AIMC Topic: Proteins

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A Hierarchical Graph Neural Network Framework for Predicting Protein-Protein Interaction Modulators With Functional Group Information and Hypergraph Structure.

IEEE journal of biomedical and health informatics Jul 2, 2024
Accurate prediction of small molecule modulators targeting protein-protein interactions (PPIMs) remains a significant challenge in drug discovery. Existing machine learning-based models rely on manual feature engineering, which is tedious and task-sp...
Proteins Protein Interaction Mapping Protein Interaction Maps Computational Biology Neural Networks, Computer Drug Discovery Deep Learning Algorithms
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Equivariant Line Graph Neural Network for Protein-Ligand Binding Affinity Prediction.

IEEE journal of biomedical and health informatics Jul 2, 2024
Binding affinity prediction of three-dimensional (3D) protein-ligand complexes is critical for drug repositioning and virtual drug screening. Existing approaches usually transform a 3D protein-ligand complex to a two-dimensional (2D) graph, and then ...
Neural Networks, Computer Computational Biology Proteins Ligands Algorithms Protein Binding
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Variant Effect Prediction in the Age of Machine Learning.

Cold Spring Harbor perspectives in biology Jul 1, 2024
Over the years, many computational methods have been created for the analysis of the impact of single amino acid substitutions resulting from single-nucleotide variants in genome coding regions. Historically, all methods have been supervised and thus...
Humans Machine Learning Amino Acid Substitution Computational Biology Proteins Deep Learning Polymorphism, Single Nucleotide
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A CNN-CBAM-BIGRU model for protein function prediction.

Statistical applications in genetics and molecular biology Jul 1, 2024
Understanding a protein's function based solely on its amino acid sequence is a crucial but intricate task in bioinformatics. Traditionally, this challenge has proven difficult. However, recent years have witnessed the rise of deep learning as a powe...
Computational Biology Proteins Amino Acid Sequence Databases, Protein Algorithms Deep Learning Humans Neural Networks, Computer
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Deep-learning map segmentation for protein X-ray crystallographic structure determination.

Acta crystallographica. Section D, Structural biology Jun 27, 2024
When solving a structure of a protein from single-wavelength anomalous diffraction X-ray data, the initial phases obtained by phasing from an anomalously scattering substructure usually need to be improved by an iterated electron-density modification...
Models, Molecular Crystallography, X-Ray Protein Conformation Proteins Databases, Protein Neural Networks, Computer Deep Learning
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Cracking AlphaFold2: Leveraging the power of artificial intelligence in undergraduate biochemistry curriculums.

PLoS computational biology Jun 27, 2024
AlphaFold2 is an Artificial Intelligence-based program developed to predict the 3D structure of proteins given only their amino acid sequence at atomic resolution. Due to the accuracy and efficiency at which AlphaFold2 can generate 3D structure predi...
Students Computational Biology Universities Artificial Intelligence Software Curriculum Protein Conformation Biochemistry Proteins Amino Acid Sequence Humans
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NPI-DCGNN: An Accurate Tool for Identifying ncRNA-Protein Interactions Using a Dual-Channel Graph Neural Network.

Journal of computational biology : a journal of computational molecular cell biology Jun 26, 2024
Noncoding RNA (NcRNA)-protein interactions (NPIs) play fundamentally important roles in carrying out cellular activities. Although various predictors based on molecular features and graphs have been published to boost the identification of NPIs, most...
Humans Neural Networks, Computer Computational Biology Proteins RNA, Untranslated Software Algorithms
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Unraveling dynamic protein structures by two-dimensional infrared spectra with a pretrained machine learning model.

Proceedings of the National Academy of Sciences of the United States of America Jun 25, 2024
Dynamic protein structures are crucial for deciphering their diverse biological functions. Two-dimensional infrared (2DIR) spectroscopy stands as an ideal tool for tracing rapid conformational evolutions in proteins. However, linking spectral charact...
Protein Folding Spectrophotometry, Infrared Protein Structure, Secondary Proteins Protein Conformation Machine Learning
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Machine learning in biological physics: From biomolecular prediction to design.

Proceedings of the National Academy of Sciences of the United States of America Jun 24, 2024
Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics. However, in this perspective, we argue that a more successful approach is a proper combination of these two methodo...
Neural Networks, Computer Machine Learning Protein Engineering Proteins Molecular Dynamics Simulation
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Protein-Protein Interaction Prediction via Structure-Based Deep Learning.

Proteins Jun 23, 2024
Protein-protein interactions (PPIs) play an essential role in life activities. Many artificial intelligence algorithms based on protein sequence information have been developed to predict PPIs. However, these models have difficulty dealing with vario...
Amino Acid Sequence Protein Interaction Mapping Proteins Databases, Protein Protein Interaction Maps Computational Biology Humans Neural Networks, Computer Animals Deep Learning Algorithms
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