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Vinh T, Nguyen L, Trinh QH, Nguyen-Vo TH, Nguyen BP. Predicting Cardiotoxicity of Molecules Using Attention-Based Graph Neural Networks. J Chem Inf Model 2024; 64:1816-1827. [PMID: 38438914 DOI: 10.1021/acs.jcim.3c01286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
In drug discovery, the search for new and effective medications is often hindered by concerns about toxicity. Numerous promising molecules fail to pass the later phases of drug development due to strict toxicity assessments. This challenge significantly increases the cost, time, and human effort needed to discover new therapeutic molecules. Additionally, a considerable number of drugs already on the market have been withdrawn or re-evaluated because of their unwanted side effects. Among the various types of toxicity, drug-induced heart damage is a severe adverse effect commonly associated with several medications, especially those used in cancer treatments. Although a number of computational approaches have been proposed to identify the cardiotoxicity of molecules, the performance and interpretability of the existing approaches are limited. In our study, we proposed a more effective computational framework to predict the cardiotoxicity of molecules using an attention-based graph neural network. Experimental results indicated that the proposed framework outperformed the other methods. The stability of the model was also confirmed by our experiments. To assist researchers in evaluating the cardiotoxicity of molecules, we have developed an easy-to-use online web server that incorporates our model.
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Affiliation(s)
- Tuan Vinh
- Department of Chemistry, Emory University, 201 Dowman Drive, Atlanta, Georgia 30322-1007, United States
| | - Loc Nguyen
- School of Mathematics and Statistics, Victoria University of Wellington, Kelburn Parade, Wellington 6012, New Zealand
| | - Quang H Trinh
- School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi 100000, Vietnam
| | - Thanh-Hoang Nguyen-Vo
- School of Mathematics and Statistics, Victoria University of Wellington, Kelburn Parade, Wellington 6012, New Zealand
- School of Innovation, Design and Technology, Wellington Institute of Technology, 21 Kensington Avenue, Lower Hutt 5012, New Zealand
| | - Binh P Nguyen
- School of Mathematics and Statistics, Victoria University of Wellington, Kelburn Parade, Wellington 6012, New Zealand
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2
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Wen S, Zhao P, Chen S, Deng B, Fang Q, Wang J. The impact of MCCK1, an inhibitor of IKBKE kinase, on acute B lymphocyte leukemia cells. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:5164-5180. [PMID: 38872531 DOI: 10.3934/mbe.2024228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
B-cell acute lymphoblastic leukemia (B-ALL) is a malignant blood disorder, particularly detrimental to children and adolescents, with recurrent or unresponsive cases contributing significantly to cancer-associated fatalities. IKBKE, associated with innate immunity, tumor promotion, and drug resistance, remains poorly understood in the context of B-ALL. Thus, this research aimed to explore the impact of the IKBKE inhibitor MCCK1 on B-ALL cells. The study encompassed diverse experiments, including clinical samples, in vitro and in vivo investigations. Quantitative real-time fluorescence PCR and protein blotting revealed heightened IKBKE mRNA and protein expression in B-ALL patients. Subsequent in vitro experiments with B-ALL cell lines demonstrated that MCCK1 treatment resulted in reduced cell viability and survival rates, with flow cytometry indicating cell cycle arrest. In vivo experiments using B-ALL mouse tumor models substantiated MCCK1's efficacy in impeding tumor proliferation. These findings collectively suggest that IKBKE, found to be elevated in B-ALL patients, may serve as a promising drug target, with MCCK1 demonstrating potential for inducing apoptosis in B-ALL cells both in vitro and in vivo.
