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Cheng N, Wang L, Liu Y, Song B, Ding C. HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction. J Chem Inf Model 2024; 64:4334-4347. [PMID: 38709204 PMCID: PMC11135324 DOI: 10.1021/acs.jcim.4c00003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 04/23/2024] [Accepted: 04/24/2024] [Indexed: 05/07/2024]
Abstract
Drug synergy therapy is a promising strategy for cancer treatment. However, the extensive variety of available drugs and the time-intensive process of determining effective drug combinations through clinical trials pose significant challenges. It requires a reliable method for the rapid and precise selection of drug synergies. In response, various computational strategies have been developed for predicting drug synergies, yet the exploitation of heterogeneous biological network features remains underexplored. In this study, we construct a heterogeneous graph that encompasses diverse biological entities and interactions, utilizing rich data sets from sources, such as DrugCombDB, PubChem, UniProt, and cancer cell line encyclopedia (CCLE). We initialize node feature representations and introduce a novel virtual node to enhance drug representation. Our proposed method, the heterogeneous graph attention network for drug-drug synergy prediction (HANSynergy), has been experimentally validated to demonstrate that the heterogeneous graph attention network can extract key node features, efficiently harness the diversity of information, and further enhance network functionality through the incorporation of a multihead attention mechanism. In the comparative experiment, the highest accuracy (Acc) and area under the curve (AUC) are 0.877 and 0.947, respectively, in DrugCombDB_early data set, demonstrating the superiority of HANSynergy over the competing methods. Moreover, protein-protein interactions are important in understanding the mechanism of action of drugs. The heterogeneous attention mechanism facilitates protein-protein interaction analysis. By analyzing the changes of attention weight before and after heterogeneous network training, we investigated proteins that may be associated with drug combinations. Additionally, case studies align our findings with existing research, underscoring the potential of HANSynergy in drug synergy prediction. This advancement not only contributes to the burgeoning field of drug synergy prediction but also holds the potential to provide valuable insights and uncover new drug synergies for combating cancer.
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Affiliation(s)
- Ning Cheng
- School
of Informatics, Hunan University of Chinese
Medicine, Changsha, Hunan 410208, China
| | - Li Wang
- Degree
Programs in Systems and information Engineering, University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan
| | - Yiping Liu
- College
of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Bosheng Song
- College
of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Changsong Ding
- School
of Informatics, Hunan University of Chinese
Medicine, Changsha, Hunan 410208, China
- Big
Data Analysis Laboratory of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China
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2
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Pang W, Chen M, Qin Y. Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning. BMC Bioinformatics 2024; 25:182. [PMID: 38724920 PMCID: PMC11080240 DOI: 10.1186/s12859-024-05669-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 01/22/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND The prediction of drug sensitivity plays a crucial role in improving the therapeutic effect of drugs. However, testing the effectiveness of drugs is challenging due to the complex mechanism of drug reactions and the lack of interpretability in most machine learning and deep learning methods. Therefore, it is imperative to establish an interpretable model that receives various cell line and drug feature data to learn drug response mechanisms and achieve stable predictions between available datasets. RESULTS This study proposes a new and interpretable deep learning model, DrugGene, which integrates gene expression, gene mutation, gene copy number variation of cancer cells, and chemical characteristics of anticancer drugs to predict their sensitivity. This model comprises two different branches of neural networks, where the first involves a hierarchical structure of biological subsystems that uses the biological processes of human cells to form a visual neural network (VNN) and an interpretable deep neural network for human cancer cells. DrugGene receives genotype input from the cell line and detects changes in the subsystem states. We also employ a traditional artificial neural network (ANN) to capture the chemical structural features of drugs. DrugGene generates final drug response predictions by combining VNN and ANN and integrating their outputs into a fully connected layer. The experimental results using drug sensitivity data extracted from the Cancer Drug Sensitivity Genome Database and the Cancer Treatment Response Portal v2 reveal that the proposed model is better than existing prediction methods. Therefore, our model achieves higher accuracy, learns the reaction mechanisms between anticancer drugs and cell lines from various features, and interprets the model's predicted results. CONCLUSIONS Our method utilizes biological pathways to construct neural networks, which can use genotypes to monitor changes in the state of network subsystems, thereby interpreting the prediction results in the model and achieving satisfactory prediction accuracy. This will help explore new directions in cancer treatment. More available code resources can be downloaded for free from GitHub ( https://github.com/pangweixiong/DrugGene ).
