Computational and structural biotechnology journal最新文献

{"title":"Anti-EBV: Artificial intelligence driven predictive modeling for repurposing drugs as potential antivirals against Epstein-Barr virus.","authors":"Hiteshi Vaidya, Sakshi Gautam, Manoj Kumar","doi":"10.1016/j.csbj.2025.04.042","DOIUrl":"10.1016/j.csbj.2025.04.042","url":null,"abstract":"<p><p>Epstein-Barr virus (EBV) is linked to various cancers like gastric carcinoma, nasopharyngeal carcinoma, and Burkitt's lymphoma, leading to around 200,000 deaths annually. Despite efforts, FDA-approved drugs to combat EBV infection are lacking. In this endeavor, we have developed an AI/ML based predictive algorithm \"Anti-EBV\" to find potential antivirals against EBV. We utilized small molecules from the ChEMBL database, which were experimentally tested for antiviral activity against EBV in lytic phase, in terms of IC₅₀ /EC₅₀ values. 17,968 molecular fingerprints and descriptors were computed for each molecule. Further, the best-performing 150 descriptors were used in the predictive model development. The molecules were then split into training/testing (T³¹⁵) and independent validation (V³⁵) datasets, followed by 10-fold cross validation to develop robust models. Various machine-learning techniques (MLTs) namely SVM, KNN, ANN, DNN, RF and XGBoost were used for predictive models development. SVM model achieved the best performance with Pearson's correlation coefficient (PCC) of 0.91 on T³¹⁵ dataset and 0.95 on V³⁵ dataset, respectively. These models were found to be robust by applicability domain, decoy dataset and chemical clustering analyses. The top-performing model was used to screen approved drugs from DrugBank, identifying potential repurposed drugs namely arzoxifene, succimer, abemaciclib and many more. To further validate these findings, top compounds were docked against key lytic proteins BZLF1 and BHRF1, demonstrating strong binding affinities for compounds like fluspirilene and suvorexant. This model is accessible as the \"Anti-EBV\" web server http://bioinfo.imtech.res.in/manojk/antiebv/ for antiviral prediction, making it the first AI/ML-based study for antiviral identification against EBV in lytic phase.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1784-1799"},"PeriodicalIF":4.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12127599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144207877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-guided structural analysis of a novel bacteriophage KPP105 against multidrug-resistant <i>Klebsiella pneumoniae</i>.","authors":"Seyoung Ko, Jaehyung Kim, Jae-Hyun Cho, Youngju Kim, Donghyuk Kim","doi":"10.1016/j.csbj.2025.04.032","DOIUrl":"10.1016/j.csbj.2025.04.032","url":null,"abstract":"<p><p>The increasing prevalence of multidrug-resistant bacteria, particularly <i>Klebsiella</i> species, poses a significant global health threat. Bacteriophages have emerged as promising alternatives due to their specificity and efficacy against bacterial targets. Characterizing phages, alongside analyzing their protein structures provide crucial insights into their host specificity, infection mechanisms, and potential applications. In this study, we isolated a novel bacteriophage, KPP105, and conducted comprehensive physiological, genomic, and structural analysis. Physiological assessments revealed that KPP105 maintains stable activity across a wide range of pHs and temperature conditions and exhibits host-specific infection properties. Genomic analysis classified KPP105 as a member of the <i>Demerecviridae</i> family and identified it as a lytic bacteriophage harboring a lytic cassette. Deep learning-based structural analysis of host-interacting proteins, including the receptor-binding protein (RBP) and endolysin derived from KPP105, was performed. Structural similarity analysis indicated that its RBP facilitates interactions with host receptors and exhibits unique sequence patterns distinguishing <i>Klebsiella</i> strains from other bacteria. Structure-based functional analysis provided comprehensive insights into cell wall degradation with various peptidoglycan fragments. In conclusion, this study reports the physiological, genomic, and structural characteristics of the novel lytic bacteriophage KPP105, offering valuable insights into its potential as an alternative agent against multidrug-resistant <i>Klebsiella</i> infections.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1827-1837"},"PeriodicalIF":4.