AIMC Topic: Proteins

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Estimating Absolute Protein-Protein Binding Free Energies by a Super Learner Model.

Journal of chemical information and modeling Feb 20, 2025
Protein-protein binding is central to most biochemical processes of all living beings. Its importance underlies mechanisms ranging from cell interactions to metabolic control, but also to biotechnology, such as the development of therapeutic monoclo...
Proteins Machine Learning Thermodynamics Protein Binding
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T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein-Ligand Binding Affinity Prediction with Uncertainty-Aware Self-Learning for Protein-Specific Alignment.

Journal of chemical information and modeling Feb 18, 2025
There is significant interest in targeting disease-causing proteins with small molecule inhibitors to restore healthy cellular states. The ability to accurately predict the binding affinity of small molecules to a protein target in silico enables the...
Humans Uncertainty Neural Networks, Computer Ligands ErbB Receptors Protein Binding SARS-CoV-2 Deep Learning Proteins Drug Discovery
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Deep Learning Protocol for Predicting Full-Spectrum Infrared and Raman Spectra of Polypeptides and Proteins Using All-Atom Models.

The journal of physical chemistry letters Feb 18, 2025
Infrared (IR) spectroscopy and Raman spectroscopy are powerful tools for probing protein and peptide structures due to their capability to provide molecular fingerprints. As a popular spectral simulation method, the quantum chemistry (QC) calculation...
Peptides Quantum Theory Deep Learning Spectrophotometry, Infrared Proteins Spectrum Analysis, Raman Models, Molecular
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AI protocol for retrieving protein dynamic structures from two-dimensional infrared spectra.

Proceedings of the National Academy of Sciences of the United States of America Feb 14, 2025
Understanding the dynamic evolution of protein structures is crucial for uncovering their biological functions. Yet, real-time prediction of these dynamic structures remains a significant challenge. Two-dimensional infrared (2DIR) spectroscopy is a p...
Proteins Protein Conformation Protein Folding Spectrophotometry, Infrared Machine Learning
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Simpler Protein Domain Identification Using Spectral Clustering.

Proteins Feb 13, 2025
The decomposition of a biomolecular complex into domains is an important step to investigate biological functions and ease structure determination. A successful approach to do so is the SPECTRUS algorithm, which provides a segmentation based on spect...
Proteins Databases, Protein Models, Molecular Protein Conformation Algorithms Computational Biology Sequence Alignment Cluster Analysis Sequence Analysis, Protein Protein Domains Amino Acid Sequence Deep Learning
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Deep Learning-Assisted Discovery of Protein Entangling Motifs.

Biomacromolecules Feb 12, 2025
Natural topological proteins exhibit unique properties including enhanced stability, controlled quaternary structures, and dynamic switching properties, highlighting topology as a unique dimension in protein engineering. Although artificial design an...
Protein Engineering Amino Acid Motifs Proteins Amino Acid Sequence Deep Learning
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Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants.

Science advances Feb 12, 2025
Machine learning (ML) is changing the world of computational protein design, with data-driven methods surpassing biophysical-based methods in experimental success. However, they are most often reported as case studies, lack integration and standardiz...
Models, Molecular Computational Biology Mutation Protein Engineering Proteins Machine Learning Supervised Machine Learning Software Algorithms
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SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein function prediction.

BMC bioinformatics Feb 10, 2025
BACKGROUND: A massive amount of protein sequences have been obtained, but their functions remain challenging to discern. In recent research on protein function prediction, Protein-Protein Interaction (PPI) Networks have played a crucial role. Uncover...
Databases, Protein Protein Interaction Mapping Artificial Intelligence Algorithms Computational Biology Proteins Protein Interaction Maps
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Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence-based methods.

Current opinion in structural biology Feb 8, 2025
This review highlights recent advances in AI-driven methods for generating Boltzmann-weighted structural ensembles, which are crucial for understanding biomolecular dynamics and drug discovery. With the rise of deep learning models such as AlphaFold2...
Artificial Intelligence Drug Discovery Models, Molecular Molecular Dynamics Simulation Protein Conformation Deep Learning Proteins
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Severe deviation in protein fold prediction by advanced AI: a case study.

Scientific reports Feb 8, 2025
Artificial intelligence (AI) and deep learning are making groundbreaking strides in protein structure prediction. AlphaFold is remarkable in this arena for its outstanding accuracy in modelling proteins fold based solely on their amino acid sequences...
Models, Molecular Protein Conformation Protein Folding Artificial Intelligence Computational Biology Deep Learning Proteins
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