New Phytol. 2025 Feb 10. doi: 10.1111/nph.20447. Online ahead of print.

NO ABSTRACT

PMID:39925315 | DOI:10.1111/nph.20447

Proteomics. 2025 Feb 9:e202400346. doi: 10.1002/pmic.202400346. Online ahead of print.

ABSTRACT

The field of clinical proteomics has seen enormous growth in the past 20 years, with over 40,000 scientific manuscripts published to date. At the same time, actual clinical application of the reported findings is obviously scarce. In this viewpoint article, we discuss the key issues that may be responsible for this apparent lack of success. We conclude that success must not be assessed based on the number of publications, but via the impact on patient management and treatment. We proceed with specific suggestions for potential solutions, which include keeping a strict focus on potential patient benefit. We hope this article can help shape the field, so it can in fact deliver on its realistic promise to bring significant improvement in management and care to patients.

PMID:39924729 | DOI:10.1002/pmic.202400346

Antiviral Res. 2025 Feb 7:106101. doi: 10.1016/j.antiviral.2025.106101. Online ahead of print.

ABSTRACT

The global response to the COVID-19 pandemic, caused by the novel SARS-CoV-2 virus, has seen an unprecedented surge in the development of antiviral therapies. Traditional antiviral strategies have primarily focused on direct-acting antivirals (DAAs), which specifically target viral components. In recent years, increasing attention was given to an alternative approach aiming to exploit host cellular pathways or immune responses to inhibit viral replication, which has led to development of so-called host-targeted antivirals (HTAs). The emergence of SARS-CoV-2 and COVID-19 has promoted a boost in this field. Numerous HTAs have been tested and demonstrated their potential against SARS-CoV-2 through in vitro and in vivo studies. However, in striking contrast, only a limited number have successfully progressed to advanced clinical trial phases (2-4), and even less have entered clinical practice. This review aims to explore the current landscape of HTAs targeting SARS-CoV-2 that have reached phase 2-4 clinical trials. Additionally, it will delve into the challenges faced in the development of HTAs and in gaining regulatory approval and market availability.

PMID:39923941 | DOI:10.1016/j.antiviral.2025.106101

J Mol Graph Model. 2025 Jan 31;136:108967. doi: 10.1016/j.jmgm.2025.108967. Online ahead of print.

ABSTRACT

Matrix metalloproteinases (MMPs) are involved in different pathophysiological conditions like cancer, COPD, asthma, and inflammatory diseases. Among these MMPs, macrophage metalloelastase is one of the prime targets for COPD, and cancer. Therefore, to combat such diseases, potent novel macrophage metalloelastase inhibitors can be considered. Here, the classification-based molecular modeling was performed on large data of macrophage metalloelastase inhibitors that identified dibenzofuran, and diphenyl ether groups as important substructures contributing towards potent macrophage metalloelastase inhibition. This information was further implicated in repurposing marketed drugs through fragment-based and molecular docking-based virtual screening with molecular dynamics (MD) simulation-based stability validation and DFT calculations. This study identified fosamprenavir and tirofiban as promising hits that can exhibit potent macrophage metalloelastase inhibition which was also validated by the MD simulation and DFT-based calculations. Therefore, this study not only revealed these repurposed drugs as effective macrophage metalloelastase inhibitors but also opened up a horizon in developing novel potent macrophage metalloelastase inhibitors for the management of cancer and COPD in the future.

PMID:39923554 | DOI:10.1016/j.jmgm.2025.108967

Pediatr Transplant. 2025 Mar;29(2):e70044. doi: 10.1111/petr.70044.

ABSTRACT

BACKGROUND: Pediatric solid organ transplantation is a complex process including a tightly orchestrated medication regimen, essential for prevention of infection, rejection, graft failure, and mortality. Pharmacogenomic (PGx) testing tailors medication therapy to the individual patient, focusing on safety, efficacy, and avoidance of adverse effects. Implementation of PGx panel results into clinical practice for pediatric transplant patients has not been evaluated.

