DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model

BMC Biology, Год журнала: 2024, Номер 22(1)

Опубликована: Янв. 2, 2024

Abstract Intrinsically disordered proteins and regions (IDPs/IDRs) are functionally important that exist as highly dynamic conformations under natural physiological conditions. IDPs/IDRs exhibit a broad range of molecular functions, their functions involve binding interactions with partners remaining native structural flexibility. The rapid increase in the number sequence databases diversity challenge existing computational methods for predicting protein intrinsic disorder functions. A region interacts different to perform multiple these dependencies correlations. In this study, we introduce DisoFLAG, method leverages graph-based interaction language model (GiPLM) jointly its potential GiPLM integrates semantic information based on pre-trained models into units enhance correlation representation DisoFLAG predictor takes amino acid sequences only inputs provides predictions six proteins, including protein-binding, DNA-binding, RNA-binding, ion-binding, lipid-binding, flexible linker. We evaluated predictive performance following Critical Assessment Intrinsic Disorder (CAID) experiments, results demonstrated offers accurate comprehensive extending current coverage computationally predicted function categories. standalone package web server have been established provide prediction tools disorders associated

Язык: Английский

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A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for follow-up experimental validation

BMC Biology, Год журнала: 2024, Номер 22(1)

Опубликована: Янв. 29, 2024

Abstract Background Circular RNAs (circRNAs) have been confirmed to play a vital role in the occurrence and development of diseases. Exploring relationship between circRNAs diseases is far-reaching significance for studying etiopathogenesis treating To this end, based on graph Markov neural network algorithm (GMNN) constructed our previous work GMNN2CD, we further considered multisource biological data that affects association circRNA disease developed an updated web server CircDA human hepatocellular carcinoma (HCC) tissue verify prediction results CircDA. Results built Tumarkov-based deep learning framework. The regards biomolecules as nodes interactions molecules edges, reasonably abstracts multiomics data, models them heterogeneous biomolecular network, which can reflect complex different biomolecules. Case studies using literature from HCC, cervical, gastric cancers demonstrate predictor identify missing associations known diseases, quantitative real-time PCR (RT-qPCR) experiment HCC samples, it was found five were significantly differentially expressed, proved predict related new circRNAs. Conclusions This efficient computational case analysis with sufficient feedback allows us circRNA-associated disease-associated Our provides method provide guidance certain For ease use, online ( http://server.malab.cn/CircDA ) provided, code open-sourced https://github.com/nmt315320/CircDA.git convenience improvement.

Язык: Английский

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CODENET: A deep learning model for COVID-19 detection

Computers in Biology and Medicine, Год журнала: 2024, Номер 171, С. 108229 - 108229

Опубликована: Фев. 29, 2024

Язык: Английский

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A BERT-based model for the prediction of lncRNA subcellular localization in Homo sapiens

International Journal of Biological Macromolecules, Год журнала: 2024, Номер 265, С. 130659 - 130659

Опубликована: Март 11, 2024

Язык: Английский

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Prediction of blood–brain barrier penetrating peptides based on data augmentation with Augur

BMC Biology, Год журнала: 2024, Номер 22(1)

Опубликована: Апрель 19, 2024

Abstract Background The blood–brain barrier serves as a critical interface between the bloodstream and brain tissue, mainly composed of pericytes, neurons, endothelial cells, tightly connected basal membranes. It plays pivotal role in safeguarding from harmful substances, thus protecting integrity nervous system preserving overall homeostasis. However, this remarkable selective transmission also poses formidable challenge realm central diseases treatment, hindering delivery large-molecule drugs into brain. In response to challenge, many researchers have devoted themselves developing drug systems capable breaching barrier. Among these, penetrating peptides emerged promising candidates. These had advantages high biosafety, ease synthesis, exceptional penetration efficiency, making them an effective solution. While previous studies developed few prediction models for peptides, their performance has often been hampered by issue limited positive data. Results study, we present Augur, novel model using borderline-SMOTE-based data augmentation machine learning. extract highly interpretable physicochemical properties while solving issues small sample size imbalance negative samples. Experimental results demonstrate superior Augur with AUC value 0.932 on training set 0.931 independent test set. Conclusions This newly demonstrates predicting offering valuable insights development targeting neurological disorders. breakthrough may enhance efficiency peptide-based discovery pave way innovative treatment strategies diseases.

