A novel methodology on distributed representations of proteins using their interacting ligands

Öztürk, Hakime; Özkırımlı, Elif; Özgür, Arzucan

doi:10.1093/bioinformatics/bty287

Cited by 38 publications

(26 citation statements)

References 45 publications

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“…LSTM architecture has been successfully employed to tasks such as detecting homology ( Hochreiter et al ., 2007 ), constructive peptide design ( Muller et al ., 2018 ) and function prediction ( Liu, 2017 ) that utilize amino-acid sequences. As future work, we also aim to utilize a recent ligand-based protein representation method proposed by our team that uses SMILES sequences of the interacting ligands to describe proteins ( Öztürk et al ., 2018 ).…”

Section: Resultsmentioning

confidence: 99%

DeepDTA: deep drug–target binding affinity prediction

2018

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MotivationThe identification of novel drug–target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein–ligand interactions assume a continuum of binding strength values, also called binding affinity and predicting this value still remains a challenge. The increase in the affinity data available in DT knowledge-bases allows the use of advanced learning techniques such as deep learning architectures in the prediction of binding affinities. In this study, we propose a deep-learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities. The few studies that focus on DT binding affinity prediction use either 3D structures of protein–ligand complexes or 2D features of compounds. One novel approach used in this work is the modeling of protein sequences and compound 1D representations with convolutional neural networks (CNNs).ResultsThe results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction. The model in which high-level representations of a drug and a target are constructed via CNNs achieved the best Concordance Index (CI) performance in one of our larger benchmark datasets, outperforming the KronRLS algorithm and SimBoost, a state-of-the-art method for DT binding affinity prediction.Availability and implementation https://github.com/hkmztrk/DeepDTA Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

DeepDTA: deep drug–target binding affinity prediction

2018

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“…Over the past decade, some researchers have successfully applied NLP techniques into biological sequences. One of the pioneering studies is from Asgari and Mofrad (2015) and it had been applied successfully in many later bioinformatics applications (Habibi et al, 2017; Hamid and Friedberg, 2018; Öztürk et al, 2018). However, most studies used the Word2Vec model or FastText model with a single level of N-gram.…”

Section: Introductionmentioning

confidence: 99%

Classifying Promoters by Interpreting the Hidden Information of DNA Sequences via Deep Learning and Combination of Continuous FastText N-Grams

Yapp

Nagasundaram

et al. 2019

Front. Bioeng. Biotechnol.

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A promoter is a short region of DNA (100–1,000 bp) where transcription of a gene by RNA polymerase begins. It is typically located directly upstream or at the 5′ end of the transcription initiation site. DNA promoter has been proven to be the primary cause of many human diseases, especially diabetes, cancer, or Huntington's disease. Therefore, classifying promoters has become an interesting problem and it has attracted the attention of a lot of researchers in the bioinformatics field. There were a variety of studies conducted to resolve this problem, however, their performance results still require further improvement. In this study, we will present an innovative approach by interpreting DNA sequences as a combination of continuous FastText N-grams, which are then fed into a deep neural network in order to classify them. Our approach is able to attain a cross-validation accuracy of 85.41 and 73.1% in the two layers, respectively. Our results outperformed the state-of-the-art methods on the same dataset, especially in the second layer (strength classification). Throughout this study, promoter regions could be identified with high accuracy and it provides analysis for further biological research as well as precision medicine. In addition, this study opens new paths for the natural language processing application in omics data in general and DNA sequences in particular.

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“…A large database of ligands was essential to test their binding affinity with the S-protein. For that, we used the data available [ 30 ] from GitHub repository [ 31 ]. We have used the data available there and converted that into a CSV file containing the SMILES code of over 615,000 distinct ligands.…”

Section: Resultsmentioning

confidence: 99%

Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug–Target Interaction Model

et al. 2021

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To fight against the present pandemic scenario of COVID-19 outbreak, medication with drugs and vaccines is extremely essential other than ventilation support. In this paper, we present a list of ligands which are expected to have the highest binding affinity with the S-glycoprotein of 2019-nCoV and thus can be used to make the drug for the novel coronavirus. Here, we implemented an architecture using 1D convolutional networks to predict drug–target interaction (DTI) values. The network was trained on the KIBA (Kinase Inhibitor Bioactivity) dataset. With this network, we predicted the KIBA scores (which gives a measure of binding affinity) of a list of ligands against the S-glycoprotein of 2019-nCoV. Based on these KIBA scores, we are proposing a list of ligands (33 top ligands based on best interactions) which have a high binding affinity with the S-glycoprotein of 2019-nCoV and thus can be used for the formation of drugs.

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A novel methodology on distributed representations of proteins using their interacting ligands

Cited by 38 publications

References 45 publications

DeepDTA: deep drug–target binding affinity prediction

DeepDTA: deep drug–target binding affinity prediction

Classifying Promoters by Interpreting the Hidden Information of DNA Sequences via Deep Learning and Combination of Continuous FastText N-Grams

Deep Learning-Based Potential Ligand Prediction Framework for COVID-19 with Drug–Target Interaction Model

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