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Patiyal, S.

Publications and source records attributed to Patiyal, S..

At least 19 recordsLinked to original sources
bioRxiv2026

Transient Splicing-Associated Isoform Remodeling Defines Early MAPKi Resistance Adaptation in Melanoma

A long-standing question in targeted therapy resistance is how tumor cells rapidly enter drug-tolerant states before stable resistance emerges. In BRAF-mutant melanoma, early adaptation to MAPK inhibitor (MAPKi) therapy is accompanied by profound phenotypic plasticity, yet the molecular mechanisms that enable this transition remain poorly understood. Here, we show that MAPKi adaptation is characterized by a transient transcript isoform remodeling that is largely invisible to conventional gene-expression analysis. We integrated and analyzed multiple different datasets, including bulk and single-cell RNA-seq datasets from melanoma cell lines, murine models, patient-derived xenografts and patient tumors, spanning no-treatment, on-treatment/minimal residual disease, and acquired-resistant states. We find that differential transcript usage peaks during the adaptive on-treatment phase and then is markedly attenuated when resistance was established. These dynamics were conserved across experimental systems and supported by independent exon-usage analyses. Notably, most genes undergoing differential transcript usage showed no corresponding change in total gene expression, and genes with isoform-level changes more strongly highlighted MAPKi resistance-associated pathways, including PI3K/AKT/mTOR and receptor tyrosine kinase signaling. Further, classifiers trained on cell-line transcript-usage features identified the adaptive state more accurately than gene-expression-based models in independent PDX and patient datasets. Interestingly, MAPKi-associated isoform changes recapitulated melanoblast-associated developmental splicing programs and were linked to a few key splicing regulators. Together, these findings identify an underappreciated regulatory layer connecting drug tolerance, developmental plasticity, and acquired resistance. Beyond the current context, our results suggest that comprehensively tracking isoform and splicing alterations may potentially advance our understanding of the emergence of treatment resistance in additional indications and treatment regimes.

cancer biology↗
bioRxiv2026

Histopathology-inferred spatial transcriptomics characterizes the tumor microenvironment in 1,500 head and neck tumors and predicts clinical outcomes

Head and neck squamous cell carcinoma (HNSC) is a prevalent malignancy associated with poor prognosis despite recent therapeutic advances. We hypothesized that a comprehensive understanding of the spatial heterogeneity and organization of the tumor microenvironment (TME) can substantially improve risk stratification and prediction of treatment response in HNSC. As spatial transcriptomics (ST) remains labor-intensive and costly, we developed HEiST (H&E-Inferred Spatial Transcriptomics), a deep learning framework that predicts spatially resolved gene expression profiles directly from routine hematoxylin and eosin (H&E)-stained histology slides. After rigorous validation across two independent external ST cohorts, we applied HEiST to infer spatial transcriptomes across 1,500 HNSC patient tumors spanning two publicly available datasets and two newly generated cohorts, one treated with concurrent chemoradiotherapy (CCRT) and one with immunotherapy. This large-scale analysis uncovered reproducible spatial clusters characterizing the HNSC TME, defining two distinct prognostic Spatiotypes, Immune-Exhausted and Immune-Activated, with significantly distinct survival outcomes. Critically, spatial cluster composition accurately predicts HPV status and yields treatment response predictors for both CCRT/radiotherapy and immunotherapy that outperform costly gene-expression and direct image-based approaches. Notably, the ST cluster-based predictor of immunotherapy response markedly surpasses the performance of commonly used FDA-approved biomarkers, including CPS, TPS, and their combination. To the best of our knowledge, this represents the first virtual spatial profiling effort and the most comprehensive large-scale spatial TME analysis in HNSC to date. HEiST thus introduces a scalable, low-cost, and spatially grounded biomarker discovery for precision oncology in HNSC.

cancer biology↗
bioRxiv2025

AI-based supervised treatment response prediction from tumor transcriptomics: A large-scale pan-cancer study