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12136712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144224627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"gdGSE: An algorithm to evaluate pathway enrichment by discretizing gene expression values.","authors":"Jiangti Luo, Qiqi Lu, Mengjiao He, Xiaobo Zhang, Xiang Yang, Xiaosheng Wang","doi":"10.1016/j.csbj.2025.04.038","DOIUrl":"10.1016/j.csbj.2025.04.038","url":null,"abstract":"<p><p>We proposed gdGSE, a novel computational framework for gene set enrichment analysis. Unlike conventional methods that rely on continuous gene expression values, gdGSE employs discretized gene expression profiles to assess pathway activity. This approach effectively mitigates discrepancies caused by data distributions. This algorithm consists of two steps: (1) applying statistical thresholds binarizing gene expression matrix, and (2) converting the binarized gene expression matrix into a gene set enrichment matrix. Our results demonstrated that gdGSE could robustly extract biological insights from a diverse array of simulated and real bulk or single-cell gene expression datasets. Notably, gene set enrichment scores by gdGSE exhibited enhanced utility in downstream applications: (1) precise quantification of cancer stemness with significant prognostic relevance; (2) enhanced clustering performance in stratifying tumor subtypes with distinct prognoses; and (3) more accurate identification of cell types. Remarkably, the pathway activity scores by gdGSE showed > 90 % concordance with experimentally validated drug mechanisms in patients-derived xenografts and estrogen receptor-positive breast cancer cell lines. Our algorithm proposes that discretizing gene expression values provides an alternative method for evaluating pathway enrichment, applicable to both bulk and single-cell data analysis.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1772-1783"},"PeriodicalIF":4.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12127574/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144207879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating spatial mapping and metabolomics: A novel platform for bioactive compound discovery and saline land reclamation.","authors":"Tushar Andriyas, Nisa Leksungnoen, Pichaya Pongchaidacha, Arashaporn Uthairangsee, Suwimon Uthairatsamee, Peerapat Doomnil, Yongkriat Ku-Or, Chatchai Ngernsaengsaruay, Sanyogita Andriyas, Arerut Yarnvudhi, Rossarin Tansawat","doi":"10.1016/j.csbj.2025.04.035","DOIUrl":"10.1016/j.csbj.2025.04.035","url":null,"abstract":"<p><p>Saline lands pose significant environmental and agricultural challenges due to high soil salinity, which disrupts water uptake and ionic balances, limiting conventional crop productivity. Yet, certain endemic plants thrive under these conditions and may offer untapped bioactive compounds. This study proposes a novel platform that integrates species distribution modeling (SDM) and advanced metabolomics to screen for bioactive secondary metabolites, using <i>Buchanania siamensis</i>, a rare native species, as a case study. An ensemble SDM model incorporating environmental and soil parameters identified salinity as a critical factor influencing the species' distribution. Leaf samples were collected from naturally growing trees at both saline (SS) and non-saline (NS) sites. LC-QTOF metabolomic analysis annotated a total of 1106 metabolites across the leaf samples, with 175 found to be significantly different between the groups. Among them, 108 metabolites exhibited higher abundance in the SS group. Additionally, antioxidant assays including DPPH, FRAP, and total phenolic content tests, were conducted. Data were further analyzed using O-PLSR models to identify key metabolites most relevant to antioxidant properties. The results indicated that afzelin was the key metabolite responsible for the antioxidant properties of <i>B. siamensis</i>, with significantly higher levels in SS compared to NS samples (<i>p</i> < 0.05), as determined by peak area. By leveraging this multidisciplinary approach, we propose a framework to support both bioactive compound discovery and saline land reclamation, offering potential environmental and pharmaceutical benefits. This integrated platform may support pharmaceutical research, particularly in drug discovery efforts.