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Affiliation(s)
- Weixiong Pang
- College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China
- Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China
| | - Ming Chen
- College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China
- Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China
| | - Yufang Qin
- College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China.
- Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China.
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3
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Han X, Wang B, Situ C, Qi Y, Zhu H, Li Y, Guo X. scapGNN: A graph neural network-based framework for active pathway and gene module inference from single-cell multi-omics data. PLoS Biol 2023; 21:e3002369. [PMID: 37956172 PMCID: PMC10681325 DOI: 10.1371/journal.pbio.3002369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 11/27/2023] [Accepted: 10/07/2023] [Indexed: 11/15/2023] Open
Abstract
Although advances in single-cell technologies have enabled the characterization of multiple omics profiles in individual cells, extracting functional and mechanistic insights from such information remains a major challenge. Here, we present scapGNN, a graph neural network (GNN)-based framework that creatively transforms sparse single-cell profile data into the stable gene-cell association network for inferring single-cell pathway activity scores and identifying cell phenotype-associated gene modules from single-cell multi-omics data. Systematic benchmarking demonstrated that scapGNN was more accurate, robust, and scalable than state-of-the-art methods in various downstream single-cell analyses such as cell denoising, batch effect removal, cell clustering, cell trajectory inference, and pathway or gene module identification. scapGNN was developed as a systematic R package that can be flexibly extended and enhanced for existing analysis processes. It provides a new analytical platform for studying single cells at the pathway and network levels.
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Affiliation(s)
- Xudong Han
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Medicine, Southeast University, Nanjing, China
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing, China
| | - Bing Wang
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Medicine, Southeast University, Nanjing, China
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing, China
| | - Chenghao Situ
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing, China
| | - Yaling Qi
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing, China
| | - Hui Zhu
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing, China
| | - Yan Li
- Department of Clinical Laboratory, Sir Run Run Hospital, Nanjing Medical University, Nanjing, China
| | - Xuejiang Guo
- State Key Laboratory of Reproductive Medicine and Offspring Health, School of Medicine, Southeast University, Nanjing, China
- Department of Histology and Embryology, State Key Laboratory of Reproductive Medicine and Offspring Health, Nanjing Medical University, Nanjing, China
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4
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Ye S, Zhao W, Shen X, Jiang X, He T. An effective multi-task learning framework for drug repurposing based on graph representation learning. Methods 2023; 218:48-56. [PMID: 37516260 DOI: 10.1016/j.ymeth.2023.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 07/04/2023] [Accepted: 07/20/2023] [Indexed: 07/31/2023] Open
Abstract
Drug repurposing, which typically applies the procedure of drug-disease associations (DDAs) prediction, is a feasible solution to drug discovery. Compared with traditional methods, drug repurposing can reduce the cost and time for drug development and advance the success rate of drug discovery. Although many methods for drug repurposing have been proposed and the obtained results are relatively acceptable, there is still some room for improving the predictive performance, since those methods fail to consider fully the issue of sparseness in known drug-disease associations. In this paper, we propose a novel multi-task learning framework based on graph representation learning to identify DDAs for drug repurposing. In our proposed framework, a heterogeneous information network is first constructed by combining multiple biological datasets. Then, a module consisting of multiple layers of graph convolutional networks is utilized to learn low-dimensional representations of nodes in the constructed heterogeneous information network. Finally, two types of auxiliary tasks are designed to help to train the target task of DDAs prediction in the multi-task learning framework. Comprehensive experiments are conducted on real data and the results demonstrate the effectiveness of the proposed method for drug repurposing.
