Predicting the binding affinities of drug-protein interaction by analyzing the images of binding sites

Erdaş, Özlem
Analysis of protein-ligand interactions plays an important role in designing safe and efficient drugs, contributing to drug discovery and development. Recently, machine learning methods have been found useful in drug design, which utilize intelligent techniques to predict unknown protein-ligand interactions by learning from specific properties of known protein-ligand interactions. The aim of this thesis is to propose a novel computational model, Compressed Images for Affinity Prediction (CIFAP), to predict binding affinities of structurally related protein-ligand complexes. The novel method presented here is based on a protein-ligand model from which computational affinity information is obtained by utilizing 2D electrostatic potential images determined for the binding site of the proteins with its inhibitors. The patterns obtained from the 2D images were used for building a predictive model whose strength was tested using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR) and Adaptive Neuro-Fuzzy Inference System (ANFIS) in comparison. The experiments were conducted on two distinct protein-ligand complex systems, which were complexes of CHK1-thienopyridine derivatives and CASP3-isatin sulfonamide derivatives. It is observed that the pixels of the images which are close to the surfaces of the interaction site have better explanation of the binding affinity. Moreover, PLSR is found to be the most promising prediction method for CIFAP as compared to SVR and ANFIS with the lowest error and the highest correlation between the observed and experimental binding affinities. The Computational algorithm presented here is proposed to have a great potential in pharmacophore-based drug design, especially in prediction of binding related properties.


Compressed images for affinity prediction (CIFAP): a study on predicting binding affinities for checkpoint kinase 1 protein inhibitors
Erdas, Ozlem; Andac, Cenk A.; Gurkan-Alp, A. Selen; Alpaslan, Ferda Nur; Buyukbingol, Erdem (Wiley, 2013-06-01)
Analyses of known protein-ligand interactions play an important role in designing novel and efficient drugs, contributing to drug discovery and development. Recently, machine learning methods have proven useful in the design of novel drugs, which utilize intelligent techniques to predict the outcome of unknown protein-ligand interactions by learning from the physical and geometrical properties of known protein-ligand interactions. The aim of this study is to work through a specific example of a novel comput...
MDeePred: Novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery
Rifaioglu, A.S.; Atalay, R. Cetin; Kahraman, Deniz Cansen; DOĞAN, TUNCA; Martin, M.; Atalay, Mehmet Volkan (2021-03-01)
Motivation: Identification of interactions between bioactive small molecules and target proteins is crucial for novel drug discovery, drug repurposing and uncovering off-target effects. Due to the tremendous size of the chemical space, experimental bioactivity screening efforts require the aid of computational approaches. Although deep learning models have been successful in predicting bioactive compounds, effective and comprehensive featurization of proteins, to be given as input to deep neural networks, r...
Ünsal Beyge, Şeyma; Tunçbağ, Nurcan; Department of Medical Informatics (2021-9-6)
Classification of cancer drugs is crucial for drug repurposing since the cost and innovation deficit make new drug development processes challenging. Heterogeneity of cancer causes drug classification purely based on known mechanism of action (MoA) and the list of target proteins to be insufficient. Multi-omic data integration is necessary for a systems biology perspective to understand molecular mechanisms and interactions between cellular entities underlying the disease. This study integrates drug-target ...
A Droplet based Multi-Drug Screening System Controlled with Electrostatic Microvalves
Yıldırım, Ender; Külah, Haluk (2012-10-28)
This paper presents a droplet-based drug effect analysis system utilizing electrostatically-actuated normallyclosed microvalves to screen the effect of multiple drugs on a single type of cell. Proposed system minimizes the need for off-chip equipment by utilizing parylene based electrostatic microvalves. Prototypes of the system were fabricated and tested using colored DI water and 3 μm diameter micro beads, emulating drugs and cells respectively. During the tests, micro beads could be successfully entrappe...
3D analysis of the binding sites for predicting binding affinities in drug design
Ataç, Ali Osman; Alpaslan, Ferda Nur; Büyükbingöl, Erdem; Department of Computer Engineering (2014)
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 have promising results in predicting the affinities. Recently, accurate estimations have been performed by extracting the electrostatic potentials from images of the drug-protein binding sites which were generated by autodocking simulator. In this study, a new ...
Citation Formats
Ö. Erdaş, “Predicting the binding affinities of drug-protein interaction by analyzing the images of binding sites,” Ph.D. - Doctoral Program, Middle East Technical University, 2013.