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Liang Y, Cao M, Zhang S. NeuroPred-ResSE: Predicting neuropeptides by integrating residual block and squeeze-excitation attention mechanism. Anal Biochem 2024; 695:115648. [PMID: 39154878 DOI: 10.1016/j.ab.2024.115648] [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: 06/10/2024] [Revised: 07/31/2024] [Accepted: 08/15/2024] [Indexed: 08/20/2024]
Abstract
Neuropeptides play crucial roles in regulating neurological function acting as signaling molecules, which provide new opportunity for developing drugs for the treatment of neurological diseases. Therefore, it is very necessary to develop a rapid and accurate prediction model for neuropeptides. Although a few prediction tools have been developed, there is room for improvement in prediction accuracy by using deep learning approach. In this paper, we establish the NeuroPred-ResSE model based on residual block and squeeze-excitation attention mechanism. Firstly, we extract multi-features by using one-hot coding based on the NT5CT5 sequence, dipeptide deviation from expected mean and natural vector. Then, we integrate residual block and squeeze-excitation attention mechanism, which can capture and identify the most relevant attribute features. Finally, the accuracies of the training set and test set are 97.16 % and 96.60 % based on the 5-fold cross-validation and independent test, respectively, and other evaluation metrics have also obtained satisfactory results. The experimental results show that the performance of the NeuroPred-ResSE model outperforms those of existing state-of-the-art models, and our model is an effective, intelligent and robust prediction tool. The datasets and source codes are available at https://github.com/yunyunliang88/NeuroPred-ResSE.
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Affiliation(s)
- Yunyun Liang
- School of Science, Xi'an Polytechnic University, Xi'an, 710048, PR China.
| | - Mengyi Cao
- School of Science, Xi'an Polytechnic University, Xi'an, 710048, PR China
| | - Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
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2
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Guo X, Zhao X, Lu X, Zhao L, Zeng Q, Chen F, Zhang Z, Xu M, Feng S, Fan T, Wei W, Zhang X, Pang J, You X, Song D, Wang Y, Jiang J. A deep learning-driven discovery of berberine derivatives as novel antibacterial against multidrug-resistant Helicobacter pylori. Signal Transduct Target Ther 2024; 9:183. [PMID: 38972904 PMCID: PMC11228022 DOI: 10.1038/s41392-024-01895-0] [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: 01/08/2024] [Revised: 05/17/2024] [Accepted: 06/14/2024] [Indexed: 07/09/2024] Open
Abstract
Helicobacter pylori (H. pylori) is currently recognized as the primary carcinogenic pathogen associated with gastric tumorigenesis, and its high prevalence and resistance make it difficult to tackle. A graph neural network-based deep learning model, employing different training sets of 13,638 molecules for pre-training and fine-tuning, was aided in predicting and exploring novel molecules against H. pylori. A positively predicted novel berberine derivative 8 with 3,13-disubstituted alkene exhibited a potency against all tested drug-susceptible and resistant H. pylori strains with minimum inhibitory concentrations (MICs) of 0.25-0.5 μg/mL. Pharmacokinetic studies demonstrated an ideal gastric retention of 8, with the stomach concentration significantly higher than its MIC at 24 h post dose. Oral administration of 8 and omeprazole (OPZ) showed a comparable gastric bacterial reduction (2.2-log reduction) to the triple-therapy, namely OPZ + amoxicillin (AMX) + clarithromycin (CLA) without obvious disturbance on the intestinal flora. A combination of OPZ, AMX, CLA, and 8 could further decrease the bacteria load (2.8-log reduction). More importantly, the mono-therapy of 8 exhibited comparable eradication to both triple-therapy (OPZ + AMX + CLA) and quadruple-therapy (OPZ + AMX + CLA + bismuth citrate) groups. SecA and BamD, playing a major role in outer membrane protein (OMP) transport and assembling, were identified and verified as the direct targets of 8 by employing the chemoproteomics technique. In summary, by targeting the relatively conserved OMPs transport and assembling system, 8 has the potential to be developed as a novel anti-H. pylori candidate, especially for the eradication of drug-resistant strains.
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Affiliation(s)
- Xixi Guo
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050, Beijing, China
| | - Xiaosa Zhao
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China
| | - Xi Lu
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050, Beijing, China
| | - Liping Zhao
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050, Beijing, China
| | - Qingxuan Zeng
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050, Beijing, China
| | - Fenbei Chen
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050, Beijing, China
| | - Zhimeng Zhang
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050, Beijing, China
| | - Mengyi Xu
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050, Beijing, China
| | - Shijiao Feng
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050, Beijing, China
| | - Tianyun Fan
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050, Beijing, China
| | - Wei Wei
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050, Beijing, China
| | - Xin Zhang
- Department of Pharmacy, Affiliated Hospital of Jining Medical University, Jining Medical University, Jining, 272029, Shandong, China
| | - Jing Pang
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050, Beijing, China.
| | - Xuefu You
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050, Beijing, China.
| | - Danqing Song
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050, Beijing, China.
| | - Yanxiang Wang
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050, Beijing, China.
