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Yu Y, Gu M, Guo H, Deng Y, Chen D, Wang J, Wang C, Liu X, Yan W, Huang J. MuCoCP: a priori chemical knowledge-based multimodal contrastive learning pre-trained neural network for the prediction of cyclic peptide membrane penetration ability. Bioinformatics 2024; 40:btae473. [PMID: 39067027 PMCID: PMC11315609 DOI: 10.1093/bioinformatics/btae473] [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: 05/07/2024] [Revised: 07/04/2024] [Accepted: 07/25/2024] [Indexed: 07/30/2024] Open
Abstract
MOTIVATION There has been a burgeoning interest in cyclic peptide therapeutics due to their various outstanding advantages and strong potential for drug formation. However, it is undoubtedly costly and inefficient to use traditional wet lab methods to clarify their biological activities. Using artificial intelligence instead is a more energy-efficient and faster approach. MuCoCP aims to build a complete pre-trained model for extracting potential features of cyclic peptides, which can be fine-tuned to accurately predict cyclic peptide bioactivity on various downstream tasks. To maximize its effectiveness, we use a novel data augmentation method based on a priori chemical knowledge and multiple unsupervised training objective functions to greatly improve the information-grabbing ability of the model. RESULTS To assay the efficacy of the model, we conducted validation on the membrane-permeability of cyclic peptides which achieved an accuracy of 0.87 and R-squared of 0.503 on CycPeptMPDB using semi-supervised training and obtained an accuracy of 0.84 and R-squared of 0.384 using a model with frozen parameters on an external dataset. This result has achieved state-of-the-art, which substantiates the stability and generalization capability of MuCoCP. It means that MuCoCP can fully explore the high-dimensional information of cyclic peptides and make accurate predictions on downstream bioactivity tasks, which will serve as a guide for the future de novo design of cyclic peptide drugs and promote the development of cyclic peptide drugs. AVAILABILITY AND IMPLEMENTATION All code used in our proposed method can be found at https://github.com/lennonyu11234/MuCoCP.
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Affiliation(s)
- Yunxiang Yu
- School of Basic Medical Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Mengyun Gu
- School of Basic Medical Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Hai Guo
- The Second Hospital Clinical Medical School, Lanzhou University, Lanzhou, 730000, China
| | - Yabo Deng
- School of Basic Medical Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Danna Chen
- School of Basic Medical Sciences, Lanzhou University, Lanzhou, 730000, China
- The Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524000, China
- Guangzhou First People’s Hospital, South China University of Technology, Guangzhou, 510180, China
| | - Jianwei Wang
- Guangzhou First People’s Hospital, South China University of Technology, Guangzhou, 510180, China
| | - Caixia Wang
- Guangzhou First People’s Hospital, South China University of Technology, Guangzhou, 510180, China
| | - Xia Liu
- School of Basic Medical Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Wenjin Yan
- School of Basic Medical Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Jinqi Huang
- The Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524000, China
- Guangzhou First People’s Hospital, South China University of Technology, Guangzhou, 510180, China
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Tang X, Luo L, Wang S. TSE-ARF: An adaptive prediction method of effectors across secretion system types. Anal Biochem 2024; 686:115407. [PMID: 38030053 DOI: 10.1016/j.ab.2023.115407] [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: 09/02/2023] [Revised: 11/12/2023] [Accepted: 11/20/2023] [Indexed: 12/01/2023]
Abstract
Bacterial effector proteins are secreted by a variety of protein secretion systems and play an important role in the interaction between the host and pathogenic bacteria. Therefore, it is important to find a fast and inexpensive method to discover bacterial effectors. In this study, we propose a multi-type secretion effector adaptive random forest (TSE-ARF) to adaptively identify secretion effectors across T1SE-T4SE and T6SE based only on protein sequences. First, we proposed two new feature descriptors by considering some characteristic protein information and fused them with some universal features to form a 290-dimensional feature vector with good versatility. Then, the TSE-ARF model was used to make classification predictions by parameter adaptation of different secretion effectors integrating Shuffled Frog Leaping Algorithm and random forest. The perfect performance in TSE-ARF under different data sets and settings shows its considerable generalization ability, with which more candidate effectors were screened in the whole genome. Source code is available at https://github.com/AIMOVE/TSE-ARF.
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Affiliation(s)
- Xianjun Tang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China
| | - Longfei Luo
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China
| | - Shunfang Wang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China; Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, Yunnan, China.
