Biology subjects

Sajjad, M.

Publications and source records attributed to Sajjad, M..

6 recordsLinked to original sources

bioRxiv2026

Computational and Structure-Guided E-Pharmacophore-Based Virtual Screening for the Identification of Novel NEK2 Kinase Inhibitors as Potential Anticancer Agents

Cancer is a serious public health problem, and is becoming more common, with a projected increase in deaths and more than 25 million new cases in coming decades. A number of molecular mechanisms are involved in the tumoral process, with one of them, never in mitosis A-related kinase 2 (NEK2), a serine/threonine protein kinase, being a frequent target of amplification in various malignancies that is responsible for chromosomal instability, aneuploidy and activation of several oncogenic pathways. Available kinase inhibitors are not yet optimized in terms of their pharmacokinetic properties for clinical use, and current therapies, such as chemotherapeutic agents or immunotherapies are often limited by their resistance. In silico methods represent an effective tool to search for novel potent inhibitors, before testing in animals, with time constraints and limited resources. To find new inhibitors of NEK2, we used E pharmacophore-based modeling and structure based virtual screening in this study. NEK2 was chosen as the target for therapeutic intervention and an energy optimized pharmacophore model was employed to screen the Enamine REAL library of millions of compounds. Pharmacodynamic and Pharmacokinetic properties of the Top hits were tested using ADMET profiling. These were further screened using molecular docking (standard precision and extra precision) and virtual screening to obtain three lead compounds 1, 2, and 3 which have docking score of -7.414, -8.037 and -7.562 respectively. MM-GBSA calculations were used to estimate the binding free energies for these complexes, which were determined to be -54.92, -54.18 and -49.23 kcal/mol. Lastly, 100 ns molecular dynamics simulations have been run to evaluate complex stability in dynamic situations. The overall results of the MD showed the overall stability of the NEK2-ligand complexes, and thus these three compounds are promising NEK2 inhibitor candidates and could be further validated in vitro and in vivo for clinical application. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/742111v1_ufig1.gif" ALT="Figure 1"> View larger version (70K): org.highwire.dtl.DTLVardef@14e9cfeorg.highwire.dtl.DTLVardef@24ec78org.highwire.dtl.DTLVardef@20be75org.highwire.dtl.DTLVardef@1b80c02_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

Explainable Artificial Intelligence for Cross-Dataset Generalizable Biomarker Discovery in Cardiovascular diseases (CVDs)

CVDs are heterogeneous, multifactorial disorders that remain the leading cause of global mortality from infancy to old age. It requires an early identification and treatment of risk factors to accelerate disease prevention and morbidity improvement. Advancements in transcriptomics technologies gives large pool of heterogenous gene expression data. The technical heterogeneity of gene expression data reduces ability to compare multiple cross-platform datasets at once. To bridge gap, we systematically evaluate three data harmonization techniques: Shambhala-2, TDM, and UPC to align heterogeneous data into a shared expression space while preserving biological signals. Our pipeline integrates 25 independent datasets comprising 983 samples across 23 distinct CVDs phenotypes from both RNA-seq and microarray platforms. The framework benchmarks 35 Machine learning (ML) and Deep learning (DL) classifiers, including Transformers and ResNets, across three data modalities such as RNA-seq, microarray hybridization and RNA-seq + microarray and multiple tissue types. To ensure clinical trustworthiness, we apply multiple Explainable artificial intelligence (XAI) methods, such as SHapley additive exPlanations (SHAP) and Integrated gradientss (IGs), and assess their reliability using quantitative metrics like Area over the perturbation curve (AOPC), Sensitivity, and Infidelity. Results indicate that Shambhala-2 provides superior harmonization by maximizing the biological signal-to-platform ratio. Evaluation of XAI methods reveals that Shapley-based approaches offer the highest stability for identifying influential genomic features in high-dimensional data. Functional enrichment and pathway analyses further confirmed the involvement of identified biomarkers in key cardiovascular processes, including inflammation, immune regulation, oxidative stress, and vascular remodeling. Collectively, this study provides a scalable and interpretable road-map that integrates XAI with cross-dataset biomarker discovery, supporting the transition toward precision cardiology.

bioinformatics↗

bioRxiv2025

Assessment of the Genetic Architecture at Early-Stage Drought Tolerance in Wheat Using UAV-Based Multispectral Imaging

