1
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Liu L, Jia R, Hou R, Huang C. Prediction of cell-type-specific cohesin-mediated chromatin loops based on chromatin state. Methods 2024; 226:151-160. [PMID: 38670416 DOI: 10.1016/j.ymeth.2024.04.014] [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: 03/02/2024] [Revised: 04/02/2024] [Accepted: 04/18/2024] [Indexed: 04/28/2024] Open
Abstract
Chromatin loop is of crucial importance for the regulation of gene transcription. Cohesin is a type of chromatin-associated protein that mediates the interaction of chromatin through the loop extrusion. Cohesin-mediated chromatin interactions have strong cell-type specificity, posing a challenge for predicting chromatin loops. Existing computational methods perform poorly in predicting cell-type-specific chromatin loops. To address this issue, we propose a random forest model to predict cell-type-specific cohesin-mediated chromatin loops based on chromatin states identified by ChromHMM and the occupancy of related factors. Our results show that chromatin state is responsible for cell-type-specificity of loops. Using only chromatin states as features, the model achieved high accuracy in predicting cell-type-specific loops between two cell types and can be applied to different cell types. Furthermore, when chromatin states are combined with the occurrence frequency of CTCF, RAD21, YY1, and H3K27ac ChIP-seq peaks, more accurate prediction can be achieved. Our feature extraction method provides novel insights into predicting cell-type-specific chromatin loops and reveals the relationship between chromatin state and chromatin loop formation.
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Affiliation(s)
- Li Liu
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324003, China.
| | - Ranran Jia
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou 571158, China.
| | - Rui Hou
- College of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010051, China.
| | - Chengbing Huang
- School of Computer Science and Technology, Aba Teachers University, Aba 623002, China.
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2
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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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3
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Zhang HQ, Liu SH, Li R, Yu JW, Ye DX, Yuan SS, Lin H, Huang CB, Tang H. MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier. ACS OMEGA 2024; 9:8439-8447. [PMID: 38405489 PMCID: PMC10882704 DOI: 10.1021/acsomega.3c09587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/16/2024] [Accepted: 01/22/2024] [Indexed: 02/27/2024]
Abstract
In biological organisms, metal ion-binding proteins participate in numerous metabolic activities and are closely associated with various diseases. To accurately predict whether a protein binds to metal ions and the type of metal ion-binding protein, this study proposed a classifier named MIBPred. The classifier incorporated advanced Word2Vec technology from the field of natural language processing to extract semantic features of the protein sequence language and combined them with position-specific score matrix (PSSM) features. Furthermore, an ensemble learning model was employed for the metal ion-binding protein classification task. In the model, we independently trained XGBoost, LightGBM, and CatBoost algorithms and integrated the output results through an SVM voting mechanism. This innovative combination has led to a significant breakthrough in the predictive performance of our model. As a result, we achieved accuracies of 95.13% and 85.19%, respectively, in predicting metal ion-binding proteins and their types. Our research not only confirms the effectiveness of Word2Vec technology in extracting semantic information from protein sequences but also highlights the outstanding performance of the MIBPred classifier in the problem of metal ion-binding protein types. This study provides a reliable tool and method for the in-depth exploration of the structure and function of metal ion-binding proteins.
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Affiliation(s)
- Hong-Qi Zhang
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Shang-Hua Liu
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Rui Li
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Jun-Wen Yu
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Dong-Xin Ye
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Shi-Shi Yuan
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Hao Lin
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Cheng-Bing Huang
- School
of Computer Science and Technology, Aba Teachers University, Aba 623002, China
| | - Hua Tang
- School
of Basic Medical Sciences, Southwest Medical
University, Luzhou 646000, China
- Central
Nervous System Drug Key Laboratory of Sichuan Province, Luzhou 646000, China
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4
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Karim T, Shaon MSH, Sultan MF, Hasan MZ, Kafy AA. ANNprob-ACPs: A novel anticancer peptide identifier based on probabilistic feature fusion approach. Comput Biol Med 2024; 169:107915. [PMID: 38171261 DOI: 10.1016/j.compbiomed.2023.107915] [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: 08/20/2023] [Revised: 12/28/2023] [Accepted: 12/29/2023] [Indexed: 01/05/2024]
Abstract
Anticancer Peptides (ACPs) offer significant potential as cancer treatment drugs in this modern era. Quickly identifying active compounds from protein sequences is crucial for healthcare and cancer treatment. In this paper ANNprob-ACPs, a novel and effective model for detecting ACPs has been implemented based on nine feature encoding techniques, including AAC, CC, W2V, DPC, PAAC, QSO, CTDC, CTDT, and CKSAAGP. After analyzing the performance of several machine learning models, the six best models were selected based on their overall performances in every evaluation metric. The probability scores of each model were subsequently aggregated and used as input of our meta- model, called ANNprob-ACPs. Our model outperformed all others and its potential to lead to phenomenal identification of ACPs. The results of this study showed notable improvement in 10-fold cross-validation and independent test, with accuracy of 93.72% and 90.62%, respectively. Our proposed model, ANNprob-ACPs outperformed existing approaches in terms of accuracy and effectiveness in discovering ACPs. By using SHAP, this study obtained the physicochemical properties of QSO, and compositional properties of DPC, AAC, and PAAC are more impactful for our model's performances, which have a major impact on a drug's interactions and future discoveries. Consequently, this model is crucial for the future and has a high probability of detecting ACPs more frequently. We developed a web server of ANNprob-ACPs, which is accessible at ANNprob-ACPs webserver.
