Yazar "Alpaslan, Ferda Nur" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • [ X ]
    Öğe
    An application of CIFAP for predicting the binding affinity of Chk1 inhibitors derived from 2-aminothiazole-4-carboxamide
    (Wiley, 2017) Konyar, Dilan; Erdaş, Özlem; Alpaslan, Ferda Nur; Büyükbingol, Erdem
    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 pIC(50) 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.
  • [ X ]
    Öğe
    Three-dimensional analysis of binding sites for predicting binding affinities in drug design
    (Amer Chemical Soc, 2019) Erdaş Çicek, Özlem; Ataç, Ali Osman; Gürkan Alp, A. Selen; Büyükbingol, Erdem; Alpaslan, Ferda Nur
    Understanding the interaction between drug molecules and proteins is one of the main challenges in drug design. Several tools have been developed recently to decrease the complexity of the process. Artificial intelligence and machine learning methods offer promising results in predicting the binding affinities. It becomes possible to do accurate predictions by using the known protein-ligand interactions. In this study, the electrostatic potential values extracted from 3-dimensional grid cubes of the drug-protein binding sites are used for predicting binding affinities of related complexes. A new algorithm with a dynamic feature selection method was implemented, which is derived from Compressed Images For Affinity Prediction (CIFAP) study, to predict binding affinities of Checkpoint Kinase 1 and Caspase 3 inhibitors.