AIMC Topic: Protein Conformation

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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 Models, Molecular Protein Conformation Data Curation Proteins Computational Biology 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...
Protein Conformation Protein Binding Models, Molecular Humans Antibodies Deep Learning
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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 ...
Amino Acid Sequence Computational Biology Protein Conformation Proteins Databases, Protein Software Models, Molecular
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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...
Binding Sites Metalloproteins Ligands Protein Conformation Metals Protein Binding Deep Learning Databases, Protein Ions Computational Biology 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...
Computational Biology Proteins Protein Conformation Binding Sites Models, Molecular Neural Networks, Computer Algorithms Protein Binding Protein Interaction Mapping 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...
Protein Conformation Protein Interaction Mapping Humans Computational Biology Machine Learning Proteomics Deep Learning Proteins
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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...
Molecular Dynamics Simulation Peptides, Cyclic Thermodynamics Protein Conformation Deep Learning
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Lessons from Deep Learning Structural Prediction of Multistate Multidomain Proteins-The Case Study of Coiled-Coil NOD-like Receptors.

International journal of molecular sciences Jan 9, 2025
We test here the prediction capabilities of the new generation of deep learning predictors in the more challenging situation of multistate multidomain proteins by using as a case study a coiled-coil family of Nucleotide-binding Oligomerization Domain...
Protein Structure, Secondary Arabidopsis Protein Domains Models, Molecular Deep Learning Protein Conformation Computational Biology NLR Proteins
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CovCysPredictor: Predicting Selective Covalently Modifiable Cysteines Using Protein Structure and Interpretable Machine Learning.

Journal of chemical information and modeling Jan 8, 2025
Targeted covalent inhibition is a powerful therapeutic modality in the drug discoverer's toolbox. Recent advances in covalent drug discovery, in particular, targeting cysteines, have led to significant breakthroughs for traditionally challenging targ...
Proteins Databases, Protein Models, Molecular Machine Learning Drug Discovery Protein Conformation Cysteine Ligands Humans
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DPFunc: accurately predicting protein function via deep learning with domain-guided structure information.

Nature communications Jan 2, 2025
Computational methods for predicting protein function are of great significance in understanding biological mechanisms and treating complex diseases. However, existing computational approaches of protein function prediction lack interpretability, mak...
Humans Algorithms Deep Learning Protein Domains Protein Conformation Databases, Protein Structure-Activity Relationship Models, Molecular Computational Biology Proteins
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