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Affiliation(s)
- Tasmin Karim
- Department of Computer Science & Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh; Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh.
| | - Md Shazzad Hossain Shaon
- Department of Computer Science & Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh; Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh.
| | - Md Fahim Sultan
- Department of Computer Science & Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh; Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh.
| | - Md Zahid Hasan
- Department of Computer Science & Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh; Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh.
| | - Abdulla-Al Kafy
- Department of Urban & Regional Planning, Rajshahi University of Engineering & Technology (RUET), Rajshahi, 6204, Bangladesh.
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5
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Yang CS, Coopersmith CM, Lyons JD. Cell death proteins in sepsis: key players and modern therapeutic approaches. Front Immunol 2024; 14:1347401. [PMID: 38274794 PMCID: PMC10808706 DOI: 10.3389/fimmu.2023.1347401] [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: 11/30/2023] [Accepted: 12/21/2023] [Indexed: 01/27/2024] Open
Abstract
Cell death proteins play a central role in host immune signaling during sepsis. These interconnected mechanisms trigger cell demise via apoptosis, necroptosis, and pyroptosis while also driving inflammatory signaling. Targeting cell death mediators with novel therapies may correct the dysregulated inflammation seen during sepsis and improve outcomes for septic patients.
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Affiliation(s)
- Chloe S. Yang
- Department of Surgery, Emory University, Atlanta, GA, United States
| | - Craig M. Coopersmith
- Department of Surgery, Emory University, Atlanta, GA, United States
- Emory Critical Care Center, Emory University, Atlanta, GA, United States
| | - John D. Lyons
- Department of Surgery, Emory University, Atlanta, GA, United States
- Emory Critical Care Center, Emory University, Atlanta, GA, United States
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6
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Pang Y, Liu B. DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model. BMC Biol 2024; 22:3. [PMID: 38166858 PMCID: PMC10762911 DOI: 10.1186/s12915-023-01803-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 12/15/2023] [Indexed: 01/05/2024] Open
Abstract
Intrinsically disordered proteins and regions (IDPs/IDRs) are functionally important proteins and regions that exist as highly dynamic conformations under natural physiological conditions. IDPs/IDRs exhibit a broad range of molecular functions, and their functions involve binding interactions with partners and remaining native structural flexibility. The rapid increase in the number of proteins in sequence databases and the diversity of disordered functions challenge existing computational methods for predicting protein intrinsic disorder and disordered functions. A disordered region interacts with different partners to perform multiple functions, and these disordered functions exhibit different dependencies and correlations. In this study, we introduce DisoFLAG, a computational method that leverages a graph-based interaction protein language model (GiPLM) for jointly predicting disorder and its multiple potential functions. GiPLM integrates protein semantic information based on pre-trained protein language models into graph-based interaction units to enhance the correlation of the semantic representation of multiple disordered functions. The DisoFLAG predictor takes amino acid sequences as the only inputs and provides predictions of intrinsic disorder and six disordered functions for proteins, including protein-binding, DNA-binding, RNA-binding, ion-binding, lipid-binding, and flexible linker. We evaluated the predictive performance of DisoFLAG following the Critical Assessment of protein Intrinsic Disorder (CAID) experiments, and the results demonstrated that DisoFLAG offers accurate and comprehensive predictions of disordered functions, extending the current coverage of computationally predicted disordered function categories. The standalone package and web server of DisoFLAG have been established to provide accurate prediction tools for intrinsic disorders and their associated functions.
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Affiliation(s)
- Yihe Pang
- School of Computer Science and Technology, Beijing Institute of Technology, No. 5, South Zhongguancun Street, Beijing, Haidian District, 100081, China
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, No. 5, South Zhongguancun Street, Beijing, Haidian District, 100081, China.