- Institute of Health and Medicine, Hefei Comprehensive National Science Center, Hefei, 230601, Anhui, China.
| | - Jiandong Jiang
- Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, 100050, Beijing, China
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Ma X, Liang Y, Zhang S. iAVPs-ResBi: Identifying antiviral peptides by using deep residual network and bidirectional gated recurrent unit. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:21563-21587. [PMID: 38124610 DOI: 10.3934/mbe.2023954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Human history is also the history of the fight against viral diseases. From the eradication of viruses to coexistence, advances in biomedicine have led to a more objective understanding of viruses and a corresponding increase in the tools and methods to combat them. More recently, antiviral peptides (AVPs) have been discovered, which due to their superior advantages, have achieved great impact as antiviral drugs. Therefore, it is very necessary to develop a prediction model to accurately identify AVPs. In this paper, we develop the iAVPs-ResBi model using k-spaced amino acid pairs (KSAAP), encoding based on grouped weight (EBGW), enhanced grouped amino acid composition (EGAAC) based on the N5C5 sequence, composition, transition and distribution (CTD) based on physicochemical properties for multi-feature extraction. Then we adopt bidirectional long short-term memory (BiLSTM) to fuse features for obtaining the most differentiated information from multiple original feature sets. Finally, the deep model is built by combining improved residual network and bidirectional gated recurrent unit (BiGRU) to perform classification. The results obtained are better than those of the existing methods, and the accuracies are 95.07, 98.07, 94.29 and 97.50% on the four datasets, which show that iAVPs-ResBi can be used as an effective tool for the identification of antiviral peptides. The datasets and codes are freely available at https://github.com/yunyunliang88/iAVPs-ResBi.
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Affiliation(s)
- Xinyan Ma
- School of Science, Xi'an Polytechnic University, Xi'an 710048, China
| | - Yunyun Liang
- School of Science, Xi'an Polytechnic University, Xi'an 710048, China
| | - Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, China
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He J, Zhang S, Fang C. AAindex-PPII: Predicting polyproline type II helix structure based on amino acid indexes with an improved BiGRU-TextCNN model. J Bioinform Comput Biol 2023; 21:2350022. [PMID: 37899354 DOI: 10.1142/s0219720023500221] [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] [Indexed: 10/31/2023]
Abstract
The polyproline-II (PPII) structure domain is crucial in organisms' signal transduction, transcription, cell metabolism, and immune response. It is also a critical structural domain for specific vital disease-associated proteins. Recognizing PPII is essential for understanding protein structure and function. To accurately predict PPII in proteins, we propose a novel method, AAindex-PPII, which only adopts amino acid index to characterize protein sequences and uses a Bidirectional Gated Recurrent Unit (BiGRU)-Improved TextCNN composite deep learning model to predict PPII in proteins. Experimental results show that, when tested on the same datasets, our method outperforms the state-of-the-art BERT-PPII method, achieving an AUC value of 0.845 on the strict data and an AUC value of 0.813 on the non-strict data, which is 0.024 and 0.03 higher than that of the BERT-PPII method. This study demonstrates that our proposed method is simple and efficient for PPII prediction without using pre-trained large models or complex features such as position-specific scoring matrices.
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Affiliation(s)
- Jiasheng He
- College of Information Engineering, Beijing Institute of Petrochemical Technology, 19 Qingyuan North Road, Daxing District, Beijing 102617, P. R. China
| | - Shun Zhang
- College of Information Engineering, Beijing Institute of Petrochemical Technology, 19 Qingyuan North Road, Daxing District, Beijing 102617, P. R. China
| | - Chun Fang
- College of Information Engineering, Beijing Institute of Petrochemical Technology, 19 Qingyuan North Road, Daxing District, Beijing 102617, P. R. China
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Jing Y, Zhang S, Wang H. DapNet-HLA: Adaptive dual-attention mechanism network based on deep learning to predict non-classical HLA binding sites. Anal Biochem 2023; 666:115075. [PMID: 36740003 DOI: 10.1016/j.ab.2023.115075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/30/2023] [Accepted: 02/02/2023] [Indexed: 02/05/2023]
Abstract
Human leukocyte antigen (HLA) plays a vital role in immunomodulatory function. Studies have shown that immunotherapy based on non-classical HLA has essential applications in cancer, COVID-19, and allergic diseases. However, there are few deep learning methods to predict non-classical HLA alleles. In this work, an adaptive dual-attention network named DapNet-HLA is established based on existing datasets. Firstly, amino acid sequences are transformed into digital vectors by looking up the table. To overcome the feature sparsity problem caused by unique one-hot encoding, the fused word embedding method is used to map each amino acid to a low-dimensional word vector optimized with the training of the classifier. Then, we use the GCB (group convolution block), SENet attention (squeeze-and-excitation networks), BiLSTM (bidirectional long short-term memory network), and Bahdanau attention mechanism to construct the classifier. The use of SENet can make the weight of the effective feature map high, so that the model can be trained to achieve better results. Attention mechanism is an Encoder-Decoder model used to improve the effectiveness of RNN, LSTM or GRU (gated recurrent neural network). The ablation experiment shows that DapNet-HLA has the best adaptability for five datasets. On the five test datasets, the ACC index and MCC index of DapNet-HLA are 4.89% and 0.0933 higher than the comparison method, respectively. According to the ROC curve and PR curve verified by the 5-fold cross-validation, the AUC value of each fold has a slight fluctuation, which proves the robustness of the DapNet-HLA. The codes and datasets are accessible at https://github.com/JYY625/DapNet-HLA.
