IL-2 for bladder urothelial carcinoma, whereas IL-5R is survival unfavorable for kidney chromophobe patients. Freely accessible to public, CancerHLA-I maintains raw and analysed data (https://webs.iiitd.edu.in/raghava/cancerhla1/).

TNFepitope: A webserver for the prediction of TNF-α inducing epitopes.

Tumor Necrosis Factor alpha (TNF-α) is a pleiotropic pro-inflammatory cytokine that is crucial in controlling the signaling pathways within the immune cells. Recent studies reported that higher expression levels of TNF-α are associated with the progression of several diseases, including cancers, cytokine release syndrome in COVID-19, and autoimmune disorders. Thus, it is the need of the hour to develop immunotherapies or subunit vaccines to manage TNF-α progression in various disease conditions. In the pilot study, we proposed a host-specific in-silico tool for predicting, designing, and scanning TNF-α inducing epitopes. The prediction models were trained and validated on the experimentally validated TNF-α inducing/non-inducing epitopes from human and mouse hosts. Firstly, we developed alignment-free (machine learning based models using composition-based features of peptides) methods for predicting TNF-α inducing peptides and achieved maximum AUROC of 0.79 and 0.74 for human and mouse hosts, respectively. Secondly, an alignment-based (using BLAST) method has been used for predicting TNF-α inducing epitopes. Finally, a hybrid method (combination of alignment-free and alignment-based method) has been developed for predicting epitopes. Hybrid approach achieved maximum AUROC of 0.83 and 0.77 on an independent dataset for human and mouse hosts, respectively. We have also identified potential TNF-α inducing peptides in different proteins of HIV-1, HIV-2, SARS-CoV-2, and human insulin. The best models developed in this study has been incorporated in the webserver TNFepitope (https://webs.iiitd.edu.in/raghava/tnfepitope/), standalone package and GitLab (https://gitlab.com/raghavalab/tnfepitope).

A Web-Based Method for the Identification of IL6-Based Immunotoxicity in Vaccine Candidates.

Interleukin 6 (IL6) is a major pro-inflammatory cytokine that plays a pivotal role in both innate and adaptive immune responses. In the past, a number of studies reported that high level of IL6 promotes the proliferation of cancer, autoimmune disorders, and cytokine storm in COVID-19 patients. Thus, it is extremely important to identify and remove the antigenic regions from a therapeutic protein or vaccine candidate that may induce IL6-associated immunotoxicity. In order to overcome this challenge, our group has developed a computational tool, IL6pred, for discovering IL6-inducing peptides in a vaccine candidate. The aim of this chapter is to describe the potential applications and methodology of IL6pred. It sheds light on the prediction, designing, and scanning modules of IL6pred webserver and standalone package ( https://webs.iiitd.edu.in/raghava/il6pred/ ).

In Silico Tool for Identification, Designing, and Searching of IL13-Inducing Peptides in Antigens.

Interleukins are a distinctive class of molecules exhibiting various immune signaling functions. Immunoregulatory cytokine, Interleukin 13 (IL13), is primarily synthesized by activated T-helper 2 cells, mast cells, and basophils. IL13, is known to stimulate many allergic and autoimmune diseases, such as asthma, rheumatoid arthritis, systemic sclerosis, ulcerative colitis, airway hyperresponsiveness, glycoprotein hypersecretion, and goblet cell hyperplasia. In addition to such disorders, IL13 also leads to carcinogenesis by inhibiting tumor immunosurveillance. Due to its role in various diseases, predicting IL13-inducing peptides or regions in a protein is vital to designing safe protein vaccines and therapeutics. IL13pred is an in silico tool which aids in identifying, predicting, and designing IL13-inducing peptides. The IL13pred web server and standalone package is easily accessible at ( https://webs.iiitd.edu.in/raghava/il13pred/ ).

An ensemble method for prediction of phage-based therapy against bacterial infections.

