Computational and structural biotechnology journal最新文献

{"title":"An explainable-by-design end-to-end AI framework based on prototypical part learning for lesion detection and classification in Digital Breast Tomosynthesis images.","authors":"Andrea Berti, Camilla Scapicchio, Chiara Iacconi, Charlotte Marguerite Lucille Trombadori, Maria Evelina Fantacci, Alessandra Retico, Sara Colantonio","doi":"10.1016/j.csbj.2025.06.008","DOIUrl":"10.1016/j.csbj.2025.06.008","url":null,"abstract":"<p><strong>Background and objective: </strong>Breast cancer is the most common cancer among women worldwide, making early detection through breast screening crucial for improving patient outcomes. Digital Breast Tomosynthesis (DBT) is an advanced radiographic technique that enhances clarity over traditional mammography by compiling multiple X-ray images into a 3D reconstruction, thereby improving cancer detection rates. However, the large data volume of DBT poses a challenge for timely analysis. This study aims to introduce a transparent AI system that not only provides a prediction but also an explanation of that prediction, expediting the analysis of DBT scans while ensuring interpretability.</p><p><strong>Methods: </strong>The study employs a two-stage deep learning process. The first stage uses state-of-the-art Neural Network (NN) models, specifically YOLOv5 and YOLOv8, to detect lesions within the scans. An ensemble method is also explored to enhance detection capabilities. The second stage involves classifying the identified lesions using ProtoPNet, an inherently transparent NN that leverages prototypical part learning to distinguish between benign and cancerous lesions. The system facilitates clear interpretability in decision-making, which is crucial for medical diagnostics.</p><p><strong>Results: </strong>The performance of the AI system demonstrates competitive metric results for both detection and classification tasks (a recall of 0.76 and an accuracy of 0.70, respectively). The evaluation metrics, together with the validation by expert radiologists through clinical feedback, highlight the potential of the system for future clinical relevance. Despite challenges such as dataset limitations and the need for more accurate ground truth annotations, which limit the final values of the metrics, the approach shows significant advancement in applying AI to DBT scans.</p><p><strong>Conclusions: </strong>This study contributes to the growing field of AI in breast cancer screening by emphasizing the need for systems that are not only accurate but also transparent and interpretable. The proposed AI system marks a significant step forward in the timely and accurate analysis of DBT scans, with potential implications for improving early breast cancer detection and patient outcomes.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2649-2660"},"PeriodicalIF":4.4,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12212108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539315","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":"<i>ColorI-DT</i>: An open-source tool for the quantitative evaluation of differences in microscopy color images.","authors":"Filippo Piccinini, Michele Tritto, Jae-Chul Pyun, Misu Lee, Bongseop Kwak, Bosung Ku, Nicola Normanno, Gastone Castellani","doi":"10.1016/j.csbj.2025.06.019","DOIUrl":"10.1016/j.csbj.2025.06.019","url":null,"abstract":"<p><p>In several fields, quantitatively comparing color images is crucial. For instance, this is important in Histopathology, where different microscopes/cameras are typically used for visualizing patient samples by causing significant color variation. No ground-truth metric exists for estimating differences between pairs of color images. A range of possible solutions is available but there is no existing open-source tool that allow clinicians and researchers to apply these metrics to microscopy images through an intuitive, easy-to-use software. In this work, we developed <i>Color Image Difference Tool</i> (<i>ColorI-DT</i>), an open-source tool for measuring quantitative differences between color images of the same subject acquired under different settings. Thanks to a user-friendly graphical user interface, it allows the selection of a pair of color images and a metric from a list of available options, and produces an output 2D pixel-wise color difference matrix between corresponding pixels in the input images. The metrics currently implemented are: (<i>1</i>) Euclidean <math><mrow><mi>Δ</mi> <mi>E</mi></mrow> </math> ; (<i>2</i>) International Commission on Illumination (CIE) 76 (Luv); (<i>3</i>) CIE76 (Lab); (<i>4</i>) CIE94; (<i>5</i>) CIE00; (<i>6</i>) Colour Measurement Committee (CMC). To demonstrate how to use the tool, microscopy images with a predominant color in the red, green, or blue channel were used. In particular, we checked which among the 6 metrics displays the most predictable and linear behavior in the case of controlled primary color alterations. For more pronounced color adjustments, a qualitative comparison would be likely sufficient for analyzing color differences, as a quantitative tool may become unreliable due to the inherent limitations of the implemented metrics.