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About 3,970 papers for “sequence-structure integration”
Yong Wang, Yu Xia, Luonan Chen · BMC Systems Biology · 2011
A report of the 4nd International Conference on Computational Systems Biology, 9-11 September 2010, Suzhou, China.
Cited by 20Open access
Blake Sweeney, Anton I. Petrov, Carlos Eduardo Ribas et al. · Nucleic Acids Research · 2020
RNAcentral is a comprehensive database of non-coding RNA (ncRNA) sequences that provides a single access point to 44 RNA resources and >18 million ncRNA sequences from a wide range of organisms and RNA types. RNAcentral now also includes secondary (2D) structure information for >13 million sequences, making RNAcentral the world's largest RNA 2D structure database. The 2D diagrams are displayed using R2DT, a new 2D structure visualization method that uses consistent, reproducible and recognizable layouts for related RNAs. The sequence similarity search has been updated with a faster interface featuring facets for filtering search results by RNA type, organism, source database or any keyword. This sequence search tool is available as a reusable web component, and has been integrated into several RNAcentral member databases, including Rfam, miRBase and snoDB. To allow for a more fine-grained assignment of RNA types and subtypes, all RNAcentral sequences have been annotated with Sequence Ontology terms. The RNAcentral database continues to grow and provide a central data resource for the RNA community. RNAcentral is freely available at https://rnacentral.org.
Cited by 407Open access
Xiling Luo, Le Ou-Yang, Yang Shen et al. · ArXiv.org · 2026
Motivation: Receptor-binding proteins (RBPs) initiate viral infection and determine host specificity, serving as key targets for phage engineering and therapy. However, the identification of RBPs is complicated by their extreme sequence divergence, which often renders traditional homology-based alignment methods ineffective. While machine learning offers a promising alternative, such approaches struggle with severe class imbalance and the difficulty of selecting informative negative samples from heterogeneous tail proteins. Existing methods often fail to balance learning from these ``hard negatives'' while maintaining generalization. Results: We present SeekRBP, a sequence--structure framework that models negative sampling as a sequential decision-making problem. By employing a multi-armed bandit strategy, SeekRBP dynamically prioritizes informative non-RBP sequences based on real-time training feedback, complemented by a multimodal fusion of protein language and structural embeddings. Benchmarking demonstrates that SeekRBP consistently outperforms static sampling strategies. Furthermore, a case study on Vibrio phages validates that SeekRBP effectively identifies RBPs to improve host prediction, highlighting its potential for large-scale annotation and synthetic biology applications.
Cited by 0Open access
Xiling Luo, Le Ou-Yang, Yang Shen et al. · arXiv (Cornell University) · 2026
Motivation: Receptor-binding proteins (RBPs) initiate viral infection and determine host specificity, serving as key targets for phage engineering and therapy. However, the identification of RBPs is complicated by their extreme sequence divergence, which often renders traditional homology-based alignment methods ineffective. While machine learning offers a promising alternative, such approaches struggle with severe class imbalance and the difficulty of selecting informative negative samples from heterogeneous tail proteins. Existing methods often fail to balance learning from these ``hard negatives'' while maintaining generalization. Results: We present SeekRBP, a sequence--structure framework that models negative sampling as a sequential decision-making problem. By employing a multi-armed bandit strategy, SeekRBP dynamically prioritizes informative non-RBP sequences based on real-time training feedback, complemented by a multimodal fusion of protein language and structural embeddings. Benchmarking demonstrates that SeekRBP consistently outperforms static sampling strategies. Furthermore, a case study on Vibrio phages validates that SeekRBP effectively identifies RBPs to improve host prediction, highlighting its potential for large-scale annotation and synthetic biology applications.
Cited by 0Open access
Guowei Zhou, Yanpeng Zhao, Song He et al. · International Journal of Molecular Sciences · 2025
Proteins are the basic building blocks of life and perform fundamental functions in biology. Predicting protein properties based on amino acid sequences and 3D structures has become a key approach to accelerating drug development. In this study, we propose a novel sequence- and structure-based framework, SST-ResNet, which consists of the multimodal language model ProSST and a multi-scale information integration module. This framework is designed to deeply explore the latent relationships between protein sequences and structures, thereby achieving superior synergistic prediction performance. Our method outperforms previous joint prediction models on Enzyme Commission (EC) numbers and Gene Ontology (GO) tasks. Furthermore, we demonstrate the necessity of multi-scale information integration for these two types of data and illustrate its exceptional performance on key tasks. We anticipate that this framework can be extended to a broader range of protein property prediction problems, ultimately facilitating drug development.
