Biology subjects

Shendre, A.

Publications and source records attributed to Shendre, A..

5 recordsLinked to original sources

bioRxiv2026

EdiProPred: Prediction of Edible Plant Tissue-Associated Proteins Using Large Language Models

Ensuring the safety and suitability of proteins considered for food and biotechnology applications is an important challenge. Computational approaches can facilitate the prioritisation of candidate proteins for further investigation, particularly given the rapid growth of genomic and metagenomic sequence datasets. In this study, we developed computational models to distinguish edible plant tissue-associated (EPTA) proteins from non-EPTA proteins. The positive class comprised 939 proteins with experimentally validated evidence of expression in plant tissues commonly consumed by humans, hereafter referred to as EPTA proteins. The negative class comprised 939 proteins annotated in UniProt as toxic, allergenic, or antinutritional, hereafter referred to as non-EPTA proteins. Models were evaluated using five-fold cross-validation with a cluster-based partitioning strategy, ensuring that no protein in the validation set shared more than 40% sequence identity with proteins in the corresponding training set. First, composition-based sequence features were evaluated to establish a baseline for EPTA or non-EPTA classification, with the Extra Trees (ET) classifier achieving a validation AUROC of 0.848. To incorporate evolutionary information beyond amino acid composition, PSSM profiles were subsequently evaluated, yielding an AUROC of 0.842 with an SVM classifier. Finally, to capture richer sequence patterns and contextual information using protein language modelling, we evaluated ESM2-based models. The fine-tuned ESM2-T33 model achieved the highest validation AUROC of 0.925 and was selected as the final predictive model. The resulting approach was implemented as the EdiProPred web server (https://webs.iiitd.edu.in/raghava/edipropred/) and standalone software for computational classification of plant proteins. The EdiProPred framework provides a useful computational tool for prioritising candidate plant proteins and facilitating the exploration of novel protein resources for food and biotechnology applications.

plant biology↗

bioRxiv2026

PCPpred: Prediction of Chemically Modified Peptide Permeability Across Multiple Assays for Oral Delivery

Chemically modified peptides, including cyclic peptides, have emerged as promising candidates for oral delivery yet they face the challenge of low membrane permeability. In this study, the datasets were sourced from CycPeptMPDB, a database for membrane permeability of peptides obtained from different assays. Our quantitative analysis showed a clear discordance between permeability measured using PAMPA and cell-based assays (Caco-2, MDCK, and, RRCK), thereby explaining its limits as surrogate for cell-based assays. Therefore, we developed assay-specific predictive models to more accurately capture permeability determinants in each system. We systematically compute diverse features of modified peptides using open-source software and used fine-tuned peptide embeddings generated using pretrained chemical language models. Baseline models were developed using the generated multi-hierarchical molecular features. We also developed a stacked ensemble architecture, which utilizes multi-hierarchical features in models as base learners. The ensemble model achieved the best PAMPA test set performance with an MSE of 0.200, R2 of 0.685, and PCC of 0.830; and a R2 of 0.783 on Caco-2 test set. Model trained on 2D Mordred descriptors attained the highest performance on the Caco-2 test-set with MSE of 0.129, R2 of 0.793, and PCC of 0.892, surpassing state-of-the-art approaches such as CPMP. To support widespread adoption, we developed an open-access web-server (https://webs.iiitd.edu.in/raghava/pcppred/) for users to design modified peptides using human comprehensible MAP (Modifications and Annotations of Proteins) format, converting MAP to SMILES format, and predict permeability across assays with result visualization. To ensure widespread adoption, and reproducibility, we also provided a standalone on GitHub (https://github.com/raghavagps/pcppred).