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2526-2536"},"PeriodicalIF":4.4,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12197881/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144505007","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":"Ranking pre-trained speech embeddings in Parkinson's disease detection: Does Wav2Vec 2.0 outperform its 1.0 version across speech modes and languages?","authors":"Ondrej Klempir, Adela Skryjova, Ales Tichopad, Radim Krupicka","doi":"10.1016/j.csbj.2025.06.022","DOIUrl":"10.1016/j.csbj.2025.06.022","url":null,"abstract":"<p><p>Speech and language technologies are effective tools for identifying the distinct speech changes associated with Parkinson's disease (PD), enabling earlier and more accurate diagnosis. Models leveraging recent advancements in self-supervised speech pretraining, such as Wav2Vec, have demonstrated superior performance over traditional feature extraction methods. While Wav2Vec 2.0 has been successfully utilized for PD detection, a rigorous quantitative comparison with Wav2Vec 1.0 is needed to comprehensively evaluate its advantages, limitations, and applicability across different speech modes in PD. This study presents a systematic comparison of Wav2Vec 1.0 and Wav2Vec 2.0 embeddings across three multilingual datasets using various classification approaches to classify normal (healthy controls; HC) and PD-affected speech. Additionally, both Wav2Vec 1.0 and 2.0 were benchmarked against traditional baseline features across diverse linguistic contexts, including spontaneous speech, non-spontaneous speech, and isolated vowels. A multicriteria TOPSIS approach was employed to rank feature extraction methods, revealing that Wav2Vec 2.0 excelled across speech modes, with its first transformer layer demonstrating the best performance for classifying read text and monologue, and its feature extractor performing best in vowel-based classification. In contrast, Wav2Vec 1.0, while generally outperformed by Wav2Vec 2.0, still provided a more efficient alternative with competitive performance. Finally, we combined selected layers from both architectures and have demonstrated improved diagnostic accuracy in vowel-based classification. This comparative analysis underscores the strengths of both Wav2Vec architectures and informs their optimal use in PD detection.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2584-2601"},"PeriodicalIF":4.4,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12206144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144526751","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":"SurvSig: Harnessing gene expression signatures to uncover heterogeneity in lung neuroendocrine neoplasms.","authors":"Kolos Nemes, Gabriella Mihalekné Fűr, Alexandra Benő, Christopher W Schultz, Petronella Topolcsányi, Éva Magó, Parth Desai, Nobuyuki Takahashi, Mirit I Aladjem, William Reinhold, Yves Pommier, Anish Thomas, Lorinc S Pongor","doi":"10.1016/j.csbj.2025.06.010","DOIUrl":"10.1016/j.csbj.2025.06.010","url":null,"abstract":"<p><p>The advances in the field of cancer genomics have enabled researchers and clinicians to identify altered pathways and regulatory networks that differentiate subtypes manifesting as differential phenotypes of lung neuroendocrine neoplasms (NENs). The clinical heterogeneity observed among lung NEN subtypes reflects underlying biological distinctions, including differential mutation patterns, epigenetic changes and immune microenvironment activities. Although in many cases only a handful of underlying genes are used to differentiate patients, broader gene signatures might result in finer separation and help identify patients with differential survival. Lung NENs are vastly underrepresented in pan-cancer studies, resulting in lacking options to explore datasets. To this end, we developed a freely available website (https://survsig.hcemm.eu/) which allows users to upload potential genes of interest, perform patient clustering, compare survival and explore gene expression signature of lung NENs. Leveraging these biological differences enhances the accuracy of gene expression-based prognostic classifiers like SurvSig.