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...
Artificial Intelligence Drug Discovery Deep Learning Molecular Dynamics Simulation Protein Conformation Proteins Models, Molecular
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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...
Protein Conformation Protein Folding Artificial Intelligence Deep Learning Proteins Models, Molecular Computational Biology
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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...
Proteins Models, Molecular Deep Learning Protein Conformation Cryoelectron Microscopy
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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...
Models, Molecular Drug Discovery Ligands Deep Learning Computational Biology Protein Conformation Protein Binding 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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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...
Protein Conformation Humans Deep Learning Protein Binding Models, Molecular Antibodies
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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...
Metalloproteins Protein Conformation Binding Sites Ions Ligands Deep Learning Metals Computational Biology Databases, Protein Protein Binding Software Algorithms Molecular Docking Simulation
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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...
Proteins Models, Molecular Binding Sites Protein Interaction Mapping Protein Conformation Protein Binding Computational Biology Neural Networks, Computer Algorithms Graph Neural Networks
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Structure-Based Approaches for Protein-Protein Interaction Prediction Using Machine Learning and Deep Learning.

Biomolecules Jan 17, 2025
Protein-Protein Interaction (PPI) prediction plays a pivotal role in understanding cellular processes and uncovering molecular mechanisms underlying health and disease. Structure-based PPI prediction has emerged as a robust alternative to sequence-ba...
Machine Learning Deep Learning Proteins Computational Biology Humans Protein Conformation Proteomics Protein Interaction Mapping
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CPconf_score: A Deep Learning Free Energy Function Trained Using Molecular Dynamics Data for Cyclic Peptides.

Journal of chemical theory and computation Jan 13, 2025
Accurate structural feature characterization of cyclic peptides (CPs), especially those with less than 10 residues and -peptide bonds, is challenging but important for the rational design of bioactive peptides. In this study, we performed high-temper...
Thermodynamics Deep Learning Peptides, Cyclic Protein Conformation Molecular Dynamics Simulation
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