ArticleJournal of computer-aided molecular design2022
Binding site identification of G protein-coupled receptors through a 3D Zernike polynomials-based method: application to C. elegans olfactory receptors.
Article in Journal of computer-aided molecular design, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 16 citations in OpenAlex.
- Investigating the Structural Basis of Diacetyl Recognition by the G-Protein-Coupled Receptor ODR-10 inThe journal of physical chemistry. B · 2025Article
- Uncovering the molecular details of the 14-3-3 recruitment by mutant Sod1 species.Scientific reports · 2025Article
- Attention-based approach to predict drug-target interactions across seven target superfamilies.Bioinformatics (Oxford, England) · 2024Article
- FP-Zernike: An Open-source Structural Database Construction Toolkit for Fast Structure Retrieval.Genomics, proteomics & bioinformatics · 2024Article
- ZMPY3D: accelerating protein structure volume analysis through vectorized 3D Zernike moments and Python-based GPU integration.Bioinformatics advances · 2024Article
- Computational structural-based GPCR optimization for user-defined ligand: Implications for the development of biosensors.Computational and structural biotechnology journal · 2023Article
- Design of protein-binding peptides with controlled binding affinity: the case of SARS-CoV-2 receptor binding domain and angiotensin-converting enzyme 2 derived peptides.Frontiers in molecular biosciences · 2023Article
- Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRs.Frontiers in bioinformatics · 2023Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Studying the binding processes of G protein-coupled receptors (GPCRs) proteins is of particular interest both to better understand the molecular mechanisms that regulate the signaling between the extracellular and intracellular environment and for drug design purposes. In this study, we propose a new computational approach for the identification of the binding site for a specific ligand on a GPCR. The method is based on the Zernike polynomials and performs the ligand-GPCR association through a shape complementarity analysis of the local molecular surfaces. The method is parameter-free and it can distinguish, working on hundreds of experimentally GPCR-ligand complexes, binding pockets from randomly sampled regions on the receptor surface, obtaining an Area Under ROC curve of 0.77. Given its importance both as a model organism and in terms of applications, we thus investigated the olfactory receptors of the C. elegans, building a list of associations between 21 GPCRs belonging to its olfactory neurons and a set of possible ligands. Thus, we can not only carry out rapid and efficient screenings of drugs proposed for GPCRs, key targets in many pathologies, but also we laid the groundwork for computational mutagenesis processes, aimed at increasing or decreasing the binding affinity between ligands and receptors.
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