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2574-2583"},"PeriodicalIF":4.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12205313/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144526752","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":"Structure analysis of human gut microbiota associated with single-celled gut protists using Next-Generation Sequencing of 16S and 18S rRNA genes.","authors":"Salem Belkessa, Edoardo Pasolli, Bachir Medrouh, Rebecca P K D Berg, Lee O 'Brien Andersen, Henrik Vedel Nielsen, Christen Rune Stensvold","doi":"10.1016/j.csbj.2025.06.006","DOIUrl":"10.1016/j.csbj.2025.06.006","url":null,"abstract":"<p><p>The gut microbiota is a complex microbial ecosystem with a major impact on health and disease. Some gut unicellular eukaryotes (particularly <i>Blastocystis</i>) have been linked to features of intestinal eubiosis. Meanwhile, little is known regarding associations between gut-pathogenic protozoa, such as <i>Giardia</i>, and gut microbiota signatures. We therefore characterized and compared gut microbiota profiles of 60 <i>Giardia</i>-positive and 31 <i>Giardia</i>-negative Algerian individuals using amplicon-based next-generation sequencing of prokaryotic and eukaryotic ribosomal genes and stratifying for co-colonization with other unicellular eukaryotes, such as species of Archamoebae or <i>Blastocystis</i>. Overall, we found that alpha and beta microbiota diversity did not differ significantly between <i>Giardia</i>-positive and <i>Giardia</i>-negative individuals, regardless of the presence or absence of Archamoebae and <i>Entamoeba</i> (<i>p </i>> 0.05). However, significant differences were observed in both alpha and beta diversity between <i>Giardia</i>-positive, <i>Blastocystis</i>-negative and <i>Giardia</i>-positive, <i>Blastocystis</i>-positive individuals (observed richness, <i>p</i> = 0.0016; ANOSIM = 0.001), and similar differences were noticed between <i>Blastocystis</i>-negative and-positive carriers (<i>p</i> < 0.05), regardless of <i>Giardia</i> carrier status. Importantly, these differences in gut microbiota were considered to be independent of factors such as sex, age, and location (<i>p</i> > 0.05). Conclusively, <i>Giardia</i>-positive individuals may exhibit features of eubiosis, but whether this depends on the presence of <i>Blastocystis</i> should be confirmed by future studies. These findings combined might indicate that <i>Blastocystis</i> could be an active driver of gut microbiota diversity.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2955-2967"},"PeriodicalIF":4.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12273226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144674059","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":"Applications of machine learning-assisted extracellular vesicles analysis technology in tumor diagnosis.","authors":"Liang Xu, Jing Li, Wei Gong","doi":"10.1016/j.csbj.2025.06.014","DOIUrl":"10.1016/j.csbj.2025.06.014","url":null,"abstract":"<p><p>Precision medicine for tumors represents a pivotal focus in contemporary medical research. Nonetheless, the diversity of tumor types and the complexity of their pathogenesis present significant challenges in the diagnostic process. Extracellular vesicles (EVs), as a category of nanoparticles, carry a wealth of biological information and play a crucial role in tumor initiation and progression, thereby offering novel approaches for early tumor diagnosis. In recent years, machine learning (ML) technology in the medical field has gained momentum, which utilize various algorithms to analyze input data, identify potential patterns and trends, develop predictive models, and generate high-precision predictions of unknown data, demonstrating its clinical potential in disease diagnosis. This review provides a comprehensive summary of advancements in EVs analysis technology based on ML for auxiliary tumor diagnosis, including early diagnosis, classification, stage recognition, and molecular diagnosis, and discusses their advantages in clinical applications. Additionally, the article anticipates future development trends in the field, aiming to serve as a reference for researchers engaged in ML-assisted liquid biopsy for tumor diagnosis.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2460-2472"},"PeriodicalIF":4.