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Zhou C, Peng D, Liao B, Jia R, Wu F. ACP_MS: prediction of anticancer peptides based on feature extraction. Brief Bioinform 2022; 23:6793775. [PMID: 36326080 DOI: 10.1093/bib/bbac462] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/10/2022] [Accepted: 09/27/2022] [Indexed: 11/06/2022] Open
Abstract
Anticancer peptides (ACPs) are bioactive peptides with antitumor activity and have become the most promising drugs in the treatment of cancer. Therefore, the accurate prediction of ACPs is of great significance to the research of cancer diseases. In the paper, we developed a more efficient prediction model called ACP_MS. Firstly, the monoMonoKGap method is used to extract the characteristic of anticancer peptide sequences and form the digital features. Then, the AdaBoost model is used to select the most discriminating features from the digital features. Finally, a stochastic gradient descent algorithm is introduced to identify anticancer peptide sequences. We adopt 7-fold cross-validation and independent test set validation, and the final accuracy of the main dataset reached 92.653% and 91.597%, respectively. The accuracy of the alternate dataset reached 98.678% and 98.317%, respectively. Compared with other advanced prediction models, the ACP_MS model improves the identification ability of anticancer peptide sequences. The data of this model can be downloaded from the public website for free https://github.com/Zhoucaimao1998/Zc.
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Affiliation(s)
- Caimao Zhou
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China.,School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Dejun Peng
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China.,School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Bo Liao
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China.,School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Ranran Jia
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China.,School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Fangxiang Wu
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China.,School of Mathematics and Statistics, Hainan Normal University, Haikou, China
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52
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Qi L, Du J, Sun Y, Xiong Y, Zhao X, Pan D, Zhi Y, Dang Y, Gao X. Umami-MRNN: Deep learning-based prediction of umami peptide using RNN and MLP. Food Chem 2022. [DOI: 10.1016/j.foodchem.2022.134935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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53
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Pandiyan S, Wang L. A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence. Comput Biol Med 2022; 150:106140. [PMID: 36179510 DOI: 10.1016/j.compbiomed.2022.106140] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 07/20/2022] [Accepted: 09/18/2022] [Indexed: 11/03/2022]
Abstract
Through the revolutionization of artificial intelligence (AI) technologies in clinical research, significant improvement is observed in diagnosis of cancer. Utilization of these AI technologies, such as machine and deep learning, is imperative for the discovery of novel anticancer drugs and improves existing/ongoing cancer therapeutics. However, building a model for complicated cancers and their types remains a challenge due to lack of effective therapeutics that hinder the establishment of effective computational tools. In this review, we exploit recent approaches and state-of-the-art in implementing AI methods for anticancer drug discovery, and discussed how advances in these applications need to be considered in the current cancer therapeutics. Considering the immense potential of AI, we explore molecular docking and their interactions to recognize metabolic activities that support drug design. Finally, we highlight corresponding strategies in applying machine and deep learning methods to various types of cancer with their pros and cons.
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Affiliation(s)
- Sanjeevi Pandiyan
- Research Center for Intelligent Information Technology, Nantong University, Nantong, China; School of Information Science and Technology, Nantong University, Nantong, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, China
| | - Li Wang
- Research Center for Intelligent Information Technology, Nantong University, Nantong, China; School of Information Science and Technology, Nantong University, Nantong, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, China.
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54
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Yan J, Cai J, Zhang B, Wang Y, Wong DF, Siu SWI. Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning. Antibiotics (Basel) 2022; 11:1451. [PMID: 36290108 PMCID: PMC9598685 DOI: 10.3390/antibiotics11101451] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/11/2022] [Accepted: 10/13/2022] [Indexed: 11/16/2022] Open
Abstract
Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.
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Affiliation(s)
- Jielu Yan
- PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macau, China
| | - Jianxiu Cai
- Faculty of Applied Sciences, Macao Polytechnic University, Macau, China
- Institute of Science and Environment, University of Saint Joseph, Estr. Marginal da Ilha Verde, Macau, China
| | - Bob Zhang
- PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macau, China
| | - Yapeng Wang
- Faculty of Applied Sciences, Macao Polytechnic University, Macau, China
| | - Derek F. Wong
- NLP2CT Lab, Department of Computer and Information Science, University of Macau, Taipa, Macau, China
| | - Shirley W. I. Siu
- Institute of Science and Environment, University of Saint Joseph, Estr. Marginal da Ilha Verde, Macau, China
- School of Pharmaceutical Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
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55
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ACP-ADA: A Boosting Method with Data Augmentation for Improved Prediction of Anticancer Peptides. Int J Mol Sci 2022; 23:ijms232012194. [PMID: 36293050 PMCID: PMC9603247 DOI: 10.3390/ijms232012194] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/08/2022] [Accepted: 10/11/2022] [Indexed: 11/30/2022] Open
Abstract
Cancer is the second-leading cause of death worldwide, and therapeutic peptides that target and destroy cancer cells have received a great deal of interest in recent years. Traditional wet experiments are expensive and inefficient for identifying novel anticancer peptides; therefore, the development of an effective computational approach is essential to recognize ACP candidates before experimental methods are used. In this study, we proposed an Ada-boosting algorithm with the base learner random forest called ACP-ADA, which integrates binary profile feature, amino acid index, and amino acid composition with a 210-dimensional feature space vector to represent the peptides. Training samples in the feature space were augmented to increase the sample size and further improve the performance of the model in the case of insufficient samples. Furthermore, we used five-fold cross-validation to find model parameters, and the cross-validation results showed that ACP-ADA outperforms existing methods for this feature combination with data augmentation in terms of performance metrics. Specifically, ACP-ADA recorded an average accuracy of 86.4% and a Mathew’s correlation coefficient of 74.01% for dataset ACP740 and 90.83% and 81.65% for dataset ACP240; consequently, it can be a very useful tool in drug development and biomedical research.
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56
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Liu J, Li M, Chen X. AntiMF: A deep learning framework for predicting anticancer peptides based on multi-view feature extraction. Methods 2022; 207:38-43. [PMID: 36100141 DOI: 10.1016/j.ymeth.2022.07.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/20/2022] [Accepted: 07/26/2022] [Indexed: 01/10/2023] Open
Abstract
In recent years, anticancer peptides have emerged as a new viable option in cancer therapy, with the ability to overcome the considerable side effects and poor outcomes of standard cancer therapies. Accurate anticancer peptide identification can facilitate its finding and speed up its application in treating cancer. However, many recent approaches are based on machine learning, which not only restricts the representation ability of the models but also requires a complex hand-crafted feature extraction process. Here, we propose AntiMF, a deep learning model that utilizes multi-view mechanism based on different feature extraction models. Comparative results show that our model has a better performance than the state-of-the-art methods in the prediction of anticancer peptides. By using an ensemble learning framework to extract representation, AntiMF can capture the different dimensional information, which can make model representation more complete. Moreover, we visualize what AntiMF learns on one of its ensemble models to intuitively show the effectivity of our model, indicating that AntiMF has the great potential ability to be an effective and useful model to identify anticancer peptides accurately.
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Affiliation(s)
- Jingjing Liu
- Eye Hospital, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - Minghao Li
- Beidahuang Industry Group General Hospital, Harbin 150001, China
| | - Xin Chen
- Eye Hospital, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China; Department of Neurosurgical Laboratory, The First Affiliated Hospital of Harbin Medical University, Harbin 150001, China.
