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Zhong G, Liu H, Deng L. Ensemble Machine Learning and Predicted Properties Promote Antimicrobial Peptide Identification. Interdiscip Sci 2024:10.1007/s12539-024-00640-z. [PMID: 38972032 DOI: 10.1007/s12539-024-00640-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 06/04/2024] [Accepted: 06/07/2024] [Indexed: 07/08/2024]
Abstract
The emergence of antibiotic-resistant microbes raises a pressing demand for novel alternative treatments. One promising alternative is the antimicrobial peptides (AMPs), a class of innate immunity mediators within the therapeutic peptide realm. AMPs offer salient advantages such as high specificity, cost-effective synthesis, and reduced toxicity. Although some computational methodologies have been proposed to identify potential AMPs with the rapid development of artificial intelligence techniques, there is still ample room to improve their performance. This study proposes a predictive framework which ensembles deep learning and statistical learning methods to screen peptides with antimicrobial activity. We integrate multiple LightGBM classifiers and convolution neural networks which leverages various predicted sequential, structural and physicochemical properties from their residue sequences extracted by diverse machine learning paradigms. Comparative experiments exhibit that our method outperforms other state-of-the-art approaches on an independent test dataset, in terms of representative capability measures. Besides, we analyse the discrimination quality under different varieties of attribute information and it reveals that combination of multiple features could improve prediction. In addition, a case study is carried out to illustrate the exemplary favorable identification effect. We establish a web application at http://amp.denglab.org to provide convenient usage of our proposal and make the predictive framework, source code, and datasets publicly accessible at https://github.com/researchprotein/amp .
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Affiliation(s)
- Guolun Zhong
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Hui Liu
- College of Computer and Information Engineering, Nanjing Tech University, Nanjing, 211816, China.
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
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2
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Shaon MSH, Karim T, Sultan MF, Ali MM, Ahmed K, Hasan MZ, Moustafa A, Bui FM, Al-Zahrani FA. AMP-RNNpro: a two-stage approach for identification of antimicrobials using probabilistic features. Sci Rep 2024; 14:12892. [PMID: 38839785 PMCID: PMC11153637 DOI: 10.1038/s41598-024-63461-6] [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: 02/16/2024] [Accepted: 05/29/2024] [Indexed: 06/07/2024] Open
Abstract
Antimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency to accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance. This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative model for detecting AMPs, which was designed with eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs. In our framework, two-stage predictions have been conducted. Initially, this study analyzed 33 models on these feature extractions. Then, we selected the best six models from these models using rigorous performance metrics. In the second stage, probabilistic features have been generated from the selected six models in each feature encoding and they are aggregated to be fed into our final meta-model called AMP-RNNpro. This study also introduced 20 features with SHAP, which are crucial in the drug development fields, where we discover AAC, ASDC, and CKSAAGP features are highly impactful for detection and drug discovery. Our proposed framework, AMP-RNNpro excels in the identification of novel Amps with 97.15% accuracy, 96.48% sensitivity, and 97.87% specificity. We built a user-friendly website for demonstrating the accurate prediction of AMPs based on the proposed approach which can be accessed at http://13.126.159.30/ .
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Affiliation(s)
- Md Shazzad Hossain Shaon
- Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh
| | - Tasmin Karim
- Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh
| | - Md Fahim Sultan
- Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh
| | - Md Mamun Ali
- Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh
- Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, S7N 5A9, Canada
- Department of Software Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka, 1216, Bangladesh
| | - Kawsar Ahmed
- Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh.
- Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, S7N 5A9, Canada.
- Group of Bio-photomatiχ, Information and Communication Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh.
| | - Md Zahid Hasan
- Department of Computer Science and 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
| | - Ahmed Moustafa
- Department of Human Anatomy and Physiology, The Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa
- School of Psychology, Centre for Data Analytics, Bond University, Gold Coast, QLD, Australia
| | - Francis M Bui
- Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, S7N 5A9, Canada
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3
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Liao YH, Chen SZ, Bin YN, Zhao JP, Feng XL, Zheng CH. UsIL-6: An unbalanced learning strategy for identifying IL-6 inducing peptides by undersampling technique. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108176. [PMID: 38677081 DOI: 10.1016/j.cmpb.2024.108176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 03/26/2024] [Accepted: 04/11/2024] [Indexed: 04/29/2024]
Abstract
BACKGROUND AND OBJECTIVE Interleukin-6 (IL-6) is the critical factor of early warning, monitoring, and prognosis in the inflammatory storm of COVID-19 cases. IL-6 inducing peptides, which can induce cytokine IL-6 production, are very important for the development of diagnosis and immunotherapy. Although the existing methods have some success in predicting IL-6 inducing peptides, there is still room for improvement in the performance of these models in practical application. METHODS In this study, we proposed UsIL-6, a high-performance bioinformatics tool for identifying IL-6 inducing peptides. First, we extracted five groups of physicochemical properties and sequence structural information from IL-6 inducing peptide sequences, and obtained a 636-dimensional feature vector, we also employed NearMiss3 undersampling method and normalization method StandardScaler to process the data. Then, a 40-dimensional optimal feature vector was obtained by Boruta feature selection method. Finally, we combined this feature vector with extreme randomization tree classifier to build the final model UsIL-6. RESULTS The AUC value of UsIL-6 on the independent test dataset was 0.87, and the BACC value was 0.808, which indicated that UsIL-6 had better performance than the existing methods in IL-6 inducing peptide recognition. CONCLUSIONS The performance comparison on independent test dataset confirmed that UsIL-6 could achieve the highest performance, best robustness, and most excellent generalization ability. We hope that UsIL-6 will become a valuable method to identify, annotate and characterize new IL-6 inducing peptides.
