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Protein folding, and especially the conformational sampling of intrinsically disordered regions (IDRs), remains a formidable challenge for classical computation. We introduce QuPepFold, a modular Python package designed to democratize hybrid quantum-classical simulations of peptide folding, with the specific aim of enabling exploration of IDR ensembles for therapeutic targeting. We compute ground-state energies using a variational quantum eigensolver (VQE) that has been tuned with a conditional value-at-risk (CVaR) objective. This CVaR approach focuses on the lowest-energy measurement results, which speeds convergence and helps the algorithm cope with noise. The software provides an interface suitable for biologists and is independent of any particular quantum hardware; it currently runs on Qiskit Aer, Braket's tensor-network simulator, and IonQ's Aria-1 device through the Amazon Braket service. In tests on short peptides up to ten amino acids long, the CVaR-optimized VQE reached the ground state roughly 30 percent faster than a standard VQE based on expectation values. When run on the IonQ Aria-1 quantum computer, it reproduced ground-state energies with over 90 percent fidelity. The agreement of results across simulators and physical devices indicates that the package yields consistent and transferable energies. QuPepFold offers an approachable yet extendable framework for integrating quantum techniques into peptide folding studies, particularly for sampling the ensembles of intrinsically disordered regions. By hiding the technical details of circuit construction and error mitigation, it lowers the barrier to using quantum computers in structural biology and opens opportunities for drug discovery against disordered proteins that have long been considered difficult to target.

Breast cancer, a heterogeneous disease, is among the most frequently diagnosed diseases and is the second leading cause of death due to cancer among women after lung cancer. Phytoactives (plant-based derivatives) and their derivatives are safer than synthetic compounds in combating chemoresistance. In the current work, a template-based design of the coumarin derivative was designed to target the ADP-sugar pyrophosphatase protein. The novel coumarin derivative (2R)-2-((S)-sec-butyl)-5-oxo-4-(2-oxochroman-4-yl)-2,5-dihydro-1H-pyrrol-3-olate was designed. Molecular docking studies provided a docking score of −6.574 kcal/mol and an MM-GBSA value of −29.15 kcal/mol. Molecular dynamics simulation studies were carried out for 500 ns, providing better insights into the interaction. An RMSD change of 2.4 Å proved that there was a stable interaction and that there was no conformational change induced to the receptor. Metadynamics studies were performed to calculate the unbinding energy of the principal compound with NUDT5, which was found to be −75.171 kcal/mol. In vitro validation via a cytotoxicity assay (MTT assay) of the principal compound was carried out with quercetin as a positive control in the MCF7 cell line and with an IC50 value of 55.57 (+/−) 0.7 μg/mL. This work promoted the research of novel natural derivatives to discover their anticancer activity.

The World Health Organization has put drug resistance in tuberculosis on its list of significant threats, with a critical emphasis on resolving the genetic differences in Mycobacterium tuberculosis. This provides an opportunity for a better understanding of the evolutionary progression leading to anti-microbial resistance. Anti-microbial resistance has a great impact on the economic stability of the global healthcare sector. We performed a timeline genomic analysis from 2003 to 2021 of 578 mycobacterium genomes to understand the pattern underlying genomic variations. Potential drug targets based on functional annotation was subjected to pharmacophore-based screening of FDA-approved phyto-actives. Reaction search, MD simulations, and metadynamics studies were performed. A total of 4,76,063 mutations with a transition/transversion ratio of 0.448 was observed. The top 10 proteins with the least number of mutations were high-confidence drug targets. Aminoglycoside 2′-N-acetyltransferase protein (AAC2′), conferring resistance to aminoglycosides, was shortlisted as a potential drug target based on its function and role in bait drug synergism. Gentamicin-AAC2′ binding pose was used as a pharmacophore template to screen 10,570 phyto-actives. A total of 66 potential hits were docked to obtain naloxone as a lead—active with a docking score of −6.317. Naloxone is an FDA-approved drug that rapidly reverses opioid overdose. This is a classic case of a repurposed phyto-active. Naloxone consists of an amine group, but the addition of the acetyl group is unfavorable, with a reaction energy of 612.248 kcal/mol. With gentamicin as a positive control, molecular dynamic simulation studies were performed for 200 ns to check the stability of binding. Metadynamics-based studies were carried out to compare unbinding energy with gentamicin. The unbinding energies were found to be −68 and −74 kcal/mol for naloxone and gentamycin, respectively. This study identifies naloxone as a potential drug candidate for a bait drug synergistic approach against Mycobacterium tuberculosis.