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57
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Akbar S, Hayat M, Tahir M, Khan S, Alarfaj FK. cACP-DeepGram: Classification of anticancer peptides via deep neural network and skip-gram-based word embedding model. Artif Intell Med 2022; 131:102349. [DOI: 10.1016/j.artmed.2022.102349] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 05/24/2022] [Accepted: 07/04/2022] [Indexed: 12/28/2022]
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58
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Ranjan A, Fahad MS, Deepak A. λ-Scaled-attention: A novel fast attention mechanism for efficient modeling of protein sequences. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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59
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Zou H, Yang F, Yin Z. Integrating multiple sequence features for identifying anticancer peptides. Comput Biol Chem 2022; 99:107711. [DOI: 10.1016/j.compbiolchem.2022.107711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 05/16/2022] [Accepted: 05/29/2022] [Indexed: 11/03/2022]
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60
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Medina-Ortiz D, Contreras S, Amado-Hinojosa J, Torres-Almonacid J, Asenjo JA, Navarrete M, Olivera-Nappa Á. Generalized Property-Based Encoders and Digital Signal Processing Facilitate Predictive Tasks in Protein Engineering. Front Mol Biosci 2022; 9:898627. [PMID: 35911960 PMCID: PMC9329607 DOI: 10.3389/fmolb.2022.898627] [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: 03/17/2022] [Accepted: 06/23/2022] [Indexed: 11/13/2022] Open
Abstract
Computational methods in protein engineering often require encoding amino acid sequences, i.e., converting them into numeric arrays. Physicochemical properties are a typical choice to define encoders, where we replace each amino acid by its value for a given property. However, what property (or group thereof) is best for a given predictive task remains an open problem. In this work, we generalize property-based encoding strategies to maximize the performance of predictive models in protein engineering. First, combining text mining and unsupervised learning, we partitioned the AAIndex database into eight semantically-consistent groups of properties. We then applied a non-linear PCA within each group to define a single encoder to represent it. Then, in several case studies, we assess the performance of predictive models for protein and peptide function, folding, and biological activity, trained using the proposed encoders and classical methods (One Hot Encoder and TAPE embeddings). Models trained on datasets encoded with our encoders and converted to signals through the Fast Fourier Transform (FFT) increased their precision and reduced their overfitting substantially, outperforming classical approaches in most cases. Finally, we propose a preliminary methodology to create de novo sequences with desired properties. All these results offer simple ways to increase the performance of general and complex predictive tasks in protein engineering without increasing their complexity.
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Affiliation(s)
- David Medina-Ortiz
- Centre for Biotechnology and Bioengineering, Universidad de Chile, Santiago, Chile
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Punta Arenas, Chile
| | - Sebastian Contreras
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
- *Correspondence: Sebastian Contreras, ; Álvaro Olivera-Nappa,
| | - Juan Amado-Hinojosa
- Centre for Biotechnology and Bioengineering, Universidad de Chile, Santiago, Chile
- Departamento de Ingeniería Química, Biotecnología y Materiales, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Santiago, Chile
| | - Jorge Torres-Almonacid
- Departamento de Ingeniería en Computación, Universidad de Magallanes, Punta Arenas, Chile
| | - Juan A. Asenjo
- Centre for Biotechnology and Bioengineering, Universidad de Chile, Santiago, Chile
- Departamento de Ingeniería Química, Biotecnología y Materiales, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Santiago, Chile
| | | | - Álvaro Olivera-Nappa
- Centre for Biotechnology and Bioengineering, Universidad de Chile, Santiago, Chile
- Departamento de Ingeniería Química, Biotecnología y Materiales, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Santiago, Chile
- *Correspondence: Sebastian Contreras, ; Álvaro Olivera-Nappa,
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61
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Rational Discovery of Antimicrobial Peptides by Means of Artificial Intelligence. MEMBRANES 2022; 12:membranes12070708. [PMID: 35877911 PMCID: PMC9320227 DOI: 10.3390/membranes12070708] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/05/2022] [Accepted: 07/06/2022] [Indexed: 11/16/2022]
Abstract
Antibiotic resistance is a worldwide public health problem due to the costs and mortality rates it generates. However, the large pharmaceutical industries have stopped searching for new antibiotics because of their low profitability, given the rapid replacement rates imposed by the increasingly observed resistance acquired by microorganisms. Alternatively, antimicrobial peptides (AMPs) have emerged as potent molecules with a much lower rate of resistance generation. The discovery of these peptides is carried out through extensive in vitro screenings of either rational or non-rational libraries. These processes are tedious and expensive and generate only a few AMP candidates, most of which fail to show the required activity and physicochemical properties for practical applications. This work proposes implementing an artificial intelligence algorithm to reduce the required experimentation and increase the efficiency of high-activity AMP discovery. Our deep learning (DL) model, called AMPs-Net, outperforms the state-of-the-art method by 8.8% in average precision. Furthermore, it is highly accurate to predict the antibacterial and antiviral capacity of a large number of AMPs. Our search led to identifying two unreported antimicrobial motifs and two novel antimicrobial peptides related to them. Moreover, by coupling DL with molecular dynamics (MD) simulations, we were able to find a multifunctional peptide with promising therapeutic effects. Our work validates our previously proposed pipeline for a more efficient rational discovery of novel AMPs.
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62
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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: 15] [Impact Index Per Article: 7.5] [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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63
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Zhu L, Ye C, Hu X, Yang S, Zhu C. ACP-check: An anticancer peptide prediction model based on bidirectional long short-term memory and multi-features fusion strategy. Comput Biol Med 2022; 148:105868. [PMID: 35868046 DOI: 10.1016/j.compbiomed.2022.105868] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/14/2022] [Accepted: 07/09/2022] [Indexed: 11/16/2022]
Abstract
The anticancer peptide is an emerging anticancer drug that has become an effective alternative to chemotherapy and targeted therapy due to fewer side effects and resistance. The traditional biological experimental method for identifying anticancer peptides is a time-consuming and complicated process that hinders large-scale, rapid, and effective identification. In this paper, we propose a model based on a bidirectional long short-term memory network and multi-features fusion, called ACP-check, which employs a bidirectional long short-term memory network to extract time-dependent information features from peptide sequences, and combines them with amino acid sequence features including binary profile feature, dipeptide composition, the composition of k-spaced amino acid group pairs, amino acid composition, and sequence-order-coupling number. To verify the performance of the model, six benchmark datasets are selected, including ACPred-Fuse, ACPred-FL, ACP240, ACP740, main and alternate datasets of AntiCP2.0. In terms of Matthews correlation coefficients, ACP-check obtains 0.37, 0.82, 0.80, 0.75, 0.56, and 0.86 on six datasets respectively, which is an improvement by 2%-86% than existing state-of-the-art anticancer peptides prediction methods. Furthermore, ACP-check achieves prediction accuracy with 0.91, 0.91, 0.90, 0.87, 0.78, and 0.93 respectively, which increases range from 1%-49%. Overall, the comparison experiment shows that ACP-check can accurately identify anticancer peptides by sequence-level information. The code and data are available at http://www.cczubio.top/ACP-check/.
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Affiliation(s)
- Lun Zhu
- School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software, Changzhou University, Changzhou, 213164, China
| | - Chenyang Ye
- School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software, Changzhou University, Changzhou, 213164, China
| | - Xuemei Hu
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China
| | - Sen Yang
- School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software, Changzhou University, Changzhou, 213164, China; Changzhou No.2 People's Hospital, the Affiliated Hospital of Nanjing Medical University, Changzhou, 213164, China.
| | - Chenyang Zhu
- School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software, Changzhou University, Changzhou, 213164, China
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64
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A convolutional neural network based tool for predicting protein AMPylation sites from binary profile representation. Sci Rep 2022; 12:11451. [PMID: 35794165 PMCID: PMC9259580 DOI: 10.1038/s41598-022-15403-3] [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: 10/24/2021] [Accepted: 06/23/2022] [Indexed: 11/09/2022] Open
Abstract
AMPylation is an emerging post-translational modification that occurs on the hydroxyl group of threonine, serine, or tyrosine via a phosphodiester bond. AMPylators catalyze this process as covalent attachment of adenosine monophosphate to the amino acid side chain of a peptide. Recent studies have shown that this post-translational modification is directly responsible for the regulation of neurodevelopment and neurodegeneration and is also involved in many physiological processes. Despite the importance of this post-translational modification, there is no peptide sequence dataset available for conducting computation analysis. Therefore, so far, no computational approach has been proposed for predicting AMPylation. In this study, we introduce a new dataset of this distinct post-translational modification and develop a new machine learning tool using a deep convolutional neural network called DeepAmp to predict AMPylation sites in proteins. DeepAmp achieves 77.7%, 79.1%, 76.8%, 0.55, and 0.85 in terms of Accuracy, Sensitivity, Specificity, Matthews Correlation Coefficient, and Area Under Curve for AMPylation site prediction task, respectively. As the first machine learning model, DeepAmp demonstrate promising results which highlight its potential to solve this problem. Our presented dataset and DeepAmp as a standalone predictor are publicly available at https://github.com/MehediAzim/DeepAmp .
