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Protein Conformation

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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) 39795072
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...
Allosteric Regulation Enzyme Inhibitors Humans Machine Learning Molecular Dynamics Simulation Protein Binding Protein Conformation Protein Tyrosine Phosphatase, Non-Receptor Type 11
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Topology-based protein classification: A deep learning approach.

Biochemical and biophysical research communications 39742787
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...
Computational Biology Databases, Protein Deep Learning Neural Networks, Computer Protein Conformation Proteins
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Evaluations of the Perturbation Resistance of the Deep-Learning-Based Ligand Conformation Optimization Algorithm.

Journal of chemical information and modeling 39724561
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...
Algorithms Deep Learning Ligands Molecular Docking Simulation Protein Binding Protein Conformation Proteins
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Structure-Based Approaches for Protein-Protein Interaction Prediction Using Machine Learning and Deep Learning.

Biomolecules 39858535
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...
Computational Biology Deep Learning Humans Machine Learning Protein Conformation Protein Interaction Mapping Proteins Proteomics
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MVGNN-PPIS: A novel multi-view graph neural network for protein-protein interaction sites prediction based on Alphafold3-predicted structures and transfer learning.

International journal of biological macromolecules 39848362
Protein-protein interactions (PPI) are crucial for understanding numerous biological processes and pathogenic mechanisms. Identifying interaction sites is essential for biomedical research and targeted drug development. Compared to experimental metho...
Algorithms Binding Sites Computational Biology Graph Neural Networks Models, Molecular Neural Networks, Computer Protein Binding Protein Conformation Protein Interaction Mapping Proteins
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DisDock: A Deep Learning Method for Metal Ion-Protein Redocking.

Proteins 39838957
The structures of metalloproteins are essential for comprehending their functions and interactions. The breakthrough of AlphaFold has made it possible to predict protein structures with experimental accuracy. However, the type of metal ion that a met...
Algorithms Binding Sites Computational Biology Databases, Protein Deep Learning Ions Ligands Metalloproteins Metals Molecular Docking Simulation Protein Binding Protein Conformation Software
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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 39801200
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...
Deep Learning Molecular Dynamics Simulation Peptides, Cyclic Protein Conformation Thermodynamics
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CovCysPredictor: Predicting Selective Covalently Modifiable Cysteines Using Protein Structure and Interpretable Machine Learning.

Journal of chemical information and modeling 39780548
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...
Cysteine Databases, Protein Drug Discovery Humans Ligands Machine Learning Models, Molecular Protein Conformation Proteins
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DPFunc: accurately predicting protein function via deep learning with domain-guided structure information.

Nature communications 39746897
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...
Algorithms Computational Biology Databases, Protein Deep Learning Humans Models, Molecular Protein Conformation Protein Domains Proteins Structure-Activity Relationship
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AFFIPred: AlphaFold2 structure-based Functional Impact Prediction of missense variations.

Protein science : a publication of the Protein Society 39840793
Protein structure holds immense potential for pathogenicity prediction, albeit structure-based predictors are limited compared to the sequence-based counterparts due to the "structure knowledge gap" between large number of available protein sequences...
Computational Biology Databases, Protein Humans Machine Learning Models, Molecular Mutation, Missense Protein Conformation Proteins Software
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