Classification of different cancer types is an essential step in designing a decision support model for early cancer predictions. Using various machine learning (ML) techniques with ensemble learning is one such method used for classifications. In the present study, various ML algorithms were explored on twenty exome datasets, belonging to 5 cancer types. Initially, a data clean-up was carried out on 4181 variants of cancer with 88 features, and a derivative dataset was obtained using natural language processing and probabilistic distribution. An exploratory dataset analysis using principal component analysis was then performed in 1 and 2D axes to reduce the high-dimensionality of the data. To significantly reduce the imbalance in the derivative dataset, oversampling was carried out using SMOTE. Further, classification algorithms such as K-nearest neighbour and support vector machine were used initially on the oversampled dataset. A 4-layer artificial neural network model with 1D batch normalization was also designed to improve the model accuracy. Ensemble ML techniques such as bagging along with using KNN, SVM and MLPs as base classifiers to improve the weighted average performance metrics of the model. However, due to small sample size, model improvement was challenging. Therefore, a novel method to augment the sample size using generative adversarial network (GAN) and triplet based variational auto encoder (TVAE) was employed that reconstructed the features and labels generating the data. The results showed that from initial scrutiny, KNN showed a weighted average of 0.74 and SVM 0.76. Oversampling ensured that the accuracy of the derivative dataset improved significantly and the ensemble classifier augmented the accuracy to 82.91%, when the data was divided into 70:15:15 ratio (training, test and holdout datasets). The overall evaluation metric value when GAN and TVAE increased the sample size was found to be 0.92 with an overall comparison model of 0.66. Therefore, the present study designed an effective model for classifying cancers which when implemented to real world samples, will play a major role in early cancer diagnosis.

Lung cancer is considered the most common and the deadliest cancer type. Lung cancer could be mainly of 2 types: small cell lung cancer and non-small cell lung cancer. Non-small cell lung cancer is affected by about 85% while small cell lung cancer is only about 14%. Over the last decade, functional genomics has arisen as a revolutionary tool for studying genetics and uncovering changes in gene expression. RNA-Seq has been applied to investigate the rare and novel transcripts that aid in discovering genetic changes that occur in tumours due to different lung cancers. Although RNA-Seq helps to understand and characterise the gene expression involved in lung cancer diagnostics, discovering the biomarkers remains a challenge. Usage of classification models helps uncover and classify the biomarkers based on gene expression levels over the different lung cancers. The current research concentrates on computing transcript statistics from gene transcript files with a normalised fold change of genes and identifying quantifiable differences in gene expression levels between the reference genome and lung cancer samples. The collected data is analysed, and machine learning models were developed to classify genes as causing NSCLC, causing SCLC, causing both or neither. An exploratory data analysis was performed to identify the probability distribution and principal features. Due to the limited number of features available, all of them were used in predicting the class. To address the imbalance in the dataset, an under-sampling algorithm Near Miss was carried out on the dataset. For classification, the research primarily focused on 4 supervised machine learning algorithms: Logistic Regression, KNN classifier, SVM classifier and Random Forest classifier and additionally, 2 ensemble algorithms were considered: XGboost and AdaBoost. Out of these, based on the weighted metrics considered, the Random Forest classifier showing 87% accuracy was considered to be the best performing algorithm and thus was used to predict the biomarkers causing NSCLC and SCLC. The imbalance and limited features in the dataset restrict any further improvement in the model’s accuracy or precision. In our present study using the gene expression values (LogFC, P Value) as the feature sets in the Random Forest Classifier BRAF, KRAS, NRAS, EGFR is predicted to be the possible biomarkers causing NSCLC and ATF6, ATF3, PGDFA, PGDFD, PGDFC and PIP5K1C is predicted to be the possible biomarkers causing SCLC from the transcriptome analysis. It gave a precision of 91.3% and 91% recall after fine tuning. Some of the common biomarkers predicted for NSCLC and SCLC were CDK4, CDK6, BAK1, CDKN1A, DDB2.