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65
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Chu Y, Guo S, Cui D, Fu X, Ma Y. DeephageTP: a convolutional neural network framework for identifying phage-specific proteins from metagenomic sequencing data. PeerJ 2022; 10:e13404. [PMID: 35698617 PMCID: PMC9188312 DOI: 10.7717/peerj.13404] [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: 12/06/2021] [Accepted: 04/18/2022] [Indexed: 01/14/2023] Open
Abstract
Bacteriophages (phages) are the most abundant and diverse biological entity on Earth. Due to the lack of universal gene markers and database representatives, there about 50-90% of genes of phages are unable to assign functions. This makes it a challenge to identify phage genomes and annotate functions of phage genes efficiently by homology search on a large scale, especially for newly phages. Portal (portal protein), TerL (large terminase subunit protein), and TerS (small terminase subunit protein) are three specific proteins of Caudovirales phage. Here, we developed a CNN (convolutional neural network)-based framework, DeephageTP, to identify the three specific proteins from metagenomic data. The framework takes one-hot encoding data of original protein sequences as the input and automatically extracts predictive features in the process of modeling. To overcome the false positive problem, a cutoff-loss-value strategy is introduced based on the distributions of the loss values of protein sequences within the same category. The proposed model with a set of cutoff-loss-values demonstrates high performance in terms of Precision in identifying TerL and Portal sequences (94% and 90%, respectively) from the mimic metagenomic dataset. Finally, we tested the efficacy of the framework using three real metagenomic datasets, and the results shown that compared to the conventional alignment-based methods, our proposed framework had a particular advantage in identifying the novel phage-specific protein sequences of portal and TerL with remote homology to their counterparts in the training datasets. In summary, our study for the first time develops a CNN-based framework for identifying the phage-specific protein sequences with high complexity and low conservation, and this framework will help us find novel phages in metagenomic sequencing data. The DeephageTP is available at https://github.com/chuym726/DeephageTP.
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Affiliation(s)
- Yunmeng Chu
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese, Shenzhen, Guangdong, P.R. China,Department of Bioengineering and Biotechnology, Huaqiao University, Xiamen, Fujian, P.R. China
| | - Shun Guo
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese, Shenzhen, Guangdong, P.R. China
| | - Dachao Cui
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese, Shenzhen, Guangdong, P.R. China
| | - Xiongfei Fu
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese, Shenzhen, Guangdong, P.R. China
| | - Yingfei Ma
- Shenzhen Key Laboratory of Synthetic Genomics, Guangdong Provincial Key Laboratory of Synthetic Genomics, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese, Shenzhen, Guangdong, P.R. China
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66
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Li Y, Li X, Liu Y, Yao Y, Huang G. MPMABP: A CNN and Bi-LSTM-Based Method for Predicting Multi-Activities of Bioactive Peptides. Pharmaceuticals (Basel) 2022; 15:707. [PMID: 35745625 PMCID: PMC9231127 DOI: 10.3390/ph15060707] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/23/2022] [Accepted: 05/30/2022] [Indexed: 12/30/2022] Open
Abstract
Bioactive peptides are typically small functional peptides with 2-20 amino acid residues and play versatile roles in metabolic and biological processes. Bioactive peptides are multi-functional, so it is vastly challenging to accurately detect all their functions simultaneously. We proposed a convolution neural network (CNN) and bi-directional long short-term memory (Bi-LSTM)-based deep learning method (called MPMABP) for recognizing multi-activities of bioactive peptides. The MPMABP stacked five CNNs at different scales, and used the residual network to preserve the information from loss. The empirical results showed that the MPMABP is superior to the state-of-the-art methods. Analysis on the distribution of amino acids indicated that the lysine preferred to appear in the anti-cancer peptide, the leucine in the anti-diabetic peptide, and the proline in the anti-hypertensive peptide. The method and analysis are beneficial to recognize multi-activities of bioactive peptides.
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Affiliation(s)
- You Li
- School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; (Y.L.); (X.L.)
| | - Xueyong Li
- School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; (Y.L.); (X.L.)
| | - Yuewu Liu
- College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China;
| | - Yuhua Yao
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China;
| | - Guohua Huang
- School of Electrical Engineering, Shaoyang University, Shaoyang 422000, China; (Y.L.); (X.L.)
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67
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Feng G, Yao H, Li C, Liu R, Huang R, Fan X, Ge R, Miao Q. ME-ACP: Multi-view neural networks with ensemble model for identification of anticancer peptides. Comput Biol Med 2022; 145:105459. [DOI: 10.1016/j.compbiomed.2022.105459] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 03/22/2022] [Accepted: 03/24/2022] [Indexed: 12/26/2022]
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68
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Multi-channel CNN based anticancer peptides identification. Anal Biochem 2022; 650:114707. [PMID: 35568159 DOI: 10.1016/j.ab.2022.114707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 01/27/2022] [Accepted: 04/27/2022] [Indexed: 11/20/2022]
Abstract
Cancer is one of the most dangerous diseases in the world that often leads to misery and death. Current treatments include different kinds of anticancer therapy which exhibit different types of side effects. Because of certain physicochemical properties, anticancer peptides (ACPs) have opened a new path of treatments for this deadly disease. That is why a well-performed methodology for identifying novel anticancer peptides has great importance in the fight against cancer. In addition to the laboratory techniques, various machine learning and deep learning methodologies have developed in recent years for this task. Although these models have shown reasonable predictive ability, there's still room for improvement in terms of performance and exploring new types of algorithms. In this work, we have proposed a novel multi-channel convolutional neural network (CNN) for identifying anticancer peptides from protein sequences. We have collected data from the existing state-of-the-art methodologies and applied binary encoding for data preprocessing. We have also employed k-fold cross-validation to train our models on benchmark datasets and compared our models' performance on the independent datasets. The comparison has indicated our models' superiority on various evaluation metrics. We think our work can be a valuable asset in finding novel anticancer peptides. We have provided a user-friendly web server for academic purposes and it is publicly available at: \texttt{http://103.99.176.239/iacp-cnn/}.
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69
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Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics. Pharmaceutics 2022; 14:pharmaceutics14050997. [PMID: 35631583 PMCID: PMC9147327 DOI: 10.3390/pharmaceutics14050997] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/28/2022] [Accepted: 05/04/2022] [Indexed: 01/27/2023] Open
Abstract
Cancer is a group of diseases causing abnormal cell growth, altering the genome, and invading or spreading to other parts of the body. Among therapeutic peptide drugs, anticancer peptides (ACPs) have been considered to target and kill cancer cells because cancer cells have unique characteristics such as a high negative charge and abundance of microvilli in the cell membrane when compared to a normal cell. ACPs have several advantages, such as high specificity, cost-effectiveness, low immunogenicity, minimal toxicity, and high tolerance under normal physiological conditions. However, the development and identification of ACPs are time-consuming and expensive in traditional wet-lab-based approaches. Thus, the application of artificial intelligence on the approaches can save time and reduce the cost to identify candidate ACPs. Recently, machine learning (ML), deep learning (DL), and hybrid learning (ML combined DL) have emerged into the development of ACPs without experimental analysis, owing to advances in computer power and big data from the power system. Additionally, we suggest that combination therapy with classical approaches and ACPs might be one of the impactful approaches to increase the efficiency of cancer therapy.
