AIMC Topic: Protein Conformation

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The State-of-the-Art Overview to Application of Deep Learning in Accurate Protein Design and Structure Prediction.

Topics in current chemistry (Cham) Jul 4, 2024
In recent years, there has been a notable increase in the scientific community's interest in rational protein design. The prospect of designing an amino acid sequence that can reliably fold into a desired three-dimensional structure and exhibit the i...
Protein Conformation Algorithms Deep Learning Proteins Databases, Protein
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Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information.

Nature communications Jul 2, 2024
The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we propose a neural network model, RMSF-net, which outperforms previous methods and...
Neural Networks, Computer Deep Learning Molecular Dynamics Simulation Protein Conformation Cryoelectron Microscopy Proteins Databases, Protein Computational Biology
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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...
Deep Learning Proteins Databases, Protein Neural Networks, Computer Protein Conformation Models, Molecular Crystallography, X-Ray
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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...
Biochemistry Universities Curriculum Software Protein Conformation Proteins Students Computational Biology Humans Artificial Intelligence Amino Acid Sequence
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TransfIGN: A Structure-Based Deep Learning Method for Modeling the Interaction between HLA-A*02:01 and Antigen Peptides.

Journal of chemical information and modeling Jun 26, 2024
The intricate interaction between major histocompatibility complexes (MHCs) and antigen peptides with diverse amino acid sequences plays a pivotal role in immune responses and T cell activity. In recent years, deep learning (DL)-based models have eme...
Humans Peptides Protein Binding Models, Molecular Deep Learning Protein Conformation Amino Acid Sequence HLA-A2 Antigen
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Accurate prediction of CDR-H3 loop structures of antibodies with deep learning.

eLife Jun 26, 2024
Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-standing challenge for antibody modeling. Here, we present the H3-OPT toolkit for predicting the 3D st...
Protein Conformation Single-Domain Antibodies Complementarity Determining Regions Antibodies, Monoclonal Models, Molecular Humans Deep Learning
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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...
Spectrophotometry, Infrared Protein Structure, Secondary Protein Conformation Machine Learning Protein Folding Proteins
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Protein loop structure prediction by community-based deep learning and its application to antibody CDR H3 loop modeling.

PLoS computational biology Jun 24, 2024
As of now, more than 60 years have passed since the first determination of protein structures through crystallography, and a significant portion of protein structures can be predicted by computers. This is due to the groundbreaking enhancement in pro...
Antibodies Protein Conformation Complementarity Determining Regions Computational Biology Databases, Protein Models, Molecular Humans Algorithms Neural Networks, Computer Deep Learning
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AlphaFold2 structures guide prospective ligand discovery.

Science (New York, N.Y.) Jun 21, 2024
AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models of the σ and serotonin 2A (5-HT2A) receptors, testing hundreds of new molecules a...
Drug Discovery Deep Learning Ligands Molecular Docking Simulation Drug Design Humans Serotonin 5-HT2 Receptor Agonists Cryoelectron Microscopy Receptor, Serotonin, 5-HT2A Small Molecule Libraries Serotonin 5-HT2 Receptor Antagonists Protein Conformation Receptors, sigma Protein Folding
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Developing a Differentiable Long-Range Force Field for Proteins with E(3) Neural Network-Predicted Asymptotic Parameters.

Journal of chemical theory and computation Jun 18, 2024
Accurately describing long-range interactions is a significant challenge in molecular dynamics (MD) simulations of proteins. High-quality long-range potential is also an important component of the range-separated machine learning force field. This st...
Protein Conformation Static Electricity Thermodynamics Molecular Dynamics Simulation Dipeptides Neural Networks, Computer Proteins
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