AIMC Topic: Models, Molecular

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Researchers turn to deep learning to decode protein structures.

Proceedings of the National Academy of Sciences of the United States of America Mar 2, 2022
Deep Learning Computational Biology Research Personnel Protein Conformation Problem Solving Proteins Models, Molecular
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Using Steady-State Kinetics to Quantitate Substrate Selectivity and Specificity: A Case Study with Two Human Transaminases.

Molecules (Basel, Switzerland) Feb 18, 2022
We examined the ability of two human cytosolic transaminases, aspartate aminotransferase (GOT1) and alanine aminotransferase (GPT), to transform their preferred substrates whilst discriminating against similar metabolites. This offers an opportunity ...
Transferases (Other Substituted Phosphate Groups) Models, Molecular Binding Sites Transaminases Amino Acids Substrate Specificity Alkyl and Aryl Transferases Humans Animals Cattle Structure-Activity Relationship Kinetics Protein Conformation Enzyme Activation
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Machine Learning Approaches for Metalloproteins.

Molecules (Basel, Switzerland) Feb 14, 2022
Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts...
Binding Sites Metalloproteins Structure-Activity Relationship Models, Molecular Protein Stability Proteolysis Amino Acid Sequence Drug Design Protein Binding Machine Learning
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A backbone-centred energy function of neural networks for protein design.

Nature Feb 9, 2022
A protein backbone structure is designable if a substantial number of amino acid sequences exist that autonomously fold into it. It has been suggested that the designability of backbones is governed mainly by side chain-independent or side chain type...
Neural Networks, Computer Proteins Models, Molecular Amino Acid Sequence Protein Conformation
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Protein sequence design with a learned potential.

Nature communications Feb 8, 2022
The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network mo...
Computer Simulation Deep Learning Models, Molecular Crystallography, X-Ray Protein Domains Protein Engineering Amino Acid Sequence Protein Folding
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Analysis of Training and Seed Bias in Small Molecules Generated with a Conditional Graph-Based Variational Autoencoder─Insights for Practical AI-Driven Molecule Generation.

Journal of chemical information and modeling Feb 7, 2022
The application of deep learning to generative molecule design has shown early promise for accelerating lead series development. However, questions remain concerning how factors like training, data set, and seed bias impact the technology's utility t...
Artificial Intelligence Receptors, Dopamine D2 Models, Molecular
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Has DeepMind's AlphaFold solved the protein folding problem?

BioTechniques Feb 4, 2022
DeepMind released AlphaFold 2.0 in 2020, an artificial intelligence model to predict the structure of proteins, which could mean that proteins can be characterized without the need for tedious and costly lab analysis.
Protein Folding Models, Molecular Protein Conformation Artificial Intelligence Proteins
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ProALIGN: Directly Learning Alignments for Protein Structure Prediction via Exploiting Context-Specific Alignment Motifs.

Journal of computational biology : a journal of computational molecular cell biology Jan 21, 2022
Template-based modeling (TBM), including homology modeling and protein threading, is one of the most reliable techniques for protein structure prediction. It predicts protein structure by building an alignment between the query sequence under predict...
Amino Acid Motifs Sequence Analysis, Protein Amino Acid Sequence Protein Conformation Algorithms Sequence Alignment Proteins Deep Learning Neural Networks, Computer Models, Molecular Computational Biology Software
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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 Datasets as Topic Neural Networks, Computer Sequence Analysis, Protein Models, Molecular 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 Ã…, exhibit prominent quantum mechanical characters and are connected to a wide range of essential biomolecular processes. However, exact determination of the geometry an...
Hydrogen Bonding Peptides Proteins Models, Molecular Amino Acid Sequence Databases, Protein Machine Learning
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