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70
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Alqahtani A. Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2022; 2022:6201067. [PMID: 35509623 PMCID: PMC9060979 DOI: 10.1155/2022/6201067] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/17/2022] [Accepted: 04/05/2022] [Indexed: 11/18/2022]
Abstract
Spectacular developments in molecular and cellular biology have led to important discoveries in cancer research. Despite cancer is one of the major causes of morbidity and mortality globally, diabetes is one of the most leading sources of group of disorders. Artificial intelligence (AI) has been considered the fourth industrial revolution machine. The most major hurdles in drug discovery and development are the time and expenditures required to sustain the drug research pipeline. Large amounts of data can be explored and generated by AI, which can then be converted into useful knowledge. Because of this, the world's largest drug companies have already begun to use AI in their drug development research. In the present era, AI has a huge amount of potential for the rapid discovery and development of new anticancer drugs. Clinical studies, electronic medical records, high-resolution medical imaging, and genomic assessments are just a few of the tools that could aid drug development. Large data sets are available to researchers in the pharmaceutical and medical fields, which can be analyzed by advanced AI systems. This review looked at how computational biology and AI technologies may be utilized in cancer precision drug development by combining knowledge of cancer medicines, drug resistance, and structural biology. This review also highlighted a realistic assessment of the potential for AI in understanding and managing diabetes.
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Affiliation(s)
- Amal Alqahtani
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, 31541, Saudi Arabia
- Department of Basic Sciences, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 34212, Saudi Arabia
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71
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Breast and Lung Anticancer Peptides Classification Using N-Grams and Ensemble Learning Techniques. BIG DATA AND COGNITIVE COMPUTING 2022. [DOI: 10.3390/bdcc6020040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Anticancer peptides (ACPs) are short protein sequences; they perform functions like some hormones and enzymes inside the body. The role of any protein or peptide is related to its structure and the sequence of amino acids that make up it. There are 20 types of amino acids in humans, and each of them has a particular characteristic according to its chemical structure. Current machine and deep learning models have been used to classify ACPs problems. However, these models have neglected Amino Acid Repeats (AARs) that play an essential role in the function and structure of peptides. Therefore, in this paper, ACPs offer a promising route for novel anticancer peptides by extracting AARs based on N-Grams and k-mers using two peptides’ datasets. These datasets pointed to breast and lung cancer cells assembled and curated manually from the Cancer Peptide and Protein Database (CancerPPD). Every dataset consists of a sequence of peptides and their synthesis and anticancer activity on breast and lung cancer cell lines. Five different feature selection methods were used in this paper to improve classification performance and reduce the experimental costs. After that, ACPs were classified using four classifiers, namely AdaBoost, Random Forest Tree (RFT), Multi-class Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). These classifiers were evaluated by applying five well-known evaluation metrics. Experimental results showed that the breast and lung ACPs classification process provided an accurate performance that reached 89.25% and 92.56%, respectively. In terms of AUC, it reached 95.35% and 96.92% for both breast and lung ACPs, respectively. The proposed classifiers performed competently somewhat equally in AUC, accuracy, precision, F-measures, and recall, except for Multi-class SVM-based feature selection, which showed superior performance. As a result, this paper significantly improved the predictive performance that can effectively distinguish ACPs as virtual inactive, experimental inactive, moderately active, and very active.
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72
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ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information. Molecules 2022; 27:molecules27051544. [PMID: 35268644 PMCID: PMC8912097 DOI: 10.3390/molecules27051544] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 02/20/2022] [Accepted: 02/23/2022] [Indexed: 12/18/2022] Open
Abstract
Cancer is one of the most dangerous threats to human health. One of the issues is drug resistance action, which leads to side effects after drug treatment. Numerous therapies have endeavored to relieve the drug resistance action. Recently, anticancer peptides could be a novel and promising anticancer candidate, which can inhibit tumor cell proliferation, migration, and suppress the formation of tumor blood vessels, with fewer side effects. However, it is costly, laborious and time consuming to identify anticancer peptides by biological experiments with a high throughput. Therefore, accurately identifying anti-cancer peptides becomes a key and indispensable step for anticancer peptides therapy. Although some existing computer methods have been developed to predict anticancer peptides, the accuracy still needs to be improved. Thus, in this study, we propose a deep learning-based model, called ACPNet, to distinguish anticancer peptides from non-anticancer peptides (non-ACPs). ACPNet employs three different types of peptide sequence information, peptide physicochemical properties and auto-encoding features linking the training process. ACPNet is a hybrid deep learning network, which fuses fully connected networks and recurrent neural networks. The comparison with other existing methods on ACPs82 datasets shows that ACPNet not only achieves the improvement of 1.2% Accuracy, 2.0% F1-score, and 7.2% Recall, but also gets balanced performance on the Matthews correlation coefficient. Meanwhile, ACPNet is verified on an independent dataset, with 20 proven anticancer peptides, and only one anticancer peptide is predicted as non-ACPs. The comparison and independent validation experiment indicate that ACPNet can accurately distinguish anticancer peptides from non-ACPs.
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73
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Mckenna A, P N Dubey S. Machine Learning Based Predictive Model for the Analysis of Sequence Activity Relationships Using Protein Spectra and Protein Descriptors. J Biomed Inform 2022; 128:104016. [PMID: 35143999 DOI: 10.1016/j.jbi.2022.104016] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 12/13/2021] [Accepted: 02/03/2022] [Indexed: 11/26/2022]
Abstract
Accurately establishing the connection between a protein sequence and its function remains a focal point within the field of protein engineering, especially in the context of predicting the effects of mutations. From this, there has been a continued drive to build accurate and reliable predictive models via machine learning that allow for the virtual screening of many protein mutant sequences, measuring the relationship between sequence and 'fitness' or 'activity', commonly known as a Sequence-Activity-Relationship (SAR). An important preliminary stage in the building of these predictive models is the encoding of the chosen sequences. Evaluated in this work is a plethora of encoding strategies using the Amino Acid Index database, where the indices are transformed into their spectral form via Digital Signal Processing (DSP) techniques, as well as numerous protein structural and physiochemical descriptors. The encoding strategies are explored on a dataset curated to measure the thermostability of various mutants from a recombination library, designed from parental cytochrome P450s. In this work it was concluded that the implementation of protein spectra in concatenation with protein descriptors, together with the Partial Least Squares Regression (PLS) algorithm, gave the most noteworthy increase in the quality of the predictive models (as described in Encoding Strategy C), highlighting their utility in identifying an SAR. The accompanying software produced for this paper is termed pySAR (Python Sequence-Activity-Relationship), which allows for a user to find the optimal arrangement of structural and or physiochemical properties to encode their specific mutant library dataset; the source code is available at: https://github.com/amckenna41/pySAR.
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Affiliation(s)
- Adam Mckenna
- School of Electronics, Electrical Engineering and Computer Science, Queen's University of Belfast, University Road, BT7 1NN, Belfast, United Kingdom.
| | - Sandhya P N Dubey
- Department of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka 576104, India.
