Improving Protein–Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge
Year: 2023
Type: article
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.
Source: Biomolecules
DOI: 10.3390/biom13010132
Authors: Nabin Giri, Jianlin Cheng
Institution: University of Missouri
Language: English
Subfield: Structural Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,558
Awards: UNIVERSITY OF MISSOURI - NEW AWARD
CONTROL NUMBER: 2107-1570
PROJECT TITLE: DEEP LEARNING PREDICTION OF PROTEIN COMPLEX STRUCTURES
PROJECT DESCRIPTION: THIS COOPERATIVE AGREEMENT IS HEREBY AWARDED TO THE CURATORS OF THE UNIVERSITY OF MISSOURI WHO PROPOSES TO DEVELOP NOVEL DEEP LEARNING METHODS TO PREDICT INTER-PROTEIN AMINO ACID INTERACTIONS (CONTACTS) AND BUILD THREE-DIMENSIONAL STRUCTURES OF PROTEIN COMPLEXES., HIGH THROUGHPUT, ACCURATE GENE ANNOTATION THROUGH AI AND HPC-ENABLED STRUCTURAL ANALYSIS, Distance-based ab initio protein structure prediction, DEEP GREEN: STRUCTURAL AND FUNCTIONAL GENOMIC CHARACTERIZATION OF CONSERVED UNANNOTATED GREEN LINEAGE PROTEINS, Deep learning methods for automated and accurate reconstruction of protein structures from cryo-EM image data
OpenAlex ID: W4313890332