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Affiliation(s)
| | - Peng Zhao
- Hematology Department, Affiliated Hospital of Guizhou Medical University, Guiyang 550004, China
| | - Siyu Chen
- The Second Affiliated Hospital, The Third Military Medical University, Chongqing 400000, China
| | - Bo Deng
- Guizhou Medical University, Guiyang 550004, China
| | - Qin Fang
- Pharmacy Department, Affiliated Hospital of Guizhou Medical University, Guiyang 550004, China
| | - Jishi Wang
- Hematology Department, Affiliated Hospital of Guizhou Medical University, Guiyang 550004, China
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3
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Wang R, Li L, Chen M, Li X, Liu Y, Xue Z, Ma Q, Chen J. Gene expression insights: Chronic stress and bipolar disorder: A bioinformatics investigation. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:392-414. [PMID: 38303428 DOI: 10.3934/mbe.2024018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Bipolar disorder (BD) is a psychiatric disorder that affects an increasing number of people worldwide. The mechanisms of BD are unclear, but some studies have suggested that it may be related to genetic factors with high heritability. Moreover, research has shown that chronic stress can contribute to the development of major illnesses. In this paper, we used bioinformatics methods to analyze the possible mechanisms of chronic stress affecting BD through various aspects. We obtained gene expression data from postmortem brains of BD patients and healthy controls in datasets GSE12649 and GSE53987, and we identified 11 chronic stress-related genes (CSRGs) that were differentially expressed in BD. Then, we screened five biomarkers (IGFBP6, ALOX5AP, MAOA, AIF1 and TRPM3) using machine learning models. We further validated the expression and diagnostic value of the biomarkers in other datasets (GSE5388 and GSE78936) and performed functional enrichment analysis, regulatory network analysis and drug prediction based on the biomarkers. Our bioinformatics analysis revealed that chronic stress can affect the occurrence and development of BD through many aspects, including monoamine oxidase production and decomposition, neuroinflammation, ion permeability, pain perception and others. In this paper, we confirm the importance of studying the genetic influences of chronic stress on BD and other psychiatric disorders and suggested that biomarkers related to chronic stress may be potential diagnostic tools and therapeutic targets for BD.
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Affiliation(s)
- Rongyanqi Wang
- School of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Lan Li
- College of Basic Medicine, Hubei University of Chinese Medicine, Wuhan 430065, China
| | - Man Chen
- College of Basic Medicine, Hubei University of Chinese Medicine, Wuhan 430065, China
| | - Xiaojuan Li
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, China
| | - Yueyun Liu
- School of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Zhe Xue
- School of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Qingyu Ma
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, China
| | - Jiaxu Chen
- School of Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
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4
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Wang R, Liu Z, Gong J, Zhou Q, Guan X, Ge G. An Uncertainty-Guided Deep Learning Method Facilitates Rapid Screening of CYP3A4 Inhibitors. J Chem Inf Model 2023; 63:7699-7710. [PMID: 38055780 DOI: 10.1021/acs.jcim.3c01241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
Cytochrome P450 3A4 (CYP3A4), a prominent member of the P450 enzyme superfamily, plays a crucial role in metabolizing various xenobiotics, including over 50% of clinically significant drugs. Evaluating CYP3A4 inhibition before drug approval is essential to avoiding potentially harmful pharmacokinetic drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Despite the development of several CYP inhibitor prediction models, the primary approach for screening CYP inhibitors still relies on experimental methods. This might stem from the limitations of existing models, which only provide deterministic classification outcomes instead of precise inhibition intensity (e.g., IC50) and often suffer from inadequate prediction reliability. To address this challenge, we propose an uncertainty-guided regression model to accurately predict the IC50 values of anti-CYP3A4 activities. First, a comprehensive data set of CYP3A4 inhibitors was compiled, consisting of 27,045 compounds with classification labels, including 4395 compounds with explicit IC50 values. Second, by integrating the predictions of the classification model trained on a larger data set and introducing an evidential uncertainty method to rank prediction confidence, we obtained a high-precision and reliable regression model. Finally, we use the evidential uncertainty values as a trustworthy indicator to perform a virtual screening of an in-house compound set. The in vitro experiment results revealed that this new indicator significantly improved the hit ratio and reduced false positives among the top-ranked compounds. Specifically, among the top 20 compounds ranked with uncertainty, 15 compounds were identified as novel CYP3A4 inhibitors, and three of them exhibited activities less than 1 μM. In summary, our findings highlight the effectiveness of incorporating uncertainty in compound screening, providing a promising strategy for drug discovery and development.