METHODS: Pediatric patients evaluated for heart, kidney, or combined heart-kidney transplant at a tertiary children's hospital from October 2021 to October 2023 received PGx panel testing.

PRIMARY OUTCOME MEASURE: Report the prevalence of actionable PGx variants for key genes impacting pharmacotherapy in pre- and post-heart and kidney transplant populations.

RESULTS: A total of 73 patients were included, predominately white (84.9%) and male (64.4%), with a mean age of 8.8 ± 6.4 years. Indications for PGx testing included evaluation for heart transplant (38.4%), kidney transplant (38.4%), combined heart-kidney transplant (4.1%), or to inform posttransplant care (19.2%). All patients had at least one actionable phenotype identified. 37 of 73 patients (50.7%) had at least one actionable phenotype for the transplant-specific genes captured including CYP3A5, SLCO1B1, G6PD, TPMT, prothrombin (Factor 2), and Factor V Leiden. 16 of 73 patients (21.9%) had actionable CYP3A5 phenotypes. 15 of 73 (20.5%) had actionable SLCO1B1 phenotypes. 9 of 73 patients (12.3%) had actionable TPMT phenotypes. 5 of 73 (6.8%) had Prothrombin or Factor V Leiden variants.

CONCLUSIONS: Routine pretransplant PGx testing provided information that was actionable and could be utilized to optimize posttransplant medications for all patients.

PMID:39924350 | DOI:10.1111/petr.70044

CNS Neurosci Ther. 2025 Feb;31(2):e70258. doi: 10.1111/cns.70258.

ABSTRACT

BACKGROUND: Antiplatelet drugs are a cornerstone in managing atherosclerotic vascular disease (ASVD). However, their interactions with other medications present significant challenges to treatment efficacy and safety. Patients with ASVD often require multiple treatment regimens due to complex comorbidities, which increases the risk of drug-drug interactions (DDIs). These interactions can lead to drug resistance, reduced therapeutic outcomes, or adverse effects. A thorough understanding of DDIs is crucial for optimizing patient care.

AIMS: This review aims to explore the clinical significance. mechanisms, and implications of DDIs in antiplatelet therapy Additionally, it seeks to identify future research directions to advance personalized treatment strategies and improve therapeutic outcomes.

MATERIALS AND METHODS: A systematic literature review was conducted using key databases, focusing on clinical studies, mechanistic research, and guidelines related to antiplatelet therapy and DDIs. Findings were analyzed to identify common interaction patterns, associated risks, and management strategies.

RESULTS: The review identifies common DDIs involving antiplatelet drugs, particularly with anticoagulants, nonsteroidal anti-inflammatory drugs, and proton pump inhibitors. These interactions primarily occur through pharmacokinetic mechanisms, such as alterations in drug metabolism via cytochrome P450 enzymes, and pharmacodynamic mechanisms, including synergistic or antagonistic effects on platelet inhibition. Clinically, DDIs can increase bleeding risk, reduce antiplatelet efficacy, and contribute to adverse cardiovascular outcomes. Strategies to mitigate these risks include individualized drug selection, dose adjustments, genetic testing, and therapeutic drug monitoring.

DISCUSSION: Effective management of DDIs in antiplatelet therapy is essential to improve clinical outcomes. A patient-specific approach, considering comorbidities, genetic predispositions, and concurrent medications, is crucial. The review categorizes DDIs based on clinical settings and underscores the need for further research on predictive biomarkers, pharmacogenomics, and advanced monitoring techniques.

CONCLUSION: DDIs significantly impact the effectiveness and safety of antiplatelet therapy, necessitating a comprehensive understanding of their mechanisms and clinical implications. Future research should focus on developing personalized treatment approaches, integrating genetic testing, and optimizing pharmacological monitoring to minimize risks and improve therapeutic outcomes. This review provides a foundation for advancing clinical practice and enhancing the management of patients with ASVD.