Precision oncology aims to guide treatment decisions using biomarkers. While DNA-based panels are increasingly applied, RNA transcriptomics remain underused due to limited datasets and the absence of robust models. We assembled the largest transcriptomic resource for drug response prediction to date, spanning 91 cohorts, 5,675 patients, nine cancer types, and six frontline therapies: anti-PD-1/PD-L1 immune-checkpoint inhibitors, trastuzumab, bevacizumab, BRAF inhibitors, paclitaxel, and FAC/FEC (Fluorouracil-Adriamycin-Cyclophosphamide/Fluorouracil-Epirubicin-Cyclophosphamide) chemotherapy. We developed EXPRESSO (EXpression-Profile-RESponSe-Optimizer), a supervised machine-learning framework that predicts treatment response from pre-treatment transcriptomes by integrating drug targets and context-specific biomarkers. EXPRESSO achieves mean ROC-AUCs of 0.62-0.73 and median odds ratios of 2.4-4.6 across therapies, outperforming 20 published transcriptomic signatures and other machine learning methods. Prospective validation on 22 independent cohorts confirms that performance generalizes beyond cross-validation. The EXPRESSO signature additionally stratifies progression-free survival in immune checkpoint blockade-treated cohorts, demonstrating prognostic value beyond binary response prediction. Robustness analysis reveals that predictive performance plateaued for some therapies with increasing training cohorts but continued to improve for others. These findings suggest inherent limits of supervised brute-force learning for certain treatments, but additional data and deeper mechanistic modeling may further enhance transcriptomics-based predictors. SIGNIFICANCEEXPRESSO forms the next step in studying the feasibility of harnessing bulk transcriptomic data to inform therapeutic decision-making, advancing the role of transcriptomics from exploratory biomarker discovery to actionable predictive modeling.

cancer biology↗
bioRxiv2025

Pan-cancer prediction of tumor immune activation and response to immune checkpoint blockade from tumor transcriptomics and histopathology

Accurately predicting which patients will respond to immune checkpoint blockade (ICB) remains a major challenge. Here, we present TIME_ACT, an unsupervised 66-gene transcriptomic signature of tumor immune activation derived from TCGA (The Cancer Genome Atlas) melanoma data. First, we demonstrate that TIME_ACT scores accurately identify tumors with activated immune microenvironments across different cancer types. Further, analysis of spatial features reveals that tumor microenvironment regions with dense lymphocyte infiltration near tumor cells have high TIME_ACT scores, successfully marking localized immune activation. Second, across 25 transcriptomic ICB cohorts encompassing nine cancer types, TIME_ACT achieves a mean AUC of 0.76 and a mean odds ratio of 5.77, significantly outperforming 30 established transcriptomic signatures and prediction methods for ICB response, including a recently developed foundation model for immunotherapy response prediction. Third, we show that TIME_ACT scores can be accurately inferred from routine tumor histopathology slides and that slide-inferred TIME_ACT scores predict ICB response across nine new independent patient cohorts spanning eight cancer types, achieving a mean AUC of 0.72 and a mean odds ratio of 4.99. These findings establish TIME_ACT as a robust, pan-cancer biomarker that enables accurate, low-cost, and clinically scalable prediction of ICB response from routine histopathology.

cancer biology↗
bioRxiv2025

PEPlife2: A Updated Repository of the Half-life of Peptides

This manuscript presents an updated version of PEPlife, a manually curated database that provides comprehensive information on peptide half-life. The updated version, PEPlife2, includes 4,412 entries, compared to 2,229 entries in the previous version. Each entry contains detailed information that include experimental methods used to determine half-life, chemical modifications, biological activity, routes of administration, and other relevant data. The database encompasses 402 proteins and 1,781 peptides, derived from 105 unique assays. In addition to natural peptide sequences, PEPlife2 includes cyclic peptides and chemically modified peptides, such as those with N- and C-terminal modifications. To provide structural insights, peptide and protein structures are sourced from the Protein Data Bank (PDB) or predicted using PEPstrMOD. To support the scientific community, we have developed a user-friendly interface and integrated advanced analytical tools, including BLAST, Smith-Waterman, GGSEARCH, CLUSTALW, and MUSTANG. The updated database is accessible at: https://webs.iiitd.edu.in/raghava/peplife2/. Authors BiographyO_LIUrooj Alam is currently pursuing a Masters degree in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIKunal Chaudhary is working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LINishant Kumar is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LIRitu Tomer is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LISumeet Patiyal is currently working as a postdoctoral visiting fellow Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. C_LIO_LIGajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗
bioRxiv2025

NfκBin: A Machine Learning based Method for Screening Nf-κB Inhibitors.