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Affiliation(s)
- Ruixuan Wang
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Zhikang Liu
- School of Mathematics and Statistics, Central South University, Changsha 410083, China
| | - Jiahao Gong
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Qingping Zhou
- School of Mathematics and Statistics, Central South University, Changsha 410083, China
| | - Xiaoqing Guan
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Guangbo Ge
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
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Ouzounis S, Panagiotopoulos V, Bafiti V, Zoumpoulakis P, Cavouras D, Kalatzis I, Matsoukas MT, Katsila T. A Robust Machine Learning Framework Built Upon Molecular Representations Predicts CYP450 Inhibition: Toward Precision in Drug Repurposing. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2023. [PMID: 37406257 PMCID: PMC10357106 DOI: 10.1089/omi.2023.0075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/07/2023]
Abstract
Human cytochrome P450 (CYP450) enzymes play a crucial role in drug metabolism and pharmacokinetics. CYP450 inhibition can lead to toxicity, in particular when drugs are co-administered with other drugs and xenobiotics or in the case of polypharmacy. Predicting CYP450 inhibition is also important for rational drug discovery and development, and precision in drug repurposing. In this overarching context, digital transformation of drug discovery and development, for example, using machine and deep learning approaches, offers prospects for prediction of CYP450 inhibition through computational models. We report here the development of a majority-voting machine learning framework to classify inhibitors and noninhibitors for seven major human liver CYP450 isoforms (CYP1A2, CYP2A6, CYP2B6, CYP2C9, CYP2C19, CYP2D6, and CYP3A4). For the machine learning models reported herein, we employed interaction fingerprints that were derived from molecular docking simulations, thus adding an additional layer of information for protein-ligand interactions. The proposed machine learning framework is based on the structure of the binding site of isoforms to produce predictions beyond previously reported approaches. Also, we carried out a comparative analysis so as to identify which representation of test compounds (molecular descriptors, molecular fingerprints, or protein-ligand interaction fingerprints) affects the predictive performance of the models. This work underlines the ways in which the structure of the enzyme catalytic site influences machine learning predictions and the need for robust frameworks toward better-informed predictions.
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Affiliation(s)
- Sotiris Ouzounis
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece
- Department of Biomedical Engineering, University of West Attica, Egaleo, Greece
- Cloudpharm PC, Athens, Greece
| | - Vasilis Panagiotopoulos
- Department of Biomedical Engineering, University of West Attica, Egaleo, Greece
- Cloudpharm PC, Athens, Greece
| | - Vivi Bafiti
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece
| | - Panagiotis Zoumpoulakis
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece
- Department of Food Science and Technology, University of West Attica, Egaleo, Greece
| | - Dionisis Cavouras
- Department of Biomedical Engineering, University of West Attica, Egaleo, Greece
| | - Ioannis Kalatzis
- Department of Biomedical Engineering, University of West Attica, Egaleo, Greece
| | - Minos-Timotheos Matsoukas
- Department of Biomedical Engineering, University of West Attica, Egaleo, Greece
- Cloudpharm PC, Athens, Greece
| | - Theodora Katsila
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece
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6
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Nguyen-Vo TH, Trinh QH, Nguyen L, Nguyen-Hoang PU, Rahardja S, Nguyen BP. i4mC-GRU: Identifying DNA N 4-Methylcytosine sites in mouse genomes using bidirectional gated recurrent unit and sequence-embedded features. Comput Struct Biotechnol J 2023; 21:3045-3053. [PMID: 37273848 PMCID: PMC10238585 DOI: 10.1016/j.csbj.2023.05.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 05/12/2023] [Accepted: 05/12/2023] [Indexed: 06/06/2023] Open
Abstract
N4-methylcytosine (4mC) is one of the most common DNA methylation modifications found in both prokaryotic and eukaryotic genomes. Since the 4mC has various essential biological roles, determining its location helps reveal unexplored physiological and pathological pathways. In this study, we propose an effective computational method called i4mC-GRU using a gated recurrent unit and duplet sequence-embedded features to predict potential 4mC sites in mouse (Mus musculus) genomes. To fairly assess the performance of the model, we compared our method with several state-of-the-art methods using two different benchmark datasets. Our results showed that i4mC-GRU achieved area under the receiver operating characteristic curve values of 0.97 and 0.89 and area under the precision-recall curve values of 0.98 and 0.90 on the first and second benchmark datasets, respectively. Briefly, our method outperformed existing methods in predicting 4mC sites in mouse genomes. Also, we deployed i4mC-GRU as an online web server, supporting users in genomics studies.