Язык: Английский

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MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier

ACS Omega, Год журнала: 2024, Номер unknown

Опубликована: Фев. 8, 2024

In biological organisms, metal ion-binding proteins participate in numerous metabolic activities and are closely associated with various diseases. To accurately predict whether a protein binds to ions the type of protein, this study proposed classifier named MIBPred. The incorporated advanced Word2Vec technology from field natural language processing extract semantic features sequence combined them position-specific score matrix (PSSM) features. Furthermore, an ensemble learning model was employed for classification task. model, we independently trained XGBoost, LightGBM, CatBoost algorithms integrated output results through SVM voting mechanism. This innovative combination has led significant breakthrough predictive performance our model. As result, achieved accuracies 95.13% 85.19%, respectively, predicting their types. Our research not only confirms effectiveness extracting information sequences but also highlights outstanding MIBPred problem provides reliable tool method in-depth exploration structure function proteins.

Язык: Английский

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iAMP-Attenpred: a novel antimicrobial peptide predictor based on BERT feature extraction method and CNN-BiLSTM-Attention combination model

Briefings in Bioinformatics, Год журнала: 2023, Номер 25(1)

Опубликована: Ноя. 22, 2023

Abstract As a kind of small molecule protein that can fight against various microorganisms in nature, antimicrobial peptides (AMPs) play an indispensable role maintaining the health organisms and fortifying defenses diseases. Nevertheless, experimental approaches for AMP identification still demand substantial allocation human resources material inputs. Alternatively, computing assist researchers effectively promptly predict AMPs. In this study, we present novel predictor called iAMP-Attenpred. far as know, is first work not only employs popular BERT model field natural language processing (NLP) AMPs feature encoding, but also utilizes idea combining multiple models to discover Firstly, treat each amino acid from preprocessed non-AMP sequences word, then input it into pre-training extraction. Moreover, features obtained method are fed composite composed one-dimensional CNN, BiLSTM attention mechanism better discriminating features. Finally, flatten layer fully connected layers utilized final classification Experimental results reveal that, compared with existing predictors, our iAMP-Attenpred achieves performance indicators, such accuracy, precision so on. This further demonstrates using approach capture effective information peptide deep learning meaningful predicting

Язык: Английский

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FEOpti-ACVP: identification of novel anti-coronavirus peptide sequences based on feature engineering and optimization

Briefings in Bioinformatics, Год журнала: 2024, Номер 25(2)

Опубликована: Янв. 22, 2024

Abstract Anti-coronavirus peptides (ACVPs) represent a relatively novel approach of inhibiting the adsorption and fusion virus with human cells. Several peptide-based inhibitors showed promise as potential therapeutic drug candidates. However, identifying such in laboratory experiments is both costly time consuming. Therefore, there growing interest using computational methods to predict ACVPs. Here, we describe model for prediction ACVPs that based on combination feature engineering (FE) optimization deep representation learning. FEOpti-ACVP was pre-trained two extraction frameworks. At next step, several machine learning approaches were tested construct final algorithm. The version outperformed existing used it has become valuable tool ACVP design. A user-friendly webserver can be accessed at http://servers.aibiochem.net/soft/FEOpti-ACVP/.

Язык: Английский

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ACVPred: Enhanced prediction of anti-coronavirus peptides by transfer learning combined with data augmentation

Future Generation Computer Systems, Год журнала: 2024, Номер 160, С. 305 - 315

Опубликована: Июнь 7, 2024

Язык: Английский

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ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree

Frontiers in Genetics, Год журнала: 2023, Номер 14

Опубликована: Март 29, 2023

Cancer is one of the most dangerous diseases in world, killing millions people every year. Drugs composed anticancer peptides have been used to treat cancer with low side effects recent years. Therefore, identifying has become a focus research. In this study, an improved peptide predictor named ACP-GBDT, based on gradient boosting decision tree (GBDT) and sequence information, proposed. To encode sequences included dataset, ACP-GBDT uses merged-feature AAIndex SVMProt-188D. A GBDT adopted train prediction model ACP-GBDT. Independent testing ten-fold cross-validation show that can effectively distinguish from non-anticancer ones. The comparison results benchmark dataset simpler more effective than other existing methods.

Язык: Английский

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