PMID:39924343 | DOI:10.1111/cns.70258

Sci Rep. 2025 Feb 9;15(1):4815. doi: 10.1038/s41598-025-88753-3.

ABSTRACT

Skin cancer is a prevalent health concern, and accurate segmentation of skin lesions is crucial for early diagnosis. Existing methods for skin lesion segmentation often face trade-offs between efficiency and feature extraction capabilities. This paper proposes Dual Skin Segmentation (DuaSkinSeg), a deep-learning model, to address this gap by utilizing dual encoders for improved performance. DuaSkinSeg leverages a pre-trained MobileNetV2 for efficient local feature extraction. Subsequently, a Vision Transformer-Convolutional Neural Network (ViT-CNN) encoder-decoder architecture extracts higher-level features focusing on long-range dependencies. This approach aims to combine the efficiency of MobileNetV2 with the feature extraction capabilities of the ViT encoder for improved segmentation performance. To evaluate DuaSkinSeg's effectiveness, we conducted experiments on three publicly available benchmark datasets: ISIC 2016, ISIC 2017, and ISIC 2018. The results demonstrate that DuaSkinSeg achieves competitive performance compared to existing methods, highlighting the potential of the dual encoder architecture for accurate skin lesion segmentation.

PMID:39924555 | DOI:10.1038/s41598-025-88753-3

Sci Rep. 2025 Feb 9;15(1):4825. doi: 10.1038/s41598-025-88579-z.

ABSTRACT

To accelerate the clinical adoption of quantitative magnetic resonance imaging (qMRI), frameworks are needed that not only allow for rapid acquisition, but also flexibility, cost efficiency, and high accuracy in parameter mapping. In this study, feed-forward deep neural network (DNN)- and iterative fitting-based frameworks are compared for multi-parametric (MP) relaxometry based on phase-cycled balanced steady-state free precession (pc-bSSFP) imaging. The performance of supervised DNNs (SVNN), self-supervised physics-informed DNNs (PINN), and an iterative fitting framework termed motion-insensitive rapid configuration relaxometry (MIRACLE) was evaluated in silico and in vivo in brain tissue of healthy subjects, including Monte Carlo sampling to simulate noise. DNNs were trained on three distinct in silico parameter distributions and at different signal-to-noise-ratios. The PINN framework, which incorporates physical knowledge into the training process, ensured more consistent inference and increased robustness to training data distribution compared to the SVNN. Furthermore, DNNs utilizing the full information of the underlying complex-valued MR data demonstrated ability to accelerate the data acquisition by a factor of 3. Whole-brain relaxometry using DNNs proved to be effective and adaptive, suggesting the potential for low-cost DNN retraining. This work emphasizes the advantages of in silico DNN MP-qMRI pipelines for rapid data generation and DNN training without extensive dictionary generation, long parameter inference times, or prolonged data acquisition, highlighting the flexible and rapid nature of lightweight machine learning applications for MP-qMRI.

PMID:39924554 | DOI:10.1038/s41598-025-88579-z

Sci Rep. 2025 Feb 9;15(1):4835. doi: 10.1038/s41598-025-85301-x.

ABSTRACT

Accurate segmentation of skin lesions is crucial for reliable clinical diagnosis and effective treatment planning. Automated techniques for skin lesion segmentation assist dermatologists in early detection and ongoing monitoring of various skin diseases, ultimately improving patient outcomes and reducing healthcare costs. To address limitations in existing approaches, we introduce a novel U-shaped segmentation architecture based on our Residual Space State Block. This efficient model, termed 'SSR-UNet,' leverages bidirectional scanning to capture both global and local features in image data, achieving strong performance with low computational complexity. Traditional CNNs struggle with long-range dependencies, while Transformers, though excellent at global feature extraction, are computationally intensive and require large amounts of data. Our SSR-UNet model overcomes these challenges by efficiently balancing computational load and feature extraction capabilities. Additionally, we introduce a spatially-constrained loss function that mitigates gradient stability issues by considering the distance between label and prediction boundaries. We rigorously evaluated SSR-UNet on the ISIC2017 and ISIC2018 skin lesion segmentation benchmarks. The results showed that the accuracy of Mean Intersection Over Union, Classification Accuracy and Specificity indexes in ISIC2017 datasets reached 80.98, 96.50 and 98.04, respectively, exceeding the best indexes of other models by 0.83, 0.99 and 0.38, respectively. The accuracy of Mean Intersection Over Union, Dice Coefficient, Classification Accuracy and Sensitivity on ISIC2018 datasets reached 82.17, 90.21, 95.34 and 88.49, respectively, exceeding the best indicators of other models by 1.71, 0.27, 0.65 and 0.04, respectively. It can be seen that SSR-UNet model has excellent performance in most aspects.