Xylooligosaccharides having various degrees of polymerization such as xylobiose, xylotriose, and xylotetraose positively affect human health by interacting with gut proteins. The present study aimed to identify proteins present in gut microflora, such as xylosidase, xylulokinase, etc., with the help of retrieved whole-genome annotations and find out the mechanistic interactions of those with the above substrates. The 3D structures of proteins, namely Endo-1,4-beta-xylanase B (XynB) from Lactobacillus brevis and beta-d-xylosidase (Xyl3) from Bifidobacterium adolescentis, were computationally predicted and validated with the help of various bioinformatics tools. Molecular docking studies identified the effectual binding of these proteins to the xylooligosaccharides, and the stabilities of the best-docked complexes were analyzed by molecular dynamic simulation. The present study demonstrated that XynB and Xyl3 showed better effectual binding toward Xylobiose with the binding energies of − 5.96 kcal/mol and − 4.2 kcal/mol, respectively. The interactions were stabilized by several hydrogen bonding having desolvation energy (− 6.59 and − 7.91). The conformational stabilities of the docked complexes were observed in the four selected complexes of XynB–xylotriose, XynB–xylotetraose, Xyl3–xylobiose, and Xyn3–xylotriose by MD simulations. This study showed that the interactions of these four complexes are stable, which means they have complex metabolic activities among each other. Extending these studies of understanding, the interaction between specific probiotics enzymes and their ligands can explore the detailed design of synbiotics in the future.

Nuclear factor-kappa B (NF-kB) plays a crucial role in numerous cellular processes, such as inflammation, immunological responses to infection, cell division, apoptosis, and the development of embryos and neurons. Cytokines, plays an important role in positive feedback loop and leads to inflammatory cell death through the release of pathogenic cytokine known to be cytokine storm which causes diseases like Acute Respiratory Disorder (ARD), multi-organ disorder, Hyperinflammation syndrome and may cause death. This cytochrome storm was identified in the people severely affected by covid-19. NF-kB presents a promising therapeutic opportunity to mitigate covid-19-induced cytokine storm and reduce the risk of severe morbidity and mortality resulting from the diseases. This paper therefore explores the modulation of the NF-kB pathway by inhibiting the binding of the transcription factor as a potential strategy to mitigate the morbidity and mortality caused by cytokine storms. To identify small molecule inhibitors of NF-kB signaling, we screened approximately 101 molecules in two identified pockets of NF-kB (p50/p65)-DNA complex. Each molecule was virtually screened in two pockets (A1 and A2). The focus library was developed starting from chemical structures obtained from the literature (Angelicin and Psolaren) which shows the inhibition of NF-kB signaling, as well as using artificial intelligence (WADDAICA) and rationally designed molecules. Among the 3 highest-scored ligands (NFAI64, NF30 and NF49) selected from the docking studies and further molecular dynamic investigations. The identified compound NF30 showed significantly higher binding affinity (ΔG binding ) in A2 pocket (60.92 ± 1.83 kJ/mol) as compared to the rest of the molecules, making it a promising molecule for the inhibition of NF-kB. The discovered novel compounds by computational studies could be of relevance to identify more potent inhibitors of NF-kB dependent biological functions beneficial to control the cytokine storm occurring in the patients affected with Covid-19.

