Interdisciplinary Sciences: Computational Life Sciences最新文献_第4页

DiTSim: A Diffusion-Transformers Based Single-Cell ATAC-seq Data Simulator. 基于扩散变压器的单cell ATAC-seq数据模拟器。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-09-01 Epub Date: 2025-10-21 DOI: 10.1007/s12539-025-00773-9

Shengze Dong, Songming Tang, Ding Liu, Shengquan Chen

{"title":"DiTSim: A Diffusion-Transformers Based Single-Cell ATAC-seq Data Simulator.","authors":"Shengze Dong, Songming Tang, Ding Liu, Shengquan Chen","doi":"10.1007/s12539-025-00773-9","DOIUrl":"10.1007/s12539-025-00773-9","url":null,"abstract":"Single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) allows for deciphering the epigenetic landscape at single-cell resolution. The inaccuracies in annotations and the scarcity of available real datasets hinder the unbiased and comprehensive evaluation of computational methods designed for scATAC-seq data analysis, which underscores the importance of scATAC-seq data simulation methods. Existing scATAC-seq data simulation methods impose strict requirements on the prior distribution of the data and fail to generate simulated data with a consistent manifold structure aligned with real data. In this study, we propose DiTSim, a scATAC-seq data simulation method based on diffusion transformers. DiTSim efficiently fits the global distribution of real scATAC-seq datasets and stably synthesizes samples with known cell type annotations for assessing scATAC-seq data analysis pipelines. Through comprehensive experiments on multiple datasets, DiTSim has demonstrated its outstanding performance in achieving consistency with real data and robustness to datasets with diverse characteristics. Moreover, extensive enrichment analysis demonstrates that DiTSim has the capability to imbue simulated data with biological significance, a critical aspect often overlooked in prior studies.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"789-806"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145344923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FRDNAC: A Future-Ready DNA Cryptography Paradigm for Secure Cloud Data Transmission Using Deep Learning-Enhanced Key Generation. FRDNAC：使用深度学习增强密钥生成的安全云数据传输的未来DNA加密范式。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-09-01 Epub Date: 2026-05-19 DOI: 10.1007/s12539-026-00840-9

Nauman Umer, Miaolei Deng, Yuhong Zhang, Khan Sheheryar, Jinfang Wang

{"title":"FRDNAC: A Future-Ready DNA Cryptography Paradigm for Secure Cloud Data Transmission Using Deep Learning-Enhanced Key Generation.","authors":"Nauman Umer, Miaolei Deng, Yuhong Zhang, Khan Sheheryar, Jinfang Wang","doi":"10.1007/s12539-026-00840-9","DOIUrl":"10.1007/s12539-026-00840-9","url":null,"abstract":"The growing reliance upon cloud settings has rendered secure transmission of information essential. This study introduces the future-ready DNA-based cryptography (FRDNAC) paradigm, which combines DNA-based encryption with the feedback-assisted archimedes optimization algorithm to achieve efficient key generation and improved security. FRDNAC was assessed in comparison to contemporary optimization approaches such as the feedback artificial tree, the archimedes optimization algorithm, the blue monkey optimization, the coot optimization algorithm, the butterfly optimization algorithm, the shark smell optimization, the whale optimization algorithm, and the lightweight encryption system, as well as traditional encryption methods such as DNA encryption, Blowfish, Rivest-Shamir-Adleman (RSA), the advanced encryption system, and the elliptic curve cryptography. Experimental findings demonstrate FRDNAC's exceptional encryption and decryption efficacy, achieving an encryption duration of 0.11 s (key length 4.0), surpassing rivals like FAT (0.29 s) and LES (0.20 s). Furthermore, FRDNAC markedly enhanced memory efficiency, rendering it suitable for resource-limited cloud settings. Security evaluations indicate its robustness against cryptographic threats, encompassing known-plaintext attack, chosen-plaintext attack, and brute force attack. Despite obstacles in real-time key generation and computational cost, FRDNAC presents itself as a highly safe and efficient cryptographic framework appropriate for cloud-based applications. Its strong security framework establishes it as a viable solution for sectors requiring high-performance encryption in evolving digital environments.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"1010-1044"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147972276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Scalable and Robust Ensemble Deep Learning Method for Predicting Drug-Target Interactions. 一种预测药物-靶标相互作用的可扩展鲁棒集成深度学习方法。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-09-01 Epub Date: 2025-10-21 DOI: 10.1007/s12539-025-00774-8

