AIMC Topic: Protein Conformation

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Druggability Assessment in TRAPP Using Machine Learning Approaches.

Journal of chemical information and modeling Mar 11, 2020
Accurate protein druggability predictions are important for the selection of drug targets in the early stages of drug discovery. Because of the flexible nature of proteins, the druggability of a binding pocket may vary due to conformational changes. ...
Proteins Protein Conformation Binding Sites Machine Learning Protein Binding
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Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps.

Scientific reports Mar 9, 2020
Cryo-electron microscopy (cryo-EM) has become a leading technology for determining protein structures. Recent advances in this field have allowed for atomic resolution. However, predicting the backbone trace of a protein has remained a challenge on a...
Proteins Models, Molecular Amino Acid Sequence Protein Conformation Cryoelectron Microscopy Sequence Homology Software Algorithms Deep Learning Humans Neural Networks, Computer
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ProNA2020 predicts protein-DNA, protein-RNA, and protein-protein binding proteins and residues from sequence.

Journal of molecular biology Mar 4, 2020
The intricate details of how proteins bind to proteins, DNA, and RNA are crucial for the understanding of almost all biological processes. Disease-causing sequence variants often affect binding residues. Here, we described a new, comprehensive system...
Binding Sites Eukaryota Sequence Analysis, Protein RNA Protein Conformation Nucleic Acid Conformation Prokaryotic Cells Software Animals Protein Binding Computational Biology Proteins DNA Humans Neural Networks, Computer Machine Learning
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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...
Fuzzy Logic Databases, Protein Models, Molecular Humans Algorithms Protein Binding Protein Conformation Protein Folding Sequence Analysis, Protein Protein Domains Amino Acid Sequence Intrinsically Disordered Proteins Protein Interaction Domains and Motifs Sequence Homology
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Prediction of peptide binding to MHC using machine learning with sequence and structure-based feature sets.

Biochimica et biophysica acta. General subjects Jan 16, 2020
Selecting peptides that bind strongly to the major histocompatibility complex (MHC) for inclusion in a vaccine has therapeutic potential for infections and tumors. Machine learning models trained on sequence data exist for peptide:MHC (p:MHC) binding...
Algorithms Animals Peptides Protein Binding Mice Alleles Machine Learning Histocompatibility Antigens Class I Amino Acid Sequence Protein Conformation
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Improved protein structure prediction using potentials from deep learning.

Nature Jan 15, 2020
Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence. This problem is of fundamental importance as the structure of a protein largely determines its function; however, protein str...
Models, Molecular Amino Acid Sequence Caspases Datasets as Topic Protein Conformation Protein Folding Software Deep Learning Proteins
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Automatic identification of crossovers in cryo-EM images of murine amyloid protein A fibrils with machine learning.

Journal of microscopy Dec 29, 2019
Detecting crossovers in cryo-electron microscopy images of protein fibrils is an important step towards determining the morphological composition of a sample. Currently, the crossover locations are picked by hand, which introduces errors and is a tim...
Reproducibility of Results Animals Image Processing, Computer-Assisted Neural Networks, Computer Machine Learning Cryoelectron Microscopy Mice Amyloid Protein Conformation
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Molecular Insights from Conformational Ensembles via Machine Learning.

Biophysical journal Dec 21, 2019
Biomolecular simulations are intrinsically high dimensional and generate noisy data sets of ever-increasing size. Extracting important features from the data is crucial for understanding the biophysical properties of molecular processes, but remains ...
Humans Machine Learning Proteins Protein Conformation
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Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning.

Nature methods Dec 9, 2019
Predicting interactions between proteins and other biomolecules solely based on structure remains a challenge in biology. A high-level representation of protein structure, the molecular surface, displays patterns of chemical and geometric features th...
Surface Properties Protein Conformation Computational Biology Proteins Algorithms Deep Learning
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Finding Reactive Configurations: A Machine Learning Approach for Estimating Energy Barriers Applied to Sirtuin 5.

Journal of chemical theory and computation Nov 25, 2019
Sirtuin 5 is a class III histone deacetylase that, unlike its classification, mainly catalyzes desuccinylation and demanoylation reactions. It is an interesting drug target that we use here to test new ideas for calculating reaction pathways of large...
Thermodynamics Sirtuins Molecular Dynamics Simulation Quantum Theory Protein Conformation Humans Machine Learning
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