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Affiliation(s)
- Shengwei Ye
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei 430079, PR China; School of Computer, Central China Normal University, Wuhan, Hubei 430079, PR China; National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei 430079, PR China
| | - Weizhong Zhao
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei 430079, PR China; School of Computer, Central China Normal University, Wuhan, Hubei 430079, PR China; National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei 430079, PR China.
| | - Xianjun Shen
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei 430079, PR China; School of Computer, Central China Normal University, Wuhan, Hubei 430079, PR China; National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei 430079, PR China
| | - Xingpeng Jiang
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei 430079, PR China; School of Computer, Central China Normal University, Wuhan, Hubei 430079, PR China; National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei 430079, PR China
| | - Tingting He
- Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan, Hubei 430079, PR China; School of Computer, Central China Normal University, Wuhan, Hubei 430079, PR China; National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, Wuhan, Hubei 430079, PR China
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5
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Xie D, Huang Q, Zhou P. Drug Discovery Targeting Post-Translational Modifications in Response to DNA Damages Induced by Space Radiation. Int J Mol Sci 2023; 24:ijms24087656. [PMID: 37108815 PMCID: PMC10142602 DOI: 10.3390/ijms24087656] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 04/07/2023] [Accepted: 04/14/2023] [Indexed: 04/29/2023] Open
Abstract
DNA damage in astronauts induced by cosmic radiation poses a major barrier to human space exploration. Cellular responses and repair of the most lethal DNA double-strand breaks (DSBs) are crucial for genomic integrity and cell survival. Post-translational modifications (PTMs), including phosphorylation, ubiquitylation, and SUMOylation, are among the regulatory factors modulating a delicate balance and choice between predominant DSB repair pathways, such as non-homologous end joining (NHEJ) and homologous recombination (HR). In this review, we focused on the engagement of proteins in the DNA damage response (DDR) modulated by phosphorylation and ubiquitylation, including ATM, DNA-PKcs, CtIP, MDM2, and ubiquitin ligases. The involvement and function of acetylation, methylation, PARylation, and their essential proteins were also investigated, providing a repository of candidate targets for DDR regulators. However, there is a lack of radioprotectors in spite of their consideration in the discovery of radiosensitizers. We proposed new perspectives for the research and development of future agents against space radiation by the systematic integration and utilization of evolutionary strategies, including multi-omics analyses, rational computing methods, drug repositioning, and combinations of drugs and targets, which may facilitate the use of radioprotectors in practical applications in human space exploration to combat fatal radiation hazards.
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Affiliation(s)
- Dafei Xie
- Department of Radiation Biology, Beijing Key Laboratory for Radiobiology (BKLRB), Beijing Institute of Radiation Medicine, Taiping Road 27th, Haidian District, Beijing 100850, China
| | - Qi Huang
- Department of Radiation Biology, Beijing Key Laboratory for Radiobiology (BKLRB), Beijing Institute of Radiation Medicine, Taiping Road 27th, Haidian District, Beijing 100850, China
- Department of Preventive Medicine, School of Public Health, University of South China, Changsheng West Road 28th, Zhengxiang District, Hengyang 421001, China
| | - Pingkun Zhou
- Department of Radiation Biology, Beijing Key Laboratory for Radiobiology (BKLRB), Beijing Institute of Radiation Medicine, Taiping Road 27th, Haidian District, Beijing 100850, China
- Department of Preventive Medicine, School of Public Health, University of South China, Changsheng West Road 28th, Zhengxiang District, Hengyang 421001, China
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6
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Patterson A, Elbasir A, Tian B, Auslander N. Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications. Cancers (Basel) 2023; 15:cancers15071958. [PMID: 37046619 PMCID: PMC10093138 DOI: 10.3390/cancers15071958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/24/2023] [Accepted: 03/09/2023] [Indexed: 03/29/2023] Open
Abstract
Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning.
