Biology subjects

Rathore, A. S.

Publications and source records attributed to Rathore, A. S..

7 recordsLinked to original sources

bioRxiv2025

A PLM-Based Method for Predicting Protein Ion Channel Modulators for Drug Discovery and Safety Evaluation

Ion channels are central to regulating neuronal communication, cardiac rhythm, and muscle contraction. Their modulation can induce therapeutic benefits but may also lead to adverse or toxic effects. This study presents IonNTXpred, a protein language model (PLM)-based method for predicting protein ion channel modulators, including channel-specific (Na{square}, K{square}, Ca{superscript 2}{square}, and others) and moonlighting proteins capable of modulating multiple ion channels. We train, test, and evaluate our models on the largest dataset of non-redundant ion channel-modulating proteins, where no two proteins have more than 40% sequence identity. Composition analysis revealed that residues Cys, Gly, and Trp are highly prevalent, whereas Ala, Glu, Leu, Gln, and Val are scarce in ion channel-modulating proteins, with Cys identified as a key discriminative residue. We explored both alignment-based (BLAST, MERCI) and alignment-free (machine learning, deep learning, and PLM-based) approaches. Among these, our evolutionary information-based PLM (ESM2-t33) achieved the best performance, with an AUROC of 0.97 across ion channels, which further improved to 0.98 when integrated with BLAST output. The proposed method outperformed existing approaches on independent datasets. We used IonNTXpred to screen FDA-approved and organismal proteins to identify candidates with ion channel-modulating potential, supporting drug repurposing, discovery of new therapeutic proteins, and safety assessment of existing biologics. We implemented these models in a user-friendly web server, IonNTXpred, which facilitates the design and discovery of ion channel-modulating protein-based drugs and supports the biosafety evaluation of therapeutic proteins through neurotoxin screening (https://webs.iiitd.edu.in/raghava/ionntxpred/). HighlightsO_LIPrediction, design, and large-scale screening of ion channel-modulating proteins. C_LIO_LIIdentifies neurotoxins to support safety evaluation of therapeutic proteins. C_LIO_LIEvolutionary information-based protein language model ESM2-t33 for prediction. C_LIO_LIPredicts moonlighting proteins capable of modulating multiple ion channels. C_LIO_LIProvides standalone software, a web server, and a PyPI package for user accessibility. C_LI

bioinformatics↗

bioRxiv2025

Hemolytik2: An Updated Database of Hemolytic Peptides and Proteins

Hemolytik 2.0 (http://webs.iiitd.edu.in/raghava/hemolytik2/) is a comprehensive, manually curated database that provides experimentally validated information on both hemolytic and non-hemolytic peptides. Data were meticulously extracted from peer-reviewed publications and established peptide repositories, including the Antimicrobial Peptide Database (APD), UniProt, the Collection of Anti-Microbial Peptides (CAMP-R4), and the Dragon Antimicrobial Peptide Database (DAMPD). This updated version of the original Hemolytik resource encompasses 13,215 entries, representing approximately 8,700 unique peptides. Each entry in Hemolytik 2.0 offers detailed annotations, including peptide name, amino acid sequence, biological source and origin, functional characterization, terminal modifications, stereochemistry, structural classification (linear or cyclic), and experimentally determined hemolytic activity. In addition, the database provides molecular representations of peptides in SMILES (Simplified Molecular Input Line Entry System) format, alongside predicted tertiary structures and annotated secondary structural states. Additionally, a RESTful API has been integrated into the Hemolytik 2.0 repository to enable programmatic access and automated retrieval of peptide data. Hemolytik 2.0 serves as a valuable resource for the scientific community, particularly for researchers involved in the design and development of therapeutic peptides, by facilitating the identification and optimization of peptide candidates with minimal hemolytic potential and enhanced safety profiles.

bioinformatics↗

bioRxiv2025

NTxPred2: A large language model for predicting neurotoxic peptides and neurotoxins

