Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 9 de 9
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
Nat Commun ; 12(1): 5465, 2021 09 15.
Artigo em Inglês | MEDLINE | ID: mdl-34526500

RESUMO

Peptide-protein interactions are involved in various fundamental cellular functions and their identification is crucial for designing efficacious peptide therapeutics. Recently, a number of computational methods have been developed to predict peptide-protein interactions. However, most of the existing prediction approaches heavily depend on high-resolution structure data. Here, we present a deep learning framework for multi-level peptide-protein interaction prediction, called CAMP, including binary peptide-protein interaction prediction and corresponding peptide binding residue identification. Comprehensive evaluation demonstrated that CAMP can successfully capture the binary interactions between peptides and proteins and identify the binding residues along the peptides involved in the interactions. In addition, CAMP outperformed other state-of-the-art methods on binary peptide-protein interaction prediction. CAMP can serve as a useful tool in peptide-protein interaction prediction and identification of important binding residues in the peptides, which can thus facilitate the peptide drug discovery process.


Assuntos
Algoritmos , Biologia Computacional/métodos , Aprendizado Profundo , Peptídeos/metabolismo , Proteínas/metabolismo , Sítios de Ligação , Modelos Moleculares , Peptídeos/química , Ligação Proteica , Domínios Proteicos , Proteínas/química , Reprodutibilidade dos Testes
2.
Nat Commun ; 12(1): 3307, 2021 06 03.
Artigo em Inglês | MEDLINE | ID: mdl-34083538

RESUMO

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.


Assuntos
Inibidores de Proteínas Quinases/farmacologia , Proteínas Quinases/metabolismo , Algoritmos , Benchmarking , Crowdsourcing , Bases de Dados de Produtos Farmacêuticos , Aprendizado Profundo , Descoberta de Drogas , Avaliação Pré-Clínica de Medicamentos , Humanos , Cinética , Aprendizado de Máquina , Modelos Biológicos , Modelos Químicos , Inibidores de Proteínas Quinases/química , Inibidores de Proteínas Quinases/farmacocinética , Proteínas Quinases/química , Proteômica , Análise de Regressão
3.
Signal Transduct Target Ther ; 6(1): 165, 2021 04 24.
Artigo em Inglês | MEDLINE | ID: mdl-33895786

RESUMO

The global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires an urgent need to find effective therapeutics for the treatment of coronavirus disease 2019 (COVID-19). In this study, we developed an integrative drug repositioning framework, which fully takes advantage of machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 can interact with the nucleocapsid (N) protein of SARS-CoV-2 and is able to suppress the LPS-induced production of several inflammatory cytokines that are highly relevant to the prevention of immunopathology induced by SARS-CoV-2 infection.


Assuntos
Antivirais/uso terapêutico , COVID-19/tratamento farmacológico , COVID-19/metabolismo , Simulação por Computador , Reposicionamento de Medicamentos , Modelos Biológicos , SARS-CoV-2/metabolismo , Humanos
4.
Proc Natl Acad Sci U S A ; 118(6)2021 02 09.
Artigo em Inglês | MEDLINE | ID: mdl-33526657

RESUMO

RNA polymerase II (Pol II) generally pauses at certain positions along gene bodies, thereby interrupting the transcription elongation process, which is often coupled with various important biological functions, such as precursor mRNA splicing and gene expression regulation. Characterizing the transcriptional elongation dynamics can thus help us understand many essential biological processes in eukaryotic cells. However, experimentally measuring Pol II elongation rates is generally time and resource consuming. We developed PEPMAN (polymerase II elongation pausing modeling through attention-based deep neural network), a deep learning-based model that accurately predicts Pol II pausing sites based on the native elongating transcript sequencing (NET-seq) data. Through fully taking advantage of the attention mechanism, PEPMAN is able to decipher important sequence features underlying Pol II pausing. More importantly, we demonstrated that the analyses of the PEPMAN-predicted results around various types of alternative splicing sites can provide useful clues into understanding the cotranscriptional splicing events. In addition, associating the PEPMAN prediction results with different epigenetic features can help reveal important factors related to the transcription elongation process. All these results demonstrated that PEPMAN can provide a useful and effective tool for modeling transcription elongation and understanding the related biological factors from available high-throughput sequencing data.


