Biology subjects

Bajiya, N.

Publications and source records attributed to Bajiya, N..

13 recordsLinked to original sources

bioRxiv2026

AntiOxiPred: Predicting Antioxidant Peptides using Machine Learning Techniques and BLAST

Antioxidant peptides (AOPs) offer significant therapeutic potential in counteracting oxidative damage associated with chronic diseases. As AOPs gain market traction in cosmeceuticals, functional foods, and clinical trials for disease intervention, efficient computational methods are essential to accelerate their discovery. In this study, a systematic attempt has been made to develop a highly accurate and reliable method for predicting AOPs from their primary sequences. The dataset comprises 1,511 experimentally validated AOPs and 1,511 randomly generated non-AOPs, partitioned into 2,417 sequences for training and 606 for testing. First, we developed machine learning-based models using one-hot encoded (OHE) representations, sequence-derived compositional features, protein language model (PLM) embeddings, and feature fusion. Our CatBoost model, developed using selected fused features, achieved the best performance, with an AUC of 0.945 and an MCC of 0.788 on the independent testing dataset. An ensemble model combining CatBoost with BLAST achieved a peak AUC of 0.956 on the same testing dataset. Furthermore, our method outperformed existing state-of-the-art predictors when benchmarked on two external validation datasets comprising 150 peptides (75 AOPs and 75 non-AOPs) and 248 peptides (124 AOPs and 124 non-AOPs). AntiOxiPred (https://webs.iiitd.edu.in/raghava/antioxipred/) is freely available as an interactive web server, a standalone package, and a pip-installable tool to support the scientific community.

bioinformatics↗

bioRxiv2026

AntiCapt: Fine-Tuned Nucleotide Language Models for Predicting and Designing Anticancer Aptamers

Over the past decade, aptamers have emerged as promising therapeutics, with cancer therapeutics as a major research area. Existing computational methods, however, either predict aptamer-target pairs or design aptamers against a specific target. In this study, we present AntiCapt, a computational method for identifying single-stranded DNA (ssDNA) anticancer aptamers (ACAs) using sequence descriptors and fine-tuned nucleotide language models. The main dataset comprises 1,021 experimentally validated ACAs and an equal number of non-aptamer sequences. Comparative analysis revealed distinct patterns in the nucleotide, dinucleotide, and trinucleotide compositions associated with ACAs. We developed machine learning (ML) models using composition, autocorrelation, binary profiles and structural features. Among these, the best-performing composition-based model achieved an AUC of 0.93 on an independent dataset, while chemical descriptor-and structural feature-based models achieved a maximum AUC of 0.89. ML models using pretrained and fine-tuned NLM embeddings were also developed, with fine-tuned HyenaDNA embeddings achieving the highest performance, with an independent AUC of 0.94. Furthermore, we developed a model for discriminating anticancer aptamers from general aptamers. The best-performing models were integrated into AntiCapt, a web server and a standalone tool for predicting, designing, and genome-scale scanning anticancer aptamers (https://webs.iiitd.edu.in/raghava/anticapt/).