Nuclear Factor kappa B (NF-{kappa}B) is a transcription factor whose upregulation is associated in chronic inflammatory diseases, including rheumatoid arthritis, inflammatory bowel disease, and asthma. In order to develop therapeutic strategies targeting NF-{kappa}B-related diseases, we developed a computational approach to predict drugs capable of inhibiting NF-{kappa}B signaling pathways. In this study, we utilized a dataset comprising 1,149 inhibitors and 1,332 non-inhibitors retrieved from PubChem. Chemical descriptors were computed using the PaDEL software, and relevant features were selected using advanced feature selection techniques. Initially, machine learning models were constructed using 2D descriptors, 3D descriptors, and molecular fingerprints, achieving maximum AUC values of 0.66, 0.56, and 0.66, respectively. To improve feature selection, we applied univariate analysis and SVC-L1 regularization to identify features that can effectively differentiate inhibitors from non-inhibitors. Using these selected features, we developed machine learning models, our support vector classifier achieved an AUC of 0.75 on the validation dataset. All models were trained using five-fold cross-validation and rigorously evaluated on an independent validation dataset, which was not used during training or hyperparameter optimization. Finally, our best-performing model was employed to screen FDA-approved drugs for potential NF-{kappa}B inhibitors. Notably, most of the predicted inhibitors corresponded to drugs previously identified as inhibitors in experimental studies, underscoring the models predictive reliability. Our best- performing models have been integrated into a standalone software and web server, Nf{kappa}Bin, to enable the scientific community to perform high-throughput screening of Nf-{kappa}B inhibitors from chemical libraries. (https://webs.iiitd.edu.in/raghava/nfkbin/). Highlights1) Inhibitors of Nf-{kappa}B can be used to manage disease like rheumatoid arthritis, asthma. 2) A novel tool Nf{kappa}Bin developed for screening Nf-{kappa}B inhibitors from chemical library. 3) Cutting-edge feature selection techniques used to identify the most relevant chemical descriptors. 4) Models were developed and validated on a robust dataset of inhibitors and non-inhibitors. 5) Integrated best-performing models into the Nf{kappa}Bin software and web server.

bioinformatics↗
bioRxiv2024

High Throughput Meta-analysis of Antimicrobial Peptides for Characterizing Class Specific Therapeutic Candidates: An in-silico Approach

The increasing incidence of antimicrobial resistance (AMR) is becoming a serious concern worldwide and requires newer drugs. Recent evidence has shown growing interest in developing peptide-based therapeutics to tackle AMR. In the current study, we performed a meta-analysis of nearly 8.6 million predicted antimicrobial peptides (AMPs) and assessed their antibacterial, antiviral, and antifungal activity. We created high-quality, class-specific datasets and performed several analyses, including amino acid composition, motif preference, physiochemical properties, etc. We observed significant differences in the residue composition, charge, molecular weight, pI value, and instability index of peptides among 3 AMP classes. We further developed multiple machine learning models to predict peptide activity using the dataset. Our Extratree model developed using composition-based features achieved the highest AUROC of 0.98 for antibacterial peptide (ABP), 0.99 for antiviral peptide (AVP), and 0.99 for antifungal peptide (AFP) prediction when tested on an independent dataset. Subsequent filtering of peptides based on moonlighting properties (toxicity, allergenicity, cell-penetrating ability, half-life, and secondary structure) yielded a list of peptides that exhibit substantial therapeutic potential. We further selected the top 10 peptides in each category based on their half-lives, predicted their 3D structures using ColabFold, a feature built into ChimeraX1.8 software, and used HDock to perform molecular docking analysis with a pathogenic protein selected from an organism in each class. Docking studies demonstrated strong interaction between peptides and proteins, and based on free energy, we ranked the peptides. Overall, we have put forth class-specific peptides with high therapeutic potential based on rigorous meta-analysis encompassing [~]8.6 million AMPs.