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1741-1753"},"PeriodicalIF":4.4,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12104716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Subtypes detection of papillary thyroid cancer from methylation assay via Deep Neural Network.","authors":"Andrea Colacino, Andrea Soricelli, Michele Ceccarelli, Ornella Affinito, Monica Franzese","doi":"10.1016/j.csbj.2025.04.034","DOIUrl":"10.1016/j.csbj.2025.04.034","url":null,"abstract":"<p><strong>Background and objective: </strong>In recent years, DNA methylation-tumor classification based on artificial intelligence algorithms has led to a notable improvement in diagnostic accuracy compared to traditional machine learning methods. In cancer, the methylation pattern likely reflects both the cell of origin and somatically acquired DNA methylation changes, making this epigenetic modification an ideal tool for tumor classification. We propose an in-depth method based on the Convolutional Neural Network for the DNA methylation-based classification of papillary thyroid carcinoma (PTC) and its follicular (fvPTC) and classical (cvPTC) subtypes.</p><p><strong>Methods: </strong>To address this issue, we first performed a pan-cancer analysis to train a convolutional 1-D Neural Network (CNN) using supervised learning. Then, we evaluated the robustness of the net on an independent PTC dataset and assessed its ability to classify normal (N=56) versus tumor (N=461) samples and fvPTC (N=102) versus cvPTC (N=359). We then compared its performance with 4 machine learning models (logistic regression with elastic net penalty, quadratic discriminant analysis, support vector classifier with RBF kernel, and random forest).</p><p><strong>Results: </strong>By using RELU activation function and leaving out liquid tumors, our results show a remarkable performance of the neural network in classifying cancer and normal samples when applied to pan-cancer data (Validation AUC = 0.9903 and Validation Loss = 0.112). When applied to the thyroid independent dataset, the proposed Neural Net architecture successfully discriminates tumor versus normal samples (AUC = 0.91 +/- 0.05) and follicular versus classical PTC subtypes (AUC = 0.80 +/- 0.05), outperforming traditional machine learning algorithms.</p><p><strong>Conclusions: </strong>In conclusion, the study highlights the effectiveness of CNNs in the methylation based classification of thyroid tumors and their subtypes, demonstrating its ability to capture subtle epigenetic differences with minimal preprocessing.This versatility makes the model adaptable for classifying other tumor types. Also, the findings emphasize the potential relevance of AI algorithms in addressing complex diagnostic challenges and supporting clinical decisions.This research lays the foundation for developing robust and generalizable models that can advance precision oncology in cancer diagnostics.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1809-1817"},"PeriodicalIF":4.4,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12136774/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144224630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-cell and multi-omics integration reveals cholesterol biosynthesis as a synergistic target with HER2 in aggressive breast cancer.","authors":"Tzu-Yang Tseng, Chiao-Hui Hsieh, Jie-Yu Liu, Hsuan-Cheng Huang, Hsueh-Fen Juan","doi":"10.1016/j.csbj.2025.04.030","DOIUrl":"10.1016/j.csbj.2025.04.030","url":null,"abstract":"<p><p>Breast cancer stands as one of the most prevalent malignancies affecting women. Alterations in molecular pathways in cancer cells represent key regulatory disruptions that drive malignancy, influencing cancer cell survival, proliferation, and potentially modulating therapeutic responsiveness. Therefore, decoding the intricate molecular mechanisms and identifying novel therapeutic targets through systematic computational approaches are essential steps toward advancing effective breast cancer treatments. In this study, we developed an integrative computational framework that combines single-cell RNA sequencing (scRNA-seq) and multi-omics analyses to delineate the functional characteristics of malignant cell subsets in breast cancer patients. Our analyses revealed a significant correlation between cholesterol biosynthesis and HER2 expression in malignant breast cancer cells, supported by proteomics data, gene expression profiles, drug treatment scores, and cell-surface HER2 intensity measurements. Given previous evidence linking cholesterol biosynthesis to HER2 membrane dynamics, we proposed a combinatorial strategy targeting both pathways. Experimental validation through clonogenic and viability assays demonstrated that simultaneous inhibition of cholesterol biosynthesis (via statins) and HER2 (via Neratinib) synergistically reduced malignant breast cancer cells, even in HER2-negative contexts. Through systematic analysis of scRNA-seq and multi-omics data, our study computationally identified and experimentally validated cholesterol biosynthesis and HER2 as novel combinatorial therapeutic targets in breast cancer. This data-driven approach highlights the potential of leveraging multiple molecular profiling techniques to uncover previously unexplored treatment strategies.