Zhixing Cheng, Qunfang Yan, Dewu Ding, Yanrui Ding

{"title":"A Scalable and Robust Ensemble Deep Learning Method for Predicting Drug-Target Interactions.","authors":"Zhixing Cheng, Qunfang Yan, Dewu Ding, Yanrui Ding","doi":"10.1007/s12539-025-00774-8","DOIUrl":"10.1007/s12539-025-00774-8","url":null,"abstract":"Accurate identification of drug-target interactions (DTIs) is a crucial step in drug discovery. Computational DTI prediction methods can significantly reduce the time and cost associated with drug development. However, effectively integrating multisource features for high-precision DTI prediction remains a challenge. In this study, we propose EDeepDTI, an ensemble deep learning framework designed to increase the accuracy and generalizability of DTI predictions by efficiently integrating multi-view features. EDeepDTI calculates multiple molecular fingerprints to extract rich substructural information from drugs, leverages several advanced pre-trained models to generate drug and protein features enriched with structural and semantic information, and calculates multiple semantic similarity features for drugs and proteins using various similarity measures. During the ensemble learning process, we design a deep learning base learner for each unique pairing of drug and protein features. This ensures that each base learner captures distinct feature interactions, enhancing both independence and diversity within the ensemble. Finally, a greedy strategy is employed to aggregate the predictions from all base learners to improve overall performance. The experimental results demonstrate that EDeepDTI and its variant consistently outperform the baseline methods across multiple datasets and prediction tasks, highlighting the superior performance, robustness, and scalability of EDeepDTI.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"807-821"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145345086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TrambaHLApan: A Transformer and Mamba-based Neoantigen Prediction Method Considering both Antigen Presentation and Immunogenicity. TrambaHLApan：一种考虑抗原呈递和免疫原性的基于变压器和曼巴的新抗原预测方法。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-09-01 Epub Date: 2025-10-14 DOI: 10.1007/s12539-025-00777-5

Yibo Zhu, Xiumin Shi, Lu Wang, Jingjuan Zhang

{"title":"TrambaHLApan: A Transformer and Mamba-based Neoantigen Prediction Method Considering both Antigen Presentation and Immunogenicity.","authors":"Yibo Zhu, Xiumin Shi, Lu Wang, Jingjuan Zhang","doi":"10.1007/s12539-025-00777-5","DOIUrl":"10.1007/s12539-025-00777-5","url":null,"abstract":"Neoantigens, tumor-specific peptides with immunogenic potential, represent pivotal targets for cancer immunotherapy. Existing methods prioritize HLA-peptide binding but often fail to adequately address immunogenicity, limiting their clinical utility. This study introduces TrambaHLApan, a novel neoantigen prediction framework that integrates Transformer and Mamba architectures to concurrently predict antigen presentation likelihood (TrambaHLApan-EL) and immunogenic potential (TrambaHLApan-IM). A Transformer-based encoding module is employed to generate unique representations for HLA molecules and peptides. Subsequently, a hybrid fusion module, which combines merged-attention mechanisms with Mamba-based sequential modeling, is deployed to evaluate interaction patterns. TrambaHLApan-IM incorporates antigen presentation scores derived from TrambaHLApan-EL to explicitly model the interplay between antigen presentation and immunogenic potential, thereby enhancing the identification of neoantigens with high confidence. Experimental results on independent datasets demonstrate that TrambaHLApan outperforms state-of-the-art methods, establishing it as a reliable tool for advancing personalized cancer immunotherapies.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"1128-1144"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145292048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MKLNID: Identifying Melanoma-related Pathogenic Genes Through Multiple Kernel Learning and Network Impulsive Dynamics. MKLNID：通过多核学习和网络脉冲动力学识别黑色素瘤相关致病基因。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-09-01 Epub Date: 2025-09-22 DOI: 10.1007/s12539-025-00755-x

