AIMC Topic: Protein Conformation

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Assessing protein homology models with docking reproducibility.

Journal of molecular graphics & modelling Feb 11, 2023
Results of the recent Critical Assessment of Protein Structure (CASP) competitions demonstrate that protein backbones can be predicted with very high accuracy. In particular, the artificial intelligence methods of AlphaFold 2 from DeepMind were able ...
Binding Sites Proteins Protein Binding Protein Conformation Ligands Artificial Intelligence Reproducibility of Results
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Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions.

Current opinion in structural biology Feb 9, 2023
Cryo-Electron Microscopy (cryo-EM) has emerged as a key technology to determine the structure of proteins, particularly large protein complexes and assemblies in recent years. A key challenge in cryo-EM data analysis is to automatically reconstruct a...
Deep Learning Proteins Protein Conformation Cryoelectron Microscopy Amino Acid Sequence Models, Molecular
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Smart de novo Macromolecular Structure Modeling from Cryo-EM Maps.

Journal of molecular biology Jan 18, 2023
The study of macromolecular structures has expanded our understanding of the amazing cell machinery and such knowledge has changed how the pharmaceutical industry develops new vaccines in recent years. Traditionally, X-ray crystallography has been th...
Proteins Artificial Intelligence Macromolecular Substances Models, Molecular Protein Conformation Software Cryoelectron Microscopy
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Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes.

Annual review of biophysics Jan 10, 2023
Advances in a scientific discipline are often measured by small, incremental steps. In this review, we report on two intertwined disciplines in the protein structure prediction field, modeling of single chains and modeling of complexes, that have ove...
Protein Conformation Artificial Intelligence
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Improving Protein-Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge.

Biomolecules Jan 9, 2023
Elucidating protein-ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein-ligand interaction is traditionally tackled by molecular docking a...
Cryoelectron Microscopy Proteins Deep Learning Molecular Docking Simulation Protein Conformation Ligands
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MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein-Protein Docking Conformations.

Biomolecules Jan 6, 2023
Protein-protein interactions play a ubiquitous role in biological function. Knowledge of the three-dimensional (3D) structures of the complexes they form is essential for understanding the structural basis of those interactions and how they orchestra...
Ligands Proteins Protein Conformation Machine Learning Protein Binding
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DeepPRObind: Modular Deep Learner that Accurately Predicts Structure and Disorder-Annotated Protein Binding Residues.

Journal of molecular biology Jan 5, 2023
Current sequence-based predictors of protein-binding residues (PBRs) belong to two distinct categories: structure-trained vs. intrinsic disorder-trained. Since disordered PBRs differ from structured PBRs in several ways, including ability to bind mul...
Deep Learning Protein Binding Databases, Protein Amino Acids Proteome Computational Biology Protein Conformation
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3D Conformational Generative Models for Biological Structures Using Graph Information-Embedded Relative Coordinates.

Molecules (Basel, Switzerland) Dec 31, 2022
Developing molecular generative models for directly generating 3D conformation has recently become a hot research area. Here, an autoencoder based generative model was proposed for molecular conformation generation. A unique feature of our method is ...
Protein Conformation Models, Molecular Ligands Neural Networks, Computer
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Personal Precise Force Field for Intrinsically Disordered and Ordered Proteins Based on Deep Learning.

Journal of chemical information and modeling Dec 19, 2022
Intrinsically disordered proteins (IDPs) are proteins without a fixed three-dimensional (3D) structure under physiological conditions and are associated with Parkinson's disease, Alzheimer's disease, cancer, cardiovascular disease, amyloidosis, diabe...
Alzheimer Disease Deep Learning Humans Protein Folding Protein Conformation Molecular Dynamics Simulation Intrinsically Disordered Proteins
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Novel machine learning approaches revolutionize protein knowledge.

Trends in biochemical sciences Dec 9, 2022
Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large scale is no l...
Machine Learning Protein Conformation Computational Biology Proteins
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