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Affiliation(s)
- Yan-Hong Liao
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China
| | - Shou-Zhi Chen
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China
| | - Yan-Nan Bin
- School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China
| | - Jian-Ping Zhao
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China.
| | - Xin-Long Feng
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China.
| | - Chun-Hou Zheng
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China; School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China
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4
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Deng Q, Zhang J, Liu J, Liu Y, Dai Z, Zou X, Li Z. Identifying Protein Phosphorylation Site-Disease Associations Based on Multi-Similarity Fusion and Negative Sample Selection by Convolutional Neural Network. Interdiscip Sci 2024:10.1007/s12539-024-00615-0. [PMID: 38457108 DOI: 10.1007/s12539-024-00615-0] [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: 08/30/2023] [Revised: 01/26/2024] [Accepted: 01/29/2024] [Indexed: 03/09/2024]
Abstract
As one of the most important post-translational modifications (PTMs), protein phosphorylation plays a key role in a variety of biological processes. Many studies have shown that protein phosphorylation is associated with various human diseases. Therefore, identifying protein phosphorylation site-disease associations can help to elucidate the pathogenesis of disease and discover new drug targets. Networks of sequence similarity and Gaussian interaction profile kernel similarity were constructed for phosphorylation sites, as well as networks of disease semantic similarity, disease symptom similarity and Gaussian interaction profile kernel similarity were constructed for diseases. To effectively combine different phosphorylation sites and disease similarity information, random walk with restart algorithm was used to obtain the topology information of the network. Then, the diffusion component analysis method was utilized to obtain the comprehensive phosphorylation site similarity and disease similarity. Meanwhile, the reliable negative samples were screened based on the Euclidean distance method. Finally, a convolutional neural network (CNN) model was constructed to identify potential associations between phosphorylation sites and diseases. Based on tenfold cross-validation, the evaluation indicators were obtained including accuracy of 93.48%, specificity of 96.82%, sensitivity of 90.15%, precision of 96.62%, Matthew's correlation coefficient of 0.8719, area under the receiver operating characteristic curve of 0.9786 and area under the precision-recall curve of 0.9836. Additionally, most of the top 20 predicted disease-related phosphorylation sites (19/20 for Alzheimer's disease; 20/16 for neuroblastoma) were verified by literatures and databases. These results show that the proposed method has an outstanding prediction performance and a high practical value.
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Affiliation(s)
- Qian Deng
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Jing Zhang
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Jie Liu
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Yuqi Liu
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Zong Dai
- School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, 510275, China
| | - Xiaoyong Zou
- School of Chemistry, Sun Yat-Sen University, Guangzhou, 510275, China.
| | - Zhanchao Li
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, China.
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5
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Jiang J, Pei H, Li J, Li M, Zou Q, Lv Z. FEOpti-ACVP: identification of novel anti-coronavirus peptide sequences based on feature engineering and optimization. Brief Bioinform 2024; 25:bbae037. [PMID: 38366802 PMCID: PMC10939380 DOI: 10.1093/bib/bbae037] [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: 08/08/2023] [Revised: 12/27/2023] [Accepted: 01/17/2024] [Indexed: 02/18/2024] Open
Abstract
Anti-coronavirus peptides (ACVPs) represent a relatively novel approach of inhibiting the adsorption and fusion of the virus with human cells. Several peptide-based inhibitors showed promise as potential therapeutic drug candidates. However, identifying such peptides in laboratory experiments is both costly and time consuming. Therefore, there is growing interest in using computational methods to predict ACVPs. Here, we describe a model for the prediction of ACVPs that is based on the combination of feature engineering (FE) optimization and deep representation learning. FEOpti-ACVP was pre-trained using two feature extraction frameworks. At the next step, several machine learning approaches were tested in to construct the final algorithm. The final version of FEOpti-ACVP outperformed existing methods used for ACVPs prediction and it has the potential to become a valuable tool in ACVP drug design. A user-friendly webserver of FEOpti-ACVP can be accessed at http://servers.aibiochem.net/soft/FEOpti-ACVP/.
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Affiliation(s)
- Jici Jiang
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Hongdi Pei
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Jiayu Li
- College of Life Science, Sichuan University, Chengdu 610065, China
| | - Mingxin Li
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| | - Zhibin Lv
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
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6
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Chung CR, Liou JT, Wu LC, Horng JT, Lee TY. Multi-label classification and features investigation of antimicrobial peptides with various functional classes. iScience 2023; 26:108250. [PMID: 38025779 PMCID: PMC10679894 DOI: 10.1016/j.isci.2023.108250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 07/15/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
The challenge of drug-resistant bacteria to global public health has led to increased attention on antimicrobial peptides (AMPs) as a targeted therapeutic alternative with a lower risk of resistance. However, high production costs and limitations in functional class prediction have hindered progress in this field. In this study, we used multi-label classifiers with binary relevance and algorithm adaptation techniques to predict different functions of AMPs across a wide range of pathogen categories, including bacteria, mammalian cells, fungi, viruses, and cancer cells. Our classifiers attained promising AUC scores varying from 0.8492 to 0.9126 on independent testing data. Forward feature selection identified sequence order and charge as critical, with specific amino acids (C and E) as discriminative. These findings provide valuable insights for the design of antimicrobial peptides (AMPs) with multiple functionalities, thus contributing to the broader effort to combat drug-resistant pathogens.
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Affiliation(s)
- Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | - Jhen-Ting Liou
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
| | - Li-Ching Wu
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan
| | - Jorng-Tzong Horng
- Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taoyuan City, Taiwan
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu City, Taiwan
- Center for Intelligent Drug Systems and Smart Biodevices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu City, Taiwan
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7
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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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8
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Nath A. Physicochemical and sequence determinants of antiviral peptides. Biol Futur 2023; 74:489-506. [PMID: 37889451 DOI: 10.1007/s42977-023-00188-x] [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: 04/22/2023] [Accepted: 10/06/2023] [Indexed: 10/28/2023]
Abstract
Antiviral peptides (AVPs) open new possibilities as an effective antiviral therapeutic in the current scenario of evolving drug-resistant viruses. Knowledge about the sequence and structure activity relationship in AVPs is still largely unknown. AVPs and antimicrobial peptides (AMPs) share several common features but as they target different life forms (living organisms and viruses), exploring the differential sequence features may facilitate in designing specific AVPs. The current work developed accurate prediction models for discriminating (a) AVPs from AMPs, (b) Coronaviridae AVPs from other virus family specific AVPs and (c) highly active AVPs (HAA) from lowly active AVPs (LAA). Further explainable machine learning methods (using model agnostic global interpretable methods) are utilized for exploring and interpreting the physicochemical spaces of AVPs, Coronaviridae AVPs and highly active AVPs. To further understand the association of physicochemical space distribution with pIC50 values, regression models were developed and analyzed using accumulated local effects and interaction strength analysis. An independent sample t-test is used to filter out the significant compositional differences between the smaller length HAA and longer length HAA groups. AVPs prefer lower charge/length ratio and basic residues in comparison with AMPs. Coronaviridae family-specific AVPs have lower propensities for basic amino acids, charge and preference for aspartic acid. Further there is prevalence for basic residues in lowly active AVPs as compared to highly active AVPs. Sequence order effects captured in terms of average amino acid pair distances proved to be more constructive in deciphering the sequences of AVPs.
