AIMC Topic: Protein Domains

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Improving protein domain classification for third-generation sequencing reads using deep learning.

BMC genomics Apr 9, 2021
BACKGROUND: With the development of third-generation sequencing (TGS) technologies, people are able to obtain DNA sequences with lengths from 10s to 100s of kb. These long reads allow protein domain annotation without assembly, thus can produce impor...
Deep Learning Sequence Analysis, DNA Protein Domains High-Throughput Nucleotide Sequencing Humans Software Algorithms
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Characterizing the function of domain linkers in regulating the dynamics of multi-domain fusion proteins by microsecond molecular dynamics simulations and artificial intelligence.

Proteins Mar 27, 2021
Multi-domain proteins are not only formed through natural evolution but can also be generated by recombinant DNA technology. Because many fusion proteins can enhance the selectivity of cell targeting, these artificially produced molecules, called mul...
Recombinant Fusion Proteins Protein Engineering Pliability Mutagenesis, Insertional Amino Acids Benchmarking Molecular Dynamics Simulation Humans Gene Expression Protein Domains Artificial Intelligence Computational Biology
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DeepCys: Structure-based multiple cysteine function prediction method trained on deep neural network: Case study on domains of unknown functions belonging to COX2 domains.

Proteins Feb 22, 2021
Cysteine (Cys) is the most reactive amino acid participating in a wide range of biological functions. In-silico predictions complement the experiments to meet the need of functional characterization. Multiple Cys function prediction algorithm is scar...
Software Models, Molecular Protein Domains Amino Acid Sequence Sulfides Deep Learning Protein Processing, Post-Translational Structure-Activity Relationship Nitroso Compounds Sulfinic Acids Glutathione Disulfides Cysteine Sulfonic Acids Cations, Divalent Protein Structure, Secondary Electron Transport Complex IV
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SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction.

International journal of molecular sciences Jan 30, 2021
Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently show...
Binding Sites Caenorhabditis elegans Protein Structure, Secondary Protein Domains Ligands Datasets as Topic Humans Deep Learning Protein Binding Animals Drug Discovery
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DEEPSMP: A deep learning model for predicting the ectodomain shedding events of membrane proteins.

Journal of bioinformatics and computational biology Jun 23, 2020
Membrane proteins play essential roles in modern medicine. In recent studies, some membrane proteins involved in ectodomain shedding events have been reported as the potential drug targets and biomarkers of some serious diseases. However, there are f...
Models, Molecular Protein Domains Deep Learning Membrane Proteins Databases, Protein Area Under Curve Neural Networks, Computer Reproducibility of Results Sensitivity and Specificity
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GPS-PBS: A Deep Learning Framework to Predict Phosphorylation Sites that Specifically Interact with Phosphoprotein-Binding Domains.

Cells May 20, 2020
Protein phosphorylation is essential for regulating cellular activities by modifying substrates at specific residues, which frequently interact with proteins containing phosphoprotein-binding domains (PPBDs) to propagate the phosphorylation signaling...
Signal Transduction Animals Databases, Protein Phosphorylation Statistics as Topic Deep Learning Humans Protein Binding Protein Domains Binding Sites Algorithms Proteome Phosphoproteins
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QUATgo: Protein quaternary structural attributes predicted by two-stage machine learning approaches with heterogeneous feature encoding.

PloS one Apr 29, 2020
Many proteins exist in natures as oligomers with various quaternary structural attributes rather than as single chains. Predicting these attributes is an essential task in computational biology for the advancement of proteomics. However, the existing...
Proteins Databases, Protein Software Protein Domains Algorithms Viral Proteins Humans Sequence Analysis, Protein Animals Machine Learning Protein Structure, Quaternary Computational Biology Support Vector Machine
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Machine Learning to Identify Flexibility Signatures of Class A GPCR Inhibition.

Biomolecules Mar 14, 2020
We show that machine learning can pinpoint features distinguishing inactive from active states in proteins, in particular identifying key ligand binding site flexibility transitions in GPCRs that are triggered by biologically active ligands. Our anal...
Ligands Machine Learning Protein Binding Receptors, G-Protein-Coupled Protein Domains Humans
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Sequence-Based Prediction of Fuzzy Protein Interactions.

Journal of molecular biology Feb 27, 2020
It is becoming increasingly recognised that disordered proteins may be fuzzy, in that they can exhibit a wide variety of binding modes. In addition to the well-known process of folding upon binding (disorder-to-order transition), many examples are em...
Protein Domains Amino Acid Sequence Intrinsically Disordered Proteins Sequence Analysis, Protein Protein Interaction Domains and Motifs Protein Conformation Protein Folding Protein Binding Fuzzy Logic Databases, Protein Models, Molecular Humans Algorithms Sequence Homology
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Learning a functional grammar of protein domains using natural language word embedding techniques.

Proteins Nov 25, 2019
In this paper, using Word2vec, a widely-used natural language processing method, we demonstrate that protein domains may have a learnable implicit semantic "meaning" in the context of their functional contributions to the multi-domain proteins in whi...
Semantics Molecular Sequence Annotation Natural Language Processing Gene Ontology Humans Protein Domains Proteins Datasets as Topic Databases, Protein
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