PMID:39924544 | DOI:10.1038/s41598-025-85301-x

Sci Rep. 2025 Feb 9;15(1):4826. doi: 10.1038/s41598-024-83597-9.

ABSTRACT

Mammography is the recommended imaging modality for breast cancer screening. Expressions of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) are critical to the development of therapeutic strategies for breast cancer. In this study, a deep learning model (CBAM ResNet-18) was developed to predict the expression of these three receptors on mammography without manual segmentation of masses. Mammography of patients with pathologically proven breast cancer was obtained from two centers. A deep learning-based model (CBAM ResNet-18) for predicting HER2, ER, and PR expressions was trained and validated using five-fold cross-validation on a training dataset. The performance of the model was further tested using an external test dataset. Area under receiver operating characteristic curve (AUC), accuracy (ACC), and F1-score were calculated to assess the ability of the model to predict each receptor. For comparison we also developed original ResNet-18 without attention module and VGG-19 with and without attention module. The AUC (95% CI), ACC, and F1-score were 0.708 (0.609, 0.808), 0.651, 0.528, respectively, in the HER2 test dataset; 0.785 (0.673, 0.897), 0.845, 0.905, respectively, in the ER test dataset; and 0.706 (0.603, 0.809), 0.678, 0.773, respectively, in the PR test dataset. The proposed model demonstrates superior performance compared to the original ResNet-18 without attention module and VGG-19 with and without attention module. The model has the potential to predict HER2, PR, and especially ER expressions, and thus serve as an adjunctive diagnostic tool for breast cancer.

PMID:39924532 | DOI:10.1038/s41598-024-83597-9

Sci Rep. 2025 Feb 10;15(1):4857. doi: 10.1038/s41598-025-88688-9.

ABSTRACT

Raindrops on the windscreen significantly impact a driver's visibility during driving, affecting safe driving. Maintaining a clear windscreen is crucial for drivers to mitigate accident risks in rainy conditions. A real-time rain detection system and an innovative wiper control method are introduced based on machine vision and deep learning. An all-weather raindrop detection model is constructed using a convolutional neural network (CNN) architecture, utilising an improved YOLOv8 model. The all-weather model achieved a precision rate of 0.89, a recall rate of 0.83, and a detection speed of 63 fps, meeting the system's real-time requirements. The raindrop area ratio is computed through target detection, which facilitates the assessment of rainfall begins and ends, as well as intensity variations. When the raindrop area ratio exceeds the wiper activation threshold, the wiper starts, and when the area ratio approaches zero, the wiper stops. The wiper control method can automatically adjust the detection frequency and the wiper operating speed according to changes in rainfall intensity. The wiper activation threshold can be adjusted to make the wiper operation more in line with the driver's habits.

PMID:39924520 | DOI:10.1038/s41598-025-88688-9

Biomed Eng Online. 2025 Feb 9;24(1):16. doi: 10.1186/s12938-025-01348-x.

ABSTRACT

PURPOSE: The aim of this study is to convert low-dose PET (L-PET) images to full-dose PET (F-PET) images based on our Diffused Multi-scale Generative Adversarial Network (DMGAN) to offer a potential balance between reducing radiation exposure and maintaining diagnostic performance.

