AIMC Topic: Binding Sites

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Ensemble learning from ensemble docking: revisiting the optimum ensemble size problem.

Scientific reports Jan 10, 2022
Despite considerable advances obtained by applying machine learning approaches in protein-ligand affinity predictions, the incorporation of receptor flexibility has remained an important bottleneck. While ensemble docking has been used widely as a so...
Machine Learning Molecular Docking Simulation Protein Binding Binding Sites Cyclin-Dependent Kinase 2 Ligands Crystallography, X-Ray Protein Conformation Support Vector Machine Structure-Activity Relationship
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Harnessing protein folding neural networks for peptide-protein docking.

Nature communications Jan 10, 2022
Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been develop...
Proteins Models, Molecular Amino Acid Sequence Neural Networks, Computer Molecular Docking Simulation Software Protein Binding Peptides Protein Interaction Domains and Motifs Binding Sites Protein Conformation, alpha-Helical Protein Interaction Mapping Protein Conformation, beta-Strand Protein Folding
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DFpin: Deep learning-based protein-binding site prediction with feature-based non-redundancy from RNA level.

Computers in biology and medicine Jan 7, 2022
The interaction between proteins and RNA is closely related to various human diseases. Computer-aided drug design can be facilitated by detecting the RNA sites that bind proteins. However, due to the aggregation of binding sites in RNA sequences, hig...
Deep Learning Protein Binding Computational Biology Humans Proteins RNA Binding Sites
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Protein embeddings and deep learning predict binding residues for various ligand classes.

Scientific reports Dec 13, 2021
One important aspect of protein function is the binding of proteins to ligands, including small molecules, metal ions, and macromolecules such as DNA or RNA. Despite decades of experimental progress many binding sites remain obscure. Here, we propose...
Sequence Analysis, Protein Nucleic Acids Protein Conformation Binding Sites Protein Binding Ligands Software Molecular Docking Simulation Deep Learning Metals
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Prediction of FMN Binding Sites in Electron Transport Chains Based on 2-D CNN and PSSM Profiles.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2021
Flavin mono-nucleotides (FMNs) are cofactors that hold responsibility for carrying and transferring electrons in the electron transport chain stage of cellular respiration. Without being facilitated by FMNs, energy production is stagnant due to the i...
Neural Networks, Computer Computational Biology Position-Specific Scoring Matrices Algorithms Flavin Mononucleotide Binding Sites Electron Transport Deep Learning
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LectinOracle: A Generalizable Deep Learning Model for Lectin-Glycan Binding Prediction.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Dec 4, 2021
Ranging from bacterial cell adhesion over viral cell entry to human innate immunity, glycan-binding proteins or lectins are abound in nature. Widely used as staining and characterization reagents in cell biology and crucial for understanding the inte...
Lectins Deep Learning Protein Binding Polysaccharides Binding Sites
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Picture-word order compound protein interaction: Predicting compound-protein interaction using structural images of compounds.

Journal of computational chemistry Nov 30, 2021
Identifying potential associations between proteins and compounds is significant and challenging in the drug discovery process. Existing deep-learning-based methods tend to treat compounds and proteins as sequences or graphs. Inspired by the rapid de...
Molecular Conformation Binding Sites Proteins Pharmaceutical Preparations Neural Networks, Computer
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CRNNTL: Convolutional Recurrent Neural Network and Transfer Learning for QSAR Modeling in Organic Drug and Material Discovery.

Molecules (Basel, Switzerland) Nov 30, 2021
Molecular latent representations, derived from autoencoders (AEs), have been widely used for drug or material discovery over the past couple of years. In particular, a variety of machine learning methods based on latent representations have shown exc...
Organic Chemicals Models, Theoretical Regression Analysis Drug Discovery Quantitative Structure-Activity Relationship Area Under Curve ROC Curve Neural Networks, Computer Binding Sites Isomerism Databases as Topic
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Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions.

BMC bioinformatics Nov 8, 2021
BACKGROUND: Accurate prediction of protein-ligand binding affinity is important for lowering the overall cost of drug discovery in structure-based drug design. For accurate predictions, many classical scoring functions and machine learning-based meth...
Protein Binding Proteins Ligands Binding Sites Machine Learning
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Multi-Scale Capsule Network for Predicting DNA-Protein Binding Sites.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
Discovering DNA-protein binding sites, also known as motif discovery, is the foundation for further analysis of transcription factors (TFs). Deep learning algorithms such as convolutional neural networks (CNN) have been introduced to motif discovery ...
Sequence Analysis, Protein Protein Binding DNA-Binding Proteins Algorithms Chromatin Immunoprecipitation Sequencing Computational Biology Binding Sites Deep Learning Transcription Factors
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