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Affiliation(s)
- Abhigyan Nath
- Department of Biochemistry, Pt. Jawahar Lal Nehru Memorial Medical College, Raipur, 492001, India.
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9
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Wang Z, Meng J, Li H, Xia S, Wang Y, Luan Y. PAMPred: A hierarchical evolutionary ensemble framework for identifying plant antimicrobial peptides. Comput Biol Med 2023; 166:107545. [PMID: 37806057 DOI: 10.1016/j.compbiomed.2023.107545] [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: 08/02/2023] [Revised: 09/04/2023] [Accepted: 09/28/2023] [Indexed: 10/10/2023]
Abstract
Antimicrobial peptides (AMPs) play a crucial role in plant immune regulation, growth and development stages, which have attracted significant attentions in recent years. As the wet-lab experiments are laborious and cost-prohibitive, it is indispensable to develop computational methods to discover novel plant AMPs accurately. In this study, we presented a hierarchical evolutionary ensemble framework, named PAMPred, which consisted of a multi-level heterogeneous architecture to identify plant AMPs. Specifically, to address the existing class imbalance problem, a cluster-based resampling method was adopted to build multiple balanced subsets. Then, several peptide features including sequence information-based and physicochemical properties-based features were fed into the different types of basic learners to increase the ensemble diversity. For boosting the predictive capability of PAMPred, the improved particle swarm optimization (PSO) algorithm and dynamic ensemble pruning strategy were used to optimize the weights at different levels adaptively. Furthermore, extensive ten-fold cross-validation and independent testing experimental results demonstrated that PAMPred achieved excellent prediction performance and generalization ability, and outperformed the state-of-the-art methods. It also indicated that the proposed method could serve as an effective auxiliary tool to identify plant AMPs, which would be conducive to explore the immune regulatory mechanism of plants.
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Affiliation(s)
- Zhaowei Wang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Jun Meng
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China.
| | - Haibin Li
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Shihao Xia
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Yu Wang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Yushi Luan
- School of Bioengineering, Dalian University of Technology, Dalian, Liaoning 116024, China
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10
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Yang Y, Wu H, Gao Y, Tong W, Li K. MFPPDB: a comprehensive multi-functional plant peptide database. FRONTIERS IN PLANT SCIENCE 2023; 14:1224394. [PMID: 37908832 PMCID: PMC10613858 DOI: 10.3389/fpls.2023.1224394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 09/29/2023] [Indexed: 11/02/2023]
Abstract
Plants produce a wide range of bioactive peptides as part of their innate defense mechanisms. With the explosive growth of plant-derived peptides, verifying the therapeutic function using traditional experimental methods are resources and time consuming. Therefore, it is necessary to predict the therapeutic function of plant-derived peptides more effectively and accurately with reduced waste of resources and thus expedite the development of plant peptides. We herein developed a repository of plant peptides predicted to have multiple therapeutic functions, named as MFPPDB (multi-functional plant peptide database). MFPPDB including 1,482,409 single or multiple functional plant origin therapeutic peptides derived from 121 fundamental plant species. The functional categories of these therapeutic peptides include 41 different features such as anti-bacterial, anti-fungal, anti-HIV, anti-viral, and anti-cancer. The detailed physicochemical information of these peptides was presented in functional search and physicochemical property search module, which can help users easily access the peptide information by the plant peptide species, ID, and functions, or by their peptide ID, isoelectric point, peptide sequence, and molecular weight through web-friendly interface. We further matched the predicted peptides to nine state-of-the-art curated functional peptide databases and found that at least 293,408 of the peptides possess functional potentials. Overall, MFPPDB integrated a massive number of plant peptides have single or multiple therapeutic functions, which will facilitate the comprehensive research in plant peptidomics. MFPPDB can be freely accessed through http://124.223.195.214:9188/mfppdb/index.
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Affiliation(s)
- Yaozu Yang
- School of Information and Computer, Anhui Agricultural University, Hefei, China
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, Anhui, China
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, Anhui, China
| | - Hongwei Wu
- School of Information and Computer, Anhui Agricultural University, Hefei, China
| | - Yu Gao
- School of Information and Computer, Anhui Agricultural University, Hefei, China
| | - Wei Tong
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, Anhui, China
| | - Ke Li
- School of Information and Computer, Anhui Agricultural University, Hefei, China
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, Anhui, China
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, Anhui, China
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11
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Liu M, Liu H, Wu T, Zhu Y, Zhou Y, Huang Z, Xiang C, Huang J. ACP-Dnnel: anti-coronavirus peptides' prediction based on deep neural network ensemble learning. Amino Acids 2023; 55:1121-1136. [PMID: 37402073 DOI: 10.1007/s00726-023-03300-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 06/25/2023] [Indexed: 07/05/2023]
Abstract
The ongoing COVID-19 pandemic has caused dramatic loss of human life. There is an urgent need for safe and efficient anti-coronavirus infection drugs. Anti-coronavirus peptides (ACovPs) can inhibit coronavirus infection. With high-efficiency, low-toxicity, and broad-spectrum inhibitory effects on coronaviruses, they are promising candidates to be developed into a new type of anti-coronavirus drug. Experiment is the traditional way of ACovPs' identification, which is less efficient and more expensive. With the accumulation of experimental data on ACovPs, computational prediction provides a cheaper and faster way to find anti-coronavirus peptides' candidates. In this study, we ensemble several state-of-the-art machine learning methodologies to build nine classification models for the prediction of ACovPs. These models were pre-trained using deep neural networks, and the performance of our ensemble model, ACP-Dnnel, was evaluated across three datasets and independent dataset. We followed Chou's 5-step rules. (1) we constructed the benchmark datasets data1, data2, and data3 for training and testing, and introduced the independent validation dataset ACVP-M; (2) we analyzed the peptides sequence composition feature of the benchmark dataset; (3) we constructed the ACP-Dnnel model with deep convolutional neural network (DCNN) merged the bi-directional long short-term memory (BiLSTM) as the base model for pre-training to extract the features embedded in the benchmark dataset, and then, nine classification algorithms were introduced to ensemble together for classification prediction and voting together; (4) tenfold cross-validation was introduced during the training process, and the final model performance was evaluated; (5) finally, we constructed a user-friendly web server accessible to the public at http://150.158.148.228:5000/ . The highest accuracy (ACC) of ACP-Dnnel reaches 97%, and the Matthew's correlation coefficient (MCC) value exceeds 0.9. On three different datasets, its average accuracy is 96.0%. After the latest independent dataset validation, ACP-Dnnel improved at MCC, SP, and ACC values 6.2%, 7.5% and 6.3% greater, respectively. It is suggested that ACP-Dnnel can be helpful for the laboratory identification of ACovPs, speeding up the anti-coronavirus peptide drug discovery and development. We constructed the web server of anti-coronavirus peptides' prediction and it is available at http://150.158.148.228:5000/ .
