AIMC Topic: Proteins

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MEGDTA: multi-modal drug-target affinity prediction based on protein three-dimensional structure and ensemble graph neural network.

BMC genomics Aug 11, 2025
BACKGROUND: Drug development is a time-consuming and costly endeavor, and utilizing computer-aided methods to predict drug-target affinity (DTA) can significantly accelerate this process. The key to accurate DTA prediction lies in selecting appropria...
Neural Networks, Computer Protein Conformation Graph Neural Networks Computational Biology Proteins
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Bag-of-words is competitive with sum-of-embeddings language-inspired representations on protein inference.

PloS one Aug 6, 2025
Inferring protein function is a fundamental and long-standing problem in biology. Laboratory experiments in this field are often expensive, and therefore large-scale computational protein inference from readily available amino acid sequences is neede...
Proteins Databases, Protein Algorithms Computational Biology Sequence Analysis, Protein Natural Language Processing Amino Acid Sequence
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PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context.

Nature communications Aug 5, 2025
Accurate prediction of the functional impact of missense variants is important for disease gene discovery, clinical genetic diagnostics, therapeutic strategies, and protein engineering. Previous efforts have focused on predicting a binary pathogenici...
Proteins Amino Acid Sequence Protein Conformation Mutation, Missense Deep Learning Computational Biology Humans Neural Networks, Computer
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Augmenting MACCS Keys with Persistent Homology Fingerprints for Protein-Ligand Binding Classification.

Journal of chemical information and modeling Jul 25, 2025
Machine learning has become an essential tool in computational drug design, enabling models to uncover patterns in molecular data and predict protein-ligand interactions. This study introduces a novel approach by integrating persistence images with M...
Cheminformatics Proteins Ligands Neural Networks, Computer Drug Design Machine Learning Protein Binding
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Benchmarking 3D Structure-Based Molecule Generators.

Journal of chemical information and modeling Jul 25, 2025
To understand the benefits and drawbacks of 3D combinatorial and deep learning generators, a novel benchmark was created focusing on the recreation of important protein-ligand interactions and 3D ligand conformations. Using the BindingMOAD data set w...
Deep Learning Benchmarking Proteins Ligands Models, Molecular Molecular Conformation
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Utilizing protein structure graph embeddings to predict the pathogenicity of missense variants.

NAR genomics and bioinformatics Jul 24, 2025
Genetic variants can impact the structure of the corresponding protein, which can have detrimental effects on protein function. While the effect of protein-truncating variants is often easier to evaluate, most genetic variants that affect the protein...
Algorithms Humans Computational Biology Protein Conformation Machine Learning Proteins Mutation, Missense
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Learning the sequence code of protein expression in human immune cells.

Science advances Jul 23, 2025
Accurate protein expression in human immune cells is essential for appropriate cellular function. The mechanisms that define protein abundance are complex and are executed on transcriptional, posttranscriptional, and posttranslational levels. Here, w...
T-Lymphocytes Promoter Regions, Genetic Humans Enhancer Elements, Genetic Receptors, Antigen, T-Cell Machine Learning Proteins Gene Expression Regulation
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Applications of enhanced sampling methods to biomolecular self-assembly: a review.

Journal of physics. Condensed matter : an Institute of Physics journal Jul 23, 2025
This review article discusses some common enhanced sampling methods in relation to the process of self-assembly of biomolecules. An introduction to self-assembly and its challenges is covered followed by a brief overview of the methods and analysis f...
Machine Learning Peptides Proteins Polymers Molecular Dynamics Simulation Nucleic Acids
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Navigating protein landscapes with a machine-learned transferable coarse-grained model.

Nature chemistry Jul 18, 2025
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics, but they come at extreme computational cost. The development of a universal, computationally efficient coarse-grained (CG) model with similar pr...
Thermodynamics Molecular Dynamics Simulation Machine Learning Protein Folding Protein Conformation Proteins
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Assisting and accelerating NMR assignment with restrained structure prediction.

Communications biology Jul 18, 2025
Accurate dynamic protein structures are essential for drug design. NMR experiments can detect protein structures and potential dynamics, but the spectrum assignment and structure determination requires expertise and is time-consuming, while deep-lear...
Magnetic Resonance Spectroscopy Protein Folding Proteins Protein Conformation Models, Molecular Nuclear Magnetic Resonance, Biomolecular
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