Phage therapy is a viable alternative to antibiotics for treating microbial infections, particularly managing drug-resistant strains of bacteria. One of the major challenges in designing phage-based therapy is to identify the most appropriate potential phage candidate to treat bacterial infections. In this study, an attempt has been made to predict phage-host interactions with high accuracy to identify the potential bacteriophage that can be used for treating a bacterial infection. The developed models have been created using a training dataset containing 826 phage- host interactions, and have been evaluated on a validation dataset comprising 1,201 phage-host interactions. Firstly, alignment-based models have been developed using similarity between phage-phage (BLASTPhage), host-host (BLASTHost) and phage-CRISPR (CRISPRPred), where we achieved accuracy between 42.4-66.2% for BLASTPhage, 55-78.4% for BLASTHost, and 43.7-80.2% for CRISPRPred across five taxonomic levels. Secondly, alignment free models have been developed using machine learning techniques. Thirdly, hybrid models have been developed by integrating the alignment-free models and the similarity-scores where we achieved maximum performance of (60.6-93.5%). Finally, an ensemble model has been developed that combines the hybrid and alignment-based models. Our ensemble model achieved highest accuracy of 67.9, 80.6, 85.5, 90, and 93.5% at Genus, Family, Order, Class, and Phylum levels on validation dataset. In order to serve the scientific community, we have also developed a webserver named PhageTB and provided a standalone software package (https://webs.iiitd.edu.in/raghava/phagetb/) for the same.

Prediction of celiac disease associated epitopes and motifs in a protein.

Introduction: Celiac disease (CD) is an autoimmune gastrointestinal disorder causes immune-mediated enteropathy against gluten. Gluten immunogenic peptides have the potential to trigger immune responses which leads to damage the small intestine. HLA-DQ2/DQ8 are major alleles that bind to epitope/antigenic region of gluten and induce celiac disease. There is a need to identify CD associated epitopes in protein-based foods and therapeutics. Methods: In this study, computational tools have been developed to predict CD associated epitopes and motifs. Dataset used for training, testing and evaluation contain experimentally validated CD associated and non-CD associate peptides. We perform positional analysis to identify the most significant position of an amino acid residue in the peptide and checked the frequency of HLA alleles. We also compute amino acid composition to develop machine learning based models. We also developed ensemble method that combines motif-based approach and machine learning based models. Results and Discussion: Our analysis support existing hypothesis that proline (P) and glutamine (Q) are highly abundant in CD associated peptides. A model based on density of P&Q in peptides has been developed for predicting CD associated peptides which achieve maximum AUROC 0.98 on independent data. We discovered motifs (e.g., QPF, QPQ, PYP) which occurs specifically in CD associated peptides. We also developed machine learning based models using peptide composition and achieved maximum AUROC 0.99. Finally, we developed ensemble method that combines motif-based approach and machine learning based models. The ensemble model-predict CD associated motifs with 100% accuracy on an independent dataset, not used for training. Finally, the best models and motifs has been integrated in a web server and standalone software package "CDpred". We hope this server anticipate the scientific community for the prediction, designing and scanning of CD associated peptides as well as CD associated motifs in a protein/peptide sequence (https://webs.iiitd.edu.in/raghava/cdpred/).

Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models.

In the last three decades, a wide range of protein features have been discovered to annotate a protein. Numerous attempts have been made to integrate these features in a software package/platform so that the user may compute a wide range of features from a single source. To complement the existing methods, we developed a method, Pfeature, for computing a wide range of protein features. Pfeature allows to compute more than 200,000 features required for predicting the overall function of a protein, residue-level annotation of a protein, and function of chemically modified peptides. It has six major modules, namely, composition, binary profiles, evolutionary information, structural features, patterns, and model building. Composition module facilitates to compute most of the existing compositional features, plus novel features. The binary profile of amino acid sequences allows to compute the fraction of each type of residue as well as its position. The evolutionary information module allows to compute evolutionary information of a protein in the form of a position-specific scoring matrix profile generated using Position-Specific Iterative Basic Local Alignment Search Tool (PSI-BLAST); fit for annotation of a protein and its residues. A structural module was developed for computing of structural features/descriptors from a tertiary structure of a protein. These features are suitable to predict the therapeutic potential of a protein containing non-natural or chemically modified residues. The model-building module allows to implement various machine learning techniques for developing classification and regression models as well as feature selection. Pfeature also allows the generation of overlapping patterns and features from a protein. A user-friendly Pfeature is available as a web server python library and stand-alone package.