Cited by 2Open access
Rafael J. Borges, Guilherme Henrique Marchi Salvador, Lucilene Delazari dos Santos et al. · Toxicon · 2019
Cited by 0
Benyamin Jamialahmadi, Mahmood Chamankhah, Mohammad Kohandel et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2024
Abstract Antibodies, or immunoglobulins, are integral to the immune response, playing a crucial role in recognizing and neutralizing external threats such as pathogens. However, the design of these molecules is complex due to the limited availability of paired structural antibody-antigen data and the intricacies of structurally non-deterministic regions. In this paper, we introduce a novel approach to designing antibodies by integrating structural and sequence information of antigens. Our approach employs a protein structural encoder to capture both sequence and conformational details of antigen. The encoded antigen information is then fed into an antibody language model (aLM) to generate antibody sequences. By adding cross-attention layers, aLM effectively incorporates the antigen information from the encoder. For optimal model training, we utilized the Causal Masked Language Modeling (CMLM) objective. Unlike other methods that require additional contextual information, such as epitope residues or a docked antibody framework, our model excels at predicting the antibody sequence without the need for any supplementary data. Our enhanced methodology demonstrates superior performance when compared to existing models in the RAbD benchmark for antibody design and SKEPMI for antibody optimization.
Cited by 0Open access
Buchholz, Patrick Christopher Frank · OPUS Publication Server of the University of Stuttgart (University of Stuttgart) · 2018
Cited by 0Open access
Daneshpour, Hosein · UTUPub (University of Turku) · 2023
In this discovery of carbonic anhydrases, several computational techniques have been used to examine different facets of these enzymes. Methods produced in this work aid for instance in the evaluation of the accuracy of Multiple Sequence Alignments and AlphaFold models, the retrieval of combinations of amino acids from targeted areas of Multiple Sequence Alignments, the evaluation of disulfides and metal-binding residues, the programmatic assessments of molecular geometry, and the production of molecular pictures. Therefore, by utilizing these data mining methods, we have learnt more about the composition, properties, and interactions of carbonic anhydrases, which will help us comprehend these enzymes and their function in biological systems better. Most importantly, the catalytic center and mechanism of the thus far poorly characterized delta carbonic anhydrases has been reliably established. Accordingly, this research offers novelty in several aspects such as combining large-scale data from AlphaFold models, analyzing the reproducibility of functional aspects in AlphaFold models, and formulating theories for the catalytic mechanism of a nearly unknown protein family using features of sequence conservation and structural evaluation.
Cited by 0
Haopeng Shi, Xiankun Zhang, Qingxu Deng · Lecture notes in computer science · 2024
Cited by 0
Ruoying Chen, Wenjing Chen, Sixiao Yang et al. · BMC Bioinformatics · 2011
Experimental results show that support vector machine classifiers are quite effective in predicting hot spots based on sequence features. Hot spots cannot be fully predicted through simple analysis based on physicochemical characteristics, but there is reason to believe that integration of features and machine learning methods can remarkably improve the predictive performance for hot spots.
Cited by 9Open access
Nan Sheng, Yunzhi Liu, Ling Gao et al. · Journal of Chemical Information and Modeling · 2025
Extensive research has shown that microRNAs (miRNAs) play a crucial role in cancer progression, treatment, and drug resistance. They have been recognized as promising potential therapeutic targets for overcoming drug resistance in cancer treatment. However, limited attention has been paid to predicting the association between miRNAs and drugs by computational methods. Existing approaches typically focus on constructing miRNA-drug interaction graphs, which may result in their performance being limited by interaction density. In this work, we propose a novel deep learning method that integrates sequence and structural information to infer miRNA-drug associations (MDAs), called DLST-MDA. This approach innovates by utilizing attribute information on miRNAs and drugs instead of relying on the commonly used interaction graph information. Specifically, considering the sequence lengths of miRNAs and drugs, DLST-MDA employs multiscale convolutional neural network (CNN) to learn sequence embeddings at different granularity levels from miRNA and drug sequences. Additionally, it leverages the power of graph neural networks to capture structural information from drug molecular graphs, providing a more representational analysis of the drug features. To evaluate DLST-MDA's effectiveness, we manually constructed a benchmark data set for various experiments based on the latest databases. Results indicate that DLST-MDA performs better than other state-of-the-art methods. Furthermore, case studies of three common anticancer drugs can evidence their usefulness in discovering novel MDAs. The data and source code are released at https://github.com/sheng-n/DLST-MDA.