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Affiliation(s)
- Andrew Patterson
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- The Wistar Institute, Philadelphia, PA 19104, USA
| | | | - Bin Tian
- The Wistar Institute, Philadelphia, PA 19104, USA
| | - Noam Auslander
- The Wistar Institute, Philadelphia, PA 19104, USA
- Department of Cancer Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Correspondence:
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7
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Wang Z, Gu Y, Zheng S, Yang L, Li J. MGREL: A multi-graph representation learning-based ensemble learning method for gene-disease association prediction. Comput Biol Med 2023; 155:106642. [PMID: 36805231 DOI: 10.1016/j.compbiomed.2023.106642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 01/15/2023] [Accepted: 02/05/2023] [Indexed: 02/12/2023]
Abstract
The identification of gene-disease associations plays an important role in the exploration of pathogenic mechanisms and therapeutic targets. Computational methods have been regarded as an effective way to discover the potential gene-disease associations in recent years. However, most of them ignored the combination of abundant genetic, therapeutic information, and gene-disease network topology. To this end, we re-organized the current gene-disease association benchmark dataset by extracting the newest gene-disease associations from the OMIM database. Then, we developed a multi-graph representation learning-based ensemble model, named MGREL to predict gene-disease associations. MGREL integrated two feature generation channels to extract gene and disease features, including a knowledge extraction channel which learned high-order representations from genetic and therapeutic information, and a graph learning channel which acquired network topological representations through multiple advanced graph representation learning methods. Then, an ensemble learning method with 5 machine learning models was used as the classifier to predict the gene-disease association. Comprehensive experiments have demonstrated the significant performance achieved by MGREL compared to 5 state-of-the-art methods. For the major measurements (AUC = 0.925, AUPR = 0.935), the relative improvements of MGREL compared to the suboptimal methods are 3.24%, and 2.75%, respectively. MGREL also achieved impressive improvements in the challenging tasks of predicting potential associations for unknown genes/diseases. In addition, case studies implied potential applications for MGREL in the discovery of potential therapeutic targets.
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Affiliation(s)
- Ziyang Wang
- Institute of Medical Information IMI, Chinese Academy of Medical Sciences and Peking Union Medical College CAMS & PUMC, Beijing, 100020, China
| | - Yaowen Gu
- Institute of Medical Information IMI, Chinese Academy of Medical Sciences and Peking Union Medical College CAMS & PUMC, Beijing, 100020, China
| | - Si Zheng
- Institute of Medical Information IMI, Chinese Academy of Medical Sciences and Peking Union Medical College CAMS & PUMC, Beijing, 100020, China; Institute for Artificial Intelligence, Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing, 100084, China
| | - Lin Yang
- Institute of Medical Information IMI, Chinese Academy of Medical Sciences and Peking Union Medical College CAMS & PUMC, Beijing, 100020, China
| | - Jiao Li
- Institute of Medical Information IMI, Chinese Academy of Medical Sciences and Peking Union Medical College CAMS & PUMC, Beijing, 100020, China.