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Affiliation(s)
- Yuanyuan Jing
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
| | - Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China.
| | - Houqiang Wang
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
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Pasca S, Linari S, Tagliaferri A, Santoro C, Zanon E. Very high risk of intracranial hemorrhage and severe outcomes in adult patients with mild hemophilia: Sub-analysis of the EMO.REC Registry. Thromb Res 2023; 221:35-36. [PMID: 36463700 DOI: 10.1016/j.thromres.2022.11.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 11/24/2022]
Affiliation(s)
- Samantha Pasca
- Biomedical Sciences Department (DSB) - Padua University Hospital, Italy; Medicine Department (DIMED) - Padua University Hospital, Italy.
| | - Silvia Linari
- Centre for Bleeding Disorders - Careggi University Hospital of Florence, Italy
| | - Annarita Tagliaferri
- Regional Reference Centre for Inherited Bleeding Disorders, Parma University Hospital, Italy
| | | | - Ezio Zanon
- Hemophilia Center, General Medicine - Padua University Hospital, Italy
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Liang Y, Ma X. iACP-GE: accurate identification of anticancer peptides by using gradient boosting decision tree and extra tree. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2023; 34:1-19. [PMID: 36562289 DOI: 10.1080/1062936x.2022.2160011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
Cancer is one of the main diseases threatening human life, accounting for millions of deaths around the world each year. Traditional physical and chemical methods for cancer treatment are extremely time-consuming, lab-intensive, expensive, inefficient and difficult to be applied in a high-throughput way. Hence, it is an urgent task to develop automated computational methods to enable fast and accurate identification of anticancer peptides (ACPs). In this paper, we develop a novel model named iACP-GE to identify ACPs. Multi-features are extracted by using binary encoding, enhanced grouped amino acid composition and BLOSUM62 encoding based on the N5C5 sequence, as well as detrended forward moving-average auto-cross correlation analysis based on physicochemical properties of 20 natural amino acids. Thus, 835 features are obtained for each sample, in order to avoid information redundancy, gradient boosting decision tree was adopted as the feature selection strategy. Then, the optimal feature subset is input to the extra tree classifier. The accuracies of ACP740 and ACP240 datasets with the 5-fold cross-validation were 90.54% and 91.25%, respectively. Experimental results indicate that iACP-GE significantly outperforms several existing models on ACP740 and ACP240 datasets and can be used as an effective tool for the identification of ACPs. The datasets and source codes for iACP-GE are available at https://github.com/yunyunliang88/iACP-GE.
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Affiliation(s)
- Y Liang
- School of Science, Xi'an Polytechnic University, Xi'an, P. R. China
| | - X Ma
- School of Science, Xi'an Polytechnic University, Xi'an, P. R. China
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Li Y, Li X, Liu Y, Yao Y, Huang G. MPMABP: A CNN and Bi-LSTM-Based Method for Predicting Multi-Activities of Bioactive Peptides. Pharmaceuticals (Basel) 2022; 15:707. [PMID: 35745625 PMCID: PMC9231127 DOI: 10.3390/ph15060707] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/23/2022] [Accepted: 05/30/2022] [Indexed: 12/30/2022] Open
Abstract
Bioactive peptides are typically small functional peptides with 2-20 amino acid residues and play versatile roles in metabolic and biological processes. Bioactive peptides are multi-functional, so it is vastly challenging to accurately detect all their functions simultaneously. We proposed a convolution neural network (CNN) and bi-directional long short-term memory (Bi-LSTM)-based deep learning method (called MPMABP) for recognizing multi-activities of bioactive peptides. The MPMABP stacked five CNNs at different scales, and used the residual network to preserve the information from loss. The empirical results showed that the MPMABP is superior to the state-of-the-art methods. Analysis on the distribution of amino acids indicated that the lysine preferred to appear in the anti-cancer peptide, the leucine in the anti-diabetic peptide, and the proline in the anti-hypertensive peptide. The method and analysis are beneficial to recognize multi-activities of bioactive peptides.
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Affiliation(s)
- You Li
- School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; (Y.L.); (X.L.)
| | - Xueyong Li
- School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; (Y.L.); (X.L.)
| | - Yuewu Liu
- College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China;
| | - Yuhua Yao
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China;
| | - Guohua Huang
- School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; (Y.L.); (X.L.)
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