METHODS: The proposed method includes two modules: the diffusion generator and the u-net discriminator. The goal of the first module is to get different information from different levels, enhancing the generalization ability of the generator to the image and improving the stability of the training. Generated images are inputted into the u-net discriminator, extracting details from both overall and specific perspectives to enhance the quality of the generated F-PET images. We conducted evaluations encompassing both qualitative assessments and quantitative measures. In terms of quantitative comparisons, we employed two metrics, structure similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) to evaluate the performance of diverse methods.

RESULTS: Our proposed method achieved the highest PSNR and SSIM scores among the compared methods, which improved PSNR by at least 6.2% compared to the other methods. Compared to other methods, the synthesized full-dose PET image generated by our method exhibits a more accurate voxel-wise metabolic intensity distribution, resulting in a clearer depiction of the epilepsy focus.

CONCLUSIONS: The proposed method demonstrates improved restoration of original details from low-dose PET images compared to other models trained on the same datasets. This method offers a potential balance between minimizing radiation exposure and preserving diagnostic performance.

PMID:39924498 | DOI:10.1186/s12938-025-01348-x

Light Sci Appl. 2025 Feb 10;14(1):75. doi: 10.1038/s41377-024-01713-w.

ABSTRACT

Free of noble-metal and high in unit internal quantum efficiency of electroluminescence, organic molecules with thermally activated delayed fluorescence (TADF) features pose the potential to substitute metal-based phosphorescence materials and serve as the new-generation emitters for the mass production of organic light emitting diodes (OLEDs) display. Predicting the function of TADF emitters beyond classic chemical synthesis and material characterization experiments remains a great challenge. The advances in deep learning (DL) based artificial intelligence (AI) offer an exciting opportunity for screening high-performance TADF materials through efficiency evaluation. However, data-driven material screening approaches with the capacity to access the excited state properties of TADF emitters remain extremely difficult and largely unaddressed. Inspired by the fundamental principle that the excited state properties of TADF molecules are strongly dependent on their D-A geometric and electronic structures, we developed the Electronic Structure-Infused Network (ESIN) for TADF emitter screening. Designed with capacities of accurate prediction of the photoluminescence quantum yields (PLQYs) of TADF molecules based on elemental molecular geometry and orbital information and integrated with frontier molecular orbitals (FMOs) weight-based representation and modeling features, ESIN is a promising interpretable tool for emission efficiency evaluation and molecular design of TADF emitters.

PMID:39924488 | DOI:10.1038/s41377-024-01713-w

BMC Bioinformatics. 2025 Feb 9;26(1):45. doi: 10.1186/s12859-025-06069-5.

ABSTRACT

BACKGROUND: Single-cell technologies enable comprehensive profiling of diverse immune cell-types through the measurement of multiple genes or proteins per individual cell. In order to translate immune signatures assayed from blood or tissue into powerful diagnostics, machine learning approaches are often employed to compute immunological summaries or per-sample featurizations, which can be used as inputs to models for outcomes of interest. Current supervised learning approaches for computing per-sample representations are trained only to accurately predict a single outcome and do not take into account relevant additional clinical features or covariates that are likely to also be measured for each sample.

RESULTS: Here, we introduce a novel approach for incorporating measured covariates in optimizing model parameters to ultimately specify per-sample encodings that accurately affect both immune signatures and additional clinical information. Our introduced method CytoCoSet is a set-based encoding method for learning per-sample featurizations, which formulates a loss function with an additional triplet term penalizing samples with similar covariates from having disparate embedding results in per-sample representations.

CONCLUSIONS: Overall, incorporating clinical covariates enables the learning of encodings for each individual sample that ultimately improve prediction of clinical outcome. This integration of information disparate more robust predictions of clinical phenotypes and holds significant potential for enhancing diagnostic and treatment strategies.