Advances in the field of phage-based therapy with special emphasis on computational resources.

In the current era, one of the major challenges is to manage the treatment of drug/antibiotic-resistant strains of bacteria. Phage therapy, a century-old technique, may serve as an alternative to antibiotics in treating bacterial infections caused by drug-resistant strains of bacteria. In this review, a systematic attempt has been made to summarize phage-based therapy in depth. This review has been divided into the following two sections: general information and computer-aided phage therapy (CAPT). In the case of general information, we cover the history of phage therapy, the mechanism of action, the status of phage-based products (approved and clinical trials) and the challenges. This review emphasizes CAPT, where we have covered primary phage-associated resources, phage prediction methods and pipelines. This review covers a wide range of databases and resources, including viral genomes and proteins, phage receptors, host genomes of phages, phage-host interactions and lytic proteins. In the post-genomic era, identifying the most suitable phage for lysing a drug-resistant strain of bacterium is crucial for developing alternate treatments for drug-resistant bacteria and this remains a challenging problem. Thus, we compile all phage-associated prediction methods that include the prediction of phages for a bacterial strain, the host for a phage and the identification of interacting phage-host pairs. Most of these methods have been developed using machine learning and deep learning techniques. This review also discussed recent advances in the field of CAPT, where we briefly describe computational tools available for predicting phage virions, the life cycle of phages and prophage identification. Finally, we describe phage-based therapy's advantages, challenges and opportunities.

DMPPred: a tool for identification of antigenic regions responsible for inducing type 1 diabetes mellitus.

There are a number of antigens that induce autoimmune response against ß-cells, leading to type 1 diabetes mellitus (T1DM). Recently, several antigen-specific immunotherapies have been developed to treat T1DM. Thus, identification of T1DM associated peptides with antigenic regions or epitopes is important for peptide based-therapeutics (e.g. immunotherapeutic). In this study, for the first time, an attempt has been made to develop a method for predicting, designing, and scanning of T1DM associated peptides with high precision. We analysed 815 T1DM associated peptides and observed that these peptides are not associated with a specific class of HLA alleles. Thus, HLA binder prediction methods are not suitable for predicting T1DM associated peptides. First, we developed a similarity/alignment based method using Basic Local Alignment Search Tool and achieved a high probability of correct hits with poor coverage. Second, we developed an alignment-free method using machine learning techniques and got a maximum AUROC of 0.89 using dipeptide composition. Finally, we developed a hybrid method that combines the strength of both alignment free and alignment-based methods and achieves maximum area under the receiver operating characteristic of 0.95 with Matthew's correlation coefficient of 0.81 on an independent dataset. We developed a web server 'DMPPred' and stand-alone server for predicting, designing and scanning T1DM associated peptides (https://webs.iiitd.edu.in/raghava/dmppred/).

Prediction of RNA-interacting residues in a protein using CNN and evolutionary profile.