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Affiliation(s)
- Thanh-Hoang Nguyen-Vo
- School of Mathematics and Statistics, Victoria University of Wellington, Wellington 6140, New Zealand
- School of Innovation, Design and Technology, Wellington Institute of Technology, Wellington 5012, New Zealand
| | - Quang H. Trinh
- School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi 100000, Vietnam
| | - Loc Nguyen
- School of Mathematics and Statistics, Victoria University of Wellington, Wellington 6140, New Zealand
| | - Phuong-Uyen Nguyen-Hoang
- Computational Biology Center, International University - VNU HCMC, Ho Chi Minh City 700000, Vietnam
| | - Susanto Rahardja
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
- Infocomm Technology Cluster, Singapore Institute of Technology, Singapore 138683, Singapore
| | - Binh P. Nguyen
- School of Mathematics and Statistics, Victoria University of Wellington, Wellington 6140, New Zealand
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Ai D, Cai H, Wei J, Zhao D, Chen Y, Wang L. DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction. Front Pharmacol 2023; 14:1099093. [PMID: 37101544 PMCID: PMC10123292 DOI: 10.3389/fphar.2023.1099093] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/31/2023] [Indexed: 04/28/2023] Open
Abstract
Cytochrome P450 (CYP) is a superfamily of heme-containing oxidizing enzymes involved in the metabolism of a wide range of medicines, xenobiotics, and endogenous compounds. Five of the CYPs (1A2, 2C9, 2C19, 2D6, and 3A4) are responsible for metabolizing the vast majority of approved drugs. Adverse drug-drug interactions, many of which are mediated by CYPs, are one of the important causes for the premature termination of drug development and drug withdrawal from the market. In this work, we reported in silicon classification models to predict the inhibitory activity of molecules against these five CYP isoforms using our recently developed FP-GNN deep learning method. The evaluation results showed that, to the best of our knowledge, the multi-task FP-GNN model achieved the best predictive performance with the highest average AUC (0.905), F1 (0.779), BA (0.819), and MCC (0.647) values for the test sets, even compared to advanced machine learning, deep learning, and existing models. Y-scrambling testing confirmed that the results of the multi-task FP-GNN model were not attributed to chance correlation. Furthermore, the interpretability of the multi-task FP-GNN model enables the discovery of critical structural fragments associated with CYPs inhibition. Finally, an online webserver called DEEPCYPs and its local version software were created based on the optimal multi-task FP-GNN model to detect whether compounds bear potential inhibitory activity against CYPs, thereby promoting the prediction of drug-drug interactions in clinical practice and could be used to rule out inappropriate compounds in the early stages of drug discovery and/or identify new CYPs inhibitors.
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8
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Nguyen QH, Ngo HH, Nguyen-Vo TH, Do TT, Rahardja S, Nguyen BP. eMIC-AntiKP: Estimating minimum inhibitory concentrations of antibiotics towards Klebsiella pneumoniae using deep learning. Comput Struct Biotechnol J 2022; 21:751-757. [PMID: 36659924 PMCID: PMC9827358 DOI: 10.1016/j.csbj.2022.12.041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 12/22/2022] [Accepted: 12/23/2022] [Indexed: 12/27/2022] Open
Abstract
Nowadays, antibiotic resistance has become one of the most concerning problems that directly affects the recovery process of patients. For years, numerous efforts have been made to efficiently use antimicrobial drugs with appropriate doses not only to exterminate microbes but also stringently constrain any chances for bacterial evolution. However, choosing proper antibiotics is not a straightforward and time-effective process because well-defined drugs can only be given to patients after determining microbic taxonomy and evaluating minimum inhibitory concentrations (MICs). Besides conventional methods, numerous computer-aided frameworks have been recently developed using computational advances and public data sources of clinical antimicrobial resistance. In this study, we introduce eMIC-AntiKP, a computational framework specifically designed to predict the MIC values of 20 antibiotics towards Klebsiella pneumoniae. Our prediction models were constructed using convolutional neural networks and k-mer counting-based features. The model for cefepime has the most limited performance with a test 1-tier accuracy of 0.49, while the model for ampicillin has the highest performance with a test 1-tier accuracy of 1.00. Most models have satisfactory performance, with test accuracies ranging from about 0.70-0.90. The significance of eMIC-AntiKP is the effective utilization of computing resources to make it a compact and portable tool for most moderately configured computers. We provide users with two options, including an online web server for basic analysis and an offline package for deeper analysis and technical modification.
