MDeePred: Novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery

Date

2021-03-01

Author

Rifaioglu, A.S.
Atalay, R. Cetin
Kahraman, Deniz Cansen
DOĞAN, TUNCA
Martin, M.
Atalay, Mehmet Volkan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

272
views

0
downloads

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, remains a challenge. Results: Here, we present a novel protein featurization approach to be used in deep learning-based compound-target protein binding affinity prediction. In the proposed method, multiple types of protein features such as sequence, structural, evolutionary and physicochemical properties are incorporated within multiple 2D vectors, which is then fed to state-of-the-art pairwise input hybrid deep neural networks to predict the real-valued compound-target protein interactions. The method adopts the proteochemometric approach, where both the compound and target protein features are used at the input level to model their interaction. The whole system is called MDeePred and it is a new method to be used for the purposes of computational drug discovery and repositioning. We evaluated MDeePred on well-known benchmark datasets and compared its performance with the state-of-the-art methods. We also performed in vitro comparative analysis of MDeePred predictions with selected kinase inhibitors' action on cancer cells. MDeePred is a scalable method with sufficiently high predictive performance. The featurization approach proposed here can also be utilized for other protein-related predictive tasks. Availability and implementation: The source code, datasets, additional information and user instructions of MDeePred are available at https://github.com/cansyl/MDeePred.

Subject Keywords

Deep Learning, Drug Discovery, Humans, Neural Networks, Computer, Protein Binding, Software

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85106439163&origin=inward
https://hdl.handle.net/11511/90855

Journal

Bioinformatics

DOI

https://doi.org/10.1093/bioinformatics/btaa858

Collections

Graduate School of Informatics, Article

Suggestions

OpenMETU
Core

Predicting the binding affinities of drug-protein interaction by analyzing the images of binding sites Erdaş, Özlem; Alpaslan, Ferda Nur; Büyükbingöl, Erdem; Department of Computer Engineering (2013) 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 ...
DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations Rifaioğlu, Ahmet Süreyya; Nalbat, Esra; Atalay, Mehmet Volkan (2020-03-07) The identification of physical interactions between drug candidate compounds and target biomolecules is an important process in drug discovery. Since conventional screening procedures are expensive and time consuming, computational approaches are employed to provide aid by automatically predicting novel drug-target interactions (DTIs). In this study, we propose a large-scale DTI prediction system, DEEPScreen, for early stage drug discovery, using deep convolutional neural networks. One of the main advantage...
Relationship of adipokines (adiponectin, resistin and RBP4) with metabolic syndrome components in pubertal obese children Boyraz, Mehmet; Cekmez, Ferhat; Karaoglu, Abdulbaki; CİNAZ, PEYAMİ; Durak, Mustafa; BİDECİ, AYSUN (Future Medicine Ltd, 2013-06-01) Aim: To investigate the relationship between serum adiponectin, resistin and RBP4 levels and the components of metabolic syndrome. Patients & methods: Serum adiponectin, resistin and RBP4 levels were detected and analyzed in 148 8-18-year-old Turkish obese pubertal children with/without metabolic syndrome. Results: Adiponectin and resistin concentrations were significantly inversely correlated with BMI standard deviation score, homeostatic model assessment insulin resistance, waist circumference, triglyceri...
Hnet-DTI: Incorporating heterogeneous information network for drug-target interaction prediction Özsarı , Gökhan; Rifaioğlu , Ahmet Süreyya; Doğan , Tunca; Atalay, Volkan (Orta Doğu Teknik Üniversitesi Enformatik Enstitüsü; 2022-10) Identifying drug-target interactions (DTIs) is crucial in drug discovery. In silico prediction of interactions between drugs and target proteins is needed to accelerate the drug discovery process. Although many DTI prediction methods have been proposed in recent years, there is still room for improvement. This study proposes a graph neural network-based method to exploit the heterogeneous information network for drug-target interaction prediction. The study includes two major contributions: Heterogeneous in...
SRC family kinase inhibitors antagonize the toxicity of multiple serotypes of botulinum neurotoxin in human embryonic stem cell-derived motor neurons. Kiriş, Erkan; Nuss, JE; Wanner, LM; Peyser, BD; Du, HT; Gomba, GY; Kota, KP; Panchal, RG; Gussio, R; Kane, CD; Tessarollo, L; Bavari, S (2015-05-01) Botulinum neurotoxins (BoNTs), the causative agents of botulism, are potent inhibitors of neurotransmitter release from motor neurons. There are currently no drugs to treat BoNT intoxication after the onset of the disease symptoms. In this study, we explored how modulation of key host pathways affects the process of BoNT intoxication in human motor neurons, focusing on Src family kinase (SFK) signaling. Motor neurons derived from human embryonic stem (hES) cells were treated with a panel of SFK inhibitors a...

Citation Formats

A. S. Rifaioglu, R. C. Atalay, D. C. Kahraman, T. DOĞAN, M. Martin, and M. V. Atalay, “MDeePred: Novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery,” Bioinformatics, pp. 693–704, 2021, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85106439163&origin=inward.