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74
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Hooshmand SE, Sabet MJ, Hasanzadeh A, Mousavi SMK, Moghadam NH, Hooshmand SA, Rabiee N, Liu Y, Hamblin MR, Karimi M. Histidine‐enhanced gene delivery systems: The state of the art. J Gene Med 2022; 24:e3415. [DOI: 10.1002/jgm.3415] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/26/2022] [Accepted: 01/29/2022] [Indexed: 11/08/2022] Open
Affiliation(s)
- Seyyed Emad Hooshmand
- Cellular and Molecular Research Center Iran University of Medical Sciences Tehran Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine Iran University of Medical Sciences Tehran Iran
| | - Makkieh Jahanpeimay Sabet
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine Iran University of Medical Sciences Tehran Iran
| | - Akbar Hasanzadeh
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine Iran University of Medical Sciences Tehran Iran
| | - Seyede Mahtab Kamrani Mousavi
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine Iran University of Medical Sciences Tehran Iran
| | - Niloofar Haeri Moghadam
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine Iran University of Medical Sciences Tehran Iran
| | - Seyed Aghil Hooshmand
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics University of Tehran Tehran Iran
| | - Navid Rabiee
- Department of Physics Sharif University of Technology Tehran Iran
- School of Engineering Macquarie University Sydney New South Wales Australia
| | - Yong Liu
- Institute of Functional Nano & Soft Materials (FUNSOM) Soochow University Suzhou Jiangsu China
| | - Michael R. Hamblin
- Laser Research Centre, Faculty of Health Science University of Johannesburg South Africa
| | - Mahdi Karimi
- Cellular and Molecular Research Center Iran University of Medical Sciences Tehran Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine Iran University of Medical Sciences Tehran Iran
- Oncopathology Research Center Iran University of Medical Sciences Tehran Iran
- Research Center for Science and Technology in Medicine Tehran University of Medical Sciences Tehran Iran
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75
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He W, Jiang Y, Jin J, Li Z, Zhao J, Manavalan B, Su R, Gao X, Wei L. Accelerating bioactive peptide discovery via mutual information-based meta-learning. Brief Bioinform 2021; 23:6457168. [PMID: 34882225 DOI: 10.1093/bib/bbab499] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/07/2021] [Accepted: 10/30/2021] [Indexed: 12/28/2022] Open
Abstract
Recently, machine learning methods have been developed to identify various peptide bio-activities. However, due to the lack of experimentally validated peptides, machine learning methods cannot provide a sufficiently trained model, easily resulting in poor generalizability. Furthermore, there is no generic computational framework to predict the bioactivities of different peptides. Thus, a natural question is whether we can use limited samples to build an effective predictive model for different kinds of peptides. To address this question, we propose Mutual Information Maximization Meta-Learning (MIMML), a novel meta-learning-based predictive model for bioactive peptide discovery. Using few samples from various functional peptides, MIMML can sufficiently learn the discriminative information amongst various functions and characterize functional differences. Experimental results show excellent performance of MIMML though using far fewer training samples as compared to the state-of-the-art methods. We also decipher the latent relationships among different kinds of functions to understand what meta-model learned to improve a specific task. In summary, this study is a pioneering work in the field of functional peptide mining and provides the first-of-its-kind solution for few-sample learning problems in biological sequence analysis, accelerating the new functional peptide discovery. The source codes and datasets are available on https://github.com/TearsWaiting/MIMML.
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Affiliation(s)
- Wenjia He
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China.,BioMap, Beijing, China
| | - Yi Jiang
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Junru Jin
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Zhongshen Li
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Jiaojiao Zhao
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | | | - Ran Su
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Xin Gao
- King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal, 23955-6900, Saudi Arabia
| | - Leyi Wei
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
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76
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Ahmed S, Muhammod R, Khan ZH, Adilina S, Sharma A, Shatabda S, Dehzangi A. ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides. Sci Rep 2021; 11:23676. [PMID: 34880291 PMCID: PMC8654959 DOI: 10.1038/s41598-021-02703-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 11/17/2021] [Indexed: 01/10/2023] Open
Abstract
Although advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chemotherapy have significant downsides and low specificity. Most recently, Anticancer peptides (ACPs) have emerged as a potential alternative to therapeutic alternatives with much fewer negative side-effects. However, the identification of ACPs through wet-lab experiments is expensive and time-consuming. Hence, computational methods have emerged as viable alternatives. During the past few years, several computational ACP identification techniques using hand-engineered features have been proposed to solve this problem. In this study, we propose a new multi headed deep convolutional neural network model called ACP-MHCNN, for extracting and combining discriminative features from different information sources in an interactive way. Our model extracts sequence, physicochemical, and evolutionary based features for ACP identification using different numerical peptide representations while restraining parameter overhead. It is evident through rigorous experiments using cross-validation and independent-dataset that ACP-MHCNN outperforms other models for anticancer peptide identification by a substantial margin on our employed benchmarks. ACP-MHCNN outperforms state-of-the-art model by 6.3%, 8.6%, 3.7%, 4.0%, and 0.20 in terms of accuracy, sensitivity, specificity, precision, and MCC respectively. ACP-MHCNN and its relevant codes and datasets are publicly available at: https://github.com/mrzResearchArena/Anticancer-Peptides-CNN . ACP-MHCNN is also publicly available as an online predictor at: https://anticancer.pythonanywhere.com/ .
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Affiliation(s)
- Sajid Ahmed
- Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
| | - Rafsanjani Muhammod
- Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
| | - Zahid Hossain Khan
- Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
| | - Sheikh Adilina
- Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
| | - Alok Sharma
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
- Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, QLD, 4111, Australia
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh.
| | - Abdollah Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ, 08102, USA.
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, 08102, USA.
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77
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Wang H, Zhao J, Zhao H, Li H, Wang J. CL-ACP: a parallel combination of CNN and LSTM anticancer peptide recognition model. BMC Bioinformatics 2021; 22:512. [PMID: 34670488 PMCID: PMC8527680 DOI: 10.1186/s12859-021-04433-9] [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: 05/10/2021] [Accepted: 10/05/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Anticancer peptides are defence substances with innate immune functions that can selectively act on cancer cells without harming normal cells and many studies have been conducted to identify anticancer peptides. In this paper, we introduce the anticancer peptide secondary structures as additional features and propose an effective computational model, CL-ACP, that uses a combined network and attention mechanism to predict anticancer peptides. RESULTS The CL-ACP model uses secondary structures and original sequences of anticancer peptides to construct the feature space. The long short-term memory and convolutional neural network are used to extract the contextual dependence and local correlations of the feature space. Furthermore, a multi-head self-attention mechanism is used to strengthen the anticancer peptide sequences. Finally, three categories of feature information are classified by cascading. CL-ACP was validated using two types of datasets, anticancer peptide datasets and antimicrobial peptide datasets, on which it achieved good results compared to previous methods. CL-ACP achieved the highest AUC values of 0.935 and 0.972 on the anticancer peptide and antimicrobial peptide datasets, respectively. CONCLUSIONS CL-ACP can effectively recognize antimicrobial peptides, especially anticancer peptides, and the parallel combined neural network structure of CL-ACP does not require complex feature design and high time cost. It is suitable for application as a useful tool in antimicrobial peptide design.
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Affiliation(s)
- Huiqing Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jian Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China.
| | - Hong Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Haolin Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Juan Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
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78
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You H, Yu L, Tian S, Ma X, Xing Y, Song J, Wu W. Anti-cancer Peptide Recognition Based on Grouped Sequence and Spatial Dimension Integrated Networks. Interdiscip Sci 2021; 14:196-208. [PMID: 34637113 DOI: 10.1007/s12539-021-00481-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 09/05/2021] [Accepted: 09/09/2021] [Indexed: 11/24/2022]
Abstract
The diversification of the characteristic sequences of anti-cancer peptides has imposed difficulties on research. To effectively predict new anti-cancer peptides, this paper proposes a more suitable feature grouping sequence and spatial dimension-integrated network algorithm for anti-cancer peptide sequence prediction called GRCI-Net. The main process is as follows: First, we implemented the fusion reduction of binary structure features and K-mer sparse matrix features through principal component analysis and generated a set of new features; second, we constructed a new bidirectional long- and short-term memory network. We used traditional convolution and dilated convolution to acquire features in the spatial dimension using the memory network's grouping sequence model, which is designed to better handle the diversification of anti-cancer peptide feature sequences and to fully learn the contextual information between features. Finally, we achieved the fusion of grouping sequence features and spatial dimensional integration features through two sets of dense network layers, achieved the prediction of anti-cancer peptides through the sigmoid function, and verified the approach with two public datasets, ACP740 (accuracy reached 0.8230) and ACP240 (accuracy reached 0.8750). The following is a link to the model code and datasets mentioned in this article: https://github.com/ YouHongfeng101/ACP-DL.