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Affiliation(s)
- Mingyou Liu
- School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, Sichuan, China
| | - Hongmei Liu
- School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China
| | - Tao Wu
- School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China
| | - Yingxue Zhu
- School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China
| | - Yuwei Zhou
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, Sichuan, China
| | - Ziru Huang
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, Sichuan, China
| | - Changcheng Xiang
- School of Computer Science and Technology, Aba Teachers University, Aba, Sichuan, China.
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, Sichuan, China.
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan, China.
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12
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Chen S, Liao Y, Zhao J, Bin Y, Zheng C. PACVP: Prediction of Anti-Coronavirus Peptides Using a Stacking Learning Strategy With Effective Feature Representation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3106-3116. [PMID: 37022025 DOI: 10.1109/tcbb.2023.3238370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Due to the global outbreak of COVID-19 and its variants, antiviral peptides with anti-coronavirus activity (ACVPs) represent a promising new drug candidate for the treatment of coronavirus infection. At present, several computational tools have been developed to identify ACVPs, but the overall prediction performance is still not enough to meet the actual therapeutic application. In this study, we constructed an efficient and reliable prediction model PACVP (Prediction of Anti-CoronaVirus Peptides) for identifying ACVPs based on effective feature representation and a two-layer stacking learning framework. In the first layer, we use nine feature encoding methods with different feature representation angles to characterize the rich sequence information and fuse them into a feature matrix. Secondly, data normalization and unbalanced data processing are carried out. Next, 12 baseline models are constructed by combining three feature selection methods and four machine learning classification algorithms. In the second layer, we input the optimal probability features into the logistic regression algorithm (LR) to train the final model PACVP. The experiments show that PACVP achieves favorable prediction performance on independent test dataset, with ACC of 0.9208 and AUC of 0.9465. We hope that PACVP will become a useful method for identifying, annotating and characterizing novel ACVPs.
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13
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Lefin N, Herrera-Belén L, Farias JG, Beltrán JF. Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides. Mol Divers 2023:10.1007/s11030-023-10718-3. [PMID: 37626205 DOI: 10.1007/s11030-023-10718-3] [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: 05/02/2023] [Accepted: 08/15/2023] [Indexed: 08/27/2023]
Abstract
Viruses constitute a constant threat to global health and have caused millions of human and animal deaths throughout human history. Despite advances in the discovery of antiviral compounds that help fight these pathogens, finding a solution to this problem continues to be a task that consumes time and financial resources. Currently, artificial intelligence (AI) has revolutionized many areas of the biological sciences, making it possible to decipher patterns in amino acid sequences that encode different functions and activities. Within the field of AI, machine learning, and deep learning algorithms have been used to discover antimicrobial peptides. Due to their effectiveness and specificity, antimicrobial peptides (AMPs) hold excellent promise for treating various infections caused by pathogens. Antiviral peptides (AVPs) are a specific type of AMPs that have activity against certain viruses. Unlike the research focused on the development of tools and methods for the prediction of antimicrobial peptides, those related to the prediction of AVPs are still scarce. Given the significance of AVPs as potential pharmaceutical options for human and animal health and the ongoing AI revolution, we have reviewed and summarized the current machine learning and deep learning-based tools and methods available for predicting these types of peptides.
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Affiliation(s)
- Nicolás Lefin
- Department of Chemical Engineering, Faculty of Engineering and Science, University of La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile
| | - Lisandra Herrera-Belén
- Departamento de Ciencias Básicas, Facultad de Ciencias, Universidad Santo Tomás, Temuco, Chile
| | - Jorge G Farias
- Department of Chemical Engineering, Faculty of Engineering and Science, University of La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile
| | - Jorge F Beltrán
- Department of Chemical Engineering, Faculty of Engineering and Science, University of La Frontera, Ave. Francisco Salazar, 01145, Temuco, Chile.
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14
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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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15
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Wang H, Li H, Gao W, Xie J. PrUb-EL: A hybrid framework based on deep learning for identifying ubiquitination sites in Arabidopsis thaliana using ensemble learning strategy. Anal Biochem 2022; 658:114935. [PMID: 36206844 DOI: 10.1016/j.ab.2022.114935] [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: 07/08/2022] [Revised: 09/25/2022] [Accepted: 09/26/2022] [Indexed: 12/30/2022]
Abstract
Identification of ubiquitination sites is central to many biological experiments. Ubiquitination is a kind of post-translational protein modification (PTM). It is a key mechanism for increasing protein diversity and plays a vital role in regulating cell function. In recent years, many models have been developed to predict ubiquitination sites in humans, mice and yeast. However, few studies have predicted ubiquitination sites in Arabidopsis thaliana. In view of this, a deep network model named PrUb-EL is proposed to predict ubiquitination sites in Arabidopsis thaliana. Firstly, six features based on the protein sequence are extracted with amino acid index database (AAindex), dipeptide deviates from the expected mean (DDE), dipeptide composition (DPC), blocks substitution matrix (BLOSUM62), enhanced amino acid composition (EAAC) and binary encoding. Secondly, the synthetic minority over-sampling technique (SMOTE) is utilized to process the imbalanced data set. Then a new classifier named DG is presented, which includes Dense block, Residual block and Gated recurrent unit (GRU) block. Finally, each of six feature extraction methods is integrated into the DG model, and the ensemble learning strategy is used to gain the final prediction result. Experimental results show that PrUb-EL has good predictive ability with the accuracy (ACC) and area under the ROC curve (auROC) values of 91.00% and 97.70% using 5-fold cross-validation, respectively. Note that the values of ACC and auROC are 88.58% and 96.09% in the independent test, respectively. Compared with previous studies, our model has significantly improved performance thus it is an excellent method for identifying ubiquitination sites in Arabidopsis thaliana. The datasets and code used for the article are available at https://github.com/Tom-Wangy/PreUb-EL.git.