This paper describes a method Pprint2, which is an improved version of Pprint developed for predicting RNA-interacting residues in a protein. Training and independent/validation datasets used in this study comprises of 545 and 161 non-redundant RNA-binding proteins, respectively. All models were trained on training dataset and evaluated on the validation dataset. The preliminary analysis reveals that positively charged amino acids such as H, R and K, are more prominent in the RNA-interacting residues. Initially, machine learning based models have been developed using binary profile and obtain maximum area under curve (AUC) 0.68 on validation dataset. The performance of this model improved significantly from AUC 0.68 to 0.76, when evolutionary profile is used instead of binary profile. The performance of our evolutionary profile-based model improved further from AUC 0.76 to 0.82, when convolutional neural network has been used for developing model. Our final model based on convolutional neural network using evolutionary information achieved AUC 0.82 with Matthews correlation coefficient of 0.49 on the validation dataset. Our best model outperforms existing methods when evaluated on the independent/validation dataset. A user-friendly standalone software and web-based server named 'Pprint2' has been developed for predicting RNA-interacting residues (https://webs.iiitd.edu.in/raghava/pprint2 and https://github.com/raghavagps/pprint2).

In silico method for predicting infectious strains of influenza A virus from its genome and protein sequences.

Influenza A is a contagious viral disease responsible for four pandemics in the past and a major public health concern. Being zoonotic in nature, the virus can cross the species barrier and transmit from wild aquatic bird reservoirs to humans via intermediate hosts. In this study, we have developed a computational method for the prediction of human-associated and non-human-associated influenza A virus sequences. The models were trained and validated on proteins and genome sequences of influenza A virus. Firstly, we have developed prediction models for 15 types of influenza A proteins using composition-based and one-hot-encoding features. We have achieved a highest AUC of 0.98 for HA protein on a validation dataset using dipeptide composition-based features. Of note, we obtained a maximum AUC of 0.99 using one-hot-encoding features for protein-based models on a validation dataset. Secondly, we built models using whole genome sequences which achieved an AUC of 0.98 on a validation dataset. In addition, we showed that our method outperforms a similarity-based approach (i.e., blast) on the same validation dataset. Finally, we integrated our best models into a user-friendly web server 'FluSPred' (https://webs.iiitd.edu.in/raghava/fluspred/index.html) and a standalone version (https://github.com/raghavagps/FluSPred) for the prediction of human-associated/non-human-associated influenza A virus strains.

A deep learning-based method for the prediction of DNA interacting residues in a protein.

DNA-protein interaction is one of the most crucial interactions in the biological system, which decides the fate of many processes such as transcription, regulation and splicing of genes. In this study, we trained our models on a training dataset of 646 DNA-binding proteins having 15 636 DNA interacting and 298 503 non-interacting residues. Our trained models were evaluated on an independent dataset of 46 DNA-binding proteins having 965 DNA interacting and 9911 non-interacting residues. All proteins in the independent dataset have less than 30% of sequence similarity with proteins in the training dataset. A wide range of traditional machine learning and deep learning (1D-CNN) techniques-based models have been developed using binary, physicochemical properties and Position-Specific Scoring Matrix (PSSM)/evolutionary profiles. In the case of machine learning technique, eXtreme Gradient Boosting-based model achieved a maximum area under the receiver operating characteristics (AUROC) curve of 0.77 on the independent dataset using PSSM profile. Deep learning-based model achieved the highest AUROC of 0.79 on the independent dataset using a combination of all three profiles. We evaluated the performance of existing methods on the independent dataset and observed that our proposed method outperformed all the existing methods. In order to facilitate scientific community, we developed standalone software and web server, which are accessible from https://webs.iiitd.edu.in/raghava/dbpred.

Prediction of risk-associated genes and high-risk liver cancer patients from their mutation profile: benchmarking of mutation calling techniques.

