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Protein Binding

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Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage.

Cell genomics 40020686
Deep learning models have advanced our ability to predict cell-type-specific chromatin patterns from transcription factor (TF) binding motifs, but their application to perturbed contexts remains limited. We applied transfer learning to predict how co...
Animals Binding Sites Chromatin Deep Learning Humans Mice Nucleosomes Protein Binding SOX9 Transcription Factor Transcription Factors
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Natural Language Processing Methods for the Study of Protein-Ligand Interactions.

Journal of chemical information and modeling 39993834
Natural Language Processing (NLP) has revolutionized the way computers are used to study and interact with human languages and is increasingly influential in the study of protein and ligand binding, which is critical for drug discovery and developmen...
Drug Discovery Humans Ligands Machine Learning Natural Language Processing Protein Binding Proteins
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Compact Assessment of Molecular Surface Complementarities Enhances Neural Network-Aided Prediction of Key Binding Residues.

Journal of chemical information and modeling 39982412
Predicting interactions between proteins is fundamental for understanding the mechanisms underlying cellular processes, since protein-protein complexes are crucial in physiological conditions but also in many diseases, for example by seeding aggregat...
Binding Sites Hydrophobic and Hydrophilic Interactions Molecular Docking Simulation Neural Networks, Computer Protein Binding Proteins ROC Curve Surface Properties
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Estimating Absolute Protein-Protein Binding Free Energies by a Super Learner Model.

Journal of chemical information and modeling 39973292
Protein-protein binding is central to most biochemical processes of all living beings. Its importance underlies mechanisms ranging from cell interactions to metabolic control, but also to biotechnology, such as the development of therapeutic monoclo...
Machine Learning Protein Binding Proteins Thermodynamics
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T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein-Ligand Binding Affinity Prediction with Uncertainty-Aware Self-Learning for Protein-Specific Alignment.

Journal of chemical information and modeling 39965912
There is significant interest in targeting disease-causing proteins with small molecule inhibitors to restore healthy cellular states. The ability to accurately predict the binding affinity of small molecules to a protein target in silico enables the...
Deep Learning Drug Discovery ErbB Receptors Humans Ligands Neural Networks, Computer Protein Binding Proteins SARS-CoV-2 Uncertainty
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ParaSurf: a surface-based deep learning approach for paratope-antigen interaction prediction.

Bioinformatics (Oxford, England) 39921885
MOTIVATION: Identifying antibody binding sites, is crucial for developing vaccines and therapeutic antibodies, processes that are time-consuming and costly. Accurate prediction of the paratope's binding site can speed up the development by improving ...
Antigens Binding Sites, Antibody Computational Biology Deep Learning Models, Molecular Protein Binding Software
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Join Persistent Homology (JPH)-Based Machine Learning for Metalloprotein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling 40082211
With the crucial role of metalloproteins in respiration, oxidative stress protection, photosynthesis, and drug metabolism, the design and discovery of drugs that can target metalloproteins are extremely important. Recently, enormous potential has bee...
Ligands Machine Learning Metalloproteins Protein Binding
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Insights into phosphorylation-induced influences on conformations and inhibitor binding of CDK6 through GaMD trajectory-based deep learning.

Physical chemistry chemical physics : PCCP 40072875
The phosphorylation of residue T177 produces a significant effect on the conformational dynamics of CDK6. Gaussian accelerated molecular dynamics (GaMD) simulations followed by deep learning (DL) are applied to explore the molecular mechanism of the ...
Cyclin-Dependent Kinase 6 Deep Learning Humans Molecular Dynamics Simulation Phosphorylation Protein Binding Protein Conformation Protein Kinase Inhibitors Thermodynamics
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Development of DeepPQK and DeepQK sequence-based deep learning models to predict protein-ligand affinity and application in the directed evolution of ferulic esterase DLfae4.

International journal of biological macromolecules 40054795
Affinity plays an essential role in the rate and stability of enzyme-catalyzed reactions, thus directly impacting the catalytic activity. In general, the predictive method for protein-ligand binding affinity mainly relies on high-resolution protein c...
Amino Acid Sequence Binding Sites Carboxylic Ester Hydrolases Deep Learning Directed Molecular Evolution Ligands Models, Molecular Neural Networks, Computer Protein Binding
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iScore: A ML-Based Scoring Function for De Novo Drug Discovery.

Journal of chemical information and modeling 40036330
In the quest for accelerating de novo drug discovery, the development of efficient and accurate scoring functions represents a fundamental challenge. This study introduces iScore, a novel machine learning (ML)-based scoring function designed to predi...
Drug Discovery Ligands Machine Learning Neural Networks, Computer Protein Binding Proteins
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