AIMC Topic: Proteins

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SOFB is a comprehensive ensemble deep learning approach for elucidating and characterizing protein-nucleic-acid-binding residues.

Communications biology Jun 3, 2024
Proteins and nucleic-acids are essential components of living organisms that interact in critical cellular processes. Accurate prediction of nucleic acid-binding residues in proteins can contribute to a better understanding of protein function. Howev...
Nucleic Acids Binding Sites Computational Biology Proteins Deep Learning Protein Binding
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[Not Available].

Proteomics Jun 2, 2024
RNA-dependent liquid-liquid phase separation (LLPS) proteins play critical roles in cellular processes such as stress granule formation, DNA repair, RNA metabolism, germ cell development, and protein translation regulation. The abnormal behavior of t...
Humans Databases, Protein RNA Phase Separation Computational Biology Proteins Artificial Intelligence
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Application of artificial intelligence and machine learning techniques to the analysis of dynamic protein sequences.

Proteins May 29, 2024
We apply methods of Artificial Intelligence and Machine Learning to protein dynamic bioinformatics. We rewrite the sequences of a large protein data set, containing both folded and intrinsically disordered molecules, using a representation developed ...
Protein Folding Sequence Analysis, Protein Intrinsically Disordered Proteins Fourier Analysis Algorithms Computational Biology Machine Learning Proteins Artificial Intelligence Databases, Protein Amino Acid Sequence
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DSSGNN-PPI: A Protein-Protein Interactions prediction model based on Double Structure and Sequence graph neural networks.

Computers in biology and medicine May 29, 2024
The process of experimentally confirming complex interaction networks among proteins is time-consuming and laborious. This study aims to address Protein-Protein Interactions (PPIs) prediction based on graph neural networks (GNN). A novel multilevel p...
Neural Networks, Computer Protein Interaction Maps Humans Databases, Protein Protein Interaction Mapping Computational Biology Proteins
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Protein-Protein Interfaces: A Graph Neural Network Approach.

International journal of molecular sciences May 28, 2024
Protein-protein interactions (PPIs) are fundamental processes governing cellular functions, crucial for understanding biological systems at the molecular level. Compared to experimental methods for PPI prediction and site identification, computationa...
Deep Learning Databases, Protein Neural Networks, Computer Computational Biology Protein Interaction Maps Algorithms Protein Interaction Mapping Proteins Protein Binding
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PfgPDI: Pocket feature-enabled graph neural network for protein-drug interaction prediction.

Journal of bioinformatics and computational biology May 27, 2024
Biomolecular interaction recognition between ligands and proteins is an essential task, which largely enhances the safety and efficacy in drug discovery and development stage. Studying the interaction between proteins and ligands can improve the unde...
Computational Biology Proteins Humans Deep Learning Drug Discovery Neural Networks, Computer Protein Binding Databases, Protein Pharmaceutical Preparations Ligands
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NPEX: Never give up protein exploration with deep reinforcement learning.

Journal of molecular graphics & modelling May 25, 2024
Elucidating unknown structures of proteins, such as metastable states, is critical in designing therapeutic agents. Protein structure exploration has been performed using advanced computational methods, especially molecular dynamics and Markov chain ...
Protein Conformation Monte Carlo Method Molecular Dynamics Simulation Markov Chains Dipeptides Deep Learning Proteins Algorithms
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Integrative modeling meets deep learning: Recent advances in modeling protein assemblies.

Current opinion in structural biology May 24, 2024
Recent progress in protein structure prediction based on deep learning revolutionized the field of Structural Biology. Beyond single proteins, it also enabled high-throughput prediction of structures of protein-protein interactions. Despite the succe...
Protein Conformation Deep Learning Protein Binding Proteins Models, Molecular Computational Biology Humans
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A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.

PLoS computational biology May 20, 2024
The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models des...
Humans Machine Learning Deep Learning Protein Kinase Inhibitors Proteins Protein Binding Small Molecule Libraries Substrate Specificity Computational Biology Amino Acid Sequence Protein Kinases
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SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks.

International journal of molecular sciences May 16, 2024
The subcellular location of a protein provides valuable insights to bioinformaticians in terms of drug designs and discovery, genomics, and various other aspects of medical research. Experimental methods for protein subcellular localization determina...
Neural Networks, Computer Software Computational Biology Humans Proteins Databases, Protein
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