Recent advances in unmanned aerial vehicles (UAVs) and multispectral sensor technologies have transformed high-throughput phenotyping as an efficient alternative to traditional approaches. In this study, we employed UAV-based multispectral imaging to monitor early-stage drought stress in wheat. A panel of 221 historical spring wheat cultivars from Pakistan, representing over a century of breeding history, were evaluated under irrigated and drought conditions. UAV flights were conducted twice at early growth stages to capture multispectral imagery, which was processed in Pix4D mapper for image alignment and orthomosaic generation. Plot segmentation and trait extraction were performed in QGIS. Eight drought-responsive vegetative indices (VIs) were analyzed to assess genotypic variation. Significant to highly significant differences were observed among genotypes, treatments, and their interactions across all VIs. Broad-sense heritability estimates were moderate to high for most traits, with predominantly additive gene action. Vegetative indices such as NDVI, GNDVI, EVI, and SAVI showed strong correlations with each other and effectively detected early canopy stress under drought. Principal component analysis based on 23.897K SNPs indicated a mixed genetic population. Genome-wide association studies identified 115 significant QTNs linked to VIs under both conditions, corresponding to 74 loci, including 26 pleiotropic loci. Of these, 10 pleiotropic loci detected under drought were annotated and six putative candidate genes were identified which showed expression in multiple tissues based on transcriptome data. Two genes i.e., TraesCS1D01G217600.1 (HSP70) and TraesCS6D01G254000.1 (ZmMDAR3), showed differential expression pattern among control and drought treatment. These findings highlight the potential of UAV-based phenotyping for early drought detection and provide candidate genes for improving drought tolerance in wheat. Future research should focus on the functional roles of these genes under drought stress.

molecular biology↗

bioRxiv2025

Artificial Intelligence Powered Biomarker Discovery: A Large-Scale Analysis of 236 Studies Across 19 Therapeutic Areas and 147 Diseases

Biomarkers are the molecular signatures that drive and reflect disease states and are indispensable for disease diagnosis, therapeutic target identification, and drug development. The landscape of biomarker discovery has undergone a transformative shift with the emergence of AI-powered predictive pipelines that can integrate complex, high-dimensional datasets. However, the field still lacks a comprehensive, cross-disciplinary foundation that unites AI pipelines with disease-specific biological insights. Together, a combined scattered knowledge of 15 review articles fails to provide a unified framework encompassing data availability, methodological trends, and disease-specific biomarker discoveries across therapeutic areas. Most prior efforts have concentrated on narrow aspects, either focusing on disease-specific AI models or individual stages of the biomarker discovery pipelines, leaving a substantial gap in translational utility. This study addresses this gap by systematically consolidating and analyzing findings from 236 AI-driven biomarker discovery studies. We systematically map the trends of datasets, data modalities, preprocessing strategies, feature engineering methods, AI models, and explainability methods across 147 diseases, which we further organize into 19 therapeutic areas. By doing so, we aim to provide a comprehensive resource that not only highlights current trends and gaps but also lays the groundwork for future advancements, including the design of multi-task learning models and multimodal AI frameworks tailored to complex biomedical data.