Cited by 9
Brian Cox · bioRxiv (Cold Spring Harbor Laboratory) · 2020
Summary In the last twenty years, three separate coronaviruses have left their typical animal hosts and became human pathogens. An area of research interest is coronavirus transcription regulation that uses an RNA-RNA mediated template-switching mechanism. It is not known how different transcriptional stoichiometries of each viral gene are generated. Analysis of SARS-CoV-2 RNA sequencing data from whole RNA transcriptomes identified TRS dependent and independent transcripts. Integration of transcripts and 5’-UTR sequence motifs identified that the pentaloop and the stem-loop 3 were also located upstream of spliced genes. TRS independent transcripts were detected as likely non-polyadenylated. Additionally, a novel conserved sequence motif was discovered at either end of the TRS independent splice junctions. While similar both SARS viruses generated similar TRS independent transcripts they were more abundant in SARS-CoV-2. TRS independent gene regulation requires investigation to determine its relationship to viral pathogenicity.
Cited by 0Open access
Yu Zhao, Bing He · Zenodo (CERN European Organization for Nuclear Research) · 2023
Structural-docking-based binding between the adaptive immune receptors (AIRs), including T cell receptor (TCR) and B cell receptor (BCR), and the antigens is one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIR. In this study, we present a deep-learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence-derived and structure-derived features of AIRs. DeepAIR consists of three feature encoders, including a trainable-embedding-layer-based gene encoder, a transformer-based sequence encoder, and a pre-trained AlphaFold2-based structure encoder. DeepAIR deploys a gating-based attention mechanism to extract important features from the three encoders, and a tensor fusion mechanism to integrate obtained features for multiple tasks, including the prediction of AIR-antigen binding affinity, AIR-antigen binding reactivity, and the classification of the immune repertoire. We systematically evaluated the performance of DeepAIR on multiple datasets. DeepAIR shows outstanding prediction performance in terms of AUC (area under the ROC curve) in predicting the binding reactivity to various antigens, as well as the classification of immune repertoire for nasopharyngeal carcinoma (NPC) and inflammatory bowel disease (IBD). We anticipate that DeepAIR can serve as a useful tool for characterizing and profiling antigen-binding AIRs, thereby informing the design of personalized immunotherapy.
Cited by 0Open access
Yu Zhao, Bing He · Zenodo (CERN European Organization for Nuclear Research) · 2023
Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens are one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIRs, omitting the structure features that are essential for binding affinity. In this study, we present a deep-learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence and structure features of AIRs. DeepAIR achieves a Pearson’s correlation of 0.813 in predicting the binding affinity of TCR, and a median AUC of 0.904 and 0.942 in predicting the binding reactivity of TCR and BCR, respectively. Meanwhile, using TCR and BCR repertoire, DeepAIR correctly identifies every patient with nasopharyngeal carcinoma (NPC) and inflammatory bowel disease (IBD) in test data. Thus, DeepAIR improves the AIR-antigen binding prediction that facilitates the study of adaptive immunity.
Cited by 0Open access
Yu Zhao, Bing He · Zenodo (CERN European Organization for Nuclear Research) · 2023
Structural-docking-based binding between the adaptive immune receptors (AIRs), including T cell receptor (TCR) and B cell receptor (BCR), and the antigens is one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIR. In this study, we present a deep-learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence-derived and structure-derived features of AIRs. DeepAIR consists of three feature encoders, including a trainable-embedding-layer-based gene encoder, a transformer-based sequence encoder, and a pre-trained AlphaFold2-based structure encoder. DeepAIR deploys a gating-based attention mechanism to extract important features from the three encoders, and a tensor fusion mechanism to integrate obtained features for multiple tasks, including the prediction of AIR-antigen binding affinity, AIR-antigen binding reactivity, and the classification of the immune repertoire. We systematically evaluated the performance of DeepAIR on multiple datasets. DeepAIR shows outstanding prediction performance in terms of AUC (area under the ROC curve) in predicting the binding reactivity to various antigens, as well as the classification of immune repertoire for nasopharyngeal carcinoma (NPC) and inflammatory bowel disease (IBD). We anticipate that DeepAIR can serve as a useful tool for characterizing and profiling antigen-binding AIRs, thereby informing the design of personalized immunotherapy.