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1719-1731"},"PeriodicalIF":4.4,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12088767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144101546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discernibility in explanations: Designing more acceptable and meaningful machine learning models for medicine.","authors":"Haomiao Wang, Julien Aligon, Julien May, Emmanuel Doumard, Nicolas Labroche, Cyrille Delpierre, Chantal Soulé-Dupuy, Louis Casteilla, Valérie Planat-Benard, Paul Monsarrat","doi":"10.1016/j.csbj.2025.04.021","DOIUrl":"10.1016/j.csbj.2025.04.021","url":null,"abstract":"<p><p>Although the benefits of machine learning are undeniable in healthcare, explainability plays a vital role in improving transparency and understanding the most decisive and persuasive variables for prediction. The challenge is to identify explanations that make sense to the biomedical expert. This work proposes <i>discernibility</i> as a new approach to faithfully reflect human cognition, based on the user's perception of a relationship between explanations and data for a given variable. A total of 50 participants (19 biomedical experts and 31 data scientists) evaluated their perception of the discernibility of explanations from both synthetic and human-based datasets (National Health and Nutrition Examination Survey). The low inter-rater reliability of discernibility (Intraclass Correlation Coefficient < 0.5), with no significant difference between areas of expertise or levels of education, highlights the need for an objective metric of discernibility. Thirteen statistical coefficients were evaluated for their ability to capture, for a given variable, the relationship between its values and its explanations using Passing-Bablok regression. Among these, dcor was shown to be a reliable metric for assessing the discernibility of explanations, effectively capturing the clarity of the relationship between the data and their explanations, and providing clues to underlying pathophysiological mechanisms not immediately apparent when examining individual predictors. Discernibility can also serve as an evaluation metric for model quality, helping to prevent overfitting and aiding in feature selection, ultimately providing medical practitioners with more accurate and persuasive results.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1800-1808"},"PeriodicalIF":4.4,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12127544/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144207878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transfer learning towards predicting viral missense mutations: A case study on SARS-CoV-2.","authors":"Shaylyn Govender, Emily Morgan, Rabelani Ramahala, Kevin Lobb, Nigel T Bishop, Özlem Tastan Bishop","doi":"10.1016/j.csbj.2025.04.029","DOIUrl":"https://doi.org/10.1016/j.csbj.2025.04.029","url":null,"abstract":"<p><p>Understanding viral evolution and predicting future mutations are crucial for overcoming drug resistance and developing long-lasting treatments. Previously, we established machine learning (ML) models using dynamic residue network (DRN) metric data and leveraging a vast amount of existing mutation data from the SARS-CoV-2 main protease (M^pro). Here, we sought to assess the generalizability and robustness of the current models across other SARS-CoV-2 proteins. To achieve this, for the first time, we employed a transfer learning (TL) approach, allowing us to determine the extent to which M^pro trained models could be applied to other SARS-CoV-2 proteins. The TL results were highly promising, with artificial neural network (ANN) and random forest (RF) correlation coefficients for M^pro closely matching those of NSP10, NSP16, and PL^pro. The ANN |R| value for M^pro was 0.564, while NSP10, NSP16, and PL^pro had values of 0.533, 0.527, and 0.464, respectively. Similarly, the RF |R| value for M^pro was 0.673, compared to 0.457, 0.460, and 0.437 for NSP10, NSP16, and PL^pro, respectively. Interestingly, we did not observe a strong correlation for the spike (S) protein monomer and its domains. The low p-values that are associated with the correlation |R| values show that the linear correlations between predicted and actual mutation frequencies are statistically significant. This indicates that TL may generalize well across structurally related viral proteins using DRN-derived ML model from M^pro. Overall, we aim to develop a universal ML model for predicting missense mutation frequencies in viral proteins, and this study lays the foundation for that goal.