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3
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Zhao C, Wang S. AttCON: With better MSAs and attention mechanism for accurate protein contact map prediction. Comput Biol Med 2024; 169:107822. [PMID: 38091726 DOI: 10.1016/j.compbiomed.2023.107822] [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: 10/06/2023] [Revised: 11/19/2023] [Accepted: 12/04/2023] [Indexed: 02/08/2024]
Abstract
Protein contact map prediction is a critical and vital step in protein structure prediction, and its accuracy is highly contingent upon the feature representations of protein sequence information and the efficacy of deep learning models. In this paper, we propose an algorithm, DeepMSA+, to generate protein multiple sequence alignments (MSAs) and to construct feature representations based on co-evolutionary information and sequence information derived from MSAs. We also propose an improved deep learning model, AttCON, for training input features to predict protein contact map. The model incorporates an attention module, and by comparing different attention modules, we find a parameter-free attention module suitable for contact map prediction. Additionally, we use the Focal Loss function to better address the data imbalance issue in protein contact map. We also developed a weighted evaluation index (W score) for model evaluation, which takes into account a wide range of metrics. W score is comprehensive in its scope, with a particular focus on the precision of predictions for medium-range and long-range contacts. Experimental results show that AttCON achieves good precision results on datasets from CASP11 to CASP15. Compared to some state-of-the-art methods, it achieves an average improvement of over 5% in both medium-range and long-range predictions, and W score is improved by an average of 2 points.
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Affiliation(s)
- Che Zhao
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China
| | - Shunfang Wang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China; Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, 650504, Yunnan, China.
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Shao H, Wang S. Deep Classification with Linearity-Enhanced Logits to Softmax Function. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25050727. [PMID: 37238482 DOI: 10.3390/e25050727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/20/2023] [Accepted: 03/27/2023] [Indexed: 05/28/2023]
Abstract
Recently, there has been a rapid increase in deep classification tasks, such as image recognition and target detection. As one of the most crucial components in Convolutional Neural Network (CNN) architectures, softmax arguably encourages CNN to achieve better performance in image recognition. Under this scheme, we present a conceptually intuitive learning objection function: Orthogonal-Softmax. The primary property of the loss function is to use a linear approximation model that is designed by Gram-Schmidt orthogonalization. Firstly, compared with the traditional softmax and Taylor-Softmax, Orthogonal-Softmax has a stronger relationship through orthogonal polynomials expansion. Secondly, a new loss function is advanced to acquire highly discriminative features for classification tasks. At last, we present a linear softmax loss to further promote the intra-class compactness and inter-class discrepancy simultaneously. The results of the widespread experimental discussion on four benchmark datasets manifest the validity of the presented method. Besides, we want to explore the non-ground truth samples in the future.
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Affiliation(s)
- Hao Shao
- School of Mathematics and Statistics, Yunnan Unverisity, Kunming 650504, China
| | - Shunfang Wang
- School of Information Science and Engineering, Yunnan Unverisity, Kunming 650504, China
- The Key Lab of Intelligent Systems and Computing of Yunnan Province, Yunnan University, Kunming 650504, China
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Arif M, Kabir M, Ahmed S, Khan A, Ge F, Khelifi A, Yu DJ. DeepCPPred: A Deep Learning Framework for the Discrimination of Cell-Penetrating Peptides and Their Uptake Efficiencies. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2749-2759. [PMID: 34347603 DOI: 10.1109/tcbb.2021.3102133] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Cell-penetrating peptides (CPPs) are special peptides capable of carrying a variety of bioactive molecules, such as genetic materials, short interfering RNAs and nanoparticles, into cells. Recently, research on CPP has gained substantial interest from researchers, and the biological mechanisms of CPPS have been assessed in the context of safe drug delivery agents and therapeutic applications. Correct identification and synthesis of CPPs using traditional biochemical methods is an extremely slow, expensive and laborious task particularly due to the large volume of unannotated peptide sequences accumulating in the World Bank repository. Hence, a powerful bioinformatics predictor that rapidly identifies CPPs with a high recognition rate is urgently needed. To date, numerous computational methods have been developed for CPP prediction. However, the available machine-learning (ML) tools are unable to distinguish both the CPPs and their uptake efficiencies. This study aimed to develop a two-layer deep learning framework named DeepCPPred to identify both CPPs in the first phase and peptide uptake efficiency in the second phase. The DeepCPPred predictor first uses four types of descriptors that cover evolutionary, energy estimation, reduced sequence and amino-acid contact information. Then, the extracted features are optimized through the elastic net algorithm and fed into a cascade deep forest algorithm to build the final CPP model. The proposed method achieved 99.45 percent overall accuracy with the CPP924 benchmark dataset in the first layer and 95.43 percent accuracy in the second layer with the CPPSite3 dataset using a 5-fold cross-validation test. Thus, our proposed bioinformatics tool surpassed all the existing state-of-the-art sequence-based CPP approaches.