bioinformatics↗

bioRxiv2026

CPPLocPred: Subcellular Localization of Cell-Penetrating Peptides

Cell-penetrating peptides (CPPs) are widely used to deliver therapeutic cargoes into cells. Although numerous computational methods have been developed for identifying CPPs and several predictors are available for protein subcellular localization, no method has been developed to predict the subcellular localization of CPPs. Here, we present CPPLocPred, a hierarchical machine-learning (ML) framework that predicts CPPs and their subcellular localization. In the first stage, we developed ML models to identify CPPs, achieving an AUC of 0.953 with an MCC of 0.7842 on an independent set, exhibiting performance equivalent to or better than existing state-of-the-art methods. In the second stage, we developed a method for predicting the subcellular localization of CPPs. Subcellular localization methods were trained (80% data using five-fold cross-validation) and validated (20% data) on experimentally validated CPPs for 663 Cytoplasm, 287 Nucleus, 57 Mitochondria, 186 Endo_lysosome, and 328 Others. Our primary analysis revealed that Mitochondrial and Nuclear associated CPPs are abundant in positively charged arginine- and lysine-rich patterns, whereas Endo_lysosomal CPPs preferentially comprise glycine-, proline-, and cysteine-rich motifs. We used a wide range of traditional peptide features, along with the embedding of protein language models, to develop ML models. Among all evaluated models, the CatBoost-based subcellular localization models with Distance Distribution of Residues (DDR) achieved AUCs of 0.814, 0.775, 0.970, 0.782, and 0.798 for Cytoplasm, Nucleus, Mitochondria, Endo_lysosome, and Others, respectively, on validation dataset. We developed CPPLocPred, which offers a practical platform for functional annotation and rational design of localization-specific CPPs for therapeutic applications (https://webs.iiitd.edu.in/raghava/cpplocpred/). HighlightsO_LIPrediction and subcellular localization of cell-penetrating peptides. C_LIO_LILocalization of CPPs depends on their amino acid and dipeptide composition. C_LIO_LIBest feature for subcellular localization was Distance Distribution of Residues. C_LIO_LICatBoost model achieved the highest performance for subcellular localization. C_LIO_LIA web server and standalone software to facilitate its use by the scientific community. C_LI

bioinformatics↗

bioRxiv2026

AptViralDB: A Repository of Experimentally Validated Antiviral Aptamers

In an era of increasing drug resistance, exploring alternative molecules is crucial for the efficient management and treatment of viral diseases. Nucleic acid aptamers have emerged as highly promising candidates due to their exceptional target specificity, low immunogenicity, and versatile mechanisms for viral blocking. This manuscript describes AptViralDB, a manually curated database providing comprehensive information on experimentally validated antiviral aptamers. It contains 1,768 entries of antiviral aptamers against 40 viral species and 104 molecular targets, compiled from literature and existing databases. Each entry provides detailed annotations, including sequence, aptamer type, target, chemical modifications, binding affinity, antiviral activity, stability, and cytotoxicity. We also provide predicted secondary structures and their corresponding minimum free energy (MFE) values. Additionally, a knowledge graph created using ArcadeDB/openCypher enables users to seamlessly explore connections among aptamers, viruses, molecular targets, and biological activities. Finally, the platform offers advanced search and browsing tools, BLAST-based sequence similarity searches, GC-content analysis, downloadable datasets, and REST API access to support computational applications. (https://webs.iiitd.edu.in/raghava/aptviraldb/).

bioinformatics↗

bioRxiv2026

AptBacterialDB: A Comprehensive, Manually Curated Database of Antibacterial Aptamers

In recent years, aptamers have transitioned from mere laboratory tools to highly potent molecular recognition agents capable of overcoming the strict limitations of conventional antibiotic therapies. We have developed AptBacterialDB, a manually curated, large, comprehensive database of experimentally validated antibacterial aptamers spanning 1996 to 2026. The database contains a total of 2131 aptamers targeting approx 75 different bacterial classes, and 124 aptamer targets with 95 entries found in UTexas databases, 97 in AptaDB, and 28 in Aptabase. It contains 1555 unique aptamer sequences, 189 unique modifications, 40 different selection approaches, and 44 different affinity methods. It integrates detailed annotations of about 20 fields, including sequence information, nucleic acid type, binding affinity, modifications, experimental and functional details. The secondary structure of the aptamers was predicted using ViennaRNA Package 2.0, demonstrating that they adopt mostly stable conformations, with a structured stem region. MySQL was implemented for database development, and a knowledge graph was integrated using ArcadeDB/openCypher for graphical visualization of aptamer-target-organisation relationships. Facilities such as different search modes, browsing, similarity search, REST API access, and entries linked to the existing database for a broader view of the aptamers have been provided. AptBacterialDB (https://webs.iiitd.edu.in/raghava/aptbacterialdb/) provides a user-friendly centralized platform to accelerate antibacterial aptamer research, therapeutic development, biosensor design, and computational modelling efforts. Authors BiographyO_LINisha Bajiya is currently working as Ph.D. Student in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India. C_LIO_LIIshika Gupta is currently working as an M.Tech Student 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 a Professor in the Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India C_LI

