Briefings in bioinformatics最新文献

{"title":"Intelligent Batch Epitope Identification and Partitioning: an intelligent tool for the identification of B cell dominant epitopes.","authors":"Yun-Fei Ma, Ye Liu","doi":"10.1093/bib/bbaf310","DOIUrl":"10.1093/bib/bbaf310","url":null,"abstract":"<p><p>Identifying B cell dominant epitopes helps to improve vaccine design and better understand immune evasion of pathogens. Herein, we present the Intelligent Batch Epitope Identification and Partitioning (IBEIP), an intelligent tool for identifying B cell dominant epitope regions based on antigen-neutralizing antibody (Ag-nAb) complex data. IBEIP can accurately map the epitopes on any appointed Ag-nAb complex by analyzing antigen-antibody interactions at a molecular level. Combined with a hierarchical iterative merging model, IBEIP can intelligently merge and analyze mapped epitopes to identify B cell dominant epitopes. It is also applicable to analyzing high-mutant antigens and complex epitope structures. We demonstrated the performance of IBEIP by analyzing 127 Ag-nAb complexes from the respiratory syncytial virus (RSV) fusion, SARS-CoV-2 spike, and high-mutant influenza hemagglutinin. Over 90% of the residues overlapped between IBEIP and reported epitopes, confirming its reliability. IBEIP also uncovered new and important B cell dominant epitope regions and structures of these pathogens for researchers. Our study provides a reliable, intelligent tool for B cell dominant epitope analysis and offers some valuable insights for preventing RSV, SARS-CoV-2, and influenza infections.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12232423/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144574845","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-enhanced clustering and classification of protein molecule tertiary structures using weighted distance matrices.","authors":"Junlong Liu, Jiaming Xiao, Xunwen Su, Yonglin Wang","doi":"10.1093/bib/bbaf331","DOIUrl":"10.1093/bib/bbaf331","url":null,"abstract":"<p><p>Protein clustering and classification are critical for understanding protein functions and interactions, particularly within structure-based predictions. Traditional sequence-based clustering often overlooks the pivotal role of tertiary structure in determining protein function. Structural clustering remains limited and challenging, with existing methods struggling to achieve high accuracy and manage complex data. This study focuses on the tertiary structures of Verticillium dahliae proteins, employing deep learning techniques for effective clustering and classification. Using AlphaFold2, we predicted protein structures and generated Cα atom distance matrices. We introduced a novel Unique Nuclear Sequence Element (UNSE) neural network to enhance feature extraction, constructing weighted distance matrices by integrating Cα distances with Pfam annotations. This method effectively captures complex structural relationships. Additionally, Basic Local Alignment Search Tool (BLAST) sequence alignments validated the sequence similarity within protein families, ensuring the biological relevance of clustering results. We applied clustering algorithms to both raw and weighted matrices, comparing their performance against traditional sequence-based and other structure-based methods, including DeepGO and DeepFRI. Evaluation metrics such as Silhouette Score, ${F}_{max}$, and AUPR demonstrated that our weighted matrix approach significantly outperforms conventional methods in accuracy and robustness. These findings confirm that integrating deep learning with weighted distance matrices effectively captures structural and functional protein characteristics, providing a robust tool for structural biology.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12234446/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144583139","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":"An artificial intelligence-based approach for identifying the proteins regulating liquid-liquid phase separation.","authors":"Zahoor Ahmed, Kiran Shahzadi, Rui Li, Yu-Qing Jiang, Yan-Ting Jin, Muhammad Arif, Juan Feng","doi":"10.1093/bib/bbaf313","DOIUrl":"10.1093/bib/bbaf313","url":null,"abstract":"<p><p>Liquid-liquid phase separation (LLPS) is a biomolecular process that underpins the formation of membrane-less organelles within living cells. This phenomenon, along with the resulting condensate bodies, is increasingly recognized for its critical roles in various biological processes, such as ribonucleic acid (RNA) metabolism, chromatin rearrangement, and signal transduction. Notably, regulator proteins play a central role in the process of LLPS. They are essential for the formation, stabilization, and maintenance of the dynamic properties of LLPS, ensuring an appropriate phase separation response to cellular signals. Targeting these regulator proteins is the key to manipulating LLPS for applications in biotechnology, materials science, and medicine, including biomaterials, drug delivery, diagnostics, and synthetic biology. Given their importance, this study focused on an artificial intelligence-based approach to identify