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Chen S, Li Q, Zhao J, Bin Y, Zheng C. NeuroPred-CLQ: incorporating deep temporal convolutional networks and multi-head attention mechanism to predict neuropeptides. Brief Bioinform 2022; 23:6672901. [DOI: 10.1093/bib/bbac319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/27/2022] [Accepted: 07/14/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Neuropeptides (NPs) are a particular class of informative substances in the immune system and physiological regulation. They play a crucial role in regulating physiological functions in various biological growth and developmental stages. In addition, NPs are crucial for developing new drugs for the treatment of neurological diseases. With the development of molecular biology techniques, some data-driven tools have emerged to predict NPs. However, it is necessary to improve the predictive performance of these tools for NPs. In this study, we developed a deep learning model (NeuroPred-CLQ) based on the temporal convolutional network (TCN) and multi-head attention mechanism to identify NPs effectively and translate the internal relationships of peptide sequences into numerical features by the Word2vec algorithm. The experimental results show that NeuroPred-CLQ learns data information effectively, achieving 93.6% accuracy and 98.8% AUC on the independent test set. The model has better performance in identifying NPs than the state-of-the-art predictors. Visualization of features using t-distribution random neighbor embedding shows that the NeuroPred-CLQ can clearly distinguish the positive NPs from the negative ones. We believe the NeuroPred-CLQ can facilitate drug development and clinical trial studies to treat neurological disorders.
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Affiliation(s)
- Shouzhi Chen
- School of Mathematics and System Science, Xinjiang University , Urumqi, China
| | - Qing Li
- School of Mathematics and System Science, Xinjiang University , Urumqi, China
| | - Jianping Zhao
- School of Mathematics and System Science, Xinjiang University , Urumqi, China
| | - Yannan Bin
- School of Computer Science and Technology, Anhui University , Hefei, China
| | - Chunhou Zheng
- School of Mathematics and System Science, Xinjiang University , Urumqi, China
- School of Computer Science and Technology, Anhui University , Hefei, China
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7
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Zou H. iAHTP-LH: Integrating Low-Order and High-Order Correlation Information for Identifying Antihypertensive Peptides. Int J Pept Res Ther 2022. [DOI: 10.1007/s10989-022-10414-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Gene prediction of aging-related diseases based on DNN and Mashup. BMC Bioinformatics 2021; 22:597. [PMID: 34920719 PMCID: PMC8680025 DOI: 10.1186/s12859-021-04518-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 11/30/2021] [Indexed: 11/17/2022] Open
Abstract
Background At present, the bioinformatics research on the relationship between aging-related diseases and genes is mainly through the establishment of a machine learning multi-label model to classify each gene. Most of the existing methods for predicting pathogenic genes mainly rely on specific types of gene features, or directly encode multiple features with different dimensions, use the same encoder to concatenate and predict the final results, which will be subject to many limitations in the applicability of the algorithm. Possible shortcomings of the above include: incomplete coverage of gene features by a single type of biomics data, overfitting of small dimensional datasets by a single encoder, or underfitting of larger dimensional datasets. Methods We use the known gene disease association data and gene descriptors, such as gene ontology terms (GO), protein interaction data (PPI), PathDIP, Kyoto Encyclopedia of genes and genomes Genes (KEGG), etc, as input for deep learning to predict the association between genes and diseases. Our innovation is to use Mashup algorithm to reduce the dimensionality of PPI, GO and other large biological networks, and add new pathway data in KEGG database, and then combine a variety of biological information sources through modular Deep Neural Network (DNN) to predict the genes related to aging diseases. Result and conclusion The results show that our algorithm is more effective than the standard neural network algorithm (the Area Under the ROC curve from 0.8795 to 0.9153), gradient enhanced tree classifier and logistic regression classifier. In this paper, we firstly use DNN to learn the similar genes associated with the known diseases from the complex multi-dimensional feature space, and then provide the evidence that the assumed genes are associated with a certain disease. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04518-5.
