AIMC Topic: Protein Conformation

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Deep learning enables rapid identification of potent DDR1 kinase inhibitors.

Nature biotechnology Sep 2, 2019
We have developed a deep generative model, generative tensorial reinforcement learning (GENTRL), for de novo small-molecule design. GENTRL optimizes synthetic feasibility, novelty, and biological activity. We used GENTRL to discover potent inhibitors...
Deep Learning Mice Rats Models, Molecular Humans Animals Microsomes, Liver Enzyme Inhibitors Protein Conformation Discoidin Domain Receptor 1 Molecular Structure Dogs Drug Evaluation, Preclinical
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Getting to Know Your Neighbor: Protein Structure Prediction Comes of Age with Contextual Machine Learning.

Journal of computational biology : a journal of computational molecular cell biology Aug 30, 2019
The folding of a protein structure is a process governed by both local and nonlocal interactions. While incorporating local dependencies into a machine learning algorithm for protein structure prediction is simple and has been exploited for some time...
Protein Conformation Neural Networks, Computer Aging Proteins Machine Learning Algorithms
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Boosting phosphorylation site prediction with sequence feature-based machine learning.

Proteins Aug 22, 2019
Protein phosphorylation is one of the essential posttranslation modifications playing a vital role in the regulation of many fundamental cellular processes. We propose a LightGBM-based computational approach that uses evolutionary, geometric, sequenc...
Humans Machine Learning Computational Biology Protein Processing, Post-Translational Proteins Databases, Protein Algorithms Reproducibility of Results Animals Sequence Analysis, Protein Threonine Binding Sites Species Specificity Phosphorylation Serine Tyrosine Models, Molecular Protein Conformation
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Sequence assignment for low-resolution modelling of protein crystal structures.

Acta crystallographica. Section D, Structural biology Jul 31, 2019
The performance of automated model building in crystal structure determination usually decreases with the resolution of the experimental data, and may result in fragmented models and incorrect side-chain assignment. Presented here are new methods for...
Datasets as Topic Crystallography, X-Ray Protein Conformation Proteins Databases, Protein Amino Acid Sequence Algorithms Molecular Docking Simulation Machine Learning Software
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Enabling full-length evolutionary profiles based deep convolutional neural network for predicting DNA-binding proteins from sequence.

Proteins Jul 8, 2019
Sequence based DNA-binding protein (DBP) prediction is a widely studied biological problem. Sliding windows on position specific substitution matrices (PSSMs) rows predict DNA-binding residues well on known DBPs but the same models cannot be applied ...
Arabidopsis Proteins Arabidopsis Models, Biological Protein Conformation Binding Sites Humans Neural Networks, Computer Animals Amino Acids DNA-Binding Proteins Mice DNA
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A Self-Consistent Sonification Method to Translate Amino Acid Sequences into Musical Compositions and Application in Protein Design Using Artificial Intelligence.

ACS nano Jun 26, 2019
We report a self-consistent method to translate amino acid sequences into audible sound, use the representation in the musical space to train a neural network, and then apply it to generate protein designs using artificial intelligence (AI). The soni...
Protein Conformation Protein Folding Proteins Mutation Amino Acid Sequence Humans Models, Molecular Artificial Intelligence Sound
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Coupling dynamics and evolutionary information with structure to identify protein regulatory and functional binding sites.

Proteins Jun 22, 2019
Binding sites in proteins can be either specifically functional binding sites (active sites) that bind specific substrates with high affinity or regulatory binding sites (allosteric sites), that modulate the activity of functional binding sites throu...
Artificial Intelligence Machine Learning Humans Binding Sites Allosteric Site Proteins Algorithms Allosteric Regulation Molecular Dynamics Simulation Protein Binding Protein Conformation Evolution, Molecular Databases, Protein
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Prediction of Sub-Monomer A2 Domain Dynamics of the von Willebrand Factor by Machine Learning Algorithm and Coarse-Grained Molecular Dynamics Simulation.

Scientific reports Jun 21, 2019
We develop a machine learning tool useful for predicting the instantaneous dynamical state of sub-monomer features within long linear polymer chains, as well as extracting the dominant macromolecular motions associated with sub-monomer behaviors of i...
Humans Protein Conformation Machine Learning Algorithms Molecular Dynamics Simulation von Willebrand Factor
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Analysis of the Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach.

International journal of molecular sciences Jun 18, 2019
Breast cancer is a leading cancer type and one of the major health issues faced by women around the world. Some of its major risk factors include body mass index, hormone replacement therapy, family history and germline mutations. Of these risk facto...
Tumor Suppressor Protein p53 Protein Conformation Estrogen Receptor alpha Protein Interaction Domains and Motifs Computational Biology Structure-Activity Relationship Humans Neural Networks, Computer Mutation Molecular Docking Simulation Machine Learning Molecular Dynamics Simulation Protein Binding Polymorphism, Single Nucleotide
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Discrimination power of knowledge-based potential dictated by the dominant energies in native protein structures.

Amino acids May 16, 2019
Extracting a well-designed energy function is important for protein structure evaluation. Knowledge-based potential functions are one type of the energy functions which can be obtained from known protein structures. The pairwise potential between ato...
Hydrophobic and Hydrophilic Interactions Proteins Amino Acids Protein Conformation Models, Molecular Amino Acid Sequence Entropy Molecular Structure Knowledge Bases
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