PMID:39924480 | DOI:10.1186/s12859-025-06069-5

J Prosthet Dent. 2025 Feb 8:S0022-3913(25)00049-6. doi: 10.1016/j.prosdent.2024.12.029. Online ahead of print.

NO ABSTRACT

PMID:39924432 | DOI:10.1016/j.prosdent.2024.12.029

JMIR Med Inform. 2025 Feb 7;13:e55825. doi: 10.2196/55825.

ABSTRACT

BACKGROUND: Chronic kidney disease (CKD) is a prevalent condition with significant global health implications. Early detection and management are critical to prevent disease progression and complications. Deep learning (DL) models using retinal images have emerged as potential noninvasive screening tools for CKD, though their performance may be limited, especially in identifying individuals with proteinuria and in specific subgroups.

OBJECTIVE: We aim to evaluate the efficacy of integrating retinal images and urine dipstick data into DL models for enhanced CKD diagnosis.

METHODS: The 3 models were developed and validated: eGFR-RIDL (estimated glomerular filtration rate-retinal image deep learning), eGFR-UDLR (logistic regression using urine dipstick data), and eGFR-MMDL (multimodal deep learning combining retinal images and urine dipstick data). All models were trained to predict an eGFR<60 mL/min/1.73 m², a key indicator of CKD, calculated using the 2009 CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation. This study used a multicenter dataset of participants aged 20-79 years, including a development set (65,082 people) and an external validation set (58,284 people). Wide Residual Networks were used for DL, and saliency maps were used to visualize model attention. Sensitivity analyses assessed the impact of numerical variables.

RESULTS: eGFR-MMDL outperformed eGFR-RIDL in both the test and external validation sets, with area under the curves of 0.94 versus 0.90 and 0.88 versus 0.77 (P<.001 for both, DeLong test). eGFR-UDLR outperformed eGFR-RIDL and was comparable to eGFR-MMDL, particularly in the external validation. However, in the subgroup analysis, eGFR-MMDL showed improvement across all subgroups, while eGFR-UDLR demonstrated no such gains. This suggested that the enhanced performance of eGFR-MMDL was not due to urine data alone, but rather from the synergistic integration of both retinal images and urine data. The eGFR-MMDL model demonstrated the best performance in individuals younger than 65 years or those with proteinuria. Age and proteinuria were identified as critical factors influencing model performance. Saliency maps indicated that urine data and retinal images provide complementary information, with urine offering insights into retinal abnormalities and retinal images, particularly the arcade vessels, being key for predicting kidney function.

CONCLUSIONS: The MMDL model integrating retinal images and urine dipstick data show significant promise for noninvasive CKD screening, outperforming the retinal image-only model. However, routine blood tests are still recommended for individuals aged 65 years and older due to the model's limited performance in this age group.

PMID:39924305 | DOI:10.2196/55825

Expert Rev Proteomics. 2025 Feb 9. doi: 10.1080/14789450.2025.2463324. Online ahead of print.

ABSTRACT

INTRODUCTION: Exogenous Cushing's syndrome is the result of long-term exposure to glucocorticoids, while endogenous Cushing's syndrome occurs due to excessive production of glucocorticoids within the body. Cushing's syndrome remains a diagnostic challenge for the treating physician.Mass spectrometry, with its better resolution, detectability and specificity, paved the way to understanding the cellular and molecular mechanisms involved in the several diseases that facilitated the evolution of biomarkers and personalized medicine, which can be applicable to manage Cushing's syndrome as well.

AREAS COVERED: There are only a few reports of mass spectrometry-based metabolomic approaches to endogenous Cushing's syndrome of certain etiologies. However, the application of this approach in the diagnosis of exogenous Cushing has not been explored much. This review attempts to discuss the application of the mass spectrometry-based metabolomic approach in the evaluation of Cushing's syndrome.

EXPERT OPINION: Global metabolomics has the potential to discover altered metabolites and associated signaling and metabolic pathways, which may serve as potential biomarkers that would help in developing tools to accelerate precision medicine. Multi-omics approaches will provide innovative solutions to develop molecular tests for multi-molecule panel assays.

