AIMC Topic: Protein Conformation

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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 ...
Alkyl and Aryl Transferases Amino Acids Kinetics Protein Conformation Transferases (Other Substituted Phosphate Groups) Transaminases Enzyme Activation Binding Sites Humans Substrate Specificity Cattle Animals Structure-Activity Relationship Models, Molecular
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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...
Amino Acid Sequence Protein Conformation Neural Networks, Computer Proteins Models, Molecular
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Deep learning methods for 3D structural proteome and interactome modeling.

Current opinion in structural biology Feb 6, 2022
Bolstered by recent methodological and hardware advances, deep learning has increasingly been applied to biological problems and structural proteomics. Such approaches have achieved remarkable improvements over traditional machine learning methods in...
Computational Biology Proteome Protein Conformation Deep Learning Protein Folding
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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.
Models, Molecular Protein Conformation Protein Folding 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 Sequence Protein Conformation Sequence Alignment Amino Acid Motifs Sequence Analysis, Protein Computational Biology Proteins Models, Molecular Algorithms Deep Learning Neural Networks, Computer Software
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Novel Big Data-Driven Machine Learning Models for Drug Discovery Application.

Molecules (Basel, Switzerland) Jan 18, 2022
Most contemporary drug discovery projects start with a 'hit discovery' phase where small chemicals are identified that have the capacity to interact, in a chemical sense, with a protein target involved in a given disease. To assist and accelerate thi...
Humans Machine Learning Ligands Protein Conformation Receptor, Adenosine A2A Receptors, Opioid, kappa Drug Discovery Algorithms Computer Simulation Big Data Protein Binding
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Ensemble learning from ensemble docking: revisiting the optimum ensemble size problem.

Scientific reports Jan 10, 2022
Despite considerable advances obtained by applying machine learning approaches in protein-ligand affinity predictions, the incorporation of receptor flexibility has remained an important bottleneck. While ensemble docking has been used widely as a so...
Support Vector Machine Structure-Activity Relationship Ligands Molecular Docking Simulation Protein Binding Cyclin-Dependent Kinase 2 Crystallography, X-Ray Protein Conformation Binding Sites Machine 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 Computational Biology Amino Acid Sequence Structure-Activity Relationship Protein Processing, Post-Translational Protein Conformation Models, Molecular Glutamic Acid
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Protein structures for all.

Science (New York, N.Y.) Dec 16, 2021
AI-powered predictions reveal the shapes of proteins by the thousands.
Artificial Intelligence Software Proteins Protein Conformation Databases, Protein Protein Folding Models, Molecular
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Protein embeddings and deep learning predict binding residues for various ligand classes.

Scientific reports Dec 13, 2021
One important aspect of protein function is the binding of proteins to ligands, including small molecules, metal ions, and macromolecules such as DNA or RNA. Despite decades of experimental progress many binding sites remain obscure. Here, we propose...
Nucleic Acids Protein Conformation Binding Sites Sequence Analysis, Protein Metals Software Protein Binding Molecular Docking Simulation Ligands Deep Learning
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