AIMC Topic: Proteins

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Emerging frontiers in protein structure prediction following the AlphaFold revolution.

Journal of the Royal Society, Interface Apr 16, 2025
Models of protein structures enable molecular understanding of biological processes. Current protein structure prediction tools lie at the interface of biology, chemistry and computer science. Millions of protein structure models have been generated ...
Protein Folding Deep Learning Computational Biology Models, Molecular Proteins Protein Conformation
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LEGOLAS: A Machine Learning Method for Rapid and Accurate Predictions of Protein NMR Chemical Shifts.

Journal of chemical theory and computation Apr 10, 2025
This work introduces LEGOLAS, a fully open source TorchANI-based neural network model designed to predict NMR chemical shifts for protein backbone atoms (N, Cα, Cβ, C', HN, Hα). LEGOLAS has been designed to be fast without loss of accuracy, as our mo...
Escherichia coli Proteins Nuclear Magnetic Resonance, Biomolecular Proteins Molecular Dynamics Simulation Machine Learning Neural Networks, Computer Escherichia coli
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The AI revolution comes to protein sequencing.

Science (New York, N.Y.) Apr 10, 2025
By identifying unknown proteins, new systems could aid research in many areas.
Humans Artificial Intelligence Proteins Sequence Analysis, Protein
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PILOT: Deep Siamese network with hybrid attention improves prediction of mutation impact on protein stability.

Neural networks : the official journal of the International Neural Network Society Apr 8, 2025
Evaluating the mutation impact on protein stability (ΔΔG) is essential in the study of protein engineering and understanding molecular mechanisms of disease-associated mutations. Here, we propose a novel deep learning framework, PILOT, for improved p...
Proteins Mutation Protein Stability Deep Learning Pilot Projects Humans Neural Networks, Computer
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Unified Deep Learning of Molecular and Protein Language Representations with T5ProtChem.

Journal of chemical information and modeling Apr 8, 2025
Deep learning has revolutionized difficult tasks in chemistry and biology, yet existing language models often treat these domains separately, relying on concatenated architectures and independently pretrained weights. These approaches fail to fully e...
Models, Molecular Computational Biology Proteins Drug Discovery Deep Learning
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QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials.

Journal of chemical information and modeling Apr 8, 2025
Accurate prediction of protein-ligand binding affinities is crucial in drug discovery, particularly during hit-to-lead and lead optimization phases, however, limitations in ligand force fields continue to impact prediction accuracy. In this work, we ...
Ligands Thermodynamics Molecular Dynamics Simulation Quantum Theory Neural Networks, Computer Protein Binding Proteins
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Edge-enhanced interaction graph network for protein-ligand binding affinity prediction.

PloS one Apr 8, 2025
Protein-ligand interactions are crucial in drug discovery. Accurately predicting protein-ligand binding affinity is essential for screening potential drugs. Graph neural networks have proven highly effective in modeling spatial relationships and thre...
Protein Binding Algorithms Proteins Neural Networks, Computer Drug Discovery Ligands
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Persistent Directed Flag Laplacian (PDFL)-Based Machine Learning for Protein-Ligand Binding Affinity Prediction.

Journal of chemical theory and computation Apr 5, 2025
Directionality in molecular and biomolecular networks plays an important role in the accurate representation of the complex, dynamic, and asymmetrical nature of interactions present in protein-ligand binding, signal transduction, and biological pathw...
Machine Learning Protein Binding Proteins Ligands
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DeepAssembly2: A Web Server for Protein Complex Structure Assembly Based on Domain-Domain Interactions.

Journal of molecular biology Apr 4, 2025
Proteins often perform biological functions by forming complexes, thereby accurately predicting the structure of protein complexes is crucial to understanding and mastering their functions, as well as facilitating drug discovery. Protein monomeric st...
Proteins Databases, Protein Deep Learning Computational Biology Internet Protein Conformation Models, Molecular Protein Domains Software
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RPI-GGCN: Prediction of RNA-Protein Interaction Based on Interpretability Gated Graph Convolution Neural Network and Co-Regularized Variational Autoencoders.

IEEE transactions on neural networks and learning systems Apr 4, 2025
RNA-protein interactions (RPIs) play an important role in several fundamental cellular physiological processes, including cell motility, chromosome replication, transcription and translation, and signaling. Predicting RPI can guide the exploration of...
Algorithms Computational Biology Humans Neural Networks, Computer RNA Autoencoder RNA-Binding Proteins Proteins
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