AIMC Topic: Protein Conformation

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Emerging Frontiers in Conformational Exploration of Disordered Proteins: Integrating Autoencoder and Molecular Simulations.

ACS chemical neuroscience Nov 18, 2024
Intrinsically disordered proteins (IDPs) are closely associated with a number of neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease. Due to the highly dynamic nature of IDPs, their structural determination and conformatio...
Protein Conformation Humans Machine Learning Molecular Dynamics Simulation Intrinsically Disordered Proteins
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Predicting Protein-Ligand Binding Affinity Using Fusion Model of Spatial-Temporal Graph Neural Network and 3D Structure-Based Complex Graph.

Interdisciplinary sciences, computational life sciences Nov 14, 2024
The investigation of molecular interactions between ligands and their target molecules is becoming more significant as protein structure data continues to develop. In this study, we introduce PLA-STGCNnet, a deep fusion spatial-temporal graph neural ...
Neural Networks, Computer Algorithms Ligands Protein Conformation Graph Neural Networks Protein Binding Proteins
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Deep learning for intrinsically disordered proteins: From improved predictions to deciphering conformational ensembles.

Current opinion in structural biology Nov 12, 2024
Intrinsically disordered proteins (IDPs) lack a stable three-dimensional structure under physiological conditions, challenging traditional structure-based prediction methods. This review explores how modern deep learning approaches, which have revolu...
Intrinsically Disordered Proteins Humans Protein Conformation Deep Learning Models, Molecular
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Interaction-Based Inductive Bias in Graph Neural Networks: Enhancing Protein-Ligand Binding Affinity Predictions From 3D Structures.

IEEE transactions on pattern analysis and machine intelligence Nov 6, 2024
Inductive bias in machine learning (ML) is the set of assumptions describing how a model makes predictions. Different ML-based methods for protein-ligand binding affinity (PLA) prediction have different inductive biases, leading to different levels o...
Ligands Protein Conformation COVID-19 Algorithms Protein Binding SARS-CoV-2 Neural Networks, Computer Humans Proteins Machine Learning
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Physics-Based Machine Learning Trains Hamiltonians and Decodes the Sequence-Conformation Relation in the Disordered Proteome.

Journal of chemical theory and computation Nov 6, 2024
Intrinsically disordered proteins and regions (IDPs) are involved in vital biological processes. To understand the IDP function, often controlled by conformation, we need to find the link between sequence and conformation. We decode this link by inte...
Proteome Molecular Dynamics Simulation Machine Learning Static Electricity Intrinsically Disordered Proteins Protein Conformation
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Ab initio characterization of protein molecular dynamics with AIBMD.

Nature Nov 6, 2024
Biomolecular dynamics simulation is a fundamental technology for life sciences research, and its usefulness depends on its accuracy and efficiency. Classical molecular dynamics simulation is fast but lacks chemical accuracy. Quantum chemistry methods...
Protein Folding Artificial Intelligence Protein Unfolding Machine Learning Proteins Nuclear Magnetic Resonance, Biomolecular Peptides Density Functional Theory Quantum Theory Time Factors Thermodynamics Molecular Dynamics Simulation Protein Conformation
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All-Atom Protein Sequence Design Based on Geometric Deep Learning.

Angewandte Chemie (International ed. in English) Nov 4, 2024
Designing sequences for specific protein backbones is a key step in creating new functional proteins. Here, we introduce GeoSeqBuilder, a deep learning framework that integrates protein sequence generation with side chain conformation prediction to p...
Models, Molecular Amino Acid Sequence Protein Conformation Deep Learning Proteins Thioredoxins
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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 Proteins Databases, Protein Ligands Protein Conformation Models, Molecular Deep Learning
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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...
Models, Molecular Protein Conformation Proteins Databases, Protein Deep Learning
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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...
Molecular Dynamics Simulation Protein Conformation Apolipoproteins E Neural Networks, Computer Molecular Docking Simulation Humans
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