bioinformatics↗
bioRxiv2024

Prediction of anti-freezing proteins from their evolutionary profile

1.Prediction of antifreeze proteins (AFPs) holds significant importance due to their diverse applications in healthcare. An inherent limitation of current AFP prediction methods is their reliance on unreviewed proteins for evaluation. This study evaluates proposed and existing methods on an independent dataset containing 81 AFPs and 73 non-AFPs obtained from Uniport, which have been already reviewed by experts. Initially, we constructed machine learning models for AFP prediction using selected composition-based protein features and achieved a peak AUC of 0.90 with an MCC of 0.69 on the independent dataset. Subsequently, we observed a notable enhancement in model performance, with the AUC increasing from 0.90 to 0.93 upon incorporating evolutionary information instead of relying solely on the primary sequence of proteins. Furthermore, we explored hybrid models integrating our machine learning approaches with BLAST-based similarity and motif-based methods. However, the performance of these hybrid models either matched or was inferior to that of our best machine-learning model. Our best model based on evolutionary information outperforms all existing methods on independent/validation dataset. To facilitate users, a user-friendly web server with a standalone package named "AFPropred" was developed (https://webs.iiitd.edu.in/raghava/afpropred). HighlightsO_LIPrediction of antifreeze proteins with high precision C_LIO_LIEvaluation of prediction models on an independent dataset C_LIO_LIMachine learning based models using sequence composition C_LIO_LIEvolutionary information based prediction models C_LIO_LIA webserver for predicting, scanning, and designing AFPs. C_LI Authors BiographyO_LINishant Kumar is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIShubham Choudhury is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LINisha Bajiya is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LISumeet Patiyal is currently working as a postdoctoral visiting fellow Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA. C_LIO_LIGajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗
bioRxiv2023

A Deep Learning method for classification of HNSCC and HPV patients using single-cell transcriptomics

Head and Neck Squamous Cell Carcinoma (HNSCC) is the seventh most highly prevalent cancer type worldwide. Early detection of HNSCC is one of the important challenges in managing the treatment of the cancer patients. Existing techniques for detecting HNSCC are costly, expensive, and invasive in nature. In this study, we aimed to address this issue by developing classification models using machine learning and deep learning techniques, focusing on single-cell transcriptomics to distinguish between HNSCC and normal samples. Additionally, we built models to classify HNSCC samples into HPV-positive (HPV+) and HPV-negative (HPV-) categories. The models developed in this study have been trained on 80% of the GSE181919 dataset and validated on the remaining 20%. To develop an efficient model, we performed feature selection using mRMR method to shortlist a small number of genes from a plethora of genes. Artificial Neural Network based model trained on 100 genes outperformed the other classifiers with an AUROC of 0.91 for HNSCC classification for the validation set. The same algorithm achieved an AUROC of 0.83 for the classification of HPV+ and HPV-patients on the validation set. We also performed Gene Ontology (GO) enrichment analysis on the 100 shortlisted genes and found that most genes were involved in binding and catalytic activities. To facilitate the scientific community, a software package has been developed in Python which allows users to identify HNSCC in patients along with their HPV status. It is available at https://webs.iiitd.edu.in/raghava/hnscpred/. Key PointsO_LIApplication of single cell transcriptomics in cancer diagnosis C_LIO_LIDevelopment of models for predicting HNSCC patients C_LIO_LIClassification of HPV+ and HPV-HNSCC patients C_LIO_LIIdentification of gene biomarkers from single cell sequencing C_LIO_LIA standalone software package HNSCpred for predicting HNSCC patients C_LI Authors BiographyO_LIAkanksha Jarwal is currently pursuing an M. Tech. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAnjali Dhall is currently pursuing a Ph.D. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAkanksha Arora is currently pursuing a Ph.D. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LISumeet Patiyal is currently pursuing a Ph.D. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAman Srivastava is currently pursuing an M. Tech. in Computational Biology at the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIGajendra P. S. Raghava is currently working as a Professor and Head of the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗
bioRxiv2023