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, No. 5, South Zhongguancun Street, Beijing, Haidian District, 100081, China.
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7
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Li M, Wei J, Zhu G, Fu S, He X, Hu X, Yu Y, Mou Y, Wang J, You X, Xiao X, Gu T, Ye Z, Zha Y. Quizartinib inhibits necroptosis by targeting receptor-interacting serine/threonine protein kinase 1. FASEB J 2023; 37:e23178. [PMID: 37698367 DOI: 10.1096/fj.202300600rr] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 08/14/2023] [Accepted: 08/23/2023] [Indexed: 09/13/2023]
Abstract
Systemic inflammatory response syndrome (SIRS), at least in part driven by necroptosis, is characterized by life-threatening multiple organ failure. Blocking the progression of SIRS and consequent multiple organ dysfunction is challenging. Receptor-interacting serine/threonine protein kinase 1 (RIPK1) is an important cell death and inflammatory mediator, making it a potential treatment target in several diseases. Here, using a drug repurposing approach, we show that inhibiting RIPK1 is also an effective treatment for SIRS. We performed cell-based high-throughput drug screening of an US Food and Drug Administration (FDA)-approved drug library that contains 1953 drugs to identify effective inhibitors of necroptotic cell death by SYTOX green staining. Dose-response validation of the top candidate, quizartinib, was conducted in two cell lines of HT-22 and MEFs. The effect of quizartinib on necroptosis-related proteins was evaluated using western blotting, immunoprecipitation, and an in vitro RIPK1 kinase assay. The in vivo effects of quizartinib were assessed in a murine tumor necrosis factor α (TNFα)-induced SIRS model. High-throughput screening identified quizartinib as the top "hit" in the compound library that rescued cells from necroptosis in vitro. Quizartinib inhibited necroptosis by directly inhibiting RIPK1 kinase activity and blocking downstream complex IIb formation. Furthermore, quizartinib protected mice against TNFα-induced SIRS. Quizartinib, as an FDA-approved drug with proven safety and efficacy, was repurposed for targeted inhibition of RIPK1. This work provides essential preclinical data for transferring quizartinib to the treatment of RIPK1-dependent necroptosis-induced inflammatory diseases, including SIRS.
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Affiliation(s)
- Min Li
- Institute of Neural Regeneration and Repair, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Department of Neurology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Yichang Central People's Hospital, Yichang, China
| | - Jun Wei
- Institute of Neural Regeneration and Repair, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Department of Neurology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Yichang Central People's Hospital, Yichang, China
| | - Guofeng Zhu
- Institute of Neural Regeneration and Repair, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Department of Neurology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Yichang Central People's Hospital, Yichang, China
| | - Shufang Fu
- Yichang Central People's Hospital, Yichang, China
- Department of Paediatrics, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
| | - Xiaoyan He
- Institute of Neural Regeneration and Repair, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Department of Neurology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Yichang Central People's Hospital, Yichang, China
| | - Xinqian Hu
- Institute of Neural Regeneration and Repair, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Department of Neurology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Yichang Central People's Hospital, Yichang, China
| | - Yajie Yu
- Institute of Neural Regeneration and Repair, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Department of Neurology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Yichang Central People's Hospital, Yichang, China
| | - Yan Mou
- Institute of Neural Regeneration and Repair, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Department of Neurology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Yichang Central People's Hospital, Yichang, China
| | - Jia Wang
- Institute of Neural Regeneration and Repair, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Department of Neurology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Yichang Central People's Hospital, Yichang, China
| | - Xiaoling You
- Institute of Neural Regeneration and Repair, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Department of Neurology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Yichang Central People's Hospital, Yichang, China
| | - Xin Xiao
- Institute of Neural Regeneration and Repair, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Department of Neurology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Yichang Central People's Hospital, Yichang, China
| | - Tanrong Gu
- Institute of Neural Regeneration and Repair, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Department of Neurology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Yichang Central People's Hospital, Yichang, China
| | - Zhi Ye
- Institute of Neural Regeneration and Repair, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Department of Neurology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Yichang Central People's Hospital, Yichang, China
| | - Yunhong Zha
- Institute of Neural Regeneration and Repair, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Department of Neurology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, China
- Yichang Central People's Hospital, Yichang, China
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8
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Huang F, Liang J, Lin Y, Chen Y, Hu F, Feng J, Zeng Q, Han Z, Lin Q, Li Y, Li J, Wu L, Li L. Repurposing of Ibrutinib and Quizartinib as potent inhibitors of necroptosis. Commun Biol 2023; 6:972. [PMID: 37741898 PMCID: PMC10517925 DOI: 10.1038/s42003-023-05353-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 09/13/2023] [Indexed: 09/25/2023] Open
Abstract
Necroptosis is a form of regulated cell death that has been implicated in multiple diseases. TNF-induced necroptosis is regulated by necrosomes, complexes consisting of RIPK1, RIPK3 and MLKL. In this study, by screening of a small-compound library, we identified dozens of compounds that inhibited TNF-induced necroptosis. According to the mechanisms by which they inhibited necroptosis, these compounds were classified into different groups. We then identified Ibrutinib as an inhibitor of RIPK3 and found that Quizartinib protected against the TNF-induced systemic inflammatory response syndrome in mice by inhibiting the activation of RIPK1. Altogether, our work revealed dozens of necroptosis inhibitors, suggesting new potential approaches for treating necroptosis-related diseases.
