AIMC Topic: Protein Conformation

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Structure-Based Approaches for Protein-Protein Interaction Prediction Using Machine Learning and Deep Learning.

Biomolecules Jan 17, 2025
Protein-Protein Interaction (PPI) prediction plays a pivotal role in understanding cellular processes and uncovering molecular mechanisms underlying health and disease. Structure-based PPI prediction has emerged as a robust alternative to sequence-ba...
Protein Conformation Protein Interaction Mapping Proteomics Proteins Humans Deep Learning Computational Biology Machine Learning
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CPconf_score: A Deep Learning Free Energy Function Trained Using Molecular Dynamics Data for Cyclic Peptides.

Journal of chemical theory and computation Jan 13, 2025
Accurate structural feature characterization of cyclic peptides (CPs), especially those with less than 10 residues and -peptide bonds, is challenging but important for the rational design of bioactive peptides. In this study, we performed high-temper...
Peptides, Cyclic Protein Conformation Thermodynamics Deep Learning Molecular Dynamics Simulation
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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...
Arabidopsis Protein Conformation Protein Structure, Secondary NLR Proteins Protein Domains Computational Biology Models, Molecular Deep Learning
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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 Protein Conformation Cysteine Ligands Drug Discovery Proteins Humans 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 Protein Domains Databases, Protein Protein Conformation Structure-Activity Relationship
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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...
Protein Conformation Proteins Ligands Molecular Docking Simulation Deep Learning Protein Binding Algorithms
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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...
Proteins Neural Networks, Computer Databases, Protein Protein Conformation Deep Learning Computational Biology
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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 Binding Humans Protein Conformation Molecular Dynamics Simulation Protein Tyrosine Phosphatase, Non-Receptor Type 11 Allosteric Regulation 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...
Machine Learning Protein Binding Intrinsically Disordered Proteins Protein Conformation alpha-Synuclein Humans 1-(5-Isoquinolinesulfonyl)-2-Methylpiperazine Thermodynamics Entropy
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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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