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Affiliation(s)
- Hongfeng You
- College of Information Science and Engineering, Xinjiang University, 666 Shengli Road, Tianshan District, Urumqi, Xinjiang, China
| | - Long Yu
- Network Center, Xinjiang University, Xinjiang, China.
| | - Shengwei Tian
- School of Software, Xinjiang University, Tianshan District, 666 Shengli Road, Urumqi, Xinjiang, China
| | - Xiang Ma
- Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830011, China
| | - Yan Xing
- Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, No. 137, LiYuShan South Road, Urumqi, Xinjiang, China
| | - Jinmiao Song
- College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang, China
| | - Weidong Wu
- People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
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79
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Cai L, Wang L, Fu X, Zeng X. Active Semisupervised Model for Improving the Identification of Anticancer Peptides. ACS OMEGA 2021; 6:23998-24008. [PMID: 34568678 PMCID: PMC8459422 DOI: 10.1021/acsomega.1c03132] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Indexed: 06/13/2023]
Abstract
Cancer is one of the most dangerous threats to human health. Accurate identification of anticancer peptides (ACPs) is valuable for the development and design of new anticancer agents. However, most machine-learning algorithms have limited ability to identify ACPs, and their accuracy is sensitive to the amount of label data. In this paper, we construct a new technology that combines active learning (AL) and label propagation (LP) algorithm to solve this problem, called (ACP-ALPM). First, we develop an efficient feature representation method based on various descriptor information and coding information of the peptide sequence. Then, an AL strategy is used to filter out the most informative data for model training, and a more powerful LP classifier is cast through continuous iterations. Finally, we evaluate the performance of ACP-ALPM and compare it with that of some of the state-of-the-art and classic methods; experimental results show that our method is significantly superior to them. In addition, through the experimental comparison of random selection and AL on three public data sets, it is proved that the AL strategy is more effective. Notably, a visualization experiment further verified that AL can utilize unlabeled data to improve the performance of the model. We hope that our method can be extended to other types of peptides and provide more inspiration for other similar work.
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Affiliation(s)
- Lijun Cai
- Department of Information
Science and Technology, Hunan University, Changsha, Hunan 410000, China
| | - Li Wang
- Department of Information
Science and Technology, Hunan University, Changsha, Hunan 410000, China
| | - Xiangzheng Fu
- Department of Information
Science and Technology, Hunan University, Changsha, Hunan 410000, China
| | - Xiangxiang Zeng
- Department of Information
Science and Technology, Hunan University, Changsha, Hunan 410000, China
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80
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Quiroz C, Saavedra YB, Armijo-Galdames B, Amado-Hinojosa J, Olivera-Nappa Á, Sanchez-Daza A, Medina-Ortiz D. Peptipedia: a user-friendly web application and a comprehensive database for peptide research supported by Machine Learning approach. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6363751. [PMID: 34478499 PMCID: PMC8415426 DOI: 10.1093/database/baab055] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/30/2021] [Accepted: 08/11/2021] [Indexed: 12/12/2022]
Abstract
Peptides have attracted attention during the last decades due to their extraordinary therapeutic properties. Different computational tools have been developed to take advantage of existing information, compiling knowledge and making available the information for common users. Nevertheless, most related tools available are not user-friendly, present redundant information, do not clearly display the data, and usually are specific for particular biological activities, not existing so far, an integrated database with consolidated information to help research peptide sequences. To solve these necessities, we developed Peptipedia, a user-friendly web application and comprehensive database to search, characterize and analyse peptide sequences. Our tool integrates the information from 30 previously reported databases with a total of 92 055 amino acid sequences, making it the biggest repository of peptides with recorded activities to date. Furthermore, we make available a variety of bioinformatics services and statistical modules to increase our tool’s usability. Moreover, we incorporated a robust assembled binary classification system to predict putative biological activities for peptide sequences. Our tools’ significant differences with other existing alternatives become a substantial contribution for developing biotechnological and bioengineering applications for peptides. Peptipedia is available for non-commercial use as an open-access software, licensed under the GNU General Public License, version GPL 3.0. The web platform is publicly available at peptipedia.cl. Database URL: Both the source code and sample data sets are available in the GitHub repository https://github.com/ProteinEngineering-PESB2/peptipedia
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Affiliation(s)
- Cristofer Quiroz
- Facultad de Ingeniería, Universidad Autonóma de Chile, Cinco Pte. 1670, Talca 3467987, Chile
| | - Yasna Barrera Saavedra
- Escuela de Ingeniería en Bioinformática, Universidad de Talca, Avenida Lircay SN, Talca 3460000, Chile
| | - Benjamín Armijo-Galdames
- Centre for Biotechnology and Bioengineering, Universidad de Chile, Beauchef 851, Santiago 8370448, Chile.,Department of Chemical Engineering, Biotechnology and Materials, Universidad de Chile, Beauchef 851, Santiago 8370448, Chile
| | - Juan Amado-Hinojosa
- Centre for Biotechnology and Bioengineering, Universidad de Chile, Beauchef 851, Santiago 8370448, Chile.,Department of Chemical Engineering, Biotechnology and Materials, Universidad de Chile, Beauchef 851, Santiago 8370448, Chile
| | - Álvaro Olivera-Nappa
- Centre for Biotechnology and Bioengineering, Universidad de Chile, Beauchef 851, Santiago 8370448, Chile.,Department of Chemical Engineering, Biotechnology and Materials, Universidad de Chile, Beauchef 851, Santiago 8370448, Chile
| | - Anamaria Sanchez-Daza
- Centre for Biotechnology and Bioengineering, Universidad de Chile, Beauchef 851, Santiago 8370448, Chile.,Institute for Cell Dynamics and Biotechnology, Beauchef 851, Santiago 8370456, Chile
| | - David Medina-Ortiz
- Centre for Biotechnology and Bioengineering, Universidad de Chile, Beauchef 851, Santiago 8370448, Chile.,Department of Chemical Engineering, Biotechnology and Materials, Universidad de Chile, Beauchef 851, Santiago 8370448, Chile
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81
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Nasiri F, Atanaki FF, Behrouzi S, Kavousi K, Bagheri M. CpACpP: In Silico Cell-Penetrating Anticancer Peptide Prediction Using a Novel Bioinformatics Framework. ACS OMEGA 2021; 6:19846-19859. [PMID: 34368571 PMCID: PMC8340416 DOI: 10.1021/acsomega.1c02569] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 07/13/2021] [Indexed: 05/12/2023]
Abstract
Cell-penetrating anticancer peptides (Cp-ACPs) are considered promising candidates in solid tumor and hematologic cancer therapies. Current approaches for the design and discovery of Cp-ACPs trust the expensive high-throughput screenings that often give rise to multiple obstacles, including instrumentation adaptation and experimental handling. The application of machine learning (ML) tools developed for peptide activity prediction is importantly of growing interest. In this study, we applied the random forest (RF)-, support vector machine (SVM)-, and eXtreme gradient boosting (XGBoost)-based algorithms to predict the active Cp-ACPs using an experimentally validated data set. The model, CpACpP, was developed on the basis of two independent cell-penetrating peptide (CPP) and anticancer peptide (ACP) subpredictors. Various compositional and physiochemical-based features were combined or selected using the multilayered recursive feature elimination (RFE) method for both data sets. Our results showed that the ACP subclassifiers obtain a mean performance accuracy (ACC) of 0.98 with an area under curve (AUC) ≈ 0.98 vis-à-vis the CPP predictors displaying relevant values of ∼0.94 and ∼0.95 via the hybrid-based features and independent data sets, respectively. Also, the predicting evaluation of Cp-ACPs gave accuracies of ∼0.79 and 0.89 on a series of independent sequences by applying our CPP and ACP classifiers, respectively, which leaves the performance of our predictors better than the earlier reported ACPred, mACPpred, MLCPP, and CPPred-RF. The described consensus-based fusion method additionally reached an AUC of 0.94 for the prediction of Cp-ACP (http://cbb1.ut.ac.ir/CpACpP/Index).
