AIMC Topic: Proteins

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Recent progress on the prospective application of machine learning to structure-based virtual screening.

Current opinion in chemical biology May 27, 2021
As more bioactivity and protein structure data become available, scoring functions (SFs) using machine learning (ML) to leverage these data sets continue to gain further accuracy and broader applicability. Advances in our understanding of the optimal...
Proteins Ligands Machine Learning Molecular Docking Simulation
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RNA Backbone Torsion and Pseudotorsion Angle Prediction Using Dilated Convolutional Neural Networks.

Journal of chemical information and modeling May 26, 2021
RNA three-dimensional structure prediction has been relied on using a predicted or experimentally determined secondary structure as a restraint to reduce the conformational sampling space. However, the secondary-structure restraints are limited to pa...
RNA Protein Structure, Secondary Neural Networks, Computer Proteins
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Structure-based protein function prediction using graph convolutional networks.

Nature communications May 26, 2021
The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting pro...
Deep Learning Models, Biological Computational Biology Amino Acid Sequence Datasets as Topic Structure-Activity Relationship Protein Structure, Tertiary Proteins Models, Molecular Databases, Protein
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Deep Learning-Based Advances in Protein Structure Prediction.

International journal of molecular sciences May 24, 2021
Obtaining an accurate description of protein structure is a fundamental step toward understanding the underpinning of biology. Although recent advances in experimental approaches have greatly enhanced our capabilities to experimentally determine prot...
Cryoelectron Microscopy Computational Biology Neural Networks, Computer Amino Acid Sequence Sequence Analysis, Protein Proteins Databases, Protein Protein Conformation Models, Molecular Software Algorithms Deep Learning
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Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins.

Journal of translational medicine May 24, 2021
BACKGROUND: Post-translational modification (PTM) is a biological process that alters proteins and is therefore involved in the regulation of various cellular activities and pathogenesis. Protein phosphorylation is an essential process and one of the...
Amino Acid Sequence Machine Learning Computational Biology Proteins Phosphorylation
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Moonlighting protein prediction using physico-chemical and evolutional properties via machine learning methods.

BMC bioinformatics May 24, 2021
BACKGROUND: Moonlighting proteins (MPs) are a subclass of multifunctional proteins in which more than one independent or usually distinct function occurs in a single polypeptide chain. Identification of unknown cellular processes, understanding novel...
Support Vector Machine Proteins Machine Learning Cell Physiological Phenomena
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ET-score: Improving Protein-ligand Binding Affinity Prediction Based on Distance-weighted Interatomic Contact Features Using Extremely Randomized Trees Algorithm.

Molecular informatics May 21, 2021
The molecular docking simulation is a key computational tool in modern drug discovery research that its predictive performance strongly depends on the employed scoring functions. Many recent studies have shown that the application of machine learning...
Molecular Docking Simulation Protein Binding Machine Learning Proteins Ligands
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Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets.

Nature communications May 21, 2021
Understanding the structural determinants of a protein's biochemical properties, such as activity and stability, is a major challenge in biology and medicine. Comparing computer simulations of protein variants with different biochemical properties is...
Myosins Computer Simulation Molecular Dynamics Simulation Deep Learning Protein Conformation Computational Biology Proteins Software Algorithms
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Machine Learning of Analytical Electron Density in Large Molecules Through Message-Passing.

Journal of chemical information and modeling May 19, 2021
Machine learning milestones in computational chemistry are overshadowed by their unaccountability and the overwhelming zoo of tools for each specific task. A promising path to tackle these problems is using machine learning to reproduce physical magn...
Neural Networks, Computer Electrons Physical Phenomena Machine Learning Proteins
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Deep learning neural network tools for proteomics.

Cell reports methods May 17, 2021
Mass-spectrometry-based proteomics enables quantitative analysis of thousands of human proteins. However, experimental and computational challenges restrict progress in the field. This review summarizes the recent flurry of machine-learning strategie...
Peptides Proteins Humans Proteomics Neural Networks, Computer Tandem Mass Spectrometry
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