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Affiliation(s)
- Houqiang Wang
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
| | - Hong Li
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China.
| | - Weifeng Gao
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
| | - Jin Xie
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
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16
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Yan K, Lv H, Guo Y, Peng W, Liu B. sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure. Bioinformatics 2022; 39:6808615. [PMID: 36342186 PMCID: PMC9805557 DOI: 10.1093/bioinformatics/btac715] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 10/24/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
MOTIVATION Antimicrobial peptides (AMPs) are essential components of therapeutic peptides for innate immunity. Researchers have developed several computational methods to predict the potential AMPs from many candidate peptides. With the development of artificial intelligent techniques, the protein structures can be accurately predicted, which are useful for protein sequence and function analysis. Unfortunately, the predicted peptide structure information has not been applied to the field of AMP prediction so as to improve the predictive performance. RESULTS In this study, we proposed a computational predictor called sAMPpred-GAT for AMP identification. To the best of our knowledge, sAMPpred-GAT is the first approach based on the predicted peptide structures for AMP prediction. The sAMPpred-GAT predictor constructs the graphs based on the predicted peptide structures, sequence information and evolutionary information. The Graph Attention Network (GAT) is then performed on the graphs to learn the discriminative features. Finally, the full connection networks are utilized as the output module to predict whether the peptides are AMP or not. Experimental results show that sAMPpred-GAT outperforms the other state-of-the-art methods in terms of AUC, and achieves better or highly comparable performance in terms of the other metrics on the eight independent test datasets, demonstrating that the predicted peptide structure information is important for AMP prediction. AVAILABILITY AND IMPLEMENTATION A user-friendly webserver of sAMPpred-GAT can be accessed at http://bliulab.net/sAMPpred-GAT and the source code is available at https://github.com/HongWuL/sAMPpred-GAT/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ke Yan
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Hongwu Lv
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yichen Guo
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Wei Peng
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Bin Liu
- To whom correspondence should be addressed.
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17
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The dynamic landscape of peptide activity prediction. Comput Struct Biotechnol J 2022; 20:6526-6533. [DOI: 10.1016/j.csbj.2022.11.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/21/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022] Open
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18
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Charoenkwan P, Kanthawong S, Schaduangrat N, Li’ P, Moni MA, Shoombuatong W. SCMRSA: a New Approach for Identifying and Analyzing Anti-MRSA Peptides Using Estimated Propensity Scores of Dipeptides. ACS OMEGA 2022; 7:32653-32664. [PMID: 36120041 PMCID: PMC9476499 DOI: 10.1021/acsomega.2c04305] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 08/22/2022] [Indexed: 06/15/2023]
Abstract
Staphylococcus aureus is deemed to be one of the major causes of hospital and community-acquired infections, especially in methicillin-resistant S. aureus (MRSA) strains. Because antimicrobial peptides have captured attention as novel drug candidates due to their rapid and broad-spectrum antimicrobial activity, anti-MRSA peptides have emerged as potential therapeutics for the treatment of bacterial infections. Although experimental approaches can precisely identify anti-MRSA peptides, they are usually cost-ineffective and labor-intensive. Therefore, computational approaches that are able to identify and characterize anti-MRSA peptides by using sequence information are highly desirable. In this study, we present the first computational approach (termed SCMRSA) for identifying and characterizing anti-MRSA peptides by using sequence information without the use of 3D structural information. In SCMRSA, we employed an interpretable scoring card method (SCM) coupled with the estimated propensity scores of 400 dipeptides. Comparative experiments indicated that SCMRSA was more effective and could outperform several machine learning-based classifiers with an accuracy of 0.960 and Matthews correlation coefficient of 0.848 on the independent test data set. In addition, we employed the SCMRSA-derived propensity scores to provide a more in-depth explanation regarding the functional mechanisms of anti-MRSA peptides. Finally, in order to serve community-wide use of the proposed SCMRSA, we established a user-friendly webserver which can be accessed online at http://pmlabstack.pythonanywhere.com/SCMRSA. SCMRSA is anticipated to be an open-source and useful tool for screening and identifying novel anti-MRSA peptides for follow-up experimental studies.
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Affiliation(s)
- Phasit Charoenkwan
- Modern
Management and Information Technology, College of Arts, Media and
Technology, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Sakawrat Kanthawong
- Department
of Microbiology, Faculty of Medicine, Khon
Kaen University, Khon Kaen 40002, Thailand
| | - Nalini Schaduangrat
- Center
of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Pietro Li’
- Department
of Computer Science and Technology, University
of Cambridge, Cambridge CB3 0FD, U.K.
| | - Mohammad Ali Moni
- Artificial
Intelligence & Digital Health, School of Health and Rehabilitation
Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland St Lucia, Queensland 4072, Australia
| | - Watshara Shoombuatong
- Center
of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
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19
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Yan W, Tang W, Wang L, Bin Y, Xia J. PrMFTP: Multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization. PLoS Comput Biol 2022; 18:e1010511. [PMID: 36094961 PMCID: PMC9499272 DOI: 10.1371/journal.pcbi.1010511] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 09/22/2022] [Accepted: 08/24/2022] [Indexed: 11/18/2022] Open
Abstract
Prediction of therapeutic peptide is a significant step for the discovery of promising therapeutic drugs. Most of the existing studies have focused on the mono-functional therapeutic peptide prediction. However, the number of multi-functional therapeutic peptides (MFTP) is growing rapidly, which requires new computational schemes to be proposed to facilitate MFTP discovery. In this study, based on multi-head self-attention mechanism and class weight optimization algorithm, we propose a novel model called PrMFTP for MFTP prediction. PrMFTP exploits multi-scale convolutional neural network, bi-directional long short-term memory, and multi-head self-attention mechanisms to fully extract and learn informative features of peptide sequence to predict MFTP. In addition, we design a class weight optimization scheme to address the problem of label imbalanced data. Comprehensive evaluation demonstrate that PrMFTP is superior to other state-of-the-art computational methods for predicting MFTP. We provide a user-friendly web server of PrMFTP, which is available at http://bioinfo.ahu.edu.cn/PrMFTP.