bioinformatics↗

bioRxiv2025

Genomic diversity analysis enables development of pan-Dengue Toehold RNA sensors

The Dengue virus (DENV) like many other RNA viruses exhibits high genome sequence diversity. This poses a challenge to nucleic acid-based diagnostics, rendering them inefficient at detecting the diverse DENV strains circulating in a population. In this study, we address this challenge by developing a Toehold sensor assay that despite significant genomic diversity is able to detect [~]99.4% of all strains of DENV. To this end, our workflow first identifies relatively conserved short stretches (36-nt) within all DENV genomes, which could potentially serve as triggers that activate Toehold RNA sensors. We then add a crucial step of mismatch-tolerance that allows related triggers with high sequence diversity and multiple mismatches in the sensor-binding region to be efficiently sensed by the same sensor. The sensitivity of the Toehold sensor assay is often increased by inclusion of an isothermal RNA amplification step. We show by employing multiple primer sets that diverse trigger RNAs from all four serotypes of the dengue virus can be successfully amplified and subsequently detected by the same sensor. Together, this approach has resulted in a pan-dengue Toehold sensor assay, which presents a powerful nucleic acid detection platform to detect viruses with high sequence diversity.

molecular biology↗

bioRxiv2023

Transcriptional regulation of Dyskerin via canonical WNT signaling modulates sphingolipid biosynthesis and drives colorectal cancer.

Targeting EGFR has been effective in RAS/RAF wild-type colorectal cancer (CRC) patients. However, residual tumor relapses, necessitating the importance of biomarker-guided novel therapeutics. We show elevated DKC1 in [~]88% of CRC patients with poor recurrence-free survival. Clinically, DKC1-positive patients exhibit similarity with CMS2 class, the canonical subtype with active WNT signaling. We show functional significance of DKC1 in cell proliferation, stemness, DNA repair, and survival. Further, mice bearing DKC1 knockdown xenografts show [~]81% reduction in tumor burden. Mechanistically, WNT/{beta}-catenin signaling orchestrates DKC1 expression, then, DKC1/SOX2 complex regulates SGPP2, modulating sphingolipids metabolism. Downregulation of DKC1 in CRC lead to reduced SGPP2 levels leading to dysregulation of sphingolipid biosynthesis. Of note, DKC1-high CRC patients show accumulation of ceramides, namely C23 and C24, signifying their utility in diagnosis. Collectively, we delineate the mechanistic circuitry involved in DKC1-mediated CRC progression, propose ceramides as biomarker, and underscore WNT-based therapeutics for DKC1-positive patients.

cancer biology↗

bioRxiv2021

PLK1 inhibition selectively kills ARID1A deficient cells through uncoupling of oxygen consumption from ATP production

Inhibitors of the mitotic kinase PLK1 yield objective responses in a subset of refractory cancers. However, PLK1 overexpression in cancer does not correlate with drug sensitivity, and the clinical development of PLK1 inhibitors has been hampered by the lack of patient selection marker. Using a high-throughput chemical screen, we discovered that cells deficient for the tumor suppressor ARID1A are highly sensitive to PLK1 inhibition. Interestingly this sensitivity was unrelated to canonical functions of PLK1 in mediating G2-M cell cycle transition. Instead, a whole-genome CRISPR screen revealed PLK1 inhibitor sensitivity in ARID1A deficient cells to be dependent on the mitochondrial translation machinery. We find that ARID1A knocked-out (KO) cells have an unusual mitochondrial phenotype with aberrant biogenesis, increased oxygen consumption/ expression of oxidative phosphorylation genes, but without increased ATP production. Using expansion microscopy and biochemical fractionation, we see that a subset of PLK1 localizes to the mitochondria in interphase cells. Inhibition of PLK1 in ARID1A KO cells further uncouples oxygen consumption from ATP production, with subsequent membrane depolarization and apoptosis. Knockdown of a key subunit of the mitochondrial ribosome reverses PLK1-inhibitor induced apoptosis in ARID1A deficient cells, confirming specificity of the phenotype. Together, these findings highlight a novel interphase role for PLK1 in maintaining mitochondrial fitness under metabolic stress, and a strategy for therapeutic use of PLK1 inhibitors. To translate these findings, we describe a quantitative microscopy assay for assessment of ARID1A protein loss, which could offer a novel patient selection strategy for the clinical development of PLK1 inhibitors in cancer. Statement of significanceCurrently, no predictive biomarkers have been identified for PLK1 inhibitors in cancer treatment. We show that ARID1A loss sensitizes cells to PLK1 inhibitors through a previously unrecognized vulnerability in mitochondrial oxygen metabolism.

cancer biology↗