AIMC Topic: Protein Conformation

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Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites.

Journal of chemical information and modeling Nov 1, 2024
In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. Traditionally, methods like X-ray crystallogra...
Allosteric Site Ligands Protein Conformation Deep Learning Databases, Protein Models, Molecular Proteins
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Using deep-learning predictions reveals a large number of register errors in PDB depositions.

IUCrJ Nov 1, 2024
The accuracy of the information in the Protein Data Bank (PDB) is of great importance for the myriad downstream applications that make use of protein structural information. Despite best efforts, the occasional introduction of errors is inevitable, e...
Protein Conformation Databases, Protein Deep Learning Models, Molecular Proteins
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Tackling APOE's structural challenges via in silico modeling in the era of neural networks: Can AlphaFold II help circumvent the problem of lacking full-length protein structure?

Archives of biochemistry and biophysics Oct 22, 2024
The APOE gene, encoding apolipoprotein E, is a predictor of longevity and age-related diseases. Despite numerous genetic studies, the data on molecular mechanisms by which apolipoprotein E affects the human phenotype remain incomplete due to the stru...
Humans Molecular Dynamics Simulation Protein Conformation Apolipoproteins E Neural Networks, Computer Molecular Docking Simulation
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Exploring Protein Conformational Changes Using a Large-Scale Biophysical Sampling Augmented Deep Learning Strategy.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Oct 10, 2024
Inspired by the success of deep learning in predicting static protein structures, researchers are now actively exploring other deep learning algorithms aimed at predicting the conformational changes of proteins. Currently, a major challenge in the de...
Molecular Dynamics Simulation Protein Conformation Humans Algorithms Deep Learning Proteins
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AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network.

Interdisciplinary sciences, computational life sciences Oct 5, 2024
The structural stability of proteins is an important topic in various fields such as biotechnology, pharmaceuticals, and enzymology. Specifically, understanding the structural stability of protein is crucial for protein design. Artificial design, whi...
Neural Networks, Computer Protein Stability Proteins Artificial Intelligence Protein Conformation Models, Molecular Algorithms Thermodynamics
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Deep learning for discriminating non-trivial conformational changes in molecular dynamics simulations of SARS-CoV-2 spike-ACE2.

Scientific reports Sep 30, 2024
Molecular dynamics (MD) simulations produce a substantial volume of high-dimensional data, and traditional methods for analyzing these data pose significant computational demands. Advances in MD simulation analysis combined with deep learning-based a...
Binding Sites Protein Binding Protein Conformation SARS-CoV-2 Spike Glycoprotein, Coronavirus Humans Angiotensin-Converting Enzyme 2 Deep Learning COVID-19 Protein Domains Mutation Molecular Dynamics Simulation
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Exploring the potential of structure-based deep learning approaches for T cell receptor design.

PLoS computational biology Sep 30, 2024
Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modeling and design field. These advancements have facilitated the creation of novel proteins, or the optim...
Humans Protein Conformation Protein Engineering Protein Binding Computational Biology Models, Molecular Deep Learning Receptors, Antigen, T-Cell
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Enhanced Sampling of Biomolecular Slow Conformational Transitions Using Adaptive Sampling and Machine Learning.

Journal of chemical theory and computation Sep 20, 2024
Biomolecular simulations often suffer from the "time scale problem", hindering the study of rare events occurring over extended time scales. Enhanced sampling techniques aim to alleviate this issue by accelerating conformational transitions, yet they...
Markov Chains Dipeptides Machine Learning Thermodynamics Protein Conformation Molecular Dynamics Simulation Alanine
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A Mode Evolution Metric to Extract Reaction Coordinates for Biomolecular Conformational Transitions.

Journal of chemical theory and computation Sep 17, 2024
The complex, multidimensional energy landscape of biomolecules makes the extraction of suitable, nonintuitive collective variables (CVs) that describe their conformational transitions challenging. At present, dimensionality reduction approaches and m...
Monte Carlo Method Thermodynamics Dipeptides Machine Learning Protein Conformation Molecular Dynamics Simulation
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Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots.

Journal of chemical information and modeling Sep 16, 2024
Identifying druggable binding sites on proteins is an important and challenging problem, particularly for cryptic, allosteric binding sites that may not be obvious from X-ray, cryo-EM, or predicted structures. The Site-Identification by Ligand Compet...
Binding Sites Ligands Protein Conformation Proteins Molecular Dynamics Simulation Drug Discovery Protein Binding Machine Learning
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