ArticlePloS one2012
Binding pocket optimization by computational protein design.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Expanding salivary biomarker detection by creating a synthetic neuraminic acid sensor via chimeragenesis.Applied and environmental microbiology · 2026Article
- Computational Approaches for Discovering Virulence Factors inJournal of fungi (Basel, Switzerland) · 2025Review
- Evaluation of Physics-Based Protein Design Methods for Predicting Single Residue Effects on Peptide Binding Specificities.Journal of computational chemistry · 2025Article
- Efficient Generation of Protein Pockets with PocketGen.bioRxiv : the preprint server for biology · 2024Article
- PocketOptimizer 2.0: A modular framework for computer-aided ligand-binding design.Protein science : a publication of the Protein Society · 2023Article
- Computation-Aided Engineering of Cytochrome P450 for the Production of Pravastatin.ACS catalysis · 2022Article
- Molecular Dynamics Simulations Reveal the Modulated Mechanism of STING Conformation.Interdisciplinary sciences, computational life sciences · 2021Article
- Computational Design of PDZ-Peptide Binding.Methods in molecular biology (Clifton, N.J.) · 2021Article
- New computational protein design methods for de novo small molecule binding sites.PLoS computational biology · 2020Article
- A Review on Applications of Computational Methods in Drug Screening and Design.Molecules (Basel, Switzerland) · 2020Review
- Adaptive landscape flattening allows the design of both enzyme: Substrate binding and catalytic power.PLoS computational biology · 2020Article
- Design of a heme-binding peptide motif adopting a β-hairpin conformation.The Journal of biological chemistry · 2018Article
- Computational tools for the evaluation of laboratory-engineered biocatalysts.Chemical communications (Cambridge, England) · 2016Review
- Multiscale Modeling in the Clinic: Drug Design and Development.Annals of biomedical engineering · 2016Review
- Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity.PLoS computational biology · 2015Article
- Computational design of protein-small molecule interfaces.Journal of structural biology · 2014Article
- Computational Models for Predictive Cardiac Ion Channel Pharmacology.Drug discovery today. Disease models · 2014Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Engineering specific interactions between proteins and small molecules is extremely useful for biological studies, as these interactions are essential for molecular recognition. Furthermore, many biotechnological applications are made possible by such an engineering approach, ranging from biosensors to the design of custom enzyme catalysts. Here, we present a novel method for the computational design of protein-small ligand binding named PocketOptimizer. The program can be used to modify protein binding pocket residues to improve or establish binding of a small molecule. It is a modular pipeline based on a number of customizable molecular modeling tools to predict mutations that alter the affinity of a target protein to its ligand. At its heart it uses a receptor-ligand scoring function to estimate the binding free energy between protein and ligand. We compiled a benchmark set that we used to systematically assess the performance of our method. It consists of proteins for which mutational variants with different binding affinities for their ligands and experimentally determined structures exist. Within this test set PocketOptimizer correctly predicts the mutant with the higher affinity in about 69% of the cases. A detailed analysis of the results reveals that the strengths of PocketOptimizer lie in the correct introduction of stabilizing hydrogen bonds to the ligand, as well as in the improved geometric complemetarity between ligand and binding pocket. Apart from the novel method for binding pocket design we also introduce a much needed benchmark data set for the comparison of affinities of mutant binding pockets, and that we use to asses programs for in silico design of ligand binding.
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