Linconghua Wang, Ju Xiang, Zihao Guo, Kaixin Zeng, Min Li

{"title":"MKLNID: Identifying Melanoma-related Pathogenic Genes Through Multiple Kernel Learning and Network Impulsive Dynamics.","authors":"Linconghua Wang, Ju Xiang, Zihao Guo, Kaixin Zeng, Min Li","doi":"10.1007/s12539-025-00755-x","DOIUrl":"10.1007/s12539-025-00755-x","url":null,"abstract":"Melanoma is a highly malignant skin cancer, and identifying its pathogenic genes is crucial for understanding its pathogenesis and developing treatment strategies. Network-based approaches effectively capture the synergistic interactions among genes and their products within biological systems, yet extracting functional insights from these complex networks remains challenging. Here, we propose a novel approach that combines multiple kernel learning and network impulsive dynamics (MKLNID) to predict melanoma-related pathogenic genes. Specifically, we construct similarity kernels of diseases and genes from the original disease-gene heterogeneous network and melanoma expression profiles. These kernels are integrated via multiple kernel learning to generate enhanced similarity networks for diseases and genes, respectively. Impulsive signals are then applied to specific nodes in the enhanced heterogeneous network, and the resulting dynamical response signatures are used to infer potential pathogenic genes. Comprehensive experiments and case analyses demonstrate the effectiveness of MKLNID in identifying melanoma-related genes. By deeply integrating heterogeneous disease networks with omics data and introducing network dynamics to simulate gene responses, MKLNID offers a new strategy for identifying melanoma-related genes, with potential implications for precision diagnosis and therapy.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"1045-1059"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13468977/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145124747","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}

引用次数: 0

Subtype-HM: A Novel Cancer Subtype Identification Method Based on Hypergraph Learning and Multi-omics Data. Subtype- hm：一种基于超图学习和多组学数据的癌症亚型识别方法。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-09-01 Epub Date: 2025-11-18 DOI: 10.1007/s12539-025-00789-1

Jie Wang, Xin Huang, Hulin Kuang, Cheng Yan

{"title":"Subtype-HM: A Novel Cancer Subtype Identification Method Based on Hypergraph Learning and Multi-omics Data.","authors":"Jie Wang, Xin Huang, Hulin Kuang, Cheng Yan","doi":"10.1007/s12539-025-00789-1","DOIUrl":"10.1007/s12539-025-00789-1","url":null,"abstract":"Cancer is a complex and lethal disease influenced by multiple factors, and accurate subtyping is crucial for personalized treatment and prognostic evaluation. Although deep learning has made progress in cancer subtype identification, existing methods still face challenges in capturing high-order biological relationships, often overlook omics-specific information, and suffer from information loss caused by conventional feature strategies. To address these challenges, we propose Subtype-HM, a novel cancer subtype identification method based on hypergraph learning and multi-omics data. We employ multi-level hypergraphs to model complex biological structures and design a hypergraph propagation network to capture both intra- and inter-omics correlations, effectively simulating high-order biological relationships. To preserve omics-specific semantics and enrich hypergraph representations, we introduce a parallel discriminator-guided attention module that extracts omics-specific features and complements the correlated representation with unique omics-specific information. Furthermore, to avoid the information loss caused by feature fusion, we propose a multi-omics contrastive entropy alignment that aligns subtype predictions across omics while retaining their unique semantics. Experimental results on TCGA cancer datasets demonstrate that Subtype-HM outperforms 14 methods in cancer subtype identification, achieving the highest average survival analysis([Formula: see text] = 5.0) and enriched clinical parameters (3.1 on average). The identified subtypes demonstrate high biological interpretability through GO and KEGG enrichment analyses.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"931-946"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145549140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DEAPLOG: Differential Expression Analysis and Pseudo-Temporal Locating and Ordering of Genes in Single-Cell Transcriptomic Data. DEAPLOG：单细胞转录组数据中基因的差异表达分析和伪时间定位和排序。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-09-01 Epub Date: 2025-10-21 DOI: 10.1007/s12539-025-00778-4