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Wang S, Deng L, Xia X, Cao Z, Fei Y. Predicting antifreeze proteins with weighted generalized dipeptide composition and multi-regression feature selection ensemble. BMC Bioinformatics 2021; 22:340. [PMID: 34162327 PMCID: PMC8220696 DOI: 10.1186/s12859-021-04251-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 06/09/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Antifreeze proteins (AFPs) are a group of proteins that inhibit body fluids from growing to ice crystals and thus improve biological antifreeze ability. It is vital to the survival of living organisms in extremely cold environments. However, little research is performed on sequences feature extraction and selection for antifreeze proteins classification in the structure and function prediction, which is of great significance. RESULTS In this paper, to predict the antifreeze proteins, a feature representation of weighted generalized dipeptide composition (W-GDipC) and an ensemble feature selection based on two-stage and multi-regression method (LRMR-Ri) are proposed. Specifically, four feature selection algorithms: Lasso regression, Ridge regression, Maximal information coefficient and Relief are used to select the feature sets, respectively, which is the first stage of LRMR-Ri method. If there exists a common feature subset among the above four sets, it is the optimal subset; otherwise we use Ridge regression to select the optimal subset from the public set pooled by the four sets, which is the second stage of LRMR-Ri. The LRMR-Ri method combined with W-GDipC was performed both on the antifreeze proteins dataset (binary classification), and on the membrane protein dataset (multiple classification). Experimental results show that this method has good performance in support vector machine (SVM), decision tree (DT) and stochastic gradient descent (SGD). The values of ACC, RE and MCC of LRMR-Ri and W-GDipC with antifreeze proteins dataset and SVM classifier have reached as high as 95.56%, 97.06% and 0.9105, respectively, much higher than those of each single method: Lasso, Ridge, Mic and Relief, nearly 13% higher than single Lasso for ACC. CONCLUSION The experimental results show that the proposed LRMR-Ri and W-GDipC method can significantly improve the accuracy of antifreeze proteins prediction compared with other similar single feature methods. In addition, our method has also achieved good results in the classification and prediction of membrane proteins, which verifies its widely reliability to a certain extent.
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Affiliation(s)
- Shunfang Wang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, China.
| | - Lin Deng
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, China
| | - Xinnan Xia
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, China.
| | - Zicheng Cao
- School of Public Health (Shenzhen), Sun Yat-Sen University, Guangzhou, 510006, China
| | - Yu Fei
- School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, 650221, China.
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10
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Holl NJ, Lee HJ, Huang YW. Evolutionary Timeline of Genetic Delivery and Gene Therapy. Curr Gene Ther 2021; 21:89-111. [PMID: 33292120 DOI: 10.2174/1566523220666201208092517] [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: 10/07/2020] [Revised: 11/17/2020] [Accepted: 11/22/2020] [Indexed: 11/22/2022]
Abstract
There are more than 3,500 genes that are being linked to hereditary diseases or correlated with an elevated risk of certain illnesses. As an alternative to conventional treatments with small molecule drugs, gene therapy has arisen as an effective treatment with the potential to not just alleviate disease conditions but also cure them completely. In order for these treatment regimens to work, genes or editing tools intended to correct diseased genetic material must be efficiently delivered to target sites. There have been many techniques developed to achieve such a goal. In this article, we systematically review a variety of gene delivery and therapy methods that include physical methods, chemical and biochemical methods, viral methods, and genome editing. We discuss their historical discovery, mechanisms, advantages, limitations, safety, and perspectives.
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Affiliation(s)
- Natalie J Holl
- Department of Biological Sciences, College of Arts, Sciences, and Business, Missouri University of Science and Technology, Rolla, MO 65409, United States
| | - Han-Jung Lee
- Department of Natural Resources and Environmental Studies, College of Environmental Studies, National Dong Hwa University, Hualien 974301, Taiwan
| | - Yue-Wern Huang
- Department of Biological Sciences, College of Arts, Sciences, and Business, Missouri University of Science and Technology, Rolla, MO 65409, United States
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11
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Fu H, Cao Z, Li M, Wang S. ACEP: improving antimicrobial peptides recognition through automatic feature fusion and amino acid embedding. BMC Genomics 2020; 21:597. [PMID: 32859150 PMCID: PMC7455913 DOI: 10.1186/s12864-020-06978-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 08/11/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Antimicrobial resistance is one of our most serious health threats. Antimicrobial peptides (AMPs), effecter molecules of innate immune system, can defend host organisms against microbes and most have shown a lowered likelihood for bacteria to form resistance compared to many conventional drugs. Thus, AMPs are gaining popularity as better substitute to antibiotics. To aid researchers in novel AMPs discovery, we design computational approaches to screen promising candidates. RESULTS In this work, we design a deep learning model that can learn amino acid embedding patterns, automatically extract sequence features, and fuse heterogeneous information. Results show that the proposed model outperforms state-of-the-art methods on recognition of AMPs. By visualizing data in some layers of the model, we overcome the black-box nature of deep learning, explain the working mechanism of the model, and find some import motifs in sequences. CONCLUSIONS ACEP model can capture similarity between amino acids, calculate attention scores for different parts of a peptide sequence in order to spot important parts that significantly contribute to final predictions, and automatically fuse a variety of heterogeneous information or features. For high-throughput AMPs recognition, open source software and datasets are made freely available at https://github.com/Fuhaoyi/ACEP .
