ArticleBiomolecules2023
Improving Protein-Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge.
Article in Biomolecules, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 19 papers.
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19 citing papers in PubMed.
- Benchmarking deep learning methods for Cα atom prediction in cryo-EM density maps.Bioinformatics (Oxford, England) · 2026Article
- A Labeled Dataset for AI-based Cryo-EM Map Enhancement.bioRxiv : the preprint server for biology · 2025Article
- An outlook on structural biology after AlphaFold: tools, limits and perspectives.FEBS open bio · 2025Review
- A labeled dataset for AI-based cryo-EM map enhancement.Computational and structural biotechnology journal · 2025Article
- Exploring G Protein-Coupled Receptors in Hematological Cancers.ACS pharmacology & translational science · 2024Article
- Correction: Giri et al. Improving Protein-Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge.Biomolecules · 2024Article
- Outcomes of the EMDataResource cryo-EM Ligand Modeling Challenge.Nature methods · 2024Article
- De novo atomic protein structure modeling for cryoEM density maps using 3D transformer and HMM.Nature communications · 2024Article
- CryoSegNet: accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net.Briefings in bioinformatics · 2024Article
- Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures.Scientific data · 2024Article
- Accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and specialized U-Net.bioRxiv : the preprint server for biology · 2024Article
- CryoTransformer: a transformer model for picking protein particles from cryo-EM micrographs.Bioinformatics (Oxford, England) · 2024Article
- Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures.bioRxiv : the preprint server for biology · 2024Article
- De Novo Atomic Protein Structure Modeling for Cryo-EM Density Maps Using 3D Transformer and Hidden Markov Model.bioRxiv : the preprint server for biology · 2024Article
- CryoTransformer: A Transformer Model for Picking Protein Particles from Cryo-EM Micrographs.bioRxiv : the preprint server for biology · 2023Article
- Novel Artificial Intelligence-Based Approaches for Ab Initio Structure Determination and Atomic Model Building for Cryo-Electron Microscopy.Micromachines · 2023Review
- A large expert-curated cryo-EM image dataset for machine learning protein particle picking.Scientific data · 2023Article
- Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions.Current opinion in structural biology · 2023Review
- CryoPPP: A Large Expert-Labelled Cryo-EM Image Dataset for Machine Learning Protein Particle Picking.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
Abstract
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 and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein-ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein-ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein-ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein-ligand interactions on cryo-EM data using prior structural information.
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