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Pang Y, Liu B. TransDFL: Identification of Disordered Flexible Linkers in Proteins by Transfer Learning. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:359-369. [PMID: 36272675 PMCID: PMC10626177 DOI: 10.1016/j.gpb.2022.10.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 09/21/2022] [Accepted: 10/14/2022] [Indexed: 11/27/2022]
Abstract
Disordered flexible linkers (DFLs) are the functional disordered regions in proteins, which are the sub-regions of intrinsically disordered regions (IDRs) and play important roles in connecting domains and maintaining inter-domain interactions. Trained with the limited available DFLs, the existing DFL predictors based on the machine learning techniques tend to predict the ordered residues as DFLs, leading to a high falsepositive rate (FPR) and low prediction accuracy. Previous studies have shown that DFLs are extremely flexible disordered regions, which are usually predicted as disordered residues with high confidence [P(D) > 0.9] by an IDR predictor. Therefore, transferring an IDR predictor to an accurate DFL predictor is of great significance for understanding the functions of IDRs. In this study, we proposed a new predictor called TransDFL for identifying DFLs by transferring the RFPR-IDP predictor for IDR identification to the DFL prediction. The RFPR-IDP was pre-trained with IDR sequences to learn the general features between IDRs and DFLs, which is helpful to reduce the false positives in the ordered regions. RFPR-IDP was fine-tuned with the DFL sequences to capture the specific features of DFLs so as to be transferred into the TransDFL. Experimental results of two application scenarios (prediction of DFLs only in IDRs or prediction of DFLs in entire proteins) showed that TransDFL consistently outperformed other existing DFL predictors with higher accuracy. The corresponding web server of TransDFL can be freely accessed at http://bliulab.net/TransDFL/.
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Affiliation(s)
- Yihe Pang
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China; Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing 100081, China.
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Jin X, Guo L, Jiang Q, Wu N, Yao S. Prediction of protein secondary structure based on an improved channel attention and multiscale convolution module. Front Bioeng Biotechnol 2022; 10:901018. [PMID: 35935483 PMCID: PMC9355137 DOI: 10.3389/fbioe.2022.901018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 06/28/2022] [Indexed: 11/13/2022] Open
Abstract
Prediction of the protein secondary structure is a key issue in protein science. Protein secondary structure prediction (PSSP) aims to construct a function that can map the amino acid sequence into the secondary structure so that the protein secondary structure can be obtained according to the amino acid sequence. Driven by deep learning, the prediction accuracy of the protein secondary structure has been greatly improved in recent years. To explore a new technique of PSSP, this study introduces the concept of an adversarial game into the prediction of the secondary structure, and a conditional generative adversarial network (GAN)-based prediction model is proposed. We introduce a new multiscale convolution module and an improved channel attention (ICA) module into the generator to generate the secondary structure, and then a discriminator is designed to conflict with the generator to learn the complicated features of proteins. Then, we propose a PSSP method based on the proposed multiscale convolution module and ICA module. The experimental results indicate that the conditional GAN-based protein secondary structure prediction (CGAN-PSSP) model is workable and worthy of further study because of the strong feature-learning ability of adversarial learning.
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Affiliation(s)
- Xin Jin
- Engineering Research Center of Cyberspace, Yunnan University, Kunming, Yunnan, China
- School of Software, Yunnan University, Kunming, Yunnan, China
| | - Lin Guo
- Engineering Research Center of Cyberspace, Yunnan University, Kunming, Yunnan, China
- School of Software, Yunnan University, Kunming, Yunnan, China
| | - Qian Jiang
- Engineering Research Center of Cyberspace, Yunnan University, Kunming, Yunnan, China
- School of Software, Yunnan University, Kunming, Yunnan, China
| | - Nan Wu
- Engineering Research Center of Cyberspace, Yunnan University, Kunming, Yunnan, China
- School of Software, Yunnan University, Kunming, Yunnan, China
| | - Shaowen Yao
- Engineering Research Center of Cyberspace, Yunnan University, Kunming, Yunnan, China
- School of Software, Yunnan University, Kunming, Yunnan, China
- *Correspondence: Shaowen Yao,
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Gong Y, Liao B, Wang P, Zou Q. DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins. Front Pharmacol 2021; 12:771808. [PMID: 34916947 PMCID: PMC8669608 DOI: 10.3389/fphar.2021.771808] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 11/15/2021] [Indexed: 01/09/2023] Open
Abstract
Drug targets are biological macromolecules or biomolecule structures capable of specifically binding a therapeutic effect with a particular drug or regulating physiological functions. Due to the important value and role of drug targets in recent years, the prediction of potential drug targets has become a research hotspot. The key to the research and development of modern new drugs is first to identify potential drug targets. In this paper, a new predictor, DrugHybrid_BS, is developed based on hybrid features and Bagging-SVM to identify potentially druggable proteins. This method combines the three features of monoDiKGap (k = 2), cross-covariance, and grouped amino acid composition. It removes redundant features and analyses key features through MRMD and MRMD2.0. The cross-validation results show that 96.9944% of the potentially druggable proteins can be accurately identified, and the accuracy of the independent test set has reached 96.5665%. This all means that DrugHybrid_BS has the potential to become a useful predictive tool for druggable proteins. In addition, the hybrid key features can identify 80.0343% of the potentially druggable proteins combined with Bagging-SVM, which indicates the significance of this part of the features for research.