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8
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Ren ZH, You ZH, Zou Q, Yu CQ, Ma YF, Guan YJ, You HR, Wang XF, Pan J. DeepMPF: deep learning framework for predicting drug-target interactions based on multi-modal representation with meta-path semantic analysis. J Transl Med 2023; 21:48. [PMID: 36698208 PMCID: PMC9876420 DOI: 10.1186/s12967-023-03876-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 01/05/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Drug-target interaction (DTI) prediction has become a crucial prerequisite in drug design and drug discovery. However, the traditional biological experiment is time-consuming and expensive, as there are abundant complex interactions present in the large size of genomic and chemical spaces. For alleviating this phenomenon, plenty of computational methods are conducted to effectively complement biological experiments and narrow the search spaces into a preferred candidate domain. Whereas, most of the previous approaches cannot fully consider association behavior semantic information based on several schemas to represent complex the structure of heterogeneous biological networks. Additionally, the prediction of DTI based on single modalities cannot satisfy the demand for prediction accuracy. METHODS We propose a multi-modal representation framework of 'DeepMPF' based on meta-path semantic analysis, which effectively utilizes heterogeneous information to predict DTI. Specifically, we first construct protein-drug-disease heterogeneous networks composed of three entities. Then the feature information is obtained under three views, containing sequence modality, heterogeneous structure modality and similarity modality. We proposed six representative schemas of meta-path to preserve the high-order nonlinear structure and catch hidden structural information of the heterogeneous network. Finally, DeepMPF generates highly representative comprehensive feature descriptors and calculates the probability of interaction through joint learning. RESULTS To evaluate the predictive performance of DeepMPF, comparison experiments are conducted on four gold datasets. Our method can obtain competitive performance in all datasets. We also explore the influence of the different feature embedding dimensions, learning strategies and classification methods. Meaningfully, the drug repositioning experiments on COVID-19 and HIV demonstrate DeepMPF can be applied to solve problems in reality and help drug discovery. The further analysis of molecular docking experiments enhances the credibility of the drug candidates predicted by DeepMPF. CONCLUSIONS All the results demonstrate the effectively predictive capability of DeepMPF for drug-target interactions. It can be utilized as a useful tool to prescreen the most potential drug candidates for the protein. The web server of the DeepMPF predictor is freely available at http://120.77.11.78/DeepMPF/ , which can help relevant researchers to further study.
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Affiliation(s)
- Zhong-Hao Ren
- grid.460132.20000 0004 1758 0275School of Information Engineering, Xijing University, Xi’an, 710100 China
| | - Zhu-Hong You
- grid.440588.50000 0001 0307 1240School of Computer Science, Northwestern Polytechnical University, Xi’an, 710129 China
| | - Quan Zou
- grid.54549.390000 0004 0369 4060Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054 China
| | - Chang-Qing Yu
- grid.460132.20000 0004 1758 0275School of Information Engineering, Xijing University, Xi’an, 710100 China
| | - Yan-Fang Ma
- grid.417234.70000 0004 1808 3203Department of Galactophore, The Third People’s Hospital of Gansu Province, Lanzhou, 730020 China
| | - Yong-Jian Guan
- grid.460132.20000 0004 1758 0275School of Information Engineering, Xijing University, Xi’an, 710100 China
| | - Hai-Ru You
- grid.440588.50000 0001 0307 1240School of Computer Science, Northwestern Polytechnical University, Xi’an, 710129 China
| | - Xin-Fei Wang
- grid.460132.20000 0004 1758 0275School of Information Engineering, Xijing University, Xi’an, 710100 China
| | - Jie Pan
- grid.460132.20000 0004 1758 0275School of Information Engineering, Xijing University, Xi’an, 710100 China
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9
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Xie D, He S, Han L, Wu L, Huang H, Tao H, Zhou P, Shi X, Bai H, Bo X. Systematic optimization of host-directed therapeutic targets and preclinical validation of repositioned antiviral drugs. Brief Bioinform 2022; 23:bbac047. [PMID: 35238349 PMCID: PMC9116211 DOI: 10.1093/bib/bbac047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 01/26/2022] [Accepted: 01/28/2022] [Indexed: 11/12/2022] Open
Abstract
Inhibition of host protein functions using established drugs produces a promising antiviral effect with excellent safety profiles, decreased incidence of resistant variants and favorable balance of costs and risks. Genomic methods have produced a large number of robust host factors, providing candidates for identification of antiviral drug targets. However, there is a lack of global perspectives and systematic prioritization of known virus-targeted host proteins (VTHPs) and drug targets. There is also a need for host-directed repositioned antivirals. Here, we integrated 6140 VTHPs and grouped viral infection modes from a new perspective of enriched pathways of VTHPs. Clarifying the superiority of nonessential membrane and hub VTHPs as potential ideal targets for repositioned antivirals, we proposed 543 candidate VTHPs. We then presented a large-scale drug-virus network (DVN) based on matching these VTHPs and drug targets. We predicted possible indications for 703 approved drugs against 35 viruses and explored their potential as broad-spectrum antivirals. In vitro and in vivo tests validated the efficacy of bosutinib, maraviroc and dextromethorphan against human herpesvirus 1 (HHV-1), hepatitis B virus (HBV) and influenza A virus (IAV). Their drug synergy with clinically used antivirals was evaluated and confirmed. The results proved that low-dose dextromethorphan is better than high-dose in both single and combined treatments. This study provides a comprehensive landscape and optimization strategy for druggable VTHPs, constructing an innovative and potent pipeline to discover novel antiviral host proteins and repositioned drugs, which may facilitate their delivery to clinical application in translational medicine to combat fatal and spreading viral infections.