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Affiliation(s)
- Farid Nasiri
- Peptide
Chemistry Laboratory, Department of Biochemistry, Institute of Biochemistry
and Biophysics (IBB), University of Tehran, Tehran 14176-14335, Iran
| | - Fereshteh Fallah Atanaki
- Laboratory
of Complex Biological Systems and Bioinformatics (CBB), Department
of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 14176-14411, Iran
| | - Saman Behrouzi
- Laboratory
of Complex Biological Systems and Bioinformatics (CBB), Department
of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 14176-14411, Iran
| | - Kaveh Kavousi
- Laboratory
of Complex Biological Systems and Bioinformatics (CBB), Department
of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 14176-14411, Iran
| | - Mojtaba Bagheri
- Peptide
Chemistry Laboratory, Department of Biochemistry, Institute of Biochemistry
and Biophysics (IBB), University of Tehran, Tehran 14176-14335, Iran
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82
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Cao R, Wang M, Bin Y, Zheng C. DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion. PeerJ 2021; 9:e11906. [PMID: 34414035 PMCID: PMC8344685 DOI: 10.7717/peerj.11906] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 07/14/2021] [Indexed: 01/10/2023] Open
Abstract
An emerging type of therapeutic agent, anticancer peptides (ACPs), has attracted attention because of its lower risk of toxic side effects. However process of identifying ACPs using experimental methods is both time-consuming and laborious. In this study, we developed a new and efficient algorithm that predicts ACPs by fusing multi-view features based on dual-channel deep neural network ensemble model. In the model, one channel used the convolutional neural network CNN to automatically extract the potential spatial features of a sequence. Another channel was used to process and extract more effective features from handcrafted features. Additionally, an effective feature fusion method was explored for the mutual fusion of different features. Finally, we adopted the neural network to predict ACPs based on the fusion features. The performance comparisons across the single and fusion features showed that the fusion of multi-view features could effectively improve the model's predictive ability. Among these, the fusion of the features extracted by the CNN and composition of k-spaced amino acid group pairs achieved the best performance. To further validate the performance of our model, we compared it with other existing methods using two independent test sets. The results showed that our model's area under curve was 0.90, which was higher than that of the other existing methods on the first test set and higher than most of the other existing methods on the second test set. The source code and datasets are available at https://github.com/wame-ng/DLFF-ACP.
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Affiliation(s)
- Ruifen Cao
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China
- Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University, Putian, Fujian, China
| | - Meng Wang
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China
| | - Yannan Bin
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui, China
| | - Chunhou Zheng
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China
- Engineering Research Center of Big Data Application in Private Health Medicine, Fujian Province University, Putian, Fujian, China
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83
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Wang Z, Hou M, Yan L, Dai Y, Yin Y, Liu X. Deep learning for tracing esophageal motility function over time. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 207:106212. [PMID: 34126411 DOI: 10.1016/j.cmpb.2021.106212] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 05/24/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Esophageal high-resolution manometry (HRM) is widely performed to evaluate the representation of manometric features in patients for diagnosing normal esophageal motility and motility disorders. Clinicians commonly assess esophageal motility function using a scheme termed the Chicago classification, which is difficult, time-consuming and inefficient with large amounts of data. METHODS Deep learning is a promising approach for diagnosing disorders and has various attractive advantages. In this study, we effectively trace esophageal motility function with HRM by using a deep learning computational model, namely, EMD-DL, which leverages three-dimensional convolution (Conv3D) and bidirectional convolutional long-short-term-memory (BiConvLSTM) models. More specifically, to fully exploit wet swallowing information, we establish an efficient swallowing representation method by localizing manometric features and swallowing box regressions from HRM. Then, EMD-DL learns how to identify major motility disorders, minor motility disorders and normal motility. To the best of our knowledge, this is the first attempt to use Conv3D and BiConvLSTM to predict esophageal motility function over esophageal HRM. RESULTS Test experiments on HRM datasets demonstrated that the overall accuracy of the proposed EMD-DL model is 91.32% with 90.5% sensitivity and 95.87% specificity. By leveraging information across swallowing motor cycles, our model can rapidly recognize esophageal motility function better than a gastroenterologist and lays the foundation for accurately diagnosing esophageal motility disorders in real time. CONCLUSIONS This approach opens new avenues for detecting and identifying esophageal motility function, thereby facilitating more efficient computer-aided diagnosis in clinical practice.
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Affiliation(s)
- Zheng Wang
- School of Mathematics and Statistics, Central South University, Changsha 410083, China; Science and Engineering School, Hunan First Normal University, Changsha 410205, China
| | - Muzhou Hou
- School of Mathematics and Statistics, Central South University, Changsha 410083, China
| | - Lu Yan
- Department of Gastroenterology of Xiangya hospital, Central South University, Changsha 410008, China
| | - Yuzhuo Dai
- School of Mathematics and Statistics, Central South University, Changsha 410083, China
| | - Yani Yin
- Department of Gastroenterology of Xiangya hospital, Central South University, Changsha 410008, China.
| | - Xiaowei Liu
- Department of Gastroenterology of Xiangya hospital, Central South University, Changsha 410008, China.
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84
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Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25:1315-1360. [PMID: 33844136 PMCID: PMC8040371 DOI: 10.1007/s11030-021-10217-3] [Citation(s) in RCA: 286] [Impact Index Per Article: 95.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/22/2021] [Indexed: 02/06/2023]
Abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Devesh Srivastava
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Swati Tiwari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India.
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85
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Chen J, Cheong HH, Siu SWI. xDeep-AcPEP: Deep Learning Method for Anticancer Peptide Activity Prediction Based on Convolutional Neural Network and Multitask Learning. J Chem Inf Model 2021; 61:3789-3803. [PMID: 34327990 DOI: 10.1021/acs.jcim.1c00181] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Cancer is one of the leading causes of death worldwide. Conventional cancer treatment relies on radiotherapy and chemotherapy, but both methods bring severe side effects to patients, as these therapies not only attack cancer cells but also damage normal cells. Anticancer peptides (ACPs) are a promising alternative as therapeutic agents that are efficient and selective against tumor cells. Here, we propose a deep learning method based on convolutional neural networks to predict biological activity (EC50, LC50, IC50, and LD50) against six tumor cells, including breast, colon, cervix, lung, skin, and prostate. We show that models derived with multitask learning achieve better performance than conventional single-task models. In repeated 5-fold cross validation using the CancerPPD data set, the best models with the applicability domain defined obtain an average mean squared error of 0.1758, Pearson's correlation coefficient of 0.8086, and Kendall's correlation coefficient of 0.6156. As a step toward model interpretability, we infer the contribution of each residue in the sequence to the predicted activity by means of feature importance weights derived from the convolutional layers of the model. The present method, referred to as xDeep-AcPEP, will help to identify effective ACPs in rational peptide design for therapeutic purposes. The data, script files for reproducing the experiments, and the final prediction models can be downloaded from http://github.com/chen709847237/xDeep-AcPEP. The web server to directly access this prediction method is at https://app.cbbio.online/acpep/home.