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Affiliation(s)
- Wenhui Yan
- Information Materials and Intelligent Sensing Laboratory of Anhui Province and Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui, China
| | - Wending Tang
- Information Materials and Intelligent Sensing Laboratory of Anhui Province and Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui, China
| | - Lihua Wang
- Information Materials and Intelligent Sensing Laboratory of Anhui Province and Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui, China
| | - Yannan Bin
- Information Materials and Intelligent Sensing Laboratory of Anhui Province and Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui, China
- * E-mail: (YB); (JX)
| | - Junfeng Xia
- Information Materials and Intelligent Sensing Laboratory of Anhui Province and Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui, China
- * E-mail: (YB); (JX)
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20
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Agüero-Chapin G, Galpert-Cañizares D, Domínguez-Pérez D, Marrero-Ponce Y, Pérez-Machado G, Teijeira M, Antunes A. Emerging Computational Approaches for Antimicrobial Peptide Discovery. Antibiotics (Basel) 2022; 11:antibiotics11070936. [PMID: 35884190 PMCID: PMC9311958 DOI: 10.3390/antibiotics11070936] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/01/2022] [Accepted: 07/08/2022] [Indexed: 02/05/2023] Open
Abstract
In the last two decades many reports have addressed the application of artificial intelligence (AI) in the search and design of antimicrobial peptides (AMPs). AI has been represented by machine learning (ML) algorithms that use sequence-based features for the discovery of new peptidic scaffolds with promising biological activity. From AI perspective, evolutionary algorithms have been also applied to the rational generation of peptide libraries aimed at the optimization/design of AMPs. However, the literature has scarcely dedicated to other emerging non-conventional in silico approaches for the search/design of such bioactive peptides. Thus, the first motivation here is to bring up some non-standard peptide features that have been used to build classical ML predictive models. Secondly, it is valuable to highlight emerging ML algorithms and alternative computational tools to predict/design AMPs as well as to explore their chemical space. Another point worthy of mention is the recent application of evolutionary algorithms that actually simulate sequence evolution to both the generation of diversity-oriented peptide libraries and the optimization of hit peptides. Last but not least, included here some new considerations in proteogenomic analyses currently incorporated into the computational workflow for unravelling AMPs in natural sources.
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Affiliation(s)
- Guillermin Agüero-Chapin
- CIIMAR—Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal;
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
- Correspondence: (G.A.-C.); (A.A.); Tel.: +351-22-340-1813 (G.A.-C. & A.A.)
| | - Deborah Galpert-Cañizares
- Departamento de Ciencia de la Computación, Universidad Central Marta Abreu de Las Villas (UCLV), Santa Clara 54830, Cuba;
| | - Dany Domínguez-Pérez
- CIIMAR—Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal;
- Proquinorte, Unipessoal, Lda, Avenida 5 de Outubro, 124, 7º Piso, Avenidas Novas, 1050-061 Lisboa, Portugal
| | - Yovani Marrero-Ponce
- Universidad San Francisco de Quito (USFQ), Grupo de Medicina Molecular y Translacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas and Instituto de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Ecuador;
| | - Gisselle Pérez-Machado
- EpiDisease S.L—Spin-Off of Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), 46980 Valencia, Spain;
| | - Marta Teijeira
- Departamento de Química Orgánica, Facultade de Química, Universidade de Vigo, 36310 Vigo, Spain;
- Instituto de Investigación Sanitaria Galicia Sur, Hospital Álvaro Cunqueiro, 36213 Vigo, Spain
| | - Agostinho Antunes
- CIIMAR—Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos, s/n, 4450-208 Porto, Portugal;
- Departamento de Biologia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
- Correspondence: (G.A.-C.); (A.A.); Tel.: +351-22-340-1813 (G.A.-C. & A.A.)
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21
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Kurata H, Tsukiyama S, Manavalan B. iACVP: markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model. Brief Bioinform 2022; 23:6623727. [PMID: 35772910 DOI: 10.1093/bib/bbac265] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 05/23/2022] [Accepted: 06/06/2022] [Indexed: 01/22/2023] Open
Abstract
The COVID-19 pandemic caused several million deaths worldwide. Development of anti-coronavirus drugs is thus urgent. Unlike conventional non-peptide drugs, antiviral peptide drugs are highly specific, easy to synthesize and modify, and not highly susceptible to drug resistance. To reduce the time and expense involved in screening thousands of peptides and assaying their antiviral activity, computational predictors for identifying anti-coronavirus peptides (ACVPs) are needed. However, few experimentally verified ACVP samples are available, even though a relatively large number of antiviral peptides (AVPs) have been discovered. In this study, we attempted to predict ACVPs using an AVP dataset and a small collection of ACVPs. Using conventional features, a binary profile and a word-embedding word2vec (W2V), we systematically explored five different machine learning methods: Transformer, Convolutional Neural Network, bidirectional Long Short-Term Memory, Random Forest (RF) and Support Vector Machine. Via exhaustive searches, we found that the RF classifier with W2V consistently achieved better performance on different datasets. The two main controlling factors were: (i) the dataset-specific W2V dictionary was generated from the training and independent test datasets instead of the widely used general UniProt proteome and (ii) a systematic search was conducted and determined the optimal k-mer value in W2V, which provides greater discrimination between positive and negative samples. Therefore, our proposed method, named iACVP, consistently provides better prediction performance compared with existing state-of-the-art methods. To assist experimentalists in identifying putative ACVPs, we implemented our model as a web server accessible via the following link: http://kurata35.bio.kyutech.ac.jp/iACVP.
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Affiliation(s)
- Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Sho Tsukiyama
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Balachandran Manavalan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
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22
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Abstract
Since 2019, the novel coronavirus (SARS-COV-2) disease (COVID-19) has caused a worldwide epidemic. Anti-coronavirus peptides (ACovPs), a type of antimicrobial peptides (AMPs), have demonstrated excellent inhibitory effects on coronaviruses. However, state-of-the-art AMP databases contain only a small number of ACovPs. Additionally, the fields of these databases are not uniform, and the units or evaluation standards of the same field are inconsistent. Most of these databases have not included the target domains of ACovPs and description of in vitro and in vivo assays to measure the inhibitory effects of ACovPs. Here, we present a database focused on ACovPs (ACovPepDB), which contains comprehensive and precise ACovPs information of 518 entries with 214 unique ACovPs manually collected from public databases and published peer-reviewed articles. We believe that ACovPepDB is of great significance for facilitating the development of new peptides and improving treatment for coronavirus infection. The database will become a portal for ACovPs and guide and help researchers perform further studies. The ACovPepDB is available at http://i.uestc.edu.cn/ACovPepDB/. Measurement(s) | Inhibition effect of anti-coronavirus peptide | Technology Type(s) | Antiviral assay |
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23
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Lv H, Yan K, Guo Y, Zou Q, Hesham AEL, Liu B. AMPpred-EL: An effective antimicrobial peptide prediction model based on ensemble learning. Comput Biol Med 2022; 146:105577. [PMID: 35576825 DOI: 10.1016/j.compbiomed.2022.105577] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/22/2022] [Accepted: 04/28/2022] [Indexed: 11/15/2022]
Abstract
Antimicrobial peptides (AMPs) are important for the human immune system and are currently applied in clinical trials. AMPs have been received much attention for accurate recognition. Recently, several computational methods for identifying AMPs have been proposed. However, existing methods have difficulty in accurately predicting AMPs. In this paper, we propose a novel AMP prediction method called AMPpred-EL based on an ensemble learning strategy. AMPred-EL is constructed based on ensemble learning combined with LightGBM and logistic regression. Experimental results demonstrate that AMPpred-EL outperforms several state-of-the-art methods on the benchmark datasets and then improves the efficiency performance.
