AIMC Topic: Protein Conformation

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Lessons from Deep Learning Structural Prediction of Multistate Multidomain Proteins-The Case Study of Coiled-Coil NOD-like Receptors.

International journal of molecular sciences Jan 9, 2025
We test here the prediction capabilities of the new generation of deep learning predictors in the more challenging situation of multistate multidomain proteins by using as a case study a coiled-coil family of Nucleotide-binding Oligomerization Domain...
Deep Learning Computational Biology Models, Molecular Protein Conformation Protein Structure, Secondary Arabidopsis NLR Proteins Protein Domains
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CovCysPredictor: Predicting Selective Covalently Modifiable Cysteines Using Protein Structure and Interpretable Machine Learning.

Journal of chemical information and modeling Jan 8, 2025
Targeted covalent inhibition is a powerful therapeutic modality in the drug discoverer's toolbox. Recent advances in covalent drug discovery, in particular, targeting cysteines, have led to significant breakthroughs for traditionally challenging targ...
Databases, Protein Models, Molecular Drug Discovery Protein Conformation Proteins Humans Cysteine Ligands Machine Learning
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DPFunc: accurately predicting protein function via deep learning with domain-guided structure information.

Nature communications Jan 2, 2025
Computational methods for predicting protein function are of great significance in understanding biological mechanisms and treating complex diseases. However, existing computational approaches of protein function prediction lack interpretability, mak...
Deep Learning Computational Biology Proteins Humans Algorithms Models, Molecular Databases, Protein Structure-Activity Relationship Protein Domains Protein Conformation
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Evaluations of the Perturbation Resistance of the Deep-Learning-Based Ligand Conformation Optimization Algorithm.

Journal of chemical information and modeling Dec 26, 2024
In recent years, the deep learning (DL) technique has rapidly developed and shown great success in scoring the protein-ligand binding affinities. The protein-ligand conformation optimization based on DL-derived scoring functions holds broad applicati...
Deep Learning Protein Binding Ligands Protein Conformation Proteins Algorithms Molecular Docking Simulation
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Topology-based protein classification: A deep learning approach.

Biochemical and biophysical research communications Dec 24, 2024
Utilizing Artificial Intelligence (AI) in computational biology techniques could offer significant advantages in alleviating the growing workloads faced by structural biologists, especially with the emergence of big data. In this study, we employed D...
Databases, Protein Protein Conformation Deep Learning Computational Biology Proteins Neural Networks, Computer
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Study on SHP2 Conformational Transition and Structural Characterization of Its High-Potency Allosteric Inhibitors by Molecular Dynamics Simulations Combined with Machine Learning.

Molecules (Basel, Switzerland) Dec 24, 2024
The src-homology 2 domain-containing phosphatase 2 (SHP2) is a human cytoplasmic protein tyrosine phosphatase that plays a crucial role in cellular signal transduction. Aberrant activation and mutations of SHP2 are associated with tumor growth and im...
Enzyme Inhibitors Protein Conformation Allosteric Regulation Protein Binding Humans Protein Tyrosine Phosphatase, Non-Receptor Type 11 Molecular Dynamics Simulation Machine Learning
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An integrated machine learning approach delineates an entropic expansion mechanism for the binding of a small molecule to α-synuclein.

eLife Dec 18, 2024
The mis-folding and aggregation of intrinsically disordered proteins (IDPs) such as α-synuclein (αS) underlie the pathogenesis of various neurodegenerative disorders. However, targeting αS with small molecules faces challenges due to the lack of defi...
Protein Conformation Thermodynamics Protein Binding 1-(5-Isoquinolinesulfonyl)-2-Methylpiperazine Humans Entropy alpha-Synuclein Machine Learning Intrinsically Disordered Proteins
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S-PLM: Structure-Aware Protein Language Model via Contrastive Learning Between Sequence and Structure.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Dec 12, 2024
Proteins play an essential role in various biological and engineering processes. Large protein language models (PLMs) present excellent potential to reshape protein research by accelerating the determination of protein functions and the design of pro...
Proteins Protein Conformation Machine Learning Computational Biology
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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 Conformation Protein Kinase Inhibitors Binding Sites Humans Protein Binding Protein Kinases Machine Learning Molecular Docking Simulation
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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...
Computational Biology Databases, Protein Protein Conformation Algorithms Proteins Models, Molecular Deep Learning
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