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Affiliation(s)
- Quang H. Nguyen
- School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi 100000, Viet Nam
| | - Hoang H. Ngo
- School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi 100000, Viet Nam
| | - Thanh-Hoang Nguyen-Vo
- School of Mathematics and Statistics, Victoria University of Wellington, Wellington 6140, New Zealand
| | - Trang T.T. Do
- School of Innovation, Design and Technology, Wellington Institute of Technology, Lower Hutt 5012, New Zealand
| | - Susanto Rahardja
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China,Infocomm Technology Cluster, Singapore Institute of Technology, Singapore 138683, Singapore,Corresponding author at: School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China.
| | - Binh P. Nguyen
- School of Mathematics and Statistics, Victoria University of Wellington, Wellington 6140, New Zealand,Corresponding author.
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9
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Wei GW, Soares TA, Wahab H, Zhu F. Computational Chemistry in Asia. J Chem Inf Model 2022; 62:5035-5037. [DOI: 10.1021/acs.jcim.2c01050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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10
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Qiu M, Liang X, Deng S, Li Y, Ke Y, Wang P, Mei H. A unified GCNN model for predicting CYP450 inhibitors by using graph convolutional neural networks with attention mechanism. Comput Biol Med 2022; 150:106177. [PMID: 36242811 DOI: 10.1016/j.compbiomed.2022.106177] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 09/19/2022] [Accepted: 10/01/2022] [Indexed: 11/17/2022]
Abstract
Undesirable drug-drug interactions (DDIs) may lead to serious adverse side effects when more than two drugs are administered to a patient simultaneously. One of the most common DDIs is caused by unexpected inhibition of a specific human cytochrome P450 (CYP450), which plays a dominant role in the metabolism of the co-administered drugs. Therefore, a unified and reliable method for predicting the potential inhibitors of CYP450 family is extremely important in drug development. In this work, graph convolutional neural network (GCN) with attention mechanism and 1-D convolutional neural network (CNN) were used to extract the features of CYP ligands and the binding sites of CYP450 respectively, which were then combined to establish a unified GCN-CNN (GCNN) model for predicting the inhibitors of 5 dominant CYP isoforms, i.e., 1A2, 2C9, 2C19, 2D6, and 3A4. Overall, the established GCNN model showed good performances on the test samples and achieved better performances than the recently proposed iCYP-MFE model by using the same datasets. Based on the heat-map analysis of the resulting molecular graphs, the key structural determinants of the CYP inhibitors were further explored.
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Affiliation(s)
- Minyao Qiu
- Key Laboratory of Biorheological Science and Technology (Ministry of Education), College of Bioengineering, Chongqing University, Chongqing, 400044, China; College of Bioengineering, Chongqing University, Chongqing, 400044, China
| | - Xiaoqi Liang
- College of Bioengineering, Chongqing University, Chongqing, 400044, China
| | - Siyao Deng
- College of Bioengineering, Chongqing University, Chongqing, 400044, China
| | - Yufang Li
- College of Bioengineering, Chongqing University, Chongqing, 400044, China
| | - Yanlan Ke
- College of Bioengineering, Chongqing University, Chongqing, 400044, China
| | - Pingqing Wang
- College of Bioengineering, Chongqing University, Chongqing, 400044, China
| | - Hu Mei
- Key Laboratory of Biorheological Science and Technology (Ministry of Education), College of Bioengineering, Chongqing University, Chongqing, 400044, China; College of Bioengineering, Chongqing University, Chongqing, 400044, China.
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11
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Ai D, Wu J, Cai H, Zhao D, Chen Y, Wei J, Xu J, Zhang J, Wang L. A multi-task FP-GNN framework enables accurate prediction of selective PARP inhibitors. Front Pharmacol 2022; 13:971369. [DOI: 10.3389/fphar.2022.971369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 09/14/2022] [Indexed: 11/13/2022] Open
Abstract
PARP (poly ADP-ribose polymerase) family is a crucial DNA repair enzyme that responds to DNA damage, regulates apoptosis, and maintains genome stability; therefore, PARP inhibitors represent a promising therapeutic strategy for the treatment of various human diseases including COVID-19. In this study, a multi-task FP-GNN (Fingerprint and Graph Neural Networks) deep learning framework was proposed to predict the inhibitory activity of molecules against four PARP isoforms (PARP-1, PARP-2, PARP-5A, and PARP-5B). Compared with baseline predictive models based on four conventional machine learning methods such as RF, SVM, XGBoost, and LR as well as six deep learning algorithms such as DNN, Attentive FP, MPNN, GAT, GCN, and D-MPNN, the evaluation results indicate that the multi-task FP-GNN method achieves the best performance with the highest average BA, F1, and AUC values of 0.753 ± 0.033, 0.910 ± 0.045, and 0.888 ± 0.016 for the test set. In addition, Y-scrambling testing successfully verified that the model was not results of chance correlation. More importantly, the interpretability of the multi-task FP-GNN model enabled the identification of key structural fragments associated with the inhibition of each PARP isoform. To facilitate the use of the multi-task FP-GNN model in the field, an online webserver called PARPi-Predict and its local version software were created to predict whether compounds bear potential inhibitory activity against PARPs, thereby contributing to design and discover better selective PARP inhibitors.