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Affiliation(s)
- Hongwu Lv
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Ke Yan
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Yichen Guo
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
| | - Abd El-Latif Hesham
- Genetics Department, Faculty of Agriculture, Beni-Suef University, Beni-Suef, 62511, Egypt.
| | - 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, China.
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24
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Manavalan B, Basith S, Lee G. Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2. Brief Bioinform 2022; 23:bbab412. [PMID: 34595489 PMCID: PMC8500067 DOI: 10.1093/bib/bbab412] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/27/2021] [Accepted: 09/07/2021] [Indexed: 01/08/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) has impacted public health as well as societal and economic well-being. In the last two decades, various prediction algorithms and tools have been developed for predicting antiviral peptides (AVPs). The current COVID-19 pandemic has underscored the need to develop more efficient and accurate machine learning (ML)-based prediction algorithms for the rapid identification of therapeutic peptides against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Several peptide-based ML approaches, including anti-coronavirus peptides (ACVPs), IL-6 inducing epitopes and other epitopes targeting SARS-CoV-2, have been implemented in COVID-19 therapeutics. Owing to the growing interest in the COVID-19 field, it is crucial to systematically compare the existing ML algorithms based on their performances. Accordingly, we comprehensively evaluated the state-of-the-art IL-6 and AVP predictors against coronaviruses in terms of core algorithms, feature encoding schemes, performance evaluation metrics and software usability. A comprehensive performance assessment was then conducted to evaluate the robustness and scalability of the existing predictors using well-constructed independent validation datasets. Additionally, we discussed the advantages and disadvantages of the existing methods, providing useful insights into the development of novel computational tools for characterizing and identifying epitopes or ACVPs. The insights gained from this review are anticipated to provide critical guidance to the scientific community in the rapid design and development of accurate and efficient next-generation in silico tools against SARS-CoV-2.
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Affiliation(s)
| | - Shaherin Basith
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Korea
| | - Gwang Lee
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Korea
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25
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Liou JW, Chang CC, Hsu HJ, Wu TY. Computer-aided discovery, design, and investigation of COVID-19 therapeutics. Tzu Chi Med J 2022; 34:276-286. [PMID: 35912059 PMCID: PMC9333103 DOI: 10.4103/tcmj.tcmj_318_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/20/2021] [Accepted: 12/30/2021] [Indexed: 11/22/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19) pandemic is currently the most serious public health threat faced by mankind. Thus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19, is being intensively investigated. Several vaccines are now available for clinical use. However, owing to the highly mutated nature of RNA viruses, the SARS-CoV-2 is changing at a rapid speed. Breakthrough infections by SARS-CoV-2 variants have been seen in vaccinated individuals. As a result, effective therapeutics for treating COVID-19 patients is urgently required. With the advance of computer technology, computational methods have become increasingly powerful in the biomedical research and pharmaceutical drug discovery. The applications of these techniques have largely reduced the costs and simplified processes of pharmaceutical drug developments. Intensive and extensive studies on SARS-CoV-2 proteins have been carried out and three-dimensional structures of the major SARS-CoV-2 proteins have been resolved and deposited in the Protein Data Bank. These structures provide the foundations for drug discovery and design using the structure-based computations, such as molecular docking and molecular dynamics simulations. In this review, introduction to the applications of computational methods in the discovery and design of novel drugs and repurposing of existing drugs for the treatments of COVID-19 is given. The examples of computer-aided investigations and screening of COVID-19 effective therapeutic compounds, functional peptides, as well as effective molecules from the herb medicines are discussed.
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26
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Jhong JH, Yao L, Pang Y, Li Z, Chung CR, Wang R, Li S, Li W, Luo M, Ma R, Huang Y, Zhu X, Zhang J, Feng H, Cheng Q, Wang C, Xi K, Wu LC, Chang TH, Horng JT, Zhu L, Chiang YC, Wang Z, Lee TY. dbAMP 2.0: updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data. Nucleic Acids Res 2021; 50:D460-D470. [PMID: 34850155 PMCID: PMC8690246 DOI: 10.1093/nar/gkab1080] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 10/16/2021] [Accepted: 10/25/2021] [Indexed: 12/26/2022] Open
Abstract
The last 18 months, or more, have seen a profound shift in our global experience, with many of us navigating a once-in-100-year pandemic. To date, COVID-19 remains a life-threatening pandemic with little to no targeted therapeutic recourse. The discovery of novel antiviral agents, such as vaccines and drugs, can provide therapeutic solutions to save human beings from severe infections; however, there is no specifically effective antiviral treatment confirmed for now. Thus, great attention has been paid to the use of natural or artificial antimicrobial peptides (AMPs) as these compounds are widely regarded as promising solutions for the treatment of harmful microorganisms. Given the biological significance of AMPs, it was obvious that there was a significant need for a single platform for identifying and engaging with AMP data. This led to the creation of the dbAMP platform that provides comprehensive information about AMPs and facilitates their investigation and analysis. To date, the dbAMP has accumulated 26 447 AMPs and 2262 antimicrobial proteins from 3044 organisms using both database integration and manual curation of >4579 articles. In addition, dbAMP facilitates the evaluation of AMP structures using I-TASSER for automated protein structure prediction and structure-based functional annotation, providing predictive structure information for clinical drug development. Next-generation sequencing (NGS) and third-generation sequencing have been applied to generate large-scale sequencing reads from various environments, enabling greatly improved analysis of genome structure. In this update, we launch an efficient online tool that can effectively identify AMPs from genome/metagenome and proteome data of all species in a short period. In conclusion, these improvements promote the dbAMP as one of the most abundant and comprehensively annotated resources for AMPs. The updated dbAMP is now freely accessible at http://awi.cuhk.edu.cn/dbAMP.