Background The unprecedented drought and frequent occurrence of pathogen infection in rice is becoming more due to climate change. Simultaneous occurrence of stresses lead to more crop loss. To cope up multiple stresses, the durable resistant cultivars needs to be developed, by identifying relevant genes from combined biotic and abiotic stress exposed plants. Results We studied the effect of drought stress, bacterial leaf blight disease causing Xanthomonas oryzae pv. oryzae (Xoo) pathogen infection and combined stress in contrasting BPT5204 and TN1 rice genotypes. Mild drought stress increased Xoo infection irrespective of the genotype. To identify relevant genes that could be used to develop multi-stress tolerant rice, RNA sequencing from individual drought, pathogen and combined stresses in contrasting genotypes has been developed. Many important genes are identified from resistant genotype and diverse group of genes are differentially expressed in contrasting genotypes under combined stress. Further, a meta-analysis from individual drought and Xoo pathogen stress from public domain data sets narrowed- down candidate differentially expressed genes. Many translation associated genes are differentially expressed suggesting their extra-ribosomal function in multi-stress adaptation. Overexpression of many of these genes showed their relevance in improving stress tolerance in rice by different scientific groups. In combined stress, many downregulated genes also showed their relevance in stress adaptation when they were over-expressed. Conclusions Our study identifies many important genes, which can be used as molecular markers and targets for genetic manipulation to develop durable resistant rice cultivars. Strategies should be developed to activate downregulated genes, to improve multi-stress tolerance in plants.

Accurate and early detection of bone cancer is critical for improving patient outcomes, yet conventional radiographic interpretation remains limited by subjectivity and variability. Conventional AI models often struggle with complex multi-modal noise distributions, non-convex and topologically entangled latent manifolds, extreme class imbalance in rare oncological conditions, and heterogeneous data fusion constraints. To address these challenges, we present a Quantum-Inspired Classical Convolutional Neural Network (QC-CNN) inspired by quantum analogies for automated bone cancer detection in radiographic images. The proposed architecture integrates classical convolutional layers for hierarchical feature extraction with a classical variational layer motivated by high-dimensional Hilbert space analogies for enhanced pattern discrimination. A curated and annotated dataset of bone X-ray images was utilized, partitioned into training, validation, and independent test cohorts. The QC-CNN was optimized using stochastic gradient descent (SGD) with adaptive learning rate scheduling, and regularization strategies were applied to mitigate overfitting. Quantitative evaluation demonstrated superior diagnostic performance, achieving high accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Results highlight the ability of classical CNN with quantum-inspired design to capture non-linear correlations and subtle radiographic biomarkers that classical CNNs may overlook. This study establishes QC-CNN as a promising framework for quantum-analogy motivated medical image analysis, providing evidence of its utility in oncology and underscoring its potential for translation into clinical decision-support systems for early bone cancer diagnosis. All computations in the present study are performed using classical algorithms, with quantum-inspired concepts serving as a conceptual framework for model design and motivating future extensions.

Using a decision support system (DSS) that classifies various cancers provides support to the clinicians/researchers to make better decisions that can aid in early cancer diagnosis, thereby reducing chances of incorrect disease diagnosis. Thus, this work aimed at designing a classification model that can predict accurately for 5 different cancer types comprising of 20 cancer exomes, using the mutations identified from whole exome cancer analysis. Initially, a basic model was designed using supervised machine learning classification algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), decision tree, naïve bayes and random forest (RF), among which decision tree and random forest performed better in terms of preliminary model accuracy. However, output predictions were incorrect due to less training scores. Thus, 16 essential features were then selected for model improvement using 2 approaches. All imbalanced datasets were balanced using SMOTE. In the first approach, all features from 20 cancer exome datasets were trained and models were designed using decision tree and random forest. Balanced datasets for decision tree model showed an accuracy of 77%, while with the RF model, the accuracy improved to 82% where all 5 cancer types were predicted correctly. Area under the curve for RF model was closer to 1, than decision tree model. In the second approach, all 15 datasets were trained, while 5 were tested. However, only 2 cancer types were predicted correctly. To cross validate RF model, Matthew’s correlation co-efficient (MCC) test was performed. For method 1, the MCC test and MCC cross validation was found to be 0.7796 and 0.9356 respectively. Likewise, for second approach, MCC was observed to be 0.9365, corroborating the accuracy of the designed model. The model was successfully deployed using Streamlit as a web application for easy use. This study presents insights for allowing easy cancer classifications.