An application of CIFAP for predicting the binding affinity of Chk1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide

• Published in: Journal of molecular recognition Vol. 30; no. 11
• Main Authors: Konyar, Dilan, Erdas, Ozlem, Alpaslan, Ferda Nur, Buyukbingol, Erdem
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01-11-2017
• Subjects: Affinity; binding affinity prediction; Binding sites; Checkpoint Kinase 1 - antagonists & inhibitors; Checkpoint Kinase 1 - chemistry; Coordination compounds; Floating structures; Humans; Imaging, Three-Dimensional; Inhibitors; Inhibitory Concentration 50; Least squares method; Least-Squares Analysis; Ligands; Models, Molecular; partial least squares regression; Predictions; Protein Kinase Inhibitors - chemistry; Protein Kinase Inhibitors - pharmacology; Proteins; Regression analysis; sequential floating forward selection; Support Vector Machine; Support vector machines; support vector regression; Thiazoles - chemistry; Thiazoles - pharmacology; Three dimensional models; Two dimensional models; support vector regression; partial least squares regression; binding affinity prediction; sequential floating forward selection
• ISSN: 0952-3499; 1099-1352
• DOI: 10.1002/jmr.2642

Investigation of protein‐ligand interactions obtained from experiments has a crucial part in the design of newly discovered and effective drugs. Analyzing the data extracted from known interactions could help scientists to predict the binding affinities of promising ligands before conducting experiments. The objective of this study is to advance the CIFAP (compressed images for affinity prediction) method, which is relevant to a protein‐ligand model, identifying 2D electrostatic potential images by separating the binding site of protein‐ligand complexes and using the images for predicting the computational affinity information represented by pIC50 values. The CIFAP method has 2 phases, namely, data modeling and prediction. In data modeling phase, the separated 3D structure of the binding pocket with the ligand inside is fitted into an electrostatic potential grid box, which is then compressed through 3 orthogonal directions into three 2D images for each protein‐ligand complex. Sequential floating forward selection technique is performed for acquiring prediction patterns from the images. In the prediction phase, support vector regression (SVR) and partial least squares regression are used for testing the quality of the CIFAP method for predicting the binding affinity of 45 CHK1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide. The results show that the CIFAP method using both support vector regression and partial least squares regression is very effective for predicting the binding affinities of CHK1‐ligand complexes with low‐error values and high correlation. As a future work, the results could be improved by working on the pose of the ligands inside the grid.
The CIFAP method constructs 2D electrostatic potential images by separating the binding site of protein‐ligand complexes and uses the images for predicting the computational affinity information represented by pIC50 values. In this study, the enhanced method is applied on 45 CHK1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide. The results show that the CIFAP method using both support vector regression and partial least squares regression is very effective for predicting the binding affinities of CHK1‐ligand complexes with low‐error values and high correlation.