bioinformatics↗

bioRxiv2026

AptCancerDB: A Curated Knowledgebase and Translational Discovery Platform for Anticancer Aptamers

Aptamers are emerging as important molecular recognition ligands in oncology, playing significant roles in cancer diagnostics, targeted therapies, drug delivery systems, and molecular imaging. Numerous aptamers have advanced to clinical trials, indicating their potential for real-world applications; however, existing databases fail to capture that. To bridge this critical gap, we developed AptCancerDB (https://webs.iiitd.edu.in/raghava/aptcancerdb/), a comprehensive, manually curated database of experimentally verified anticancer aptamers. The current release contains 1,941 entries collected from studies published between 2000 and 2025, covering 29 cancer types, approximately 200 cancer cell lines, and direct links to 22 clinical trials. Each entry is annotated with sequence information, target details, cancer type, cell line, SELEX methodology, affinity determination data, chemical modifications, and biological activities. The dataset is dominated by 82.7% ssDNA, reflecting its superior stability and ease of synthesis, while only 16.6% is ssRNA and appears primarily in studies targeting complex intracellular or protein-protein interactions. To facilitate structural analysis, predicted secondary structures, dot-bracket notations, specific structural elements, and minimum free energy values were also included. AptCancerDB integrates a MySQL backend with an ArcadeDB/OpenCypher-based Knowledge Graph, enabling exploration of relationships among aptamers, targets, cancer types, cell lines, and functional applications. The platform provides advanced search and browsing facilities, BLASTn-based similarity searching, and GC Calculator. Built on a modern, responsive frontend (React/TypeScript/Tailwind CSS), the platform includes a REST API for data retrieval. By integrating fragmented experimental data into a unified cancer-focused resource, AptCancerDB serves as a valuable resource for comparative analysis, aptamer discovery, and the development of next-generation aptamer-based diagnostics and therapeutics. HighlightsO_LICurated knowledge base of experimentally validated anticancer aptamers. C_LIO_LIAptCancerDB contain therapeutic, tumor-homing and cell-penetrating aptamers. C_LIO_LISummarizes clinical progress and translational trends in anticancer aptamer research. C_LIO_LISupports rational aptamer design using molecular, functional, and clinical annotations C_LIO_LIDisease-focused resource for cancer diagnosis, therapy, and drug delivery C_LI TeaserAptCancerDB maintains experimentally validated anticancer aptamers relevant to diagnosis, drug delivery, and therapy.

cancer biology↗

bioRxiv2025

Prediction of lncRNA-protein interacting pairs using LLM embeddings based on evolutionary information

Interactions of long non-coding RNAs (lncRNAs) with proteins is responsible for numerous cellular processes, including transcriptional regulation, chromatin remodeling, cell differentiation, and intracellular signaling. In the past, numerous computational methods have been developed for predicting lncRNA-protein interacting (LPI) pairs. This study describes a highly accurate and reliable method for predicting LPI pairs built on largest possible non-redundant dataset having 262,244 interacting and equal number of non-interacting pairs. Initially, similarity-based approach BLAST has been tried which have poor discriminative power, due to low sequence similarity. Subsequently, we developed CNN based models and machine learning based models using traditional features and embedding. Our CatBoost model developed using embedding generated by DNABERT-2 and ESM-2-t30 achieved AUC of 0.989 with MCC 0.915 on an independent dataset. Our method performs better than existing methods on an independent dataset. We developed standalone software and web server lncrnaPI for predicting LPI pairs, scanning lncRNA interacting proteins in proteome and protein interacting lncRNA in genomes (https://webs.iiitd.edu.in/raghava/lncrnapi/). HIGHLIGHTSO_LIDiscrimination of LncRNA-protein interacting and non-interacting pairs. C_LIO_LINon-redundant dataset of 262,244 interacting and 262,244 non-interacting pairs. C_LIO_LIEmbedding of LncRNA using DNABERT-2 and protein using ESM-2-t30. C_LIO_LIRapid scanning of lncRNA interacting proteins at genome scale. C_LIO_LIA web server and software for predicting LncRNA-protein interacting pairs. C_LI