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12180947/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144474176","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 new approach combining a whole-slide foundation model and gradient boosting for predicting BRAF mutation status in dermatopathology.","authors":"Mohamed Albahri, Daniel Sauter, Felix Nensa, Georg Lodde, Elisabeth Livingstone, Dirk Schadendorf, Markus Kukuk","doi":"10.1016/j.csbj.2025.06.017","DOIUrl":"10.1016/j.csbj.2025.06.017","url":null,"abstract":"<p><p>Determining the mutation status of proto-oncogene B-Rapidly Accelerated Fibrosarcoma (BRAF) is crucial in melanoma for guiding targeted therapies and improving patient outcomes. While genetic testing has become more accessible, histopathological examination remains central to routine diagnostics, and an image-based strategy could further streamline the associated time and cost. In this study, we propose a new machine learning framework that integrates a large-scale, pretrained foundation model (Prov-GigaPath) with a gradient-boosting classifier (XGBoost) to predict BRAF-V600 mutation status directly from histopathological slides. Our approach was trained and cross-validated on the Skin Cutaneous Melanoma (SKCM) dataset from The Cancer Genome Atlas (TCGA; 275 slides), where the fine-tuned Prov-GigaPath model alone achieved an average Area Under the Curve (AUC) of 0.653 during cross-validation. An additional test on 68 slides from the University Hospital Essen (UHE), Germany, yielded an AUC of 0.697 (95 % CI: 0.553-0.821). Incorporating XGBoost significantly improved performance, reaching an AUC of 0.824 (SD=0.043) during cross-validation and 0.772 (95 % CI: 0.650-0.886) on the independent set-representing a new state-of-the-art for image-only BRAF mutation prediction in melanoma. By employing a weakly supervised, data-efficient pipeline, this method reduces the need for extensive annotations and costly molecular assays. While these results are not intended to replace genetic testing at this stage, they mark a new milestone in predicting BRAF mutation status solely from histopathological slides-a concept not yet fully established in prior research-and underscore the potential for seamlessly integrating automated, AI-driven decision-support tools into diagnostic workflows, thereby expediting personalized therapy decisions and advancing precision oncology.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2503-2514"},"PeriodicalIF":4.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12182775/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144474175","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 novel method for endometrial cancer patient stratification considering ARID1A protein expression and activity with effective use of multi-omics data.","authors":"Junsoo Song, Ayako Ui, Kenji Mizuguchi, Reiko Watanabe","doi":"10.1016/j.csbj.2025.06.015","DOIUrl":"10.1016/j.csbj.2025.06.015","url":null,"abstract":"<p><p>AT-rich interactive domain-containing protein 1A (ARID1A) is frequently mutated in endometrial cancers. Although patient stratification based on mutations or mRNA expression is commonly performed, this approach may not accurately reflect the functional state of ARID1A. This functional state is not only directly reflected in upstream events such as gene expression but also influenced by various regulatory including protein expression and the presence and type of mutations. Although protein expression is more directly correlated with phenotypic outcomes, integrating different omics data remains challenging due to disparities in data availability. To address this challenge, we developed a novel patient stratification method that integrates proteomics and transcriptomics to assess the functional state of ARID1A in patients with uterine corpus endometrial carcinoma. Initially, missing protein expression data were imputed using machine learning, and the patients were labelled based on their ARID1A protein expression. We then labelled the patients according to ARID1A activity, inferred by analysing the transcriptional regulation of genes directly controlled by ARID1A. Finally, patients were stratified by ARID1A functional state, considering both protein expression and the inferred activity label. This approach identified different gene expression patterns that are undetectable using conventional methods based on mRNA expression and mutation. Gene set enrichment and over-representation analyses confirmed that the proposed method revealed immune-related differences in patients with ARID1A-deficient uterine corpus endometrial carcinoma. These results highlight its potential to identify novel therapeutic targets and immune alterations that are undetected by conventional techniques.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2614-2625"},"PeriodicalIF":4.