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Xiao C, Zhou Z, She J, Yin J, Cui F, Zhang Z. PEL-PVP: Application of plant vacuolar protein discriminator based on PEFT ESM-2 and bilayer LSTM in an unbalanced dataset. Int J Biol Macromol 2024; 277:134317. [PMID: 39094861 DOI: 10.1016/j.ijbiomac.2024.134317] [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: 04/27/2024] [Revised: 07/10/2024] [Accepted: 07/28/2024] [Indexed: 08/04/2024]
Abstract
Plant vacuoles, play a crucial role in maintaining cellular stability, adapting to environmental changes, and responding to external pressures. The accurate identification of vacuolar proteins (PVPs) is crucial for understanding the biosynthetic mechanisms of intracellular vacuoles and the adaptive mechanisms of plants. In order to more accurately identify vacuole proteins, this study developed a new predictive model PEL-PVP based on ESM-2. Through this study, the feasibility and effectiveness of using advanced pre-training models and fine-tuning techniques for bioinformatics tasks were demonstrated, providing new methods and ideas for plant vacuolar protein research. In addition, previous datasets for vacuolar proteins were balanced, but imbalance is more closely related to the actual situation. Therefore, this study constructed an imbalanced dataset UB-PVP from the UniProt database,helping the model better adapt to the complexity and uncertainty in real environments, thereby improving the model's generalization ability and practicality. The experimental results show that compared with existing recognition techniques, achieving significant improvements in multiple indicators, with 6.08 %, 13.51 %, 11.9 %, and 5 % improvements in ACC, SP, MCC, and AUC, respectively. The accuracy reaches 94.59 %, significantly higher than the previous best model GraphIdn. This provides an efficient and precise tool for the study of plant vacuole proteins.
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Affiliation(s)
- Cuilin Xiao
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Zheyu Zhou
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Jiayi She
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Jinfen Yin
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Feifei Cui
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Zilong Zhang
- School of Computer Science and Technology, Hainan University, Haikou 570228, China.
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Lin L, Long Y, Liu J, Deng D, Yuan Y, Liu L, Tan B, Qi H. FRP-XGBoost: Identification of ferroptosis-related proteins based on multi-view features. Int J Biol Macromol 2024; 262:130180. [PMID: 38360239 DOI: 10.1016/j.ijbiomac.2024.130180] [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: 12/05/2023] [Revised: 02/11/2024] [Accepted: 02/12/2024] [Indexed: 02/17/2024]
Abstract
Ferroptosis represents a novel form of programmed cell death. Pan-cancer bioinformatics analysis indicates that identifying and modulating ferroptosis offer innovative approaches for preventing and treating diverse tumor pathologies. However, the precise detection of ferroptosis-related proteins via conventional wet-laboratory techniques remains a formidable challenge, largely due to the constraints of existing methodologies. These traditional approaches are not only labor-intensive but also financially burdensome. Consequently, there is an imperative need for the development of more sophisticated and efficient computational tools to facilitate the detection of these proteins. In this paper, we presented a XGBoost and multi-view features-based machine learning prediction method for predicting ferroptosis-related proteins, which was referred to as FRP-XGBoost. In this study, we explored four types of protein feature extraction methods and evaluated their effectiveness in predicting ferroptosis-related proteins using six of the most commonly used traditional classifiers. To enhance the representational power of the hybrid features, we employed a two-step feature selection technique to identify the optimal subset of features. Subsequently, we constructed a prediction model using the XGBoost algorithm. The FRP-XGBoost achieved an accuracy of 96.74 % in 10-fold cross-validation and a further accuracy of 91.52 % in an independent test. The implementation source code of FRP-XGBoost is available at https://github.com/linli5417/FRP-XGBoost.
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Affiliation(s)
- Li Lin
- Department of Obstetrics and Gynecology, Women and Children's Hospital of Chongqing Medical University, Chongqing 401147, China; Department of Obstetrics and Gynecology, Chongqing Health Center for Women and Children, Chongqing 401147, China
| | - Yao Long
- Chongqing Key Laboratory of Maternal and Fetal Medicine, Chongqing Medical University, Chongqing 400016, China; Joint International Research Laboratory of Reproduction and Development, Chinese Ministry of Education, Chongqing Medical University, 400016, China; Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Jinkai Liu
- Chongqing Key Laboratory of Maternal and Fetal Medicine, Chongqing Medical University, Chongqing 400016, China; Joint International Research Laboratory of Reproduction and Development, Chinese Ministry of Education, Chongqing Medical University, 400016, China; Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Dongliang Deng
- Department of Oncology, Chongqing Traditional Chinese Medicine Hospital, Chongqing 400021, China
| | - Yu Yuan
- Department of Obstetrics and Gynecology, Women and Children's Hospital of Chongqing Medical University, Chongqing 401147, China; Department of Obstetrics and Gynecology, Chongqing Health Center for Women and Children, Chongqing 401147, China
| | - Lubin Liu
- Department of Obstetrics and Gynecology, Women and Children's Hospital of Chongqing Medical University, Chongqing 401147, China; Department of Obstetrics and Gynecology, Chongqing Health Center for Women and Children, Chongqing 401147, China
| | - Bin Tan
- Chongqing Key Laboratory of Maternal and Fetal Medicine, Chongqing Medical University, Chongqing 400016, China; Joint International Research Laboratory of Reproduction and Development, Chinese Ministry of Education, Chongqing Medical University, 400016, China; Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
| | - Hongbo Qi
- Department of Obstetrics and Gynecology, Women and Children's Hospital of Chongqing Medical University, Chongqing 401147, China; Department of Obstetrics and Gynecology, Chongqing Health Center for Women and Children, Chongqing 401147, China; Chongqing Key Laboratory of Maternal and Fetal Medicine, Chongqing Medical University, Chongqing 400016, China; Joint International Research Laboratory of Reproduction and Development, Chinese Ministry of Education, Chongqing Medical University, 400016, China.