bioinformatics↗

bioRxiv2025

FROM CANCER MOLECULAR SUBTYPE TO AI HYPE: BENCHMARKING AI IN CANCER MOLECULAR SUBTYPING

BackgroundCancer molecular subtype classification is an essential component of precision oncology which provides insights into cancer prognosis and guides targeted therapy. Despite the growing applications of AI for cancer molecular subtype classification, challenges persist due to non-standardized dataset configurations, diverse omics modalities, and inconsistent evaluation measures. These issues limit the comparability, reproducibility, and generalizability of AI classifiers across different cancers and hinder the development of robust and accurate AI-driven tools. ResultsThis study benchmarks 35 unique AI classifiers across 153 datasets, covering 8 omics modalities and 20 different cancers. Particularly, it investigates 6 different research questions, and based on comprehensive performance analyses of the 35 AI classifiers it elucidates the research questions with the following answers: (i) Out of 17 different configurations for 5/8 omics modalities, RPPA (RPPA), Gistic2-all-data-by-genes (CNV), HM27 (Meth), and HiSeqV2-exon (Exon) configurations consistently yield better performance; (ii) In terms of 8 omics modalities, RNASeq, miRNA, CNV, and Exon generally achieve higher macro-accuracy compared to Meth., Array, SNP and RPPA; (iii) SNP and RPPA modalities are prone to biases due to technical noise and data imbalance; (iv) Traditional machine learning (ML) models (SVM, XGB, HGB) perform best on small and low-dimensional datasets, while deep learning (DL) models (ResNet18, CNN, NN, MLP) excel on large and high-dimensional datasets; (v) SVM achieves the highest mean macro-accuracy across all classifiers, with NN, ResNet18, DEEPGENE, and MLP also demonstrate strong performance; and (vi) DL classifiers show superior macro accuracy as compared to ML classifiers in 12 out of 20 cancers. ConclusionsThe findings offer key insights to guide the development of standardized, robust, and efficient AI-driven pipelines for cancer molecular subtype classification. This study enhances reproducibility and facilitates better comparison across AI methods, ultimately advancing precision oncology. Key PointsO_LIThis study benchmarks 35 unique AI classifiers, ranging from simpler ML models such as Support Vector Machines (SVM), Histogram-Based Gradient Boosting (HGB), and K-Nearest Neighbors (KNN), to complex DL classifiers including Convolutional Neural Networks (CNNs), computer vision models like DenseNet and ResNet, sequential models such as Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), Long Short-Term Memory networks (LSTM), and their hybrid combinations (e.g., CNN-LSTM, CNN-GRU), as well as transformer-based models, across 153 datasets spanning 8 omics modalities and 20 cancers. It identifies optimal data configurations and evaluates the performance of these classifiers in cancer molecular subtype classification. C_LIO_LIThe study highlights biases in specific omics modalities: SNP, RPPA, and Array exhibit higher variability and precision-recall imbalances, while RNASeq, miRNA, Exon, and CNV deliver more consistent and reliable results. C_LIO_LIML models (e.g., SVM, XGB, HGB) demonstrate strong performance on smaller datasets with fewer features, whereas DL models (e.g., ResNet18, CNN, NN, MLP, and DEEPGENE transformer) excel in handling high-dimensional datasets with large sample sizes. C_LIO_LIThe findings provide critical insights for developing robust, standardized AI pipelines for precision oncology, enhancing reproducibility and enabling meaningful cross-method comparisons. C_LI

bioinformatics↗

bioRxiv2023

Pyruvate dehydrogenase dependent metabolic program affects oligodendrocyte maturation and remyelination

The metabolic need of the premature oligodendrocytes (Pre-OLs) and mature oligodendrocytes (OLs) are distinct. The metabolic control of oligodendrocyte maturation is not fully understood. Here we show that the terminal maturation and higher mitochondrial respiration in the oligodendrocyte is an integrated process controlled through pyruvate dehydrogenase (Pdh). Combined bioenergetics and metabolic studies show that mature oligodendrocytes show elevated TCA cycle activity than the premature oligodendrocytes. Our signaling studies show that the increased TCA cycle activity is mediated by the activation of Pdh due to inhibition of pyruvate dehydrogenases isoform-1 (Pdhk1) that phosphorylates and inhibits Pdh. Accordingly, when Pdhk1 is directly expressed in the premature oligodendrocytes, they fail to mature. While Pdh converts pyruvate into the acetyl-CoA by its oxidative decarboxylation, our study shows that Pdh also activates a unique molecular switch required for oligodendrocyte maturation by acetylating the bHLH family transcription factor Olig1. Pdh inhibition via Pdhk1 blocks the Olig1-acetylation and hence, oligodendrocyte maturation. Using the cuprizone model of demyelination, we show that Pdh is deactivated during the demyelination phase, which is reversed in the remyelination phase upon cuprizone withdrawal. In addition, Pdh activity status correlates with the Olig1-acetylation status. Hence, the Pdh metabolic node activation allows a robust mitochondrial respiration and activation of a molecular program necessary for the terminal maturation of oligodendrocytes. Our findings open a new dialogue in the developmental biology that links cellular development and metabolism. These findings have far-reaching implications for the development of therapies for a variety of demyelinating disorders including multiple sclerosis.

neuroscience↗