Identification of somatic mutations with high precision is one of the major challenges in the prediction of high-risk liver cancer patients. In the past, number of mutations calling techniques has been developed that include MuTect2, MuSE, Varscan2, and SomaticSniper. In this study, an attempt has been made to benchmark the potential of these techniques in predicting the prognostic biomarkers for liver cancer. Initially, we extracted somatic mutations in liver cancer patients using Variant Call Format (VCF) and Mutation Annotation Format (MAF) files from the cancer genome atlas. In terms of size, the MAF files are 42 times smaller than VCF files and containing only high-quality somatic mutations. Furthermore, machine learning-based models have been developed for predicting high-risk cancer patients using mutations obtained from different techniques. The performance of different techniques and data files has been compared based on their potential to discriminate high- and low-risk liver cancer patients. Based on correlation analysis, we selected 80 genes having significant negative correlation with the overall survival of liver cancer patients. The univariate survival analysis revealed the prognostic role of highly mutated genes. Single gene-based analysis showed that MuTect2 technique-based MAF file has achieved maximum hazard ratio (HRLAMC3) of 9.25 with P-value of 1.78E-06. Further, we developed various prediction models using risk-associated top-10 genes for each technique. Our results indicate that MuTect2 technique-based VCF files outperform all other methods with maximum Area Under the Receiver-Operating Characteristic curve of 0.765 and HR = 4.50 (P-value = 3.83E-15). Eventually, VCF file generated using MuTect2 technique performs better among other mutation calling techniques for the prediction of high-risk liver cancer patients. We hope that our findings will provide a useful and comprehensive comparison of various mutation-calling techniques for the prognostic analysis of cancer patients. In order to serve the scientific community, we have provided a Python-based pipeline to develop the prediction models using mutation profiles (VCF/MAF) of cancer patients. It is available on GitHub at https://github.com/raghavagps/mutation_bench.

HLAncPred: a method for predicting promiscuous non-classical HLA binding sites.

Human leukocyte antigens (HLA) regulate various innate and adaptive immune responses and play a crucial immunomodulatory role. Recent studies revealed that non-classical HLA-(HLA-E & HLA-G) based immunotherapies have many advantages over traditional HLA-based immunotherapy, particularly against cancer and COVID-19 infection. In the last two decades, several methods have been developed to predict the binders of classical HLA alleles. In contrast, limited attempts have been made to develop methods for predicting non-classical HLA binding peptides, due to the scarcity of sufficient experimental data. Of note, in order to facilitate the scientific community, we have developed an artificial intelligence-based method for predicting binders of class-Ib HLA alleles. All the models were trained and tested on experimentally validated data obtained from the recent release of IEDB. The machine learning models achieved more than 0.98 AUC for HLA-G alleles on validation dataset. Similarly, our models achieved the highest AUC of 0.96 and 0.94 on the validation dataset for HLA-E*01:01 and HLA-E*01:03, respectively. We have summarized the models developed in the past for non-classical HLA and validated the performance with the models developed in this study. Moreover, to facilitate the community, we have utilized our tool for predicting the potential non-classical HLA binding peptides in the spike protein of different variants of virus causing COVID-19, including Omicron (B.1.1.529). One of the major challenges in the field of immunotherapy is to identify the promiscuous binders or antigenic regions that can bind to a large number of HLA alleles. To predict the promiscuous binders for the non-classical HLA alleles, we developed a web server HLAncPred (https://webs.iiitd.edu.in/raghava/hlancpred) and standalone package.

In silico tools and databases for designing cancer immunotherapy.

Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.

IL13Pred: A method for predicting immunoregulatory cytokine IL-13 inducing peptides.