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Affiliation(s)
- Dafei Xie
- Beijing Institute of Radiation Medicine, Beijing, China, 100850
| | - Song He
- Beijing Institute of Radiation Medicine, Beijing, China, 100850
| | - Lu Han
- Beijing Institute of Pharmacology and Toxicology, Beijing, China, 100850
| | - Lianlian Wu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China, 300072
| | - Hai Huang
- Department of Biological Medicines, School of Pharmacy, Fudan University, Shanghai, China, 201203
| | - Huan Tao
- Beijing Institute of Radiation Medicine, Beijing, China, 100850
| | - Pingkun Zhou
- Beijing Institute of Radiation Medicine, Beijing, China, 100850
| | - Xunlong Shi
- Department of Biological Medicines, School of Pharmacy, Fudan University, Shanghai, China, 201203
| | - Hui Bai
- BioMap (Beijing) Intelligence Technology Limited, Beijing, China, 100005
| | - Xiaochen Bo
- Beijing Institute of Radiation Medicine, Beijing, China, 100850
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10
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Gu Y, Zheng S, Xu Z, Yin Q, Li L, Li J. An efficient curriculum learning-based strategy for molecular graph learning. Brief Bioinform 2022; 23:6562682. [DOI: 10.1093/bib/bbac099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/18/2022] [Accepted: 02/27/2022] [Indexed: 12/14/2022] Open
Abstract
Abstract
Computational methods have been widely applied to resolve various core issues in drug discovery, such as molecular property prediction. In recent years, a data-driven computational method-deep learning had achieved a number of impressive successes in various domains. In drug discovery, graph neural networks (GNNs) take molecular graph data as input and learn graph-level representations in non-Euclidean space. An enormous amount of well-performed GNNs have been proposed for molecular graph learning. Meanwhile, efficient use of molecular data during training process, however, has not been paid enough attention. Curriculum learning (CL) is proposed as a training strategy by rearranging training queue based on calculated samples' difficulties, yet the effectiveness of CL method has not been determined in molecular graph learning. In this study, inspired by chemical domain knowledge and task prior information, we proposed a novel CL-based training strategy to improve the training efficiency of molecular graph learning, called CurrMG. Consisting of a difficulty measurer and a training scheduler, CurrMG is designed as a plug-and-play module, which is model-independent and easy-to-use on molecular data. Extensive experiments demonstrated that molecular graph learning models could benefit from CurrMG and gain noticeable improvement on five GNN models and eight molecular property prediction tasks (overall improvement is 4.08%). We further observed CurrMG’s encouraging potential in resource-constrained molecular property prediction. These results indicate that CurrMG can be used as a reliable and efficient training strategy for molecular graph learning.
Availability: The source code is available in https://github.com/gu-yaowen/CurrMG.
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Affiliation(s)
- Yaowen Gu
- Institute of Medical Information (IMI), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100020, China
| | - Si Zheng
- Institute of Medical Information (IMI), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100020, China
- Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
| | - Zidu Xu
- Institute of Medical Information (IMI), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100020, China
| | - Qijin Yin
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Liang Li
- Key Laboratory of Antibiotic Bioengineering of National Health and Family Planning Commission (NHFPC), Institute of Medicinal Biotechnology (IMB), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100020, China
| | - Jiao Li
- Institute of Medical Information (IMI), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100020, China
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