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Affiliation(s)
- Yuxin Gong
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China.,Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, China
| | - Bo Liao
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China.,Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, China
| | - Peng Wang
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China.,Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
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Hu J, Zheng LL, Bai YS, Zhang KW, Yu DJ, Zhang GJ. Accurate prediction of protein-ATP binding residues using position-specific frequency matrix. Anal Biochem 2021; 626:114241. [PMID: 33971164 DOI: 10.1016/j.ab.2021.114241] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 04/27/2021] [Accepted: 05/01/2021] [Indexed: 10/21/2022]
Abstract
Knowledge of protein-ATP interaction can help for protein functional annotation and drug discovery. Accurately identifying protein-ATP binding residues is an important but challenging task to gain the knowledge of protein-ATP interactions, especially for the case where only protein sequence information is given. In this study, we propose a novel method, named DeepATPseq, to predict protein-ATP binding residues without using any information about protein three-dimension structure or sequence-derived structural information. In DeepATPseq, the HHBlits-generated position-specific frequency matrix (PSFM) profile is first employed to extract the feature information of each residue. Then, for each residue, the PSFM-based feature is fed into two prediction models, which are generated by the algorithms of deep convolutional neural network (DCNN) and support vector machine (SVM) separately. The final ATP-binding probability of the corresponding residue is calculated by the weighted sum of the outputted values of DCNN-based and SVM-based models. Experimental results on the independent validation data set demonstrate that DeepATPseq could achieve an accuracy of 77.71%, covering 57.42% of all ATP-binding residues, while achieving a Matthew's correlation coefficient value (0.655) that is significantly higher than that of existing sequence-based methods and comparable to that of the state-of-the-art structure-based predictors. Detailed data analysis show that the major advantage of DeepATPseq lies at the combination utilization of DCNN and SVM that helps dig out more discriminative information from the PSFM profiles. The online server and standalone package of DeepATPseq are freely available at: https://jun-csbio.github.io/DeepATPseq/for academic use.
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Affiliation(s)
- Jun Hu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.
| | - Lin-Lin Zheng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Yan-Song Bai
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Ke-Wen Zhang
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology,Xiaolingwei 200, Nanjing, 210094, China.
| | - Gui-Jun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.
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Wang H, Liang P, Zheng L, Long C, Li H, Zuo Y. eHSCPr discriminating the cell identity involved in endothelial to hematopoietic transition. Bioinformatics 2021; 37:2157-2164. [PMID: 33532815 DOI: 10.1093/bioinformatics/btab071] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 01/15/2021] [Accepted: 01/28/2021] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION Hematopoietic stem cells (HSCs) give rise to all blood cells and play a vital role throughout the whole lifespan through their pluripotency and self-renewal properties. Accurately identifying the stages of early HSCs is extremely important, as it may open up new prospects for extracorporeal blood research. Existing experimental techniques for identifying the early stages of HSCs development are time-consuming and expensive. Machine learning has shown its excellence in massive single-cell data processing and it is desirable to develop related computational models as good complements to experimental techniques. RESULTS In this study, we presented a novel predictor called eHSCPr specifically for predicting the early stages of HSCs development. To reveal the distinct genes at each developmental stage of HSCs, we compared F-score with three state-of-art differential gene selection methods (limma, DESeq2, edgeR) and evaluated their performance. F-score captured the more critical surface markers of endothelial cells and hematopoietic cells, and the area under receiver operating characteristic curve (ROC) value was 0.987. Based on SVM, the 10-fold cross-validation accuracy of eHSCpr in the independent dataset and the training dataset reached 94.84% and 94.19%, respectively. Importantly, we performed transcription analysis on the F-score gene set, which indeed further enriched the signal markers of HSCs development stages. eHSCPr can be a powerful tool for predicting early stages of HSCs development, facilitating hypothesis-driven experimental design and providing crucial clues for the in vitro blood regeneration studies. AVAILABILITY http://bioinfor.imu.edu.cn/ehscpr. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hao Wang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Pengfei Liang
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Lei Zheng
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - ChunShen Long
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - HanShuang Li
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
| | - Yongchun Zuo
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China
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