Bao Zhang, Jing Wang, Weiwei Wang, Hongbo Zhang

{"title":"DEAPLOG: Differential Expression Analysis and Pseudo-Temporal Locating and Ordering of Genes in Single-Cell Transcriptomic Data.","authors":"Bao Zhang, Jing Wang, Weiwei Wang, Hongbo Zhang","doi":"10.1007/s12539-025-00778-4","DOIUrl":"10.1007/s12539-025-00778-4","url":null,"abstract":"Differential expression analysis constitutes a crucial step in the analysis of single-cell transcriptomic data. Numerous statistical methods have been developed to conduct differential expression analysis by addressing the sparsity or heterogeneity of gene expression. Nevertheless, these approaches often overlook other critical characteristics of single-cell transcriptomic data, such as the high dimensionality of gene expression at the cellular level, which may consequently lead to suboptimal performance. Furthermore, to date, there remains a significant gap in methodologies capable of locating and ordering genes along cell trajectories. Here, we integrate polynomial fitting with hypergeometric testing to develop a new tool, DEAPLOG (Differential Expression Analysis and Pseudo-temporal Locating and Ordering of Genes), leveraging the high-dimensional gene expression characteristics at the cellular level in single-cell transcriptomic data. Benchmarking analyses on synthetic single-cell datasets demonstrate that while DEAPLOG exhibits performance comparable to existing methods on datasets comprising only two cell clusters, it demonstrates superior performance in differential expression analysis when applied to datasets with multiple cell clusters. Furthermore, the applications of DEAPLOG to real single-cell and spatial transcriptomic dataset not only validate its superior performance in terms of accuracy but also computational efficiency. Notably, when applied to single-cell transcriptomic data from the developmental hematopoietic system, DEAPLOG demonstrate precise gene localization and accurate ordering along developmental trajectories. Collectively, these findings establish DEAPLOG as a robust and highly effective tool for single-cell transcriptomic data analysis.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"822-837"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145337026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Protein-DNA Binding Sites Prediction via Integrating Pretrained Large Language Models and Contrastive Learning. 通过整合预训练的大语言模型和对比学习来预测蛋白质- dna结合位点。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-09-01 Epub Date: 2025-11-24 DOI: 10.1007/s12539-025-00788-2

Zhen Feng, Hui Yu, Xiaoya Guan, Zhenyu Yue, Lichuan Gu, Ke Li, Xiaobo Zhou

引用次数: 0

SANNO: A Graph-Transformer Enhanced Optimal Transport Tool for Spatial Transcriptomic Annotation. SANNO：一个图形转换器增强的最佳传输工具，用于空间转录组注释。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-09-01 Epub Date: 2025-08-11 DOI: 10.1007/s12539-025-00752-0

Yuansong Zeng, Yuanze Chen, Ningyuan Shangguan, Wenbing Li, Xiaoming Cai, Hongyu Zhang, Zheng Wang, Huiying Zhao