Assuntos
Genoma Humano , Aprendizado de Máquina , Modelos Biológicos , Elongação da Transcrição Genética , Sequência de Bases , Sítios de Ligação , Metilação de DNA/genética , Epigênese Genética , Células HEK293 , Células HeLa , Histonas/metabolismo , Humanos , Motivos de Nucleotídeos/genética , Processamento de Proteína Pós-Traducional , RNA Polimerase II/metabolismo , Sítios de Splice de RNA/genética , Splicing de RNA/genética
5.
Front Pharmacol ; 11: 112, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32184722

RESUMO

Synthetic lethality (SL), an important type of genetic interaction, can provide useful insight into the target identification process for the development of anticancer therapeutics. Although several well-established SL gene pairs have been verified to be conserved in humans, most SL interactions remain cell-line specific. Here, we demonstrated that the cell-line-specific gene expression profiles derived from the shRNA perturbation experiments performed in the LINCS L1000 project can provide useful features for predicting SL interactions in human. In this paper, we developed a semi-supervised neural network-based method called EXP2SL to accurately identify SL interactions from the L1000 gene expression profiles. Through a systematic evaluation on the SL datasets of three different cell lines, we demonstrated that our model achieved better performance than the baseline methods and verified the effectiveness of using the L1000 gene expression features and the semi-supervise training technique in SL prediction.

6.
Bioinformatics ; 36(9): 2872-2880, 2020 05 01.
Artigo em Inglês | MEDLINE | ID: mdl-31950974

RESUMO

MOTIVATION: Quantitative structure-activity relationship (QSAR) and drug-target interaction (DTI) prediction are both commonly used in drug discovery. Collaboration among pharmaceutical institutions can lead to better performance in both QSAR and DTI prediction. However, the drug-related data privacy and intellectual property issues have become a noticeable hindrance for inter-institutional collaboration in drug discovery. RESULTS: We have developed two novel algorithms under secure multiparty computation (MPC), including QSARMPC and DTIMPC, which enable pharmaceutical institutions to achieve high-quality collaboration to advance drug discovery without divulging private drug-related information. QSARMPC, a neural network model under MPC, displays good scalability and performance and is feasible for privacy-preserving collaboration on large-scale QSAR prediction. DTIMPC integrates drug-related heterogeneous network data and accurately predicts novel DTIs, while keeping the drug information confidential. Under several experimental settings that reflect the situations in real drug discovery scenarios, we have demonstrated that DTIMPC possesses significant performance improvement over the baseline methods, generates novel DTI predictions with supporting evidence from the literature and shows the feasible scalability to handle growing DTI data. All these results indicate that QSARMPC and DTIMPC can provide practically useful tools for advancing privacy-preserving drug discovery. AVAILABILITY AND IMPLEMENTATION: The source codes of QSARMPC and DTIMPC are available on the GitHub: https://github.com/rongma6/QSARMPC_DTIMPC.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Descoberta de Drogas , Privacidade , Algoritmos , Desenvolvimento de Medicamentos
7.
Bioinformatics ; 35(23): 4946-4954, 2019 12 01.
Artigo em Inglês | MEDLINE | ID: mdl-31120490