BACKGROUND: Interleukin 13 (IL-13) is an immunoregulatory cytokine, primarily released by activated T-helper 2 cells. IL-13 induces the pathogenesis of many allergic diseases, such as airway hyperresponsiveness, glycoprotein hypersecretion, and goblet cell hyperplasia. In addition, IL-13 inhibits tumor immunosurveillance, leading to carcinogenesis. Since elevated IL-13 serum levels are severe in COVID-19 patients, predicting IL-13 inducing peptides or regions in a protein is vital to designing safe protein therapeutics particularly immunotherapeutic. OBJECTIVE: The present study describes a method to develop, predict, design, and scan IL-13 inducing peptides. METHODS: The dataset experimentally validated 313 IL-13 inducing peptides, and 2908 non-inducing homo-sapiens peptides extracted from the immune epitope database (IEDB). A total of 95 key features using the linear support vector classifier with the L1 penalty (SVC-L1) technique was extracted from the originally generated 9165 features using Pfeature. These key features were ranked based on their prediction ability, and the top 10 features were used to build machine learning prediction models. Various machine learning techniques were deployed to develop models for predicting IL-13 inducing peptides. These models were trained, tested, and evaluated using five-fold cross-validation techniques; the best model was evaluated on an independent dataset. RESULTS: Our best model based on XGBoost achieves a maximum AUC of 0.83 and 0.80 on the training and independent dataset, respectively. Our analysis indicates that certain SARS-COV2 variants are more prone to induce IL-13 in COVID-19 patients. CONCLUSION: The best performing model was incorporated in web-server and standalone package named 'IL-13Pred' for precise prediction of IL-13 inducing peptides. For large dataset analysis standalone package of IL-13Pred is available at (https://webs.iiitd.edu.in/raghava/il13pred/) webserver and over GitHub link: https://github.com/raghavagps/il13pred.

ProCanBio: A Database of Manually Curated Biomarkers for Prostate Cancer.

Prostate cancer (PCa) is the second lethal malignancy in men worldwide. In the past, numerous research groups investigated the omics profiles of patients and scrutinized biomarkers for the diagnosis and prognosis of PCa. However, information related to the biomarkers is widely scattered across numerous resources in complex textual format, which poses hindrance to understand the tumorigenesis of this malignancy and scrutinization of robust signature. To create a comprehensive resource, we collected all the relevant literature on PCa biomarkers from the PubMed. We scrutinize the extensive information about each biomarker from a total of 412 unique research articles. Each entry of the database incorporates PubMed ID, biomarker name, biomarker type, biomolecule, source, subjects, validation status, and performance measures such as sensitivity, specificity, and hazard ratio (HR). In this study, we present ProCanBio, a manually curated database that maintains detailed data on 2053 entries of potential PCa biomarkers obtained from 412 publications in user-friendly tabular format. Among them are 766 protein-based, 507 RNA-based, 157 genomic mutations, 260 miRNA-based, and 122 metabolites-based biomarkers. To explore the information in the resource, a web-based interactive platform was developed with searching and browsing facilities. To the best of the authors' knowledge, there is no resource that can consolidate the information contained in all the published literature. Besides this, ProCanBio is freely available and is compatible with most web browsers and devices. Eventually, we anticipate this resource will be highly useful for the research community involved in the area of prostate malignancy.

Computer-aided prediction of inhibitors against STAT3 for managing COVID-19 associated cytokine storm.

BACKGROUND: Proinflammatory cytokines are correlated with the severity of disease in patients with COVID-19. IL6-mediated activation of STAT3 proliferates proinflammatory responses that lead to cytokine storm promotion. Thus, STAT3 inhibitors may play a crucial role in managing the COVID-19 pathogenesis. The present study discusses a method for predicting inhibitors against the STAT3 signaling pathway. METHOD: The main dataset comprises 1565 STAT3 inhibitors and 1671 non-inhibitors used for training, testing, and evaluation of models. A number of machine learning classifiers have been implemented to develop the models. RESULTS: The outcomes of the data analysis show that rings and aromatic groups are significantly abundant in STAT3 inhibitors compared to non-inhibitors. First, we developed models using 2-D and 3-D chemical descriptors and achieved a maximum AUC of 0.84 and 0.73, respectively. Second, fingerprints are used to build predictive models and achieved 0.86 AUC with an accuracy of 78.70% on the validation dataset. Finally, models were developed using hybrid descriptors, which achieved a maximum of 0.87 AUC with 78.55% accuracy on the validation dataset. CONCLUSION: We used the best model to identify STAT3 inhibitors in FDA-approved drugs and found few drugs (e.g., Tamoxifen and Perindopril) to manage the cytokine storm in COVID-19 patients. A webserver "STAT3In" (https://webs.iiitd.edu.in/raghava/stat3in/) has been developed to predict and design STAT3 inhibitors.