bioinformatics↗

bioRxiv2025

In Silico Tool for Predicting and Scanning Rheumatoid Arthritis-Inducing Peptides in an Antigen

Rheumatoid arthritis (RA) is an autoimmune disorder in which the immune system mounts an abnormal response to self-antigens, leading to chronic inflammation and joint damage. Therefore, identifying antigenic regions in a protein that trigger RA is crucial for developing protein-based therapeutics. In this study, we developed models for predicting RA-inducing peptides using a dataset comprising of 291 experimentally confirmed RA-inducing peptides and 165 RA non-inducing peptides. Our initial analysis revealed that certain residues, such as glycine, proline, and tyrosine, are significantly enriched in RA-inducing peptides. While alignment-based techniques like BLAST and MERCI offered high precision, they suffered from limited coverage. We developed machine/deep learning based prediction and obtained highest performance (AUC = 0.75) using XGboost on an independent dataset. We also developed prediction methods using large language models and achieved highest performance (AUC 0.72) using ProtBERT. Our ensemble model achieved highest performance (AUC = 0.80 & MCC = 0.45) on an independent dataset that combine XGBoost and MERCI-derived motifs. All models were rigorously evaluated on an independent dataset not used during training or testing of models. This study will be valuable for assessing the risk of proteins used in probiotics, genetically modified foods, and protein-based therapeutics. Our most effective approach has been implemented in RAIpred, a web server and standalone software tool for predicting and scanning RA-inducing peptides. (https://webs.iiitd.edu.in/raghava/raipred/). HighlightsO_LIRheumatoid arthritis (RA), an incurable chronic joint disorder with diverse systemic complications. C_LIO_LIAn attempt to identify antigenic regions in a protein which trigger this severe disease. C_LIO_LIUtilizing sequence composition based features for developing models. C_LIO_LIImplementation of ML, DL and LLM based models for prediction of RA-inducing peptides. C_LIO_LIDevelopment of webserver, standalone, pypi and GitHub package for users. C_LI

bioinformatics↗

bioRxiv2024

Prediction of plant resistance proteins using alignment-based and alignment-free approaches

Plant Disease Resistance (PDR) proteins are critical in identifying and killing plant pathogens. Predicting PDR protein is essential for understanding plant-pathogen interactions and developing strategies for crop protection. This study proposes a hybrid model for predicting and designing PDR proteins against plant-invading pathogens. Initially, we tried alignment-based approaches, such as BLAST for similarity search and MERCI for motif search. These alignment-based approaches exhibit very poor coverage or sensitivity. To overcome these limitations, we developed alignment-free or machine learning-based methods using compositional features of proteins. Our machine learning-based model, developed using compositional features of proteins, achieved a maximum performance AUROC of 0.92. The performance of our model improved significantly from AUROC of 0.92 to 0.95 when we used evolutionary information instead of protein sequence. Finally, we developed a hybrid or ensemble model that combined our best machine learning model with BLAST and obtained the highest AUROC of 0.98 on the validation dataset. We trained and tested our models on a training dataset and evaluated them on a validation dataset. None of the proteins in our validation dataset are more than 40% similar to proteins in the training dataset. One of the objectives of this study is to facilitate the scientific community working in plant biology. Thus, we developed an online platform for predicting and designing plant resistance proteins, "PlantDRPpred" (https://webs.iiitd.edu.in/raghava/plantdrppred). HighlightsO_LIDevelopment of a Machine-learning model for resistance protein prediction. C_LIO_LIUsed alignment-based and alignment-free ensemble methods. C_LIO_LIWeb server development and standalone package. C_LIO_LIPrediction and design of PDR proteins. C_LI