Cited by 0Open access
Yu Zhao, Bing He, Chen Li et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2022
Abstract Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens is one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIRs, omitting the structure features that are essential for binding affinity. In this study, we present a deep-learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence and structure features of AIRs. DeepAIR consists of three feature encoders (a trainable-embedding-layer-based gene encoder, a transformer-based sequence encoder, and a pre-trained AlphaFold2-based structure encoder), a gating-based attention mechanism to extract important features, and a tensor fusion mechanism to integrate obtained features. We train and evaluate DeepAIR on three downstream prediction tasks, including the prediction of AIR-antigen binding affinity, the prediction of AIR-antigen binding reactivity, and the classification of the immune repertoire. On five representative datasets, DeepAIR shows outstanding prediction performance in terms of AUC (area under the ROC curve) in predicting the binding reactivity to various antigens, as well as the classification of immune repertoire for nasopharyngeal carcinoma (NPC) and inflammatory bowel disease (IBD). DeepAIR is freely available for academic purposes at https://github.com/TencentAILabHealthcare/DeepAIR . We anticipate that DeepAIR can serve as a useful tool for characterizing and profiling antigen binding AIRs, thereby informing the design of personalized immunotherapy. Highlights Integrating predicted AIR structures using AlphaFold2 significantly improves the prediction accuracy of the binding reactivity between AIRs and antigens. DeepAIR is featured by a novel deep learning architecture that leverages both the gating-based attention mechanism and tensor fusion mechanism to effectively extract and integrate informative features from three feature encoders, including a trainable embedding-layer-based gene encoder, a transformer-based sequence encoder, and a pre-trained AlphaFold2-based structure encoder. DeepAIR is implemented as a biologically interpretable deep learning framework that highlights the key residues in both α and β chains that are critical for predicting the AIR-antigen binding.
Cited by 4Open access
Yu Zhao, Bing He, Fan Xu et al. · Science Advances · 2023
Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens are one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIRs, omitting the structure features that are essential for binding affinity. In this study, we present a deep learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence and structure features of AIRs. DeepAIR achieves a Pearson's correlation of 0.813 in predicting the binding affinity of TCR, and a median area under the receiver-operating characteristic curve (AUC) of 0.904 and 0.942 in predicting the binding reactivity of TCR and BCR, respectively. Meanwhile, using TCR and BCR repertoire, DeepAIR correctly identifies every patient with nasopharyngeal carcinoma and inflammatory bowel disease in test data. Thus, DeepAIR improves the AIR-antigen binding prediction that facilitates the study of adaptive immunity.
Cited by 43Open access
Zhao, Yu, He, Bing · Zenodo (CERN European Organization for Nuclear Research) · 2023
Structural-docking-based binding between the adaptive immune receptors (AIRs), including T cell receptor (TCR) and B cell receptor (BCR), and the antigens is one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIR. In this study, we present a deep-learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence-derived and structure-derived features of AIRs. DeepAIR consists of three feature encoders, including a trainable-embedding-layer-based gene encoder, a transformer-based sequence encoder, and a pre-trained AlphaFold2-based structure encoder. DeepAIR deploys a gating-based attention mechanism to extract important features from the three encoders, and a tensor fusion mechanism to integrate obtained features for multiple tasks, including the prediction of AIR-antigen binding affinity, AIR-antigen binding reactivity, and the classification of the immune repertoire. We systematically evaluated the performance of DeepAIR on multiple datasets. DeepAIR shows outstanding prediction performance in terms of AUC (area under the ROC curve) in predicting the binding reactivity to various antigens, as well as the classification of immune repertoire for nasopharyngeal carcinoma (NPC) and inflammatory bowel disease (IBD). We anticipate that DeepAIR can serve as a useful tool for characterizing and profiling antigen-binding AIRs, thereby informing the design of personalized immunotherapy.
Cited by 0Open access
Zhao, Yu, He, Bing · Zenodo (CERN European Organization for Nuclear Research) · 2023
Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens are one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIRs, omitting the structure features that are essential for binding affinity. In this study, we present a deep-learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence and structure features of AIRs. DeepAIR achieves a Pearson’s correlation of 0.813 in predicting the binding affinity of TCR, and a median AUC of 0.904 and 0.942 in predicting the binding reactivity of TCR and BCR, respectively. Meanwhile, using TCR and BCR repertoire, DeepAIR correctly identifies every patient with nasopharyngeal carcinoma (NPC) and inflammatory bowel disease (IBD) in test data. Thus, DeepAIR improves the AIR-antigen binding prediction that facilitates the study of adaptive immunity.
Cited by 0Open access