AIMC Topic: Protein Conformation

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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...
Proteins Models, Molecular Protein Conformation Molecular Dynamics Simulation Drug Discovery Deep Learning Artificial Intelligence
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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...
Proteins Models, Molecular Deep Learning Computational Biology Protein Conformation Protein Folding Artificial Intelligence
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Advancing structure modeling from cryo-EM maps with deep learning.

Biochemical Society transactions Feb 7, 2025
Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling the determination of biomolecular structures that are challenging to resolve using conventional methods. Interpreting a cryo-EM map requires accurate modeling of the...
Cryoelectron Microscopy Deep Learning Models, Molecular Protein Conformation Proteins
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Protein ligand structure prediction: From empirical to deep learning approaches.

Current opinion in structural biology Feb 5, 2025
Protein-ligand structure prediction methods, aiming to predict the three-dimensional complex structure and binding energy of a compound and target protein, are essential in many structure-based drug discovery pipelines, including virtual screening an...
Drug Discovery Deep Learning Protein Binding Protein Conformation Models, Molecular Computational Biology Ligands Proteins
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Modern machine learning methods for protein property prediction.

Current opinion in structural biology Jan 28, 2025
Recent progress and development of artificial intelligence and machine learning (AI/ML) techniques have enabled addressing complex biomolecular problems. AI/ML models learn the underlying distribution of data they are trained on and when exposed to n...
Proteins Machine Learning Protein Conformation Computational Biology
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PDB-IHM: A System for Deposition, Curation, Validation, and Dissemination of Integrative Structures.

Journal of molecular biology Jan 27, 2025
Structures of many large biomolecular assemblies are now being determined using integrative approaches. In these approaches, information derived from multiple experimental and computational methods is combined to compute three-dimensional structures ...
Software Computational Biology Models, Molecular Protein Conformation Data Curation Proteins Databases, Protein
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Challenges and compromises: Predicting unbound antibody structures with deep learning.

Current opinion in structural biology Jan 24, 2025
Therapeutic antibodies are manufactured, stored and administered in the free state; this makes understanding the unbound form key to designing and improving development pipelines. Prediction of unbound antibodies is challenging, specifically modellin...
Humans Protein Conformation Deep Learning Models, Molecular Protein Binding Antibodies
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Phyre2.2: A Community Resource for Template-based Protein Structure Prediction.

Journal of molecular biology Jan 23, 2025
Template-based modelling, also known as homology modelling, is a powerful approach to predict the structure of a protein from its amino acid sequence. The approach requires one to identify a sequence similarity between the query sequence and that of ...
Protein Conformation Software Proteins Models, Molecular Databases, Protein Amino Acid Sequence Computational Biology
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DisDock: A Deep Learning Method for Metal Ion-Protein Redocking.

Proteins Jan 22, 2025
The structures of metalloproteins are essential for comprehending their functions and interactions. The breakthrough of AlphaFold has made it possible to predict protein structures with experimental accuracy. However, the type of metal ion that a met...
Molecular Docking Simulation Deep Learning Protein Binding Computational Biology Metals Software Algorithms Metalloproteins Ligands Databases, Protein Protein Conformation Ions Binding Sites
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MVGNN-PPIS: A novel multi-view graph neural network for protein-protein interaction sites prediction based on Alphafold3-predicted structures and transfer learning.

International journal of biological macromolecules Jan 21, 2025
Protein-protein interactions (PPI) are crucial for understanding numerous biological processes and pathogenic mechanisms. Identifying interaction sites is essential for biomedical research and targeted drug development. Compared to experimental metho...
Algorithms Protein Interaction Mapping Protein Binding Graph Neural Networks Neural Networks, Computer Computational Biology Models, Molecular Protein Conformation Proteins Binding Sites
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