AIMC Topic: Binding Sites

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Predicting TF-DNA Binding Motifs from ChIP-seq Datasets Using the Bag-Based Classifier Combined With a Multi-Fold Learning Scheme.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
The rapid development of high-throughput sequencing technology provides unique opportunities for studying of transcription factor binding sites, but also brings new computational challenges. Recently, a series of discriminative motif discovery (DMD) ...
Humans Machine Learning DNA Binding Sites Chromatin Immunoprecipitation Sequencing Algorithms Computational Biology Transcription Factors
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A deep-learning framework for multi-level peptide-protein interaction prediction.

Nature communications Sep 15, 2021
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...
Algorithms Proteins Deep Learning Models, Molecular Protein Binding Protein Domains Binding Sites Computational Biology Reproducibility of Results Peptides
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Evaluation of deep learning approaches for modeling transcription factor sequence specificity.

Genomics Sep 14, 2021
As a key component of gene regulation, transcription factors (TFs) play an important role in a number of biological processes. To fully understand the underlying mechanism of TF-mediated gene regulation, it is therefore critical to accurately identif...
Humans DNA Binding Sites Neural Networks, Computer Deep Learning Transcription Factors
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rBPDL:Predicting RNA-Binding Proteins Using Deep Learning.

IEEE journal of biomedical and health informatics Sep 3, 2021
RNA-binding protein (RBP) is a powerful and wide-ranging regulator that plays an important role in cell development, differentiation, metabolism, health and disease. The prediction of RBPs provides valuable guidance for biologists. Although experimen...
Binding Sites Protein Binding RNA-Binding Proteins Neural Networks, Computer Deep Learning
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Atomic Structure-Free Representation of Active Motifs for Expedited Catalyst Discovery.

Journal of chemical information and modeling Aug 23, 2021
To discover new catalysts using density functional theory (DFT) calculations, binding energies of reaction intermediates are considered as descriptors to predict catalytic activities. Recently, machine learning methods have been developed to reduce t...
Binding Sites Machine Learning Catalysis
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Optimization of C-to-G base editors with sequence context preference predictable by machine learning methods.

Nature communications Aug 12, 2021
Efficient and precise base editors (BEs) for C-to-G transversion are highly desirable. However, the sequence context affecting editing outcome largely remains unclear. Here we report engineered C-to-G BEs of high efficiency and fidelity, with the seq...
Codon Gene Library Caenorhabditis elegans Cytidine Deaminase Base Sequence Gene Editing Binding Sites HEK293 Cells Uracil-DNA Glycosidase Female Reproducibility of Results Animals Mice Escherichia coli Humans CRISPR-Cas Systems Machine Learning
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DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks.

Journal of chemical information and modeling Aug 10, 2021
A structure-based drug design pipeline involves the development of potential drug molecules or ligands that form stable complexes with a given receptor at its binding site. A prerequisite to this is finding druggable and functionally relevant binding...
Ligands Binding Sites Algorithms Proteins Neural Networks, Computer Software
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MCN-CPI: Multiscale Convolutional Network for Compound-Protein Interaction Prediction.

Biomolecules Jul 29, 2021
In the process of drug discovery, identifying the interaction between the protein and the novel compound plays an important role. With the development of technology, deep learning methods have shown excellent performance in various situations. Howeve...
Protein Conformation, beta-Strand Proteins Drugs, Investigational Kinetics High-Throughput Screening Assays Protein Interaction Domains and Motifs Datasets as Topic Protein Conformation, alpha-Helical Binding Sites Humans Drug Discovery Molecular Docking Simulation Deep Learning Protein Binding
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Deep learning-based prediction of TFBSs in plants.

Trends in plant science Jul 24, 2021
Deep Learning Binding Sites Protein Binding Computational Biology Transcription Factors
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Logistic matrix factorisation and generative adversarial neural network-based method for predicting drug-target interactions.

Molecular diversity Jul 23, 2021
Identifying drug-target protein association pairs is a prerequisite and a crucial task in drug discovery and development. Numerous computational models, based on different assumptions and algorithms, have been proposed as an alternative to the labori...
Protein Binding Models, Theoretical Humans Drug Development Neural Networks, Computer Biomarkers Models, Molecular Algorithms Ligands Drug Discovery Databases, Factual Binding Sites Deep Learning
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