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1686-1692"},"PeriodicalIF":4.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12063013/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143986916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent detection for Polycystic Ovary Syndrome (PCOS): Taxonomy, datasets and detection tools.","authors":"Meng Li, Zanxiang He, Liyun Shi, Mengyuan Lin, Minge Li, Yanjun Cheng, Hongwei Liu, Lei Xue, Kabir Sulaiman Said, Murtala Yusuf, Hadiza Shehu Galadanci, Liming Nie","doi":"10.1016/j.csbj.2025.04.011","DOIUrl":"https://doi.org/10.1016/j.csbj.2025.04.011","url":null,"abstract":"<p><p>Recent research on Polycystic Ovary Syndrome (PCOS) detection increasingly employs intelligent algorithms to assist gynecologists in more accurate and efficient diagnoses. However, intelligent PCOS detection faces notable challenges: absence of standardized feature taxonomies, limited research on available datasets, and insufficient understanding of existing detection tools' capabilities. This paper addresses these gaps by introducing a novel analytical framework for PCOS diagnostic research and developing a comprehensive taxonomy comprising 108 features across 8 categories. Furthermore, we analyzed available datasets and assessed current intelligent detection tools. Our findings reveal that 12 publicly accessible datasets cover only 54% of the 108 features identified in our taxonomy. These datasets frequently lack multimodal integration, regular updates, and clear license information-constraints that potentially limit detection tool development. Additionally, our analysis of 42 detection tools identifies several limitations: high computational resource requirements, inadequate multimodal data processing, insufficient longitudinal analysis capabilities, and limited clinical validation. Based on these observations, we highlight critical challenges and future research directions for advancing intelligent PCOS detection tools.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1578-1599"},"PeriodicalIF":4.4,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12032871/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143984478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A molecular dynamics study of membrane positioning for 7-transmembrane RGS proteins to modulate G-protein-mediated signaling in plants.","authors":"Celio Cabral Oliveira, Eduardo Bassi Simoni, Mariana Abrahão Bueno Morais, Elizabeth Pacheco Batista Fontes, Pedro A Braga Dos Reis, Daisuke Urano, Alan M Jones","doi":"10.1016/j.csbj.2025.04.013","DOIUrl":"https://doi.org/10.1016/j.csbj.2025.04.013","url":null,"abstract":"<p><p>Protein phosphorylation regulates G protein signaling in plants. AtRGS1 primarily modulates AtGPA1, the canonical Gα subunit in the heterotrimeric G protein complex. AtRGS1 possesses both a seven-transmembrane (7TM) domain connected to a cytoplasmic Regulator of G Protein Signaling domain (RGS box domain) by a flexible linker region. This study presents the novel function of a highly conserved, known phosphorylation site, Ser278, within this linker region utilizing molecular dynamics (MD) simulations with <i>in vivo</i> experimental validation. We show that phosphorylation at Ser278 is crucial for establishing specific AtRGS1 interactions with AtGPA1, primarily by stabilizing the positioning and orientation of the RGS domain within the membrane. Phosphorylation at Ser278 enhances the formation of stable hydrogen bonds between phosphorylated Ser278 and conserved residues within the RGS box domain, influencing the flexibility of RGS domain mobility and thus modulating its interface to AtGPA1. Consistent with the MD simulations, <i>in vivo</i> assays demonstrated that this phosphorylation reduced the binding of AtRGS1 to AtGPA1 and conferred changes in physiology. Specifically, the non-phosphorylation mutation of Ser278 decreased both plant immune responses and AtRGS1 endocytosis evoked by the bacterial effector, flg22. MD simulations and sequence analysis of diverse plant 7TM-RGS proteins suggest conservation of this mechanism across land plants, emphasizing the critical role of this previously overlooked linker region.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"1529-1537"},"PeriodicalIF":4.4,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12017998/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143983707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}