AIMC Topic: Protein Conformation

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Docking-Informed Machine Learning for Kinome-wide Affinity Prediction.

Journal of chemical information and modeling Dec 10, 2024
Kinase inhibitors are an important class of anticancer drugs, with 80 inhibitors clinically approved and >100 in active clinical testing. Most bind competitively in the ATP-binding site, leading to challenges with selectivity for a specific kinase, r...
Protein Kinase Inhibitors Binding Sites Machine Learning Molecular Docking Simulation Protein Kinases Protein Conformation Protein Binding Humans
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Prediction of Inter-Residue Multiple Distances and Exploration of Protein Multiple Conformations by Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics Dec 10, 2024
AlphaFold2 has achieved a major breakthrough in end-to-end prediction for static protein structures. However, protein conformational change is considered to be a key factor in protein biological function. Inter-residue multiple distances prediction i...
Models, Molecular Protein Conformation Algorithms Deep Learning Proteins Databases, Protein Computational Biology
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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...
Protein Conformation Deep Learning Protein Binding Proteins Ligands Humans Algorithms Software Molecular Docking Simulation
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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...
Substrate Specificity Machine Learning Catalytic Domain Humans Pancreatic alpha-Amylases Protein Conformation Oligosaccharides
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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 Conformation Protein Interaction Mapping Databases, Protein Models, Molecular Protein Binding Proteins Neural Networks, Computer
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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...
Machine Learning Molecular Dynamics Simulation Humans Intrinsically Disordered Proteins 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 ...
Ligands Protein Conformation Neural Networks, Computer Protein Binding Algorithms Proteins Graph Neural Networks
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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...
Models, Molecular Intrinsically Disordered Proteins Protein Conformation Humans Deep Learning
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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...
Humans Neural Networks, Computer Algorithms SARS-CoV-2 Protein Binding Machine Learning COVID-19 Protein Conformation Proteins Ligands
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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...
Protein Conformation Static Electricity Molecular Dynamics Simulation Intrinsically Disordered Proteins Machine Learning Proteome
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