» Articles » PMID: 18172838

Assessment of Programs for Ligand Binding Affinity Prediction

Overview
Journal J Comput Chem
Publisher Wiley
Specialties Biology
Chemistry
Date 2008 Jan 4
PMID 18172838
Citations 52
Authors
Affiliations
Soon will be listed here.
Abstract

The prediction of the binding free energy between a ligand and a protein is an important component in the virtual screening and lead optimization of ligands for drug discovery. To determine the quality of current binding free energy estimation programs, we examined FlexX, X-Score, AutoDock, and BLEEP for their performance in binding free energy prediction in various situations including cocrystallized complex structures, cross docking of ligands to their non-cocrystallized receptors, docking of thermally unfolded receptor decoys to their ligands, and complex structures with "randomized" ligand decoys. In no case was there a satisfactory correlation between the experimental and estimated binding free energies over all the datasets tested. Meanwhile, a strong correlation between ligand molecular weight-binding affinity correlation and experimental predicted binding affinity correlation was found. Sometimes the programs also correctly ranked ligands' binding affinities even though native interactions between the ligands and their receptors were essentially lost because of receptor deformation or ligand randomization, and the programs could not decisively discriminate randomized ligand decoys from their native ligands; this suggested that the tested programs miss important components for the accurate capture of specific ligand binding interactions.

Citing Articles

Exploring the Antidiarrheal Properties of Papaya Leaf: Insights Study in Mice-Model and Analysis at M3 Muscarinic Acetylcholine Receptor Interaction.

Saptarini N, Kelutur F, Corpuz M Scientifica (Cairo). 2024; 2024:1558620.

PMID: 38962530 PMC: 11221971. DOI: 10.1155/2024/1558620.


Molecular interactions of the Omicron, Kappa, and Delta SARS-CoV-2 spike proteins with quantum dots of graphene oxide.

da Silva Arouche T, Lobato J, Dos Santos Borges R, Santana de Oliveira M, de Jesus Chaves Neto A J Mol Model. 2024; 30(7):203.

PMID: 38858279 DOI: 10.1007/s00894-024-05996-z.


Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein-Ligand Scoring Functions.

Yang C, Zhang Y J Chem Inf Model. 2022; 62(11):2696-2712.

PMID: 35579568 PMC: 9197983. DOI: 10.1021/acs.jcim.2c00485.


Rapid and accurate estimation of protein-ligand relative binding affinities using site-identification by ligand competitive saturation.

Goel H, Hazel A, Ustach V, Jo S, Yu W, MacKerell Jr A Chem Sci. 2021; 12(25):8844-8858.

PMID: 34257885 PMC: 8246086. DOI: 10.1039/d1sc01781k.


Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts.

Karimi M, Wu D, Wang Z, Shen Y J Chem Inf Model. 2020; 61(1):46-66.

PMID: 33347301 PMC: 7987499. DOI: 10.1021/acs.jcim.0c00866.


References
1.
Puvanendrampillai D, Mitchell J . L/D Protein Ligand Database (PLD): additional understanding of the nature and specificity of protein-ligand complexes. Bioinformatics. 2003; 19(14):1856-7. DOI: 10.1093/bioinformatics/btg243. View

2.
Sanner M . Python: a programming language for software integration and development. J Mol Graph Model. 2000; 17(1):57-61. View

3.
Bindewald E, Skolnick J . A scoring function for docking ligands to low-resolution protein structures. J Comput Chem. 2005; 26(4):374-83. DOI: 10.1002/jcc.20175. View

4.
Wei B, Weaver L, Ferrari A, Matthews B, Shoichet B . Testing a flexible-receptor docking algorithm in a model binding site. J Mol Biol. 2004; 337(5):1161-82. DOI: 10.1016/j.jmb.2004.02.015. View

5.
Good A, Cheney D, Sitkoff D, Tokarski J, Stouch T, Bassolino D . Analysis and optimization of structure-based virtual screening protocols. 2. Examination of docked ligand orientation sampling methodology: mapping a pharmacophore for success. J Mol Graph Model. 2003; 22(1):31-40. DOI: 10.1016/S1093-3263(03)00124-4. View