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12
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Nguyen-Vo TH, Trinh QH, Nguyen L, Nguyen-Hoang PU, Rahardja S, Nguyen BP. iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features. BMC Genomics 2022; 23:681. [PMID: 36192696 PMCID: PMC9531353 DOI: 10.1186/s12864-022-08829-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 08/08/2022] [Indexed: 11/30/2022] Open
Abstract
Background Promoters, non-coding DNA sequences located at upstream regions of the transcription start site of genes/gene clusters, are essential regulatory elements for the initiation and regulation of transcriptional processes. Furthermore, identifying promoters in DNA sequences and genomes significantly contributes to discovering entire structures of genes of interest. Therefore, exploration of promoter regions is one of the most imperative topics in molecular genetics and biology. Besides experimental techniques, computational methods have been developed to predict promoters. In this study, we propose iPromoter-Seqvec – an efficient computational model to predict TATA and non-TATA promoters in human and mouse genomes using bidirectional long short-term memory neural networks in combination with sequence-embedded features extracted from input sequences. The promoter and non-promoter sequences were retrieved from the Eukaryotic Promoter database and then were refined to create four benchmark datasets. Results The area under the receiver operating characteristic curve (AUCROC) and the area under the precision-recall curve (AUCPR) were used as two key metrics to evaluate model performance. Results on independent test sets showed that iPromoter-Seqvec outperformed other state-of-the-art methods with AUCROC values ranging from 0.85 to 0.99 and AUCPR values ranging from 0.86 to 0.99. Models predicting TATA promoters in both species had slightly higher predictive power compared to those predicting non-TATA promoters. With a novel idea of constructing artificial non-promoter sequences based on promoter sequences, our models were able to learn highly specific characteristics discriminating promoters from non-promoters to improve predictive efficiency. Conclusions iPromoter-Seqvec is a stable and robust model for predicting both TATA and non-TATA promoters in human and mouse genomes. Our proposed method was also deployed as an online web server with a user-friendly interface to support research communities. Links to our source codes and web server are available at https://github.com/mldlproject/2022-iPromoter-Seqvec. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08829-6.
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Affiliation(s)
- Thanh-Hoang Nguyen-Vo
- School of Mathematics and Statistics, Victoria University of Wellington, Gate 7, Kelburn Parade, 6140, Wellington, New Zealand
| | - Quang H Trinh
- School of Information and Communication Technology, Hanoi University of Science and Technology, 1 Dai Co Viet, 100000, Hanoi, Vietnam
| | - Loc Nguyen
- School of Mathematics and Statistics, Victoria University of Wellington, Gate 7, Kelburn Parade, 6140, Wellington, New Zealand
| | - Phuong-Uyen Nguyen-Hoang
- Computational Biology Center, International University - VNU HCMC, Quarter 6, Linh Trung Ward, Thu Duc District, 700000, Ho Chi Minh City, Vietnam
| | - Susanto Rahardja
- School of Marine Science and Technology, Northwestern Polytechnical University, 127 West Youyi Road, 710072, Xi'an, China. .,Infocomm Technology Cluster, Singapore Institute of Technology, 10 Dover Drive, 138683, Singapore, Singapore.
| | - Binh P Nguyen
- School of Mathematics and Statistics, Victoria University of Wellington, Gate 7, Kelburn Parade, 6140, Wellington, New Zealand.