bioinformatics↗

bioRxiv2024

Prediction of inhibitory peptides against E. coli with desired MIC value

In the past, several methods have been developed for predicting antibacterial and antimicrobial peptides, but only limited attempts have been made to predict their minimum inhibitory concentration (MIC) values. In this study, we trained our models on 3,143 peptides and validated them on 786 peptides whose MIC values have been determined experimentally against Escherichia coli (E. coli). The correlational analysis reveals that the Composition Enhanced Transition and Distribution (CeTD) attributes strongly correlate with MIC values. We initially employed the similarity search strategy utilizing BLAST to estimate MIC values of peptides but found it inadequate for prediction. Next, we developed machine learning techniques-based regression models using a wide range of features, including peptide composition, binary profile, and embeddings of large language models. We implemented feature selection techniques like minimum Redundancy Maximum Relevance (mRMR) to select the best relevant features for developing prediction models. Our Random forest-based regressor, based on selected features, achieved a correlation coefficient (R) of 0.78, R-squared (R{superscript 2}) of 0.59, and a root mean squared error (RMSE) of 0.53 on the validation dataset. Our best model outperforms the existing methods when benchmarked on an independent dataset of 498 inhibitory peptides of E. coli. One of the major features of the web-based platform EIPpred developed in this study is that it allows users to identify or design peptides that can inhibit E. coli with the desired MIC value (https://webs.iiitd.edu.in/raghava/eippred). HighlightsO_LIPrediction of MIC value of peptides against E.coli. C_LIO_LIAn independent dataset was generated for comparison. C_LIO_LIFeature selection using the mRMR method. C_LIO_LIA regressor method for designing novel inhibitory peptides. C_LIO_LIA web server and standalone package for predicting the inhibitory activity of peptides. C_LI

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

AntiBP3: A hybrid method for predicting antibacterial peptides against gram-positive/negative/variable bacteria

This study focuses on the development of in silico models for predicting antibacterial peptides as a potential solution for combating antibiotic-resistant strains of bacteria. Existing methods for predicting antibacterial peptides are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we introduce a novel approach that enables the prediction of antibacterial peptides against several bacterial groups, including gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify antibacterial peptides and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict antibacterial peptides and obtained high precision with low sensitivity. To address the similarity issue, we developed machine learning-based models using a variety of compositional and binary features. 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, when evaluated on a validation/independent dataset. Our attempts to develop hybrid or ensemble methods by merging machine learning models with similarity and motif-based techniques did not yield any improvements. To ensure robust evaluation, we employed standard techniques such as five-fold cross-validation, internal validation, and external validation. Our method performs better than existing methods when we compare our method with existing approaches on an independent dataset. In summary, this study makes significant contributions to the field of antibacterial peptide prediction by providing a comprehensive set of methods tailored to different bacterial groups. As part of our contribution, we have developed the AntiBP3 web server and standalone package, which will assist researchers in the discovery of novel antibacterial peptides for combating bacterial infections (https://webs.iiitd.edu.in/raghava/antibp3/). Key Points BLAST-based similarity for annotating antibacterial peptides. Machine learning-based models developed using composition and binary profiles. Identification and mapping of motifs exclusively found in antibacterial peptides Improved version of AntiBP and AntiBP2 for predicting antibacterial peptides. Web server for predicting/designing/scanning antibacterial peptides for all groups of bacteria Authors BiographyO_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_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_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_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 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↗