PMID:39924469 | DOI:10.1080/14789450.2025.2463324

ChemSusChem. 2025 Feb 9:e202402536. doi: 10.1002/cssc.202402536. Online ahead of print.

ABSTRACT

Lignocellulosic biomasses have the potential to generate by-products with biological activity (i.e., polyphenols) as well as biopolymers (i.e., cellulose, hemicellulose, pectins, lignin). The wine industry is one of the pillars of Italian agri-food sector. Nevertheless, large quantities of by-products such as grape stems are produced, which are usually disposed of at a cost, and therefore represent an attractive negative-cost feedstock for biorefinery. In this work, a sequential protocol for biomass valorization is proposed, characterized by a multidisciplinary strategy using enabling technologies and subcritical water as a green solvent, where physical/chemical treatments synergistically interact with biological treatments. The first phase involved the sequential fractionation of grape stalks, obtaining several product streams rich in polyphenols, hemicellulose, pectin (13.15% of cumulative yield on biomass), lignin and cellulose. A membrane treatment was employed to recycle materials within the process. Finally, the cellulose-rich residue was exploited as a fermentation substrate for the last step, producing up to 5.8 g/L of lactic acid by harnessing suitably engineered Clostridium thermocellum strains. The polyphenolic fraction successfully inhibited the growth of Brettanomyces bruxellensis and Acetobacter pasteurianus, microorganisms responsible for major wine off-flavors. Globally, this study represents a proof-of-concept of a second-generation biorefining process based on locally available waste biomass.

PMID:39924442 | DOI:10.1002/cssc.202402536

Plant Genome. 2025 Mar;18(1):e20536. doi: 10.1002/tpg2.20536.

ABSTRACT

The organization of subcellular components in a cell is critical for its function and studying cellular processes, protein-protein interactions, identifying potential drug targets, network analysis, and other systems biology mechanisms. Determining protein localization experimentally is time-consuming and expensive. Due to the need for meticulous experimentation, validation, and data analysis, computational methods provide a quick and accurate alternative. Arabidopsis thaliana, a beneficial model organism in plant biology, facilitates experimentation and applies to other plants. Predicting its proteins' subcellular localization can improve our understanding of cellular processes and have applications in crop improvement and biotechnology. We propose AtSubP-2.0, an extension of our previously developed and widely used AtSubP v1.0 tool for annotating the Arabidopsis proteome. For precise protein subcellular localization prediction, AtSubP-2.0 employs a four-phase strategy. The first phase differentiates between single and dual localization with accuracy (97.66% in fivefold training/testing, 98.10% on independent data) and high Matthews correlation coefficient (0.88 training, 0.90 independent). Single localized proteins are classified into 12 locations at the second phase, with accuracy (98.37% in fivefold training/testing, 97.43% on independent data) and Matthews correlation coefficient (0.94 training, 0.91 independent). The third phase categorizes dual location proteins into nine classes with accuracy (99.65% in fivefold training/testing, 98.16% on independent data) and Matthews correlation coefficient (0.92 training, 0.87 independent). We also employed a fourth phase that classifies the membrane type proteins predicted in phase I into single-pass and multi-pass membrane with accuracy (98% in fivefold training/testing, 98.55% on independent data) and a high Matthews correlation coefficient (0.95 training, 0.97 independent). A web-based prediction server has been implemented for community use and is freely available at https://kaabil.net/AtSubP2/, including a standalone version. AtSubP2 will help researchers to better understand organelle-specific functions, cellular processes, and regulatory mechanisms important for plant growth, development, and response to environmental stimuli.

PMID:39924294 | DOI:10.1002/tpg2.20536

Biochim Biophys Acta Biomembr. 2025 Feb 7:184416. doi: 10.1016/j.bbamem.2025.184416. Online ahead of print.

NO ABSTRACT

PMID:39924097 | DOI:10.1016/j.bbamem.2025.184416