Most of the existing methods including our NTxPred, use a single model to predict both neurotoxic peptides and neurotoxic proteins (neurotoxins). In this study, we developed distinct models for predicting neurotoxic peptides and neurotoxins. Our peptide dataset consists of 877 neurotoxic and 877 non-neurotoxic peptides, while our protein dataset includes 775 neurotoxins and 775 non-neurotoxins. Preliminary analysis reveals that certain residues, such as cysteine, are more prevalent in both neurotoxic peptides and proteins, though their abundance differs in magnitude. First, we developed machine learning models using composition and binary profiles, achieving a maximum AUC of 0.97 for peptides and 0.85 for proteins. The performance for proteins improved from an AUC of 0.85 to 0.89 when evolutionary information was incorporated. Next, we built machine learning models using embeddings from protein language models, attaining an AUC of 0.96 for peptides and 0.94 for proteins. We also developed protein language models and achieved an AUC of 0.98 for peptides using esm2-t30 and 0.91 for proteins using esm2-t6. All models were trained and tested using 5-fold cross-validation, and final models were evaluated on an independent dataset not used in training. We further assessed protein models on the peptide dataset and vice versa, highlighting the necessity of separate models. The proposed models outperform existing methods on independent datasets. Our neurotoxicity prediction models will aid in the safety assessment of genetically modified foods and therapeutic proteins by minimizing the need for animal testing. To support the scientific community, we developed a standalone software and web server NTxPred2, for predicting and scanning neurotoxins (https://webs.iiitd.edu.in/raghava/ntxpred2/, https://github.com/raghavagps/ntxpred2/). HighlightsO_LIDevelopment of models for predicting neurotoxic peptides and neurotoxins C_LIO_LIML Models Using LLM Embeddings for Neurotoxin Prediction C_LIO_LIProtein language models esm2-t30 for predicting neurotoxic peptides C_LIO_LIImportant for risk assessment of genetically modified foods/proteins C_LIO_LINTxPred2 is an updated version of NTxPred for predicting neurotoxicity C_LI

bioinformatics↗

bioRxiv2024

Prediction of Hemolytic Peptides and their Hemolytic Concentration (HC50)

Several peptide-based drugs fail in clinical trials due to their toxicity or hemolytic activity against red blood cells (RBCs). Existing methods predict hemolytic peptides but not the concentration (HC50) required to lyse 50% of RBCs. In this study, we developed a classification model and regression model to identify and quantify the hemolytic activity of peptides. Our models were trained and validated on 1924 peptides with experimentally determined HC50 against mammalian RBCs. Analysis indicates that hydrophobic and positively charged residues were associated with higher hemolytic activity. Our classification models achieved a maximum AUC of 0.909 using a hybrid model of ESM-2 and a motif-based approach. Regression models using compositional features achieved R of 0.739 with R{superscript 2} of 0.543. Our models outperform existing methods and are implemented in the web-based platform HemoPI2 and standalone software for designing hemolytic peptides with desired HC50 values (http://webs.iiitd.edu.in/raghava/hemopi2/). HighlightsO_LIDeveloped classification and regression models to predict hemolytic activity and HC50 values of peptides. C_LIO_LIA hybrid model combining machine learning and motif prediction excels in accuracy. C_LIO_LIBenchmarking of the existing classification methods on independent datasets. C_LIO_LIWeb server, standalone software, and pip package for hemolytic activity prediction of peptides/proteins. C_LI

