AIMC Topic: Proteins

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Compact Assessment of Molecular Surface Complementarities Enhances Neural Network-Aided Prediction of Key Binding Residues.

Journal of chemical information and modeling Feb 21, 2025
Predicting interactions between proteins is fundamental for understanding the mechanisms underlying cellular processes, since protein-protein complexes are crucial in physiological conditions but also in many diseases, for example by seeding aggregat...
Surface Properties Binding Sites Proteins Hydrophobic and Hydrophilic Interactions ROC Curve Neural Networks, Computer Protein Binding Molecular Docking Simulation
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Toward deep learning sequence-structure co-generation for protein design.

Current opinion in structural biology Feb 20, 2025
Deep generative models that learn from the distribution of natural protein sequences and structures may enable the design of new proteins with valuable functions. While the majority of today's models focus on generating either sequences or structures...
Deep Learning Models, Molecular Proteins Protein Conformation Protein Engineering
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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...
Machine Learning Thermodynamics Protein Binding Proteins
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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 SARS-CoV-2 Drug Discovery Neural Networks, Computer Uncertainty Proteins Deep Learning Ligands Protein Binding ErbB Receptors
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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...
Spectrophotometry, Infrared Models, Molecular Quantum Theory Spectrum Analysis, Raman Proteins Peptides Deep Learning
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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...
Machine Learning Protein Folding Protein Conformation Spectrophotometry, Infrared Proteins
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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...
Deep Learning Computational Biology Cluster Analysis Algorithms Amino Acid Sequence Protein Conformation Sequence Analysis, Protein Sequence Alignment Models, Molecular Proteins Protein Domains Databases, Protein
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Identification and understanding of allostery hotspots in proteins: Integration of deep mutational scanning and multi-faceted computational analyses.

Journal of molecular biology Feb 12, 2025
Motivated by recent deep mutational scanning (DMS) experiments, we have carried out a diverse set of computations to better understand the distribution and contributions of allostery hotspot residues in a transcription factor, TetR. These include ext...
Proteins Machine Learning Models, Molecular Mutation Thermodynamics Allosteric Site Protein Binding Molecular Dynamics Simulation Allosteric Regulation Computational Biology Protein Conformation
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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...
Proteins Amino Acid Sequence Protein Engineering Deep Learning Amino Acid Motifs
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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...
Protein Engineering Computational Biology Proteins Mutation Supervised Machine Learning Software Models, Molecular Algorithms Machine Learning
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