{"title":"SANNO: A Graph-Transformer Enhanced Optimal Transport Tool for Spatial Transcriptomic Annotation.","authors":"Yuansong Zeng, Yuanze Chen, Ningyuan Shangguan, Wenbing Li, Xiaoming Cai, Hongyu Zhang, Zheng Wang, Huiying Zhao","doi":"10.1007/s12539-025-00752-0","DOIUrl":"10.1007/s12539-025-00752-0","url":null,"abstract":"The latest progress in spatial transcriptomics has empowered scientists to investigate spatial heterogeneity with single-cell precision. A pivotal yet demanding aspect of spatial transcriptomics data analysis is cell type annotation. However, current methods exhibit limited performance as they are primarily designed for scRNA-seq data. Especially, these approaches often neglect spatial coordinate information and encounter challenges in identifying novel cell types. Here, we introduce SANNO, a novel approach that employs Optimal Transport (OT) to concurrently identify both known and novel cell types in spatially resolved single-cell data. Specifically, SANNO leverages a graph-Transformer module to model spatial coordinates and gene expression. This produces unified representations for both reference and query data. Building on this, SANNO employs a dual-strategy classifier. The first is an Unbalanced Optimal Transport (UOT) module that aligns query data with reference prototypes. The second is a self-supervised OT-based module that enhances global cluster separation and local cellular consistency, effectively eliminating batch effects. To further improve prediction accuracy, SANNO integrates an entropy-based re-weighted loss function. This significantly boosts the confidence of query cell predictions. Comprehensive experiments reveal that SANNO surpasses state-of-the-art techniques across both intra- and cross-spatial datasets, particularly in the identification of novel cell types. Additionally, SANNO demonstrates commendable performance in annotating cells within single-cell data, underscoring its potential as a versatile tool for cell annotation across single-cell and spatial transcriptomics datasets.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":"1113-1127"},"PeriodicalIF":5.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144821315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MBPBERT: A Large Language Model for Metal-Binding Peptide Discovery. MBPBERT：金属结合肽发现的大型语言模型。

IF 5.2 2区生物学

Interdisciplinary Sciences: Computational Life Sciences Pub Date : 2026-08-28 DOI: 10.1007/s12539-026-00847-2

Guifen Jian, Xinwei Li, Yu Chen, Junjie Liu, Ziyang Liu, Xing Shang, Heng Chen, Jian Huang, Bifang He

{"title":"MBPBERT: A Large Language Model for Metal-Binding Peptide Discovery.","authors":"Guifen Jian, Xinwei Li, Yu Chen, Junjie Liu, Ziyang Liu, Xing Shang, Heng Chen, Jian Huang, Bifang He","doi":"10.1007/s12539-026-00847-2","DOIUrl":"https://doi.org/10.1007/s12539-026-00847-2","url":null,"abstract":"Metal-binding peptides (MBPs) are a class of peptides capable of selectively coordinating metal ions and play critical roles in various biological processes, such as metal ion transport, storage, catalysis, and signal transduction. However, traditional experimental approaches, such as mass spectrometry, chromatography, and nuclear magnetic resonance spectroscopy, are labor-intensive, time-consuming, and poorly suited for the large-scale screening of MBPs. To overcome these limitations, we introduce MBPBERT, a deep learning-based predictive framework for accurate identification of MBPs and discrimination of metal-specific binding subtypes. MBPBERT builds upon the ProteinBERT architecture and was developed using two curated datasets: a peptide pretraining corpus comprising 33,095 sequences and a labeled dataset containing 909 MBPs and non-MBPs. For rigorous external validation, an independent test dataset of 101 peptides was constructed. MBPBERT attained areas under the receiver operating characteristic curve (AUROCs) of 0.9414 and 0.8882 on the test dataset for the prediction of MBPs and the classification of their specific binding subtypes, respectively. These results underscore the framework's strong predictive capability and generalization performance. Thus, MBPBERT provides a scalable and efficient in silico solution for high-throughput discovery of novel MBPs and screening of peptides with metal-specific binding preferences, potentially reducing the reliance on resource-intensive experimental validation.","PeriodicalId":13670,"journal":{"name":"Interdisciplinary Sciences: Computational Life Sciences","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148850611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0