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Affiliation(s)
- Haoyi Fu
- School of Information Science and Engineering, Yunnan University, Kunming, 650500, China
| | - Zicheng Cao
- School of Public Health (Shenzhen), Sun Yat-sen University, Guangzhou, 510006, China
| | - Mingyuan Li
- School of Information Science and Engineering, Yunnan University, Kunming, 650500, China
| | - Shunfang Wang
- School of Information Science and Engineering, Yunnan University, Kunming, 650500, China.
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12
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Bin Y, Zhang W, Tang W, Dai R, Li M, Zhu Q, Xia J. Prediction of Neuropeptides from Sequence Information Using Ensemble Classifier and Hybrid Features. J Proteome Res 2020; 19:3732-3740. [DOI: 10.1021/acs.jproteome.0c00276] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Yannan Bin
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China
- School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China
| | - Wei Zhang
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China
| | - Wending Tang
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China
| | - Ruyu Dai
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China
| | - Menglu Li
- School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China
| | - Qizhi Zhu
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China
| | - Junfeng Xia
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China
- School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China
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Yan J, Bhadra P, Li A, Sethiya P, Qin L, Tai HK, Wong KH, Siu SWI. Deep-AmPEP30: Improve Short Antimicrobial Peptides Prediction with Deep Learning. MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 20:882-894. [PMID: 32464552 PMCID: PMC7256447 DOI: 10.1016/j.omtn.2020.05.006] [Citation(s) in RCA: 117] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 04/08/2020] [Accepted: 05/06/2020] [Indexed: 12/12/2022]
Abstract
Antimicrobial peptides (AMPs) are a valuable source of antimicrobial agents and a potential solution to the multi-drug resistance problem. In particular, short-length AMPs have been shown to have enhanced antimicrobial activities, higher stability, and lower toxicity to human cells. We present a short-length (≤30 aa) AMP prediction method, Deep-AmPEP30, developed based on an optimal feature set of PseKRAAC reduced amino acids composition and convolutional neural network. On a balanced benchmark dataset of 188 samples, Deep-AmPEP30 yields an improved performance of 77% in accuracy, 85% in the area under the receiver operating characteristic curve (AUC-ROC), and 85% in area under the precision-recall curve (AUC-PR) over existing machine learning-based methods. To demonstrate its power, we screened the genome sequence of Candida glabrata—a gut commensal fungus expected to interact with and/or inhibit other microbes in the gut—for potential AMPs and identified a peptide of 20 aa (P3, FWELWKFLKSLWSIFPRRRP) with strong anti-bacteria activity against Bacillus subtilis and Vibrio parahaemolyticus. The potency of the peptide is remarkably comparable to that of ampicillin. Therefore, Deep-AmPEP30 is a promising prediction tool to identify short-length AMPs from genomic sequences for drug discovery. Our method is available at https://cbbio.cis.um.edu.mo/AxPEP for both individual sequence prediction and genome screening for AMPs.
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Affiliation(s)
- Jielu Yan
- Department of Computer and Information Science, University of Macau, Macau, China
| | - Pratiti Bhadra
- Department of Computer and Information Science, University of Macau, Macau, China
| | - Ang Li
- Faculty of Health Sciences, University of Macau, Macau, China
| | - Pooja Sethiya
- Faculty of Health Sciences, University of Macau, Macau, China
| | - Longguang Qin
- Faculty of Health Sciences, University of Macau, Macau, China
| | - Hio Kuan Tai
- Department of Computer and Information Science, University of Macau, Macau, China
| | - Koon Ho Wong
- Faculty of Health Sciences, University of Macau, Macau, China; Institute of Translational Medicines, University of Macau, Macau, China
| | - Shirley W I Siu
- Department of Computer and Information Science, University of Macau, Macau, China.
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