HLA-DR4Pred2: an improved method for predicting HLA-DRB1*04:01 binders

HLA-DRB1*04:01 is associated with many disease that include sclerosis, arthritis, diabetes and Covid19. Thus, it is important to scan binders of HLA-DRB1*04:01 in an antigen to develop immunotherapy, vaccine and protection against these diseases. One of the major limitations of existing methods for predicting with HLA-DRB1*04:01 binders is that these methods trained on small datasets. This study present a method HLA-DR4Pred2 developed on a large dataset contain 12676 binders and equal number of non-binders. It is an improved version of HLA-DR4Pred, which was trained on a small dataset contain only 576 binders and equal number of binders. All models in this study were trained, optimized and tested on 80% of data called training datasets using five-fold cross-validation; final models were evaluated on 20% of data called validation/independent dataset. A wide range of machine learning techniques have been employed to develop prediction models and achieved maximum AUC of 0.90 and 0.87 on validation dataset using composition and binary profile features respectively. The performance of our composition based model increased from 0.90 to 0.93 when combined with BLAST search. In addition, we also developed our models on alternate or realistic dataset that contain 12676 binders and 86300 non-binders and achieved maximum AUC 0.99. Our method perform better than existing methods when we compare the performance of our best model with performance of existing methods on validation dataset. Finally, we developed standalone and online version of HLA-DR4Pred2 for predicting, designing and virtual scanning of HLA- DRB1*04:01(https://webs.iiitd.edu.in/raghava/hladr4pred2/; https://github.com/raghavagps/hladr4pred2). Key PointsO_LIHLADR4Pred2.0 is an update of HLADR4Pred C_LIO_LIPredict the binding or non-binding peptides for MHC-Class II allele HLA- DRB1*04:01 C_LIO_LIUsed alignment free and alignment based hybrid approach C_LIO_LIMotifs which are highly specific to HLA-DRB1*04:01 binders C_LIO_LIBenchmark the performance of the other existing methods with HLADR4Pred2.0 C_LI Authors BiographyO_LISumeet Patiyal is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LIAnjali Dhall is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LINishant Kumar is currently working as Ph.D. in Computational biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LIO_LIGajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗
bioRxiv2023

Prediction of Alzheimer's Disease from Single Cell Transcriptomics Using Deep Learning

Alzheimers disease (AD) is a progressive neurological disorder characterized by brain cell death, brain atrophy, and cognitive decline. Early diagnosis of AD remains a significant challenge in effectively managing this debilitating disease. In this study, we aimed to harness the potential of single-cell transcriptomics data from 12 Alzheimers patients and 9 normal controls (NC) to develop a predictive model for identifying AD patients. The dataset comprised gene expression profiles of 33,538 genes across 169,469 cells, with 90,713 cells belonging to AD patients and 78,783 cells belonging to NC individuals. Employing machine learning and deep learning techniques, we developed prediction models. Initially, we performed data processing to identify genes expressed in most cells. These genes were then ranked based on their ability to classify AD and NC groups. Subsequently, two sets of genes, consisting of 35 and 100 genes, respectively, were used to develop machine learning-based models. Although these models demonstrated high performance on the training dataset, their performance on the validation/independent dataset was notably poor, indicating potential overoptimization. To address this challenge, we developed a deep learning method utilizing dropout regularization technique. Our deep learning approach achieved an AUC of 0.75 and 0.84 on the validation dataset using the sets of 35 and 100 genes, respectively. Furthermore, we conducted gene ontology enrichment analysis on the selected genes to elucidate their biological roles and gain insights into the underlying mechanisms of Alzheimers disease. While this study presents a prototype method for predicting AD using single-cell genomics data, it is important to note that the limited size of the dataset represents a major limitation. To facilitate the scientific community, we have created a website to provide with code and service. It is freely available at https://webs.iiitd.edu.in/raghava/alzscpred. Key PointsO_LIPredictive Model for Alzheimers Disease Using Single Cell Transcriptomics Data C_LIO_LIOveroptimization of models trained on single-cell genomics data. C_LIO_LIApplication of dropout regularization technique of ANN for reducing overoptimization C_LIO_LIRanking of genes based on their ability to predict patients Alzheimers Disease C_LIO_LIStandalone software package for predicting Alzheimers Disease C_LI Authors BiographyO_LIAman Srivastava is pursuing M. Tech. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAnjali Dhall is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LISumeet Patiyal is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAkanksha Arora is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIAkanksha Jarwal is pursuing M. Tech. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIGajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LI