regulator proteins in LLPS. We constructed a dataset of 913 positive and 6584 negative protein sequences, and divided it into eight balanced training datasets and a test dataset. Semantic information from protein sequences was extracted using the ESM2_t36 pretrained protein language model, followed by training a multilayer perceptron classifier. The model achieved 0.78 accuracy on the test dataset, outperforming traditional sequence-based methods, one-hot encoding, and other pretrained embedding methods. SHapley Additive exPlanations (SHAP)-based interpretation revealed key biophysical patterns enriched in regulator proteins, including higher levels of charged and disordered residues. Our results show that deep contextual protein representations combined with neural network-based classifiers can accurately identify LLPS regulator proteins. This tool offers new opportunities for understanding condensate biology and designing synthetic phase-separating systems. All data and code are available at: https://github.com/bioplusAI/LLPS_regulators_pred.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12239617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144590464","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":"Multimodal zero-shot learning of previously unseen epitranscriptomes from RNA-seq data.","authors":"Yiyou Song, Bowen Song, Daiyun Huang, Anh Nguyen, Lihong Hu, Jia Meng, Yue Wang","doi":"10.1093/bib/bbaf332","DOIUrl":"10.1093/bib/bbaf332","url":null,"abstract":"<p><p>Precise identification of condition-specific epitranscriptomes is of critical importance for investigating the dynamics and versatile functions of RNA modification under various biological contexts. Existing approaches for predicting condition-specific RNA modification are usually trained on epitranscriptome data obtained from the same condition, which limited their usage, as such data are available only for a small number of conditions due to the technical difficulties and high expenses of epitranscriptome profiling technologies. We present ExpressRM, a multimodal zero-shot learning framework for predicting condition-specific RNA modification sites in previously unseen contexts from genome and RNA-seq data. Different from existing in-condition learning approaches, this method does not rely on matched epitranscriptome data for training, which greatly expands its applicability. On a benchmark dataset comprising epitranscriptomes and matched transcriptomes of 37 human tissues, we demonstrate that ExpressRM can accurately predict epitranscriptomes of previously unseen conditions from their transcriptomes only, and the performance is comparable to existing in-condition learning algorithms that require epitranscriptome data from the same condition. Additionally, the method has the capability of differentiating highly dynamic RNA methylation sites from more static (or house-keeping) ones. With a case study, we show that ExpressRM can uncover N6-methyladenosine RNA methylation sites in glioblastoma using only its RNA-seq data, and unveils novel and previously validated pathological insights. Together, these results suggest that the proposed multimodal zero-shot learning framework can effectively leverage transcriptome knowledge to explore the dynamic roles of RNA modifications in previously unseen experimental setups, providing valuable insights into vast biological contexts where RNA-seq is routinely used but epitranscriptome profiling has not yet been covered.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12239634/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144590476","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":"Incorporating local ancestry information to predict genetically associated DNA methylation in admixed populations.","authors":"Youshu Cheng, Geyu Zhou, Hongyu Li, Xinyu Zhang, Amy Justice, Claudia Martinez, Bradley E Aouizerat, Ke Xu, Hongyu Zhao","doi":"10.1093/bib/bbaf325","DOIUrl":"10.1093/bib/bbaf325","url":null,"abstract":"<p><p>Methylome-wide association studies (MWASs) have identified many 5'-cytosine-phosphate-guanine-3' (CpG) sites associated with complex traits. Several methods have been developed to predict CpG methylation levels from genotypes when the direct measurements of methylation are unavailable. To date, the published methods have mostly used datasets from populations of European ancestry to train prediction models for methylations, which limits the generalizability of methylome-wide association study to non-European populations. To address this gap, we proposed a new model by incorporating local ancestry (LA) information, called LA Methylation Predictor with Preselection (LAMPP), to improve the prediction accuracy of DNA methylation in admixed populations. We showed that LAMPP outperformed the conventional model and other LA models in prediction accuracy using an admixed African American population. We further applied our model to identify significant CpG sites for seven complex traits. Together, our LAMPP model is a valuable tool to reveal epigenetic underpinnings of complex traits in the admixed populations.