AIMC Topic: Databases, Protein

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MAI-TargetFisher: A proteome-wide drug target prediction method synergetically enhanced by artificial intelligence and physical modeling.

Acta pharmacologica Sinica Jan 27, 2025
Computational target identification plays a pivotal role in the drug development process. With the significant advancements of deep learning methods for protein structure prediction, the structural coverage of human proteome has increased substantial...
Proteins Humans Databases, Protein Algorithms Artificial Intelligence Binding Sites Drug Discovery Proteome
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DisDock: A Deep Learning Method for Metal Ion-Protein Redocking.

Proteins Jan 22, 2025
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...
Software Metalloproteins Metals Protein Binding Computational Biology Algorithms Ligands Protein Conformation Molecular Docking Simulation Binding Sites Deep Learning Databases, Protein Ions
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PPILS: Protein-protein interaction prediction with language of biological coding.

Computers in biology and medicine Jan 19, 2025
Protein-protein interactions within a cell are essential for various fundamental biological processes. Computational techniques have arisen in bioinformatics due to the challenging and resource-intensive nature of experimental protein pair interactio...
Humans Machine Learning Protein Interaction Maps Computational Biology Proteins Databases, Protein Protein Interaction Mapping
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Supervised learning approaches for predicting Ebola-Human Protein-Protein interactions.

Gene Jan 17, 2025
The goal of this research work is to predict protein-protein interactions (PPIs) between the Ebola virus and the host who is at risk of infection. Since there are very limited databases available on the Ebola virus; we have prepared a comprehensive d...
Protein Interaction Mapping Hemorrhagic Fever, Ebola Databases, Protein Supervised Machine Learning Protein Interaction Maps Ebolavirus Humans Host-Pathogen Interactions Computational Biology Gene Ontology Support Vector Machine Viral Proteins
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Integrating CNN and Bi-LSTM for protein succinylation sites prediction based on Natural Language Processing technique.

Computers in biology and medicine Jan 10, 2025
Protein succinylation, a post-translational modification wherein a succinyl group (-CO-CH₂-CH₂-CO-) attaches to lysine residues, plays a critical regulatory role in cellular processes. Dysregulated succinylation has been implicated in the onset and p...
Protein Processing, Post-Translational Proteins Databases, Protein Natural Language Processing Succinic Acid Humans Neural Networks, Computer Computational Biology
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Identifying Protein-Nucleotide Binding Residues via Grouped Multi-task Learning and Pre-trained Protein Language Models.

Journal of chemical information and modeling Jan 9, 2025
The accurate identification of protein-nucleotide binding residues is crucial for protein function annotation and drug discovery. Numerous computational methods have been proposed to predict these binding residues, achieving remarkable performance. H...
Nucleotides Binding Sites Protein Binding Proteins Databases, Protein 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...
Humans Databases, Protein Models, Molecular Ligands Machine Learning Drug Discovery Proteins Protein Conformation Cysteine
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Deep Learning Approaches for the Prediction of Protein Functional Sites.

Molecules (Basel, Switzerland) Jan 7, 2025
Knowing which residues of a protein are important for its function is of paramount importance for understanding the molecular basis of this function and devising ways of modifying it for medical or biotechnological applications. Due to the difficulty...
Binding Sites Humans Deep Learning Sequence Analysis, Protein Proteins Computational Biology Databases, Protein
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π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing.

Nature communications Jan 2, 2025
Peptide sequencing via tandem mass spectrometry (MS/MS) is essential in proteomics. Unlike traditional database searches, deep learning excels at de novo peptide sequencing, even for peptides missing from existing databases. Current deep learning mod...
Sequence Analysis, Protein Peptides Phosphopeptides Humans Software Protein Processing, Post-Translational Deep Learning Algorithms Proteomics Databases, Protein Tandem Mass Spectrometry
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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...
Algorithms Deep Learning Computational Biology Humans Models, Molecular Protein Domains Protein Conformation Proteins Databases, Protein Structure-Activity Relationship
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