bioinformatics↗
bioRxiv2023

Hmrbase2: A comprehensive database of hormones and their receptors

Background and objectiveHormones are essential for cell communication and hence regulate various physiological processes. The discrepancies in the hormones or their receptors can break this communication and cause major endocrinological disorders. It is, therefore, indispensable for the therapeutics and diagnostics of hormonal diseases. MethodsWe collected widespread information on peptide and non-peptide hormones and hormone receptors. The information was collected from HMDB, UniProt, HORDB, ENDONET, PubChem and literature. ResultsHmrbase2 is an updated version of Hmrbase. The current version contains a total of 12056 entries which is more than twice the entries in the previous version. These include 7406, 753, and 3897 entries for peptide hormones, non-peptide hormones and hormone receptors, respectively, from 803 organisms compared to the 562 organisms in the previous version. The database also hosts 5662 hormone receptor pairs. The source organism, function, and subcellular location are provided for peptide hormones and receptors and properties like melting point; water solubility is provided for non-peptide hormones. Besides browsing and keyword search, an advanced search option has also been provided. Additionally, a similarity search module has been incorporated, enabling users to run similarity searches against peptide hormone sequences using BLAST and Smith-Waterman. ConclusionsTo make the database accessible to various users, we designed a user-friendly, responsive website that can be easily used on smartphones, tablets, and desktop computers. The updated database version, Hmrbase2, offers improved data content compared to the previous version. Homebase 2.0 is freely available at https://webs.iiitd.edu.in/raghava/hmrbase2.

bioinformatics↗
bioRxiv2023

A hybrid method for discovering interferon-gamma inducing peptides in human and mouse

A host-specific technique has been developed for annotating interferon-gamma (IFN-{gamma}) inducing peptides, it is an updated version of IFNepitope. In this study, dataset used for developing prediction method contain experimentally validated 25492 and 7983 IFN-{gamma} inducing peptides in human and mouse host, respectively. In initial phase, machine learning techniques have been exploited to develop classification model using wide range of peptide features. In most of the case, models based on extra tree perform better than other machine learning techniques. In case of peptide features, compositional feature particularly dipeptide composition performs better than one-hot encoding or binary profile. Our best machine learning based models achieved AUROC 0.89 and 0.83 for human and mouse host, respectively. In order to improve machine learning based models or alignment free models, we explore potential of similarity-based technique BLAST. Finally, a hybrid model has been developed that combine best machine learning based model with BLAST and achieved AUROC 0.90 and 0.85 for human and mouse host, respectively. All models have been evaluated on an independent/validation dataset not used for training or testing these models. Newly developed method performs better than existing method on independent dataset. The major objective of this study is to predict, design and scan IFN-{gamma} inducing peptides, thus server/software have been developed (https://webs.iiitd.edu.in/raghava/ifnepitope2/). HighlightsO_LIAn updated method for predicting interferon-gamma (IFN-{gamma}) inducing peptides. C_LIO_LIA wide range of features have been generated using Pfeature tool. C_LIO_LIModels were trained and tested on experimentally validated datasets. C_LIO_LIHybrid models developed by combining machine-learning and BLAST. C_LIO_LIIFNepitope2 server is available to design subunit or peptide-based vaccines. C_LI

bioinformatics↗
bioRxiv2023

A random forest model for predicting exosomal proteins using evolutionary information and motifs