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12212153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144539314","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":"Computational vaccine development against protozoa.","authors":"Omar Hashim, Isabelle Dimier-Poisson","doi":"10.1016/j.csbj.2025.06.011","DOIUrl":"10.1016/j.csbj.2025.06.011","url":null,"abstract":"<p><p>Protozoan parasites remain a major global health and economic burden, particularly in low- and middle-income countries. Conventional strategies such as chemotherapy and vector control face growing limitations due to resistance, toxicity, and implementation challenges. Vaccination represents a sustainable solution, but the complexity of protozoan life cycles and antigenic diversity has hindered vaccine development. Computational vaccinology offers innovative tools to overcome these barriers, combining immuno-informatics, reverse vaccinology, and artificial intelligence to accelerate the identification of immunogenic epitopes and streamline vaccine design. This review explores the current landscape of computational vaccine development against protozoa, highlighting advances in epitope prediction, population-specific vaccine design, and digital twin technologies. Applications include multivalent vaccines targeting conserved antigens across species, personalized formulations based on host immunogenetics, and the emerging use of protozoan vectors in cancer immunotherapy. Despite these promising avenues, significant challenges remain, particularly the need for robust experimental validation, improved delivery systems for short peptides, and greater acceptance of in silico methods by the broader scientific community. We argue that integrating computational tools with experimental immunology, high-throughput genomics, and translational research will be the key to developing safe, effective, and broadly accessible vaccines against protozoan infections. This convergence of disciplines has the potential to not only address neglected tropical diseases but also to establish new paradigms in precision vaccinology and immunotherapy.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2386-2393"},"PeriodicalIF":4.4,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12172979/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144316014","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":"Investigating and evaluating potential antigen binding sites for monoclonal anti-HER2 antibodies: The LightDock approach.","authors":"Emil Stefańczyk, Agata Mitura, Marta Utratna, Magdalena Staniszewska","doi":"10.1016/j.csbj.2025.06.001","DOIUrl":"10.1016/j.csbj.2025.06.001","url":null,"abstract":"<p><p>Monoclonal antibodies targeting HER2, a receptor overexpressed in certain cancer cells, have greatly improved the treatment of HER2-positive cancers. In addition, anti-HER2 antibodies play a critical role in diagnostic applications, enabling accurate detection of HER2 expression levels. Advancing antibody-based therapies and diagnostic tools require a thorough understanding of binding interactions, but it remains challenging due to complex antibody protein structure and its flexibility, particularly within their complementarity-determining regions. In this study we utilized LightDock, a molecular docking tool simulating protein-protein interactions which can incorporate flexibility that allows the <i>in silico</i> analysis of flexible proteins like antibody. Using LightDock we investigated interaction sites between the recently developed by our group anti-HER2 antibodies and their specific antigen HER2 protein. Despite the high variability in the obtained results, a statistics-based approach identified two recurring HER2 regions as potential binding sites and functionally relevant areas in receptor biology. This variability in predicted docking interfaces reflects the inherent complexity of antibody-antigen interactions. This structure based docking approach provides a cost-effective method to analyze antibody-protein interactions and offers preliminary insight into possible epitopes targeted by the novel anti-HER2 antibodies. However, our data indicates that at this time point further validation using experimental techniques will be beneficial to refine and increase the accuracy of the results obtained <i>in silico</i>. This report highlights the value of the computational docking in antibody-protein interaction studies, demonstrating significant potential with present and upcoming advancements in computer-based approaches.</p>","PeriodicalId":10715,"journal":{"name":"Computational and structural biotechnology journal","volume":"27 ","pages":"2515-2525"},"PeriodicalIF":4.4,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12182776/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144474153","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}