RESUMO

MOTIVATION: Prediction of peptide binding to the major histocompatibility complex (MHC) plays a vital role in the development of therapeutic vaccines for the treatment of cancer. Algorithms with improved correlations between predicted and actual binding affinities are needed to increase precision and reduce the number of false positive predictions. RESULTS: We present ACME (Attention-based Convolutional neural networks for MHC Epitope binding prediction), a new pan-specific algorithm to accurately predict the binding affinities between peptides and MHC class I molecules, even for those new alleles that are not seen in the training data. Extensive tests have demonstrated that ACME can significantly outperform other state-of-the-art prediction methods with an increase of the Pearson correlation coefficient between predicted and measured binding affinities by up to 23 percentage points. In addition, its ability to identify strong-binding peptides has been experimentally validated. Moreover, by integrating the convolutional neural network with attention mechanism, ACME is able to extract interpretable patterns that can provide useful and detailed insights into the binding preferences between peptides and their MHC partners. All these results have demonstrated that ACME can provide a powerful and practically useful tool for the studies of peptide-MHC class I interactions. AVAILABILITY AND IMPLEMENTATION: ACME is available as an open source software at https://github.com/HYsxe/ACME. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Redes Neurais de Computação , Algoritmos , Atenção , Sítios de Ligação , Biologia Computacional , Antígenos de Histocompatibilidade Classe I , Peptídeos , Ligação Proteica
8.
Genomics Proteomics Bioinformatics ; 17(5): 478-495, 2019 10.
Artigo em Inglês | MEDLINE | ID: mdl-32035227

RESUMO

Accurate identification of compound-protein interactions (CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development. Conventional similarity- or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled compound and protein data and often limit their usage to relatively small-scale datasets. In the present study, we propose DeepCPI, a novel general and scalable computational framework that combines effective feature embedding (a technique of representation learning) with powerful deep learning methods to accurately predict CPIs at a large scale. DeepCPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unlabeled data. Evaluations of the measured CPIs in large-scale databases, such as ChEMBL and BindingDB, as well as of the known drug-target interactions from DrugBank, demonstrated the superior predictive performance of DeepCPI. Furthermore, several interactions among small-molecule compounds and three G protein-coupled receptor targets (glucagon-like peptide-1 receptor, glucagon receptor, and vasoactive intestinal peptide receptor) predicted using DeepCPI were experimentally validated. The present study suggests that DeepCPI is a useful and powerful tool for drug discovery and repositioning. The source code of DeepCPI can be downloaded from https://github.com/FangpingWan/DeepCPI.


Assuntos
Aprendizado Profundo , Interface Usuário-Computador , Área Sob a Curva , Bases de Dados de Compostos Químicos , Preparações Farmacêuticas/química , Preparações Farmacêuticas/metabolismo , Proteínas/química , Proteínas/metabolismo , Curva ROC
9.
Bioinformatics ; 35(1): 104-111, 2019 01 01.
Artigo em Inglês | MEDLINE | ID: mdl-30561548

RESUMO

Motivation: Accurately predicting drug-target interactions (DTIs) in silico can guide the drug discovery process and thus facilitate drug development. Computational approaches for DTI prediction that adopt the systems biology perspective generally exploit the rationale that the properties of drugs and targets can be characterized by their functional roles in biological networks. Results: Inspired by recent advance of information passing and aggregation techniques that generalize the convolution neural networks to mine large-scale graph data and greatly improve the performance of many network-related prediction tasks, we develop a new nonlinear end-to-end learning model, called NeoDTI, that integrates diverse information from heterogeneous network data and automatically learns topology-preserving representations of drugs and targets to facilitate DTI prediction. The substantial prediction performance improvement over other state-of-the-art DTI prediction methods as well as several novel predicted DTIs with evidence supports from previous studies have demonstrated the superior predictive power of NeoDTI. In addition, NeoDTI is robust against a wide range of choices of hyperparameters and is ready to integrate more drug and target related information (e.g. compound-protein binding affinity data). All these results suggest that NeoDTI can offer a powerful and robust tool for drug development and drug repositioning. Availability and implementation: The source code and data used in NeoDTI are available at: https://github.com/FangpingWan/NeoDTI. Supplementary information: Supplementary data are available at Bioinformatics online.


Assuntos
Simulação por Computador , Desenvolvimento de Medicamentos/métodos , Software , Descoberta de Drogas , Reposicionamento de Medicamentos , Ligação Proteica
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...