Identification of secretory proteins in body fluids is one of the key challenges in the development of non-invasive diagnostics. It has been shown in the part that a significant number of proteins are secreted by cells via exosomes called exosomal proteins. In this study, an attempt has been made to build a model that can predict exosomal proteins with high precision. All models are trained, tested, and evaluated on a non-redundant dataset comprising 2831 exosomal and 2831 non-exosomal proteins, where no two proteins have more than 40% similarity. Initially, the standard similarity-based method BLAST was used to predict exosomal proteins, which failed due to low-level similarity in the dataset. To overcome this challenge, machine learning based models have been developed using compositional features of proteins and achieved highest AUROC of 0.70. The performance of the ML-based models improved significantly to AUROC of 0.73 when evolutionary information in the form of PSSM profiles was used for building models. Our analysis indicates that exosomal proteins have wide range of motifs. In addition, it was observed that exosomal proteins contain different types of sequence-based motifs, which can be used for predicting exosomal proteins. Finally, a hybrid method has been developed that combines a motif-based approach and an ML-based model for predicting exosomal proteins, achieving a maximum AUROC 0.85 and MCC of 0.56 on an independent dataset. The hybrid model in this study performs better than the presently available methods when assessed on an independent dataset. A web server and a standalone software ExoProPred has been created for the scientific community to provide service, code, and data. (https://webs.iiitd.edu.in/raghava/exopropred/). KeypointsO_LIExosomal proteins or non-classical secretory proteins are secreted by via exosomes C_LIO_LIA method has been developed for predicting exosomal proteins C_LIO_LIModels have been trained, tested, and evaluated on non-redundant dataset C_LIO_LIWide range of sequence motifs have been discovered in exosomal proteins C_LIO_LIA web server and standalone software have been developed C_LI

bioinformatics↗
bioRxiv2023

A method for predicting linear and conformational B-cell epitopes in an antigen from its primary sequence

B-cell is an essential component of the immune system that plays a vital role in providing the immune response against any pathogenic infection by producing antibodies. Existing methods either predict linear or conformational B-cell epitopes in an antigen. In this study, a single method was developed for predicting both types (linear/conformational) of B-cell epitopes. The dataset used in this study contains 3875 B-cell epitopes and 3996 non-B-cell epitopes, where B-cell epitopes consist of both linear and conformational B-cell epitopes. Our primary analysis indicates that certain residues (like Asp, Glu, Lys, Asn) are more prominent in B-cell epitopes. We developed machine-learning based methods using different types of sequence composition and achieved the highest AUC of 0.80 using dipeptide composition. In addition, models were developed on selected features, but no further improvement was observed. Our similarity-based method implemented using BLAST shows a high probability of correct prediction with poor sensitivity. Finally, we came up with a hybrid model that combine alignment free (dipeptide based random forest model) and alignment-based (BLAST based similarity) model. Our hybrid model attained maximum AUC 0.83 with MCC 0.49 on the independent dataset. Our hybrid model performs better than existing methods on an independent dataset used in this study. All models trained and tested on 80% data using cross-validation technique and final model was evaluated on 20% data called independent or validation dataset. A webserver and standalone package named "CLBTope" has been developed for predicting, designing, and scanning B-cell epitopes in an antigen sequence (https://webs.iiitd.edu.in/raghava/clbtope/).

bioinformatics↗
bioRxiv2023

A hybrid approach for predicting multi-label subcellular localization of mRNA at genome scale

