AIMC Topic: Proteins

Clear Filters Showing 931 to 940 of 1969 articles

PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features.

International journal of molecular sciences Mar 8, 2021
Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyrosine is a prerequisite to understanding the molecular functio...
Machine Learning Computational Biology Proteins Tyrosine Sequence Analysis, Protein Support Vector Machine Protein Processing, Post-Translational
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Advances in machine learning for directed evolution.

Current opinion in structural biology Feb 26, 2021
Machine learning (ML) can expedite directed evolution by allowing researchers to move expensive experimental screens in silico. Gathering sequence-function data for training ML models, however, can still be costly. In contrast, raw protein sequence d...
Machine Learning Computer Simulation Amino Acid Sequence Proteins
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Androgen Receptor Binding Category Prediction with Deep Neural Networks and Structure-, Ligand-, and Statistically Based Features.

Molecules (Basel, Switzerland) Feb 26, 2021
Substances that can modify the androgen receptor pathway in humans and animals are entering the environment and food chain with the proven ability to disrupt hormonal systems and leading to toxicity and adverse effects on reproduction, brain developm...
Ligands Receptors, Androgen Rats Proteins Neural Networks, Computer Software Humans Deep Learning Protein Binding Logistic Models Algorithms Animals
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Improved protein structure refinement guided by deep learning based accuracy estimation.

Nature communications Feb 26, 2021
We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy and residue-residue distance signed error in protein models and uses these predictions to guide Rosetta protein structure refinement. The network uses 3D convolutio...
Protein Conformation Models, Biological Caspases Algorithms Deep Learning Proteins Models, Molecular Computational Biology Software
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Generating functional protein variants with variational autoencoders.

PLoS computational biology Feb 26, 2021
The vast expansion of protein sequence databases provides an opportunity for new protein design approaches which seek to learn the sequence-function relationship directly from natural sequence variation. Deep generative models trained on protein sequ...
Proteins Solubility Computational Biology Escherichia coli Oxidoreductases Photorhabdus Machine Learning Algorithms Reproducibility of Results Recombinant Proteins Computer Simulation Neural Networks, Computer
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Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning.

Journal of chemical information and modeling Feb 25, 2021
The development of efficient models for predicting specific properties through machine learning is of great importance for the innovation of chemistry and material science. However, predicting global electronic structure properties like Frontier mole...
Neural Networks, Computer Machine Learning Proteins Attention Thermodynamics
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PFP-WGAN: Protein function prediction by discovering Gene Ontology term correlations with generative adversarial networks.

PloS one Feb 25, 2021
Understanding the functionality of proteins has emerged as a critical problem in recent years due to significant roles of these macro-molecules in biological mechanisms. However, in-laboratory techniques for protein function prediction are not as eff...
Gene Ontology Sequence Analysis, Protein Computational Biology Proteins Humans Gene Regulatory Networks
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Deep learning techniques have significantly impacted protein structure prediction and protein design.

Current opinion in structural biology Feb 24, 2021
Protein structure prediction and design can be regarded as two inverse processes governed by the same folding principle. Although progress remained stagnant over the past two decades, the recent application of deep neural networks to spatial constrai...
Neural Networks, Computer Protein Folding Deep Learning Proteins
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Generative Adversarial Learning of Protein Tertiary Structures.

Molecules (Basel, Switzerland) Feb 24, 2021
Protein molecules are inherently dynamic and modulate their interactions with different molecular partners by accessing different tertiary structures under physiological conditions. Elucidating such structures remains challenging. Current momentum in...
Protein Structure, Tertiary Neural Networks, Computer Proteins
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Comprehensive Study on Enhancing Low-Quality Position-Specific Scoring Matrix with Deep Learning for Accurate Protein Structure Property Prediction: Using Bagging Multiple Sequence Alignment Learning.

Journal of computational biology : a journal of computational molecular cell biology Feb 22, 2021
Sequence Alignment Position-Specific Scoring Matrices Deep Learning Computational Biology Protein Conformation Protein Structure, Secondary Proteins Neural Networks, Computer Algorithms
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