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Affiliation(s)
- Fangmin Huang
- The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Jiankun Liang
- The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Yingying Lin
- The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Yushi Chen
- The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Fen Hu
- The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Jianting Feng
- The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Qiang Zeng
- The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Zeteng Han
- The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Qiaofa Lin
- The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Yan Li
- The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Jingyi Li
- The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Lanqin Wu
- The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.
| | - Lisheng Li
- The School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, 1 Xueyuan Road, Minhou, Fuzhou, China.
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9
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Li Y, Ma D, Chen D, Chen Y. ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree. Front Genet 2023; 14:1165765. [PMID: 37065496 PMCID: PMC10090421 DOI: 10.3389/fgene.2023.1165765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 03/09/2023] [Indexed: 03/31/2023] Open
Abstract
Cancer is one of the most dangerous diseases in the world, killing millions of people every year. Drugs composed of anticancer peptides have been used to treat cancer with low side effects in recent years. Therefore, identifying anticancer peptides has become a focus of research. In this study, an improved anticancer peptide predictor named ACP-GBDT, based on gradient boosting decision tree (GBDT) and sequence information, is proposed. To encode the peptide sequences included in the anticancer peptide dataset, ACP-GBDT uses a merged-feature composed of AAIndex and SVMProt-188D. A GBDT is adopted to train the prediction model in ACP-GBDT. Independent testing and ten-fold cross-validation show that ACP-GBDT can effectively distinguish anticancer peptides from non-anticancer ones. The comparison results of the benchmark dataset show that ACP-GBDT is simpler and more effective than other existing anticancer peptide prediction methods.
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Affiliation(s)
- Yanjuan Li
- College of Electrical and Information Engineering, Quzhou University, Quzhou, China
| | - Di Ma
- College of Computer, Hangzhou Dianzi University, Hangzhou, China
| | - Dong Chen
- College of Electrical and Information Engineering, Quzhou University, Quzhou, China
- *Correspondence: Dong Chen, ; Yu Chen,
| | - Yu Chen
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
- *Correspondence: Dong Chen, ; Yu Chen,
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10
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Zulfiqar H, Ahmed Z, Kissanga Grace-Mercure B, Hassan F, Zhang ZY, Liu F. Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique. Front Microbiol 2023; 14:1170785. [PMID: 37125199 PMCID: PMC10133480 DOI: 10.3389/fmicb.2023.1170785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 03/17/2023] [Indexed: 05/02/2023] Open
Abstract
Promotors are those genomic regions on the upstream of genes, which are bound by RNA polymerase for starting gene transcription. Because it is the most critical element of gene expression, the recognition of promoters is crucial to understand the regulation of gene expression. This study aimed to develop a machine learning-based model to predict promotors in Agrobacterium tumefaciens (A. tumefaciens) strain C58. In the model, promotor sequences were encoded by three different kinds of feature descriptors, namely, accumulated nucleotide frequency, k-mer nucleotide composition, and binary encodings. The obtained features were optimized by using correlation and the mRMR-based algorithm. These optimized features were inputted into a random forest (RF) classifier to discriminate promotor sequences from non-promotor sequences in A. tumefaciens strain C58. The examination of 10-fold cross-validation showed that the proposed model could yield an overall accuracy of 0.837. This model will provide help for the study of promoters in A. tumefaciens C58 strain.
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Affiliation(s)
- Hasan Zulfiqar
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
- *Correspondence: Hasan Zulfiqar
| | - Zahoor Ahmed
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China
| | - Bakanina Kissanga Grace-Mercure
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Farwa Hassan
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhao-Yue Zhang
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
- Zhao-Yue Zhang
| | - Fen Liu
- Department of Radiation Oncology, Peking University Cancer Hospital (Inner Mongolia Campus), Affiliated Cancer Hospital of Inner Mongolia Medical University, Inner Mongolia Cancer Hospital, Hohhot, China
- Fen Liu
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