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12232425/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144574842","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":"Beyond static structures: protein dynamic conformations modeling in the post-AlphaFold era.","authors":"Xinyue Cui, Lingyu Ge, Xia Chen, Zexin Lv, Suhui Wang, Xiaogen Zhou, Guijun Zhang","doi":"10.1093/bib/bbaf340","DOIUrl":"10.1093/bib/bbaf340","url":null,"abstract":"<p><p>The emergence of deep learning, particularly AlphaFold, has revolutionized static protein structure prediction, marking a transformative milestone in structural biology. However, protein function is not solely determined by static three-dimensional structures but is fundamentally governed by dynamic transitions between multiple conformational states. This shift from static to multi-state representations is crucial for understanding the mechanistic basis of protein function and regulation. This review outlines the fundamental concepts of protein dynamic conformations, surveys recent computational advances in modeling these dynamics in the post-AlphaFold era, and highlights key challenges, including data limitations, methodological constraints, and evaluation metrics. We also discuss potential strategies to address these challenges and explore future research directions to deepen our understanding of protein dynamics and their functional implications. This work aims to provide insights and perspectives to facilitate the ongoing development of protein conformation studies in the era of artificial intelligence-driven structural biology.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12262120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144641764","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":"Paradigms, innovations, and biological applications of RNA velocity: a comprehensive review.","authors":"Yajunzi Wang, Jing Li, Haoruo Zha, Shuhe Liu, Daiyun Huang, Lei Fu, Xin Liu","doi":"10.1093/bib/bbaf339","DOIUrl":"10.1093/bib/bbaf339","url":null,"abstract":"<p><p>Single-cell RNA sequencing enables unprecedented insights into cellular heterogeneity and lineage dynamics. RNA velocity, by modeling the temporal relationship between spliced and unspliced transcripts, extends this capability to predict future transcriptional states and uncover the directionality of cellular transitions. Since the introduction of foundational frameworks such as Velocyto and scVelo, an expanding array of computational tools has emerged, each based on distinct biophysical assumptions and modeling paradigms. To provide a structured overview of this rapidly evolving field, we categorize RNA velocity models into three classes: steady-state methods, trajectory methods, and state extrapolation methods, according to their underlying approaches to transcriptional kinetics inference. For each category, we systematically analyze both the overarching principles and the individual methods, comparing their assumptions, kinetic models, and computational strategies and assessing their respective strengths and limitations. To demonstrate the biological utility of these tools, we summarize representative applications of RNA velocity across developmental biology and diseased microenvironments. We further introduce emerging extensions of RNA velocity methods that go beyond classical splicing kinetics. Finally, we discuss existing limitations regarding model assumptions, preprocessing procedures, and velocity visualization and offer practical recommendations for model selection and application. This review offers a comprehensive guide to the RNA velocity landscape, supporting its effective implementation in dynamic transcriptomic research.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12265890/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144641768","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":"AEPMA: peptide-microbe association prediction based on autoevolutionary heterogeneous graph learning.","authors":"Zhiyang Hu, Linqiang Pan, Daijun Zhang, Yannan Bin, Yansen Su","doi":"10.1093/bib/bbaf334","DOIUrl":"10.1093/bib/bbaf334","url":null,"abstract":"<p><p>The inappropriate use of antibiotics has precipitated the emergence of multidrug-resistant bacteria, prompting significant interest in antimicrobial peptides (AMPs) as potential alternatives to traditional antibiotics. Given the prohibitive costs and time-consuming nature of biological experiments, computational methods provide an efficient alternative for the development of AMP-based drugs. However, existing computational studies primarily focus on identifying AMPs with antimicrobial activity, lacking a targeted identification of AMPs against specific microbial species. To address this gap, we propose a peptide-microbe association (PMA) prediction framework, termed AEPMA, which is constructed based on an autoevolutionary heterogeneous graph. Within AEPMA, we construct an innovative peptide-microbe-disease network (PMDHAN). Furthermore, we design an autoevolutionary information aggregation mechanism that facilitates the representation learning of the heterogeneous graph. This model automatically aggregates semantic information within the heterogeneous network while thoroughly accounting for the spatiotemporal dependencies and heterogeneous interactions in the PMDHAN. Experiments conducted on one peptide-microbe and three drug-microbe association datasets demonstrate that the performance of AEPMA outperforms five state-of-the-art methods, demonstrating its robust modeling capability and exceptional generalization ability. In addition, this study identifies a novel anti-Staphylococcus aureus peptide and an anti-Escherichia coli peptide, thereby contributing valuable information for the development of antimicrobial drugs and strategies for mitigating antibiotic resistance.