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Affiliation(s)
- Jhih-Hua Jhong
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Lantian Yao
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Yuxuan Pang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Zhongyan Li
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan
| | - Rulan Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Shangfu Li
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Wenshuo Li
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Mengqi Luo
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Renfei Ma
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Yuqi Huang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Xiaoning Zhu
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Jiahong Zhang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Hexiang Feng
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Qifan Cheng
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Chunxuan Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Kun Xi
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Li-Ching Wu
- Department of Biomedical Sciences and Engineering, National Central University, Taoyuan 32001, Taiwan
| | - Tzu-Hao Chang
- Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei 10675, Taiwan
| | - Jorng-Tzong Horng
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan
| | - Lizhe Zhu
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Ying-Chih Chiang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Zhuo Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China
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27
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Singh V, Shrivastava S, Kumar Singh S, Kumar A, Saxena S. StaBle-ABPpred: a stacked ensemble predictor based on biLSTM and attention mechanism for accelerated discovery of antibacterial peptides. Brief Bioinform 2021; 23:6423526. [PMID: 34750606 DOI: 10.1093/bib/bbab439] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 09/06/2021] [Accepted: 09/24/2021] [Indexed: 01/29/2023] Open
Abstract
Due to the rapid emergence of multi-drug resistant (MDR) bacteria, existing antibiotics are becoming ineffective. So, researchers are looking for alternatives in the form of antibacterial peptides (ABPs) based medicines. The discovery of novel ABPs using wet-lab experiments is time-consuming and expensive. Many machine learning models have been proposed to search for new ABPs, but there is still scope to develop a robust model that has high accuracy and precision. In this work, we present StaBle-ABPpred, a stacked ensemble technique-based deep learning classifier that uses bidirectional long-short term memory (biLSTM) and attention mechanism at base-level and an ensemble of random forest, gradient boosting and logistic regression at meta-level to classify peptides as antibacterial or otherwise. The performance of our model has been compared with several state-of-the-art classifiers, and results were subjected to analysis of variance (ANOVA) test and its post hoc analysis, which proves that our model performs better than existing classifiers. Furthermore, a web app has been developed and deployed at https://stable-abppred.anvil.app to identify novel ABPs in protein sequences. Using this app, we identified novel ABPs in all the proteins of the Streptococcus phage T12 genome. These ABPs have shown amino acid similarities with experimentally tested antimicrobial peptides (AMPs) of other organisms. Hence, they could be chemically synthesized and experimentally validated for their activity against different bacteria. The model and app developed in this work can be further utilized to explore the protein diversity for identifying novel ABPs with broad-spectrum activity, especially against MDR bacterial pathogens.
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Affiliation(s)
- Vishakha Singh
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, Uttar Pradesh, India
| | - Sameer Shrivastava
- Division of Veterinary Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar, 243122, Uttar Pradesh, India
| | - Sanjay Kumar Singh
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, Uttar Pradesh, India
| | - Abhinav Kumar
- Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, Uttar Pradesh, India
| | - Sonal Saxena
- Division of Veterinary Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar, 243122, Uttar Pradesh, India
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28
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Timmons PB, Hewage CM. ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides. Brief Bioinform 2021; 22:bbab258. [PMID: 34297817 PMCID: PMC8575049 DOI: 10.1093/bib/bbab258] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/09/2021] [Accepted: 06/18/2021] [Indexed: 11/14/2022] Open
Abstract
Viruses represent one of the greatest threats to human health, necessitating the development of new antiviral drug candidates. Antiviral peptides often possess excellent biological activity and a favourable toxicity profile, and therefore represent a promising field of novel antiviral drugs. As the quantity of sequencing data grows annually, the development of an accurate in silico method for the prediction of peptide antiviral activities is important. This study leverages advances in deep learning and cheminformatics to produce a novel sequence-based deep neural network classifier for the prediction of antiviral peptide activity. The method outperforms the existent best-in-class, with an external test accuracy of 93.9%, Matthews correlation coefficient of 0.87 and an Area Under the Curve of 0.93 on the dataset of experimentally validated peptide activities. This cutting-edge classifier is available as an online web server at https://research.timmons.eu/ennavia, facilitating in silico screening and design of peptide antiviral drugs by the wider research community.
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Affiliation(s)
- Patrick Brendan Timmons
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland
| | - Chandralal M Hewage
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland
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29
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Pang Y, Yao L, Jhong JH, Wang Z, Lee TY. AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches. Brief Bioinform 2021; 22:6323205. [PMID: 34279599 DOI: 10.1093/bib/bbab263] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 06/07/2021] [Accepted: 06/21/2021] [Indexed: 02/06/2023] Open
Abstract
Antiviral peptide (AVP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against virus infection. Machine learning-based prediction with a computational biology approach can facilitate the development of the novel therapeutic agents. In this study, we proposed a double-stage classification scheme, named AVPIden, for predicting the AVPs and their functional activities against different viruses. The first stage is to distinguish the AVP from a broad-spectrum peptide collection, including not only the regular peptides (non-AMP) but also the AMPs without antiviral functions (non-AVP). The second stage is responsible for characterizing one or more virus families or species that the AVP targets. Imbalanced learning is utilized to improve the performance of prediction. The AVPIden uses multiple descriptors to precisely demonstrate the peptide properties and adopts explainable machine learning strategies based on Shapley value to exploit how the descriptors impact the antiviral activities. Finally, the evaluation performance of the proposed model suggests its ability to predict the antivirus activities and their potential functions against six virus families (Coronaviridae, Retroviridae, Herpesviridae, Paramyxoviridae, Orthomyxoviridae, Flaviviridae) and eight kinds of virus (FIV, HCV, HIV, HPIV3, HSV1, INFVA, RSV, SARS-CoV). The AVPIden gives an option for reinforcing the development of AVPs with the computer-aided method and has been deployed at http://awi.cuhk.edu.cn/AVPIden/.
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Affiliation(s)
- Yuxuan Pang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, PR China
| | - Lantian Yao
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, PR China
| | - Jhih-Hua Jhong
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, PR China
| | - Zhuo Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, PR China
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, PR China
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