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Fu X, Yuan Y, Qiu H, Suo H, Song Y, Li A, Zhang Y, Xiao C, Li Y, Dou L, Zhang Z, Cui F. AGF-PPIS: A protein-protein interaction site predictor based on an attention mechanism and graph convolutional networks. Methods 2024; 222:142-151. [PMID: 38242383 DOI: 10.1016/j.ymeth.2024.01.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 01/04/2024] [Accepted: 01/13/2024] [Indexed: 01/21/2024] Open
Abstract
Protein-protein interactions play an important role in various biological processes. Interaction among proteins has a wide range of applications. Therefore, the correct identification of protein-protein interactions sites is crucial. In this paper, we propose a novel predictor for protein-protein interactions sites, AGF-PPIS, where we utilize a multi-head self-attention mechanism (introducing a graph structure), graph convolutional network, and feed-forward neural network. We use the Euclidean distance between each protein residue to generate the corresponding protein graph as the input of AGF-PPIS. On the independent test dataset Test_60, AGF-PPIS achieves superior performance over comparative methods in terms of seven different evaluation metrics (ACC, precision, recall, F1-score, MCC, AUROC, AUPRC), which fully demonstrates the validity and superiority of the proposed AGF-PPIS model. The source codes and the steps for usage of AGF-PPIS are available at https://github.com/fxh1001/AGF-PPIS.
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Affiliation(s)
- Xiuhao Fu
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Ye Yuan
- Beidahuang Industry Group General Hospital, Harbin 150001, China
| | - Haoye Qiu
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Haodong Suo
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Yingying Song
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Anqi Li
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Yupeng Zhang
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Cuilin Xiao
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Yazi Li
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Lijun Dou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland, OH 44106, USA
| | - Zilong Zhang
- School of Computer Science and Technology, Hainan University, Haikou 570228, China.
| | - Feifei Cui
- School of Computer Science and Technology, Hainan University, Haikou 570228, China.
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Sui J, Chen J, Chen Y, Iwamori N, Sun J. Identification of plant vacuole proteins by using graph neural network and contact maps. BMC Bioinformatics 2023; 24:357. [PMID: 37740195 PMCID: PMC10517492 DOI: 10.1186/s12859-023-05475-x] [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: 05/13/2023] [Accepted: 09/12/2023] [Indexed: 09/24/2023] Open
Abstract
Plant vacuoles are essential organelles in the growth and development of plants, and accurate identification of their proteins is crucial for understanding their biological properties. In this study, we developed a novel model called GraphIdn for the identification of plant vacuole proteins. The model uses SeqVec, a deep representation learning model, to initialize the amino acid sequence. We utilized the AlphaFold2 algorithm to obtain the structural information of corresponding plant vacuole proteins, and then fed the calculated contact maps into a graph convolutional neural network. GraphIdn achieved accuracy values of 88.51% and 89.93% in independent testing and fivefold cross-validation, respectively, outperforming previous state-of-the-art predictors. As far as we know, this is the first model to use predicted protein topology structure graphs to identify plant vacuole proteins. Furthermore, we assessed the effectiveness and generalization capability of our GraphIdn model by applying it to identify and locate peroxisomal proteins, which yielded promising outcomes. The source code and datasets can be accessed at https://github.com/SJNNNN/GraphIdn .
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Affiliation(s)
- Jianan Sui
- School of Information Science and Engineering, University of Jinan, Jinan, China
| | - Jiazi Chen
- Laboratory of Zoology, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, Fukuoka-Shi, Fukuoka, Japan
| | - Yuehui Chen
- School of Artificial Intelligence Institute and Information Science and Engineering, University of Jinan, Jinan, China.
| | - Naoki Iwamori
- Laboratory of Zoology, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, Fukuoka-Shi, Fukuoka, Japan
| | - Jin Sun
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
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