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12240738/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144599484","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":"Inference of gene coexpression networks from single-cell transcriptome data based on variance decomposition analysis.","authors":"Bin Lian, Haohui Zhang, Tao Wang, Yongtian Wang, Xuequn Shang, N Ahmad Aziz, Jialu Hu","doi":"10.1093/bib/bbaf309","DOIUrl":"10.1093/bib/bbaf309","url":null,"abstract":"<p><p>Gene regulation varies across different cell types and developmental stages, leading to distinct cellular roles across cellular populations. Investigating cell type-specific gene coexpression is therefore crucial for understanding gene functions and disease pathology. However, reconstructing gene coexpression networks from single-cell transcriptome data is challenging due to artifacts, noise, and data sparsity. Here, we present an efficient method for inference of gene coexpression networks via variance decomposition analysis (GCNVDA) to explore the underlying gene regulatory mechanisms from single-cell transcriptome data. Our model incorporates multiple sources of variability, including a random effect term $G$ to capture gene-level variance and a random effect term $E$ to account for residual errors. We applied GCNVDA to three real-world single-cell datasets, demonstrating that our method outperforms existing state-of-the-art algorithms in both sensitivity and specificity for identifying tissue- or state-specific gene regulations. Furthermore, GCNVDA facilitates the discovery of functional modules that play critical roles in key biological processes such as embryonic development. These findings provide new insights into cell-specific regulatory mechanisms and have the potential to significantly advance research in developmental biology and disease pathology.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12229094/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144574843","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":"Multi-level association rule mining and network pharmacology to identify the polypharmacological effects of herbal materials and compounds in traditional medicine.","authors":"Hyejin Yu, Kwanyong Choi, Ji Yeon Kim, Sunyong Yoo","doi":"10.1093/bib/bbaf328","DOIUrl":"10.1093/bib/bbaf328","url":null,"abstract":"<p><p>Many cultures worldwide have widely used traditional medicine (TM) to prevent or treat diseases. Herbal materials and their compounds used in TM offer many advantages for drug discovery, including cost-effectiveness, fewer side effects, and improved metabolism. However, the multi-compound and multi-target characteristics of TM prescriptions complicate drug discovery; meanwhile, previous studies have been limited by a lack of high-quality data, complex interpretation, and/or narrow analytical ranges. Thus, this study proposed a framework to identify potential therapeutic combinations of herbal materials and their compounds currently used in TM by integrating association rule mining (ARM) and network pharmacology analysis across multiple TM and biological levels. Subsequently, we collected prescriptions, herbal materials, compounds, genes, phenotypes, and all ensuing interactions to identify effective combinations of herbal materials and compounds using ARM for various symptoms and diseases. This proposed analytical approach was also applied to screen effective herbal material combinations and compounds for five phenotypes: asthma, diabetes, arthritis, stroke, and inflammation. The potential pharmacological effects of the inferred candidates were identified at the molecular level using structural network analysis and a literature review. In addition, compounds from Morus alba, Ephedra sinica, Perilla frutescens, and Pinellia ternata, which were strongly associated with asthma, were validated in vitro. Collectively, our study provides ethnopharmacological and biological evidence for the polypharmacological effects of herbal materials and their compounds, thus enhancing the understanding of the mechanisms involved in TM and suggesting potential candidates for prescriptions, dietary supplements, and drug combinations. The source code and results are available at https://github.com/bmil-jnu/InPETM.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"26 4","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12232419/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144574846","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}