bioinformatics↗

bioRxiv2023

ToxinPred 3.0: An improved method for predicting the toxicity of peptides

Toxicity emerges as a prominent challenge in the design of therapeutic peptides, causing the failure of numerous peptides during clinical trials. In 2013, our group developed ToxinPred, a computational method that has been extensively adopted by the scientific community for predicting peptide toxicity. In this paper, we propose a refined variant of ToxinPred that showcases improved reliability and accuracy in predicting peptide toxicity. Initially, we used BLAST for alignment-based toxicity prediction, yet coverage was limited. We adopted a motif-based approach with MERCI software to identify unique toxic patterns. Despite specificity gains, sensitivity was compromised. We developed alignment-free methods using machine/deep learning, achieving a balance sensitivity and specificity of prediction. A deep learning model (ANN - LSTM with fixed sequence length) developed using one-hot encoding attained a 0.93 AUROC and 0.71 MCC on independent data. The machine learning model (extra tree) developed using compositional features of peptides achieved 0.95 AUROC and 0.78 MCC. Lastly, we developed hybrid or ensemble methods combining two or more models to enhance performance. Hybrid approaches, including motif-based and machine learning, achieved a 0.98 AUROC and 0.81 MCC. Evaluation on independent data demonstrated our methods superiority. To cater to the needs of the scientific community, we have developed a standalone software, pip package and web-based server ToxinPred3 (https://github.com/raghavagps/toxinpred3 and https://webs.iiitd.edu.in/raghava/toxinpred3/). Authors BiographyO_LIAnand Singh Rathore 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_LIShubham Choudhury 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_LIPurava Tijare is a Project Fellow 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 HighlightsO_LIImplementation of alignment or similarly based techniques for predicting toxic peptides. C_LIO_LIDiscovery of toxicity-associated patterns and identification of toxic regions in peptides. C_LIO_LIDevelopment of machine and deep learning-based models for toxicity prediction. C_LIO_LIEnsemble methods that combine alignment-based and alignment-free methods. C_LIO_LIWeb server and standalone software package for screening toxicity in peptides/proteins. C_LI

bioinformatics↗

bioRxiv2022

Template-based design of peptides to inhibit SARS-CoV-2 RNA-dependent RNA polymerase complexation.

The RNA-dependent RNA polymerase (RdRp) complex of SARS-CoV-2 lies at the core of its replication and transcription processes. The interfaces between the subunits of the RdRp complex are highly conserved, facilitating the design of inhibitors with high affinity for the interaction hotspots of the complex. Here, we report development and application of a structural bioinformatics protocol to design peptides that can inhibit RdRp complex formation by targeting the interface of its core subunit nonstructural protein (nsp) 12 with accesory factor nsp7. We adopt a top-down approach for protein design by using interaction hotspots of the nsp7-nsp12 complex obtained from a long molecular dynamics trajectory as template. A large library of peptide sequences constructed from multiple hotspot motifs of nsp12 is screened in silico to determine peptide sequences with highest shape and interaction complementarity for the nsp7-nsp12 interface. Two lead designed peptide are extensively characterized using orthogonal bioanalytical methods to determine their suitability for inhibition of RdRp complexation and anti-viral activity. Their binding affinity to nsp7 (target), as determined from surface plasmon resonance (SPR) assay, is found to be comparable to that of the nsp7-nsp12 complex. Further, one of the designed peptides gives 46 % inhibition of nsp7-nsp12 complex at 10:1 peptide:nsp7 molar concentration (from ELISA assay). Further optimization of cell penetrability and target affinity of these designed peptides is expected to provide lead candidates with high anti-viral activity against SARS-CoV-2.

bioinformatics↗

bioRxiv2021

Dimerization of SARS-CoV-2 nucleocapsid protein affects sensitivity of ELISA based diagnostics of COVID-19

Diagnostics has played a significant role in effective management of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nucleocapsid protein (N protein) is the primary antigen of the virus for development of sensitive diagnostic assays. Thus far, limited knowledge exists about the antigenic properties of the N protein. In this paper, we demonstrate the significant impact of dimerization of SARS-CoV-2 nucleocapsid protein on sensitivity of enzyme-linked immunosorbent assay (ELISA) based diagnostics of COVID-19. The expressed purified protein from E.coli consists of two forms, dimeric and monomeric forms, which have been further characterized by biophysical and immunological means. Indirect ELISA indicated elevated susceptibility of the dimeric form of the nucleocapsid protein for identification of protein-specific monoclonal antibody as compared to the monomeric form of the protein. These findings have also been confirmed with the modelled structure of monomeric and dimeric nucleocapsid protein via HHPred software and its solvent accessible surface area, which indicates higher stability and antigenicity of the dimeric type as compared to the monomeric form. It is evident that use of the dimeric form will increase the sensitivity of the current nucleocapsid dependent ELISA for rapid COVID-19 diagnostic. Further, the results indicate that monitoring and maintaining of the monomerdimer composition is critical for accurate and robust diagnostics. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/445305v2_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@16bf153org.highwire.dtl.DTLVardef@1b0be6corg.highwire.dtl.DTLVardef@a5b3aeorg.highwire.dtl.DTLVardef@b784a3_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