AIMC Topic: Protein Conformation

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SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction.

Nature methods Nov 27, 2024
Accurately predicting protein-ligand interactions is crucial for understanding cellular processes. We introduce SurfDock, a deep-learning method that addresses this challenge by integrating protein sequence, three-dimensional structural graphs and su...
Deep Learning Algorithms Ligands Protein Binding Molecular Docking Simulation Proteins Protein Conformation Humans Software
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Correlating enzymatic reactivity for different substrates using transferable data-driven collective variables.

Proceedings of the National Academy of Sciences of the United States of America Nov 26, 2024
Machine learning (ML) is transforming the investigation of complex biological processes. In enzymatic catalysis, one significant challenge is identifying the reactive conformations (RC) of the enzyme:substrate complex where the substrate assumes a pr...
Oligosaccharides Catalytic Domain Humans Machine Learning Pancreatic alpha-Amylases Protein Conformation Substrate Specificity
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ProAffinity-GNN: A Novel Approach to Structure-Based Protein-Protein Binding Affinity Prediction via a Curated Data Set and Graph Neural Networks.

Journal of chemical information and modeling Nov 18, 2024
Protein-protein interactions (PPIs) are crucial for understanding biological processes and disease mechanisms, contributing significantly to advances in protein engineering and drug discovery. The accurate determination of binding affinities, essenti...
Protein Interaction Mapping Models, Molecular Protein Conformation Proteins Databases, Protein Neural Networks, Computer Protein Binding
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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...
Humans Molecular Dynamics Simulation Intrinsically Disordered Proteins Machine Learning Protein Conformation
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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...
Protein Conformation Deep Learning Models, Molecular Intrinsically Disordered Proteins Humans
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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...
Machine Learning Protein Conformation COVID-19 SARS-CoV-2 Proteins Protein Binding Ligands Algorithms Humans Neural Networks, Computer
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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...
Molecular Dynamics Simulation Intrinsically Disordered Proteins Protein Conformation Machine Learning Proteome Static Electricity
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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 Conformation Nuclear Magnetic Resonance, Biomolecular Protein Folding Protein Unfolding Thermodynamics Molecular Dynamics Simulation Quantum Theory Proteins Density Functional Theory Artificial Intelligence Peptides Time Factors Machine Learning
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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...
Proteins Models, Molecular Amino Acid Sequence Deep Learning Protein Conformation Thioredoxins
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