In the past, number of methods have been developed for predicting single label subcellular localization of mRNA in a cell. Only limited methods had been built to predict multi-label subcellular localization of mRNA. Most of the existing methods are slow and cannot be implemented at transcriptome scale. In this study, a fast and reliable method had been developed for predicting multi-label subcellular localization of mRNA that can be implemented at genome scale. Firstly, deep learning method based on convolutional neural network method have been developed using one-hot encoding and attained an average AUROC - 0.584 (0.543 - 0.605). Secondly, machine learning based methods have been developed using mRNA sequence composition, our XGBoost classifier achieved an average AUROC - 0.709 (0.668 - 0.732). In addition to alignment free methods, we also developed alignment-based methods using similarity and motif search techniques. Finally, a hybrid technique has been developed that combine XGBoost models and motif-based searching and achieved an average AUROC 0.742 (0.708 - 0.816). Our method - MRSLpred, developed in this study is complementary to the existing method. One of the major advantages of our method over existing methods is its speed, it can scan all mRNA of a transcriptome in few hours. A publicly accessible webserver and a standalone tool has been developed to facilitate researchers (Webserver: https://webs.iiitd.edu.in/raghava/mrslpred/). Key PointsO_LIPrediction of Subcellular localization of mRNA C_LIO_LIClassification of mRNA based on Motif and BLAST search C_LIO_LICombination of alignment based and alignment free techniques C_LIO_LIA fast method for subcellular localization of mRNA C_LIO_LIA web server and standalone software C_LI

bioinformatics↗
bioRxiv2022

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 various types of cancers. In order to design effective immunotherapy, it is important to understand immune response of a patient based on its genomic profile. We compute prognostic biomarkers from 8346 cancer patients for twenty types of cancer. These prognostic biomarkers has been computed based on i) presence of 352 human leucocyte antigen class-I (HLA-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 LIHC cancer patients with HLA-A*03:01 are on lower risk. Our analysis indicate 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 LUSC and -0.77 with KICH cancer patients. It is clear from above analysis that HLA and their binders have major role in survival of cancer patients suffering from different types of cancer. In addition, we compute prognostic biomarkers for 20 types of cancer based on each type of cytokine expression. Higher expression of few cytokines is survival favourable like IL-2 for BLCA cancer patients whereas IL-5R survival unfavourable for KICH cancer patients. In order to facilitate scientific community we developed a web-based platform CancerHLA1 that maintain raw and analyzed data (https://webs.iiitd.edu.in/raghava/cancerhla1/).

cancer biology↗
bioRxiv2022

Prediction, scanning and designing of TNF-α inducing epitopes for human and mouse

Tumor Necrosis Factor alpha (TNF-) is a pleiotropic pro-inflammatory cytokine that plays a crucial role in controlling signaling pathways within the immune cells. Recent studies reported that the higher expression levels of TNF- is 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- progression in various disease conditions. In the pilot study, we have proposed a host-specific in-silico tool for the prediction, designing and scanning of TNF- inducing epitopes. The prediction models were trained and validated on the experimentally validated TNF- inducing/non-inducing for human and mouse hosts. Firstly, we developed alignment free (machine learning based models using composition of peptides) methods for predicting TNF- inducing peptides and achieved maximum AUROC of 0.79 and 0.74 for human and mouse hosts, respectively. Secondly, alignment based (using BLAST) method has been used for predicting TNF- inducing epitopes. Finally, a hybrid method (combination of alignment free and alignment-based method) has been developed for predicting epitopes. Our hybrid method achieved maximum AUROC of 0.83 and 0.77 on an independent dataset for human and mouse hosts, respectively. We have also identified the potential TNF- inducing peptides in different proteins of HIV-1, HIV-2, SARS-CoV-2 and human insulin. Best models developed in this study has been incorporated in a webserver TNFepitope (https://webs.iiitd.edu.in/raghava/tnfepitope/), standalone package and GitLab (https://gitlab.com/raghavalab/tnfepitope). Key PointsO_LITNF- is a multifunctional pleiotropic pro-inflammatory cytokine. C_LIO_LIAnti-TNF- therapy used as an effective treatment in several autoimmune disorders. C_LIO_LIComposition-based features generated using Pfeature for each peptide sequence. C_LIO_LIAlignment-based and alignment-free models developed. C_LIO_LIPrediction and scanning of TNF- inducing regions in antigens. C_LIO_LITNFepitope is available as a web-server, standalone package and GitLab. C_LI

bioinformatics↗