AIMC Topic: Proteins

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Enhancing protein inter-residue real distance prediction by scrutinising deep learning models.

Scientific reports Jan 17, 2022
Protein structure prediction (PSP) has achieved significant progress lately via prediction of inter-residue distances using deep learning models and exploitation of the predictions during conformational search. In this context, prediction of large in...
Deep Learning Proteins Models, Molecular Neural Networks, Computer Sequence Analysis, Protein Amino Acid Sequence Datasets as Topic
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GCRNN: graph convolutional recurrent neural network for compound-protein interaction prediction.

BMC bioinformatics Jan 11, 2022
BACKGROUND: Compound-protein interaction prediction is necessary to investigate health regulatory functions and promotes drug discovery. Machine learning is becoming increasingly important in bioinformatics for applications such as analyzing protein-...
Neural Networks, Computer Computational Biology Proteins Machine Learning Amino Acid Sequence
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Effective prediction of short hydrogen bonds in proteins via machine learning method.

Scientific reports Jan 10, 2022
Short hydrogen bonds (SHBs), whose donor and acceptor heteroatoms lie within 2.7 Å, exhibit prominent quantum mechanical characters and are connected to a wide range of essential biomolecular processes. However, exact determination of the geometry an...
Models, Molecular Amino Acid Sequence Hydrogen Bonding Peptides Proteins Machine Learning Databases, Protein
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Harnessing protein folding neural networks for peptide-protein docking.

Nature communications Jan 10, 2022
Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been develop...
Protein Conformation, beta-Strand Protein Interaction Domains and Motifs Protein Conformation, alpha-Helical Protein Binding Protein Interaction Mapping Proteins Protein Folding Models, Molecular Peptides Binding Sites Amino Acid Sequence Molecular Docking Simulation Neural Networks, Computer Software
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DFpin: Deep learning-based protein-binding site prediction with feature-based non-redundancy from RNA level.

Computers in biology and medicine Jan 7, 2022
The interaction between proteins and RNA is closely related to various human diseases. Computer-aided drug design can be facilitated by detecting the RNA sites that bind proteins. However, due to the aggregation of binding sites in RNA sequences, hig...
Binding Sites Proteins RNA Protein Binding Computational Biology Humans Deep Learning
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Computational identification of 4-carboxyglutamate sites to supplement physiological studies using deep learning.

Scientific reports Jan 7, 2022
In biological systems, Glutamic acid is a crucial amino acid which is used in protein biosynthesis. Carboxylation of glutamic acid is a significant post-translational modification which plays important role in blood coagulation by activating prothrom...
Reproducibility of Results Deep Learning Proteins Structure-Activity Relationship Models, Molecular Computational Biology Protein Processing, Post-Translational Glutamic Acid Amino Acid Sequence Protein Conformation
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Precipitate-Supported Thermal Proteome Profiling Coupled with Deep Learning for Comprehensive Screening of Drug Target Proteins.

ACS chemical biology Jan 6, 2022
Although thermal proteome profiling (TPP) acts as a popular modification-free approach for drug target deconvolution, some key problems are still limiting screening sensitivity. In the prevailing TPP workflow, only the soluble fractions are analyzed ...
Drug Delivery Systems Deep Learning Proteome Proteins Humans Proteomics K562 Cells Algorithms Chemical Precipitation Hot Temperature
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Secondary structure specific simpler prediction models for protein backbone angles.

BMC bioinformatics Jan 4, 2022
MOTIVATION: Protein backbone angle prediction has achieved significant accuracy improvement with the development of deep learning methods. Usually the same deep learning model is used in making prediction for all residues regardless of the categories...
Neural Networks, Computer Machine Learning Proteins Protein Structure, Secondary
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PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks.

Journal of chemical information and modeling Jan 3, 2022
Deep learning has been successfully applied to structure-based protein-ligand affinity prediction, yet the black box nature of these models raises some questions. In a previous study, we presented K, a convolutional neural network that predicted the ...
Neural Networks, Computer Proteins Ligands
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Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks.

PloS one Dec 31, 2021
Characterization of protein complexes, i.e. sets of proteins assembling into a single larger physical entity, is important, as such assemblies play many essential roles in cells such as gene regulation. From networks of protein-protein interactions, ...
Protein Binding Protein Interaction Mapping SARS-CoV-2 Humans Supervised Machine Learning Computational Biology COVID-19 Proteins Protein Interaction Maps Viral Proteins
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