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13
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Wang J, Lou C, Liu G, Li W, Wu Z, Tang Y. Profiling prediction of nuclear receptor modulators with multi-task deep learning methods: toward the virtual screening. Brief Bioinform 2022; 23:6673852. [PMID: 35998896 DOI: 10.1093/bib/bbac351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 07/13/2022] [Accepted: 07/27/2022] [Indexed: 11/13/2022] Open
Abstract
Nuclear receptors (NRs) are ligand-activated transcription factors, which constitute one of the most important targets for drug discovery. Current computational strategies mainly focus on a single target, and the transfer of learned knowledge among NRs was not considered yet. Herein we proposed a novel computational framework named NR-Profiler for prediction of potential NR modulators with high affinity and specificity. First, we built a comprehensive NR data set including 42 684 interactions to connect 42 NRs and 31 033 compounds. Then, we used multi-task deep neural network and multi-task graph convolutional neural network architectures to construct multi-task multi-classification models. To improve the predictive capability and robustness, we built a consensus model with an area under the receiver operating characteristic curve (AUC) = 0.883. Compared with conventional machine learning and structure-based approaches, the consensus model showed better performance in external validation. Using this consensus model, we demonstrated the practical value of NR-Profiler in virtual screening for NRs. In addition, we designed a selectivity score to quantitatively measure the specificity of NR modulators. Finally, we developed a freely available standalone software for users to make profiling predictions for their compounds of interest. In summary, our NR-Profiler provides a useful tool for NR-profiling prediction and is expected to facilitate NR-based drug discovery.
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Affiliation(s)
- Jiye Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Chaofeng Lou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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Goldwaser E, Laurent C, Lagarde N, Fabrega S, Nay L, Villoutreix BO, Jelsch C, Nicot AB, Loriot MA, Miteva MA. Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9. PLoS Comput Biol 2022; 18:e1009820. [PMID: 35081108 PMCID: PMC8820617 DOI: 10.1371/journal.pcbi.1009820] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 02/07/2022] [Accepted: 01/10/2022] [Indexed: 11/19/2022] Open
Abstract
Cytochrome P450 2C9 (CYP2C9) is a major drug-metabolizing enzyme that represents 20% of the hepatic CYPs and is responsible for the metabolism of 15% of drugs. A general concern in drug discovery is to avoid the inhibition of CYP leading to toxic drug accumulation and adverse drug-drug interactions. However, the prediction of CYP inhibition remains challenging due to its complexity. We developed an original machine learning approach for the prediction of drug-like molecules inhibiting CYP2C9. We created new predictive models by integrating CYP2C9 protein structure and dynamics knowledge, an original selection of physicochemical properties of CYP2C9 inhibitors, and machine learning modeling. We tested the machine learning models on publicly available data and demonstrated that our models successfully predicted CYP2C9 inhibitors with an accuracy, sensitivity and specificity of approximately 80%. We experimentally validated the developed approach and provided the first identification of the drugs vatalanib, piriqualone, ticagrelor and cloperidone as strong inhibitors of CYP2C9 with IC values <18 μM and sertindole, asapiprant, duvelisib and dasatinib as moderate inhibitors with IC50 values between 40 and 85 μM. Vatalanib was identified as the strongest inhibitor with an IC50 value of 0.067 μM. Metabolism assays allowed the characterization of specific metabolites of abemaciclib, cloperidone, vatalanib and tarafenacin produced by CYP2C9. The obtained results demonstrate that such a strategy could improve the prediction of drug-drug interactions in clinical practice and could be utilized to prioritize drug candidates in drug discovery pipelines.
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Affiliation(s)
- Elodie Goldwaser
- INSERM U1268 « Medicinal Chemistry and Translational Research », UMR 8038 CiTCoM, CNRS—University of Paris, Paris, France
| | | | - Nathalie Lagarde
- Laboratoire GBCM, EA7528, Conservatoire National des Arts et Métiers, 2 Rue Conté, Hésam Université, Paris, France
| | - Sylvie Fabrega
- Viral Vector for Gene Transfer core facility, Université de Paris—Structure Fédérative de Recherche Necker, INSERM US24/CNRS UMS3633, Paris, France
| | - Laure Nay
- Viral Vector for Gene Transfer core facility, Université de Paris—Structure Fédérative de Recherche Necker, INSERM US24/CNRS UMS3633, Paris, France
| | | | | | - Arnaud B. Nicot
- INSERM, Nantes Université, Center for Research in Transplantation and Translational Immunology, UMR 1064, ITUN, Nantes, France
| | - Marie-Anne Loriot
- University of Paris, INSERM U1138, Paris, France
- Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Service de Biochimie, Paris, France
| | - Maria A. Miteva
- INSERM U1268 « Medicinal Chemistry and Translational Research », UMR 8038 CiTCoM, CNRS—University of Paris, Paris, France
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