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Affiliation(s)
- Jiarui Chen
- Department of Computer and Information Science, University of Macau, Avenida da Universidade, Taipa, Macau 999078, China
| | - Hong Hin Cheong
- Department of Computer and Information Science, University of Macau, Avenida da Universidade, Taipa, Macau 999078, China
| | - Shirley W I Siu
- Department of Computer and Information Science, University of Macau, Avenida da Universidade, Taipa, Macau 999078, China.,School of Pharmaceutical Sciences, Universiti Sains Malaysia, 11800 USM, Penang, Malaysia
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86
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He W, Wang Y, Cui L, Su R, Wei L. Learning embedding features based on multi-sense-scaled attention architecture to improve the predictive performance of anticancer peptides. Bioinformatics 2021; 37:4684-4693. [PMID: 34323948 DOI: 10.1093/bioinformatics/btab560] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 07/03/2021] [Accepted: 07/28/2021] [Indexed: 01/10/2023] Open
Abstract
MOTIVATION Anticancer peptides (ACPs) have recently emerged as effective anticancer drugs in cancer therapy. Machine-learning-based predictors have been developed to identify ACPs and achieve satisfactory performance. However, existing methods suffer from experience-based feature engineering, which not only restricts the representation ability of the models to a certain extent but also lacks adaptivity for different data, limiting the further improvement of the predictive performance and impacting the robustness of the predictive models. To alleviate the above problems, we propose a novel deep-learning-based predictor named ACPred-LAF, in which we propose a novel multi-sense and multi-scaled embedding algorithm to automatically learn and extract context sequential characteristics of ACPs. RESULTS Through the feature comparative analysis, we demonstrate that our learnable and self-adaptive embedding features are better than hand-crafted features in capturing discriminative information, which can effectively benefit the performance improvement for ACP prediction. In addition, benchmarking comparison results demonstrate that our ACPred-LAF outperforms the state-of-the-art methods both on existing benchmark datasets and our newly constructed dataset. Furthermore, we also prove and validate the robustness of the model via the data interference experiment. To avoid potential evaluation bias, here we construct a new ACP benchmark dataset named ACP-Mixed by integrating existing datasets. We expect our newly constructed dataset to be a golden standard benchmark dataset in this field. To facilitate the use of our model, we develop a web server as the implementation of ACPred-LAF. AVAILABILITY Our proposed ACPred-LAF, newly constructed benchmark dataset ACP-Mixed are open source collaborative initiatives available in the GitHub repository (https://github.com/TearsWaiting/ACPred-LAF). Besides, a webserver as the implementation of ACPred-LAF that can be accessed via: http://server.malab.cn/ACPred-LAF. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Wenjia He
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Yu Wang
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Lizhen Cui
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Ran Su
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Leyi Wei
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
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87
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Liang X, Li F, Chen J, Li J, Wu H, Li S, Song J, Liu Q. Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification. Brief Bioinform 2021; 22:bbaa312. [PMID: 33316035 PMCID: PMC8294543 DOI: 10.1093/bib/bbaa312] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 09/30/2020] [Accepted: 08/25/2020] [Indexed: 12/13/2022] Open
Abstract
Anti-cancer peptides (ACPs) are known as potential therapeutics for cancer. Due to their unique ability to target cancer cells without affecting healthy cells directly, they have been extensively studied. Many peptide-based drugs are currently evaluated in the preclinical and clinical trials. Accurate identification of ACPs has received considerable attention in recent years; as such, a number of machine learning-based methods for in silico identification of ACPs have been developed. These methods promote the research on the mechanism of ACPs therapeutics against cancer to some extent. There is a vast difference in these methods in terms of their training/testing datasets, machine learning algorithms, feature encoding schemes, feature selection methods and evaluation strategies used. Therefore, it is desirable to summarize the advantages and disadvantages of the existing methods, provide useful insights and suggestions for the development and improvement of novel computational tools to characterize and identify ACPs. With this in mind, we firstly comprehensively investigate 16 state-of-the-art predictors for ACPs in terms of their core algorithms, feature encoding schemes, performance evaluation metrics and webserver/software usability. Then, comprehensive performance assessment is conducted to evaluate the robustness and scalability of the existing predictors using a well-prepared benchmark dataset. We provide potential strategies for the model performance improvement. Moreover, we propose a novel ensemble learning framework, termed ACPredStackL, for the accurate identification of ACPs. ACPredStackL is developed based on the stacking ensemble strategy combined with SVM, Naïve Bayesian, lightGBM and KNN. Empirical benchmarking experiments against the state-of-the-art methods demonstrate that ACPredStackL achieves a comparative performance for predicting ACPs. The webserver and source code of ACPredStackL is freely available at http://bigdata.biocie.cn/ACPredStackL/ and https://github.com/liangxiaoq/ACPredStackL, respectively.
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Affiliation(s)
- Xiao Liang
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China
| | - Fuyi Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Monash Centre for Data Science, Monash University, Melbourne, VIC 3800, Australia
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Victoria, Australia
| | - Jinxiang Chen
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
| | - Junlong Li
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
| | - Hao Wu
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
| | - Shuqin Li
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Monash Centre for Data Science, Monash University, Melbourne, VIC 3800, Australia
- ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia
| | - Quanzhong Liu
- College of Information Engineering, Northwest A&F University, Yangling, 712100, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China
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88
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Wang Q, Ye J, Xu T, Zhou N, Lu Z, Ying J. Prediction of prokaryotic transposases from protein features with machine learning approaches. Microb Genom 2021; 7:000611. [PMID: 34309504 PMCID: PMC8477400 DOI: 10.1099/mgen.0.000611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 05/18/2021] [Indexed: 11/18/2022] Open
Abstract
Identification of prokaryotic transposases (Tnps) not only gives insight into the spread of antibiotic resistance and virulence but the process of DNA movement. This study aimed to develop a classifier for predicting Tnps in bacteria and archaea using machine learning (ML) approaches. We extracted a total of 2751 protein features from the training dataset including 14852 Tnps and 14852 controls, and selected 75 features as predictive signatures using the combined mutual information and least absolute shrinkage and selection operator algorithms. By aggregating these signatures, an ensemble classifier that integrated a collection of individual ML-based classifiers, was developed to identify Tnps. Further validation revealed that this classifier achieved good performance with an average AUC of 0.955, and met or exceeded other common methods. Based on this ensemble classifier, a stand-alone command-line tool designated TnpDiscovery was established to maximize the convenience for bioinformaticians and experimental researchers toward Tnp prediction. This study demonstrates the effectiveness of ML approaches in identifying Tnps, facilitating the discovery of novel Tnps in the future.
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Affiliation(s)
- Qian Wang
- Department of Clinical Laboratory, Wenzhou People's Hospital, The Third Affiliated Hospital of Shanghai University, The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou, PR China
| | - Jun Ye
- Department of Clinical Laboratory, The Second Affiliated Hospital of Guizhou Medical University, Kaili, PR China
| | - Teng Xu
- Institute of Translational Medicine, Baotou Central Hospital, Baotou, PR China
| | - Ning Zhou
- Wenzhou Key Laboratory of Emergency, Critical Care, and Disaster Medicine, Department of Emergency, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, PR China
| | - Zhongqiu Lu
- Wenzhou Key Laboratory of Emergency, Critical Care, and Disaster Medicine, Department of Emergency, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, PR China
| | - Jianchao Ying
- Wenzhou Key Laboratory of Emergency, Critical Care, and Disaster Medicine, Department of Emergency, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, PR China
- Central Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, PR China
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89
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Chen XG, Zhang W, Yang X, Li C, Chen H. ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation. Front Genet 2021; 12:698477. [PMID: 34276801 PMCID: PMC8279753 DOI: 10.3389/fgene.2021.698477] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 06/07/2021] [Indexed: 12/09/2022] Open
Abstract
Anticancer peptides (ACPs) have provided a promising perspective for cancer treatment, and the prediction of ACPs is very important for the discovery of new cancer treatment drugs. It is time consuming and expensive to use experimental methods to identify ACPs, so computational methods for ACP identification are urgently needed. There have been many effective computational methods, especially machine learning-based methods, proposed for such predictions. Most of the current machine learning methods try to find suitable features or design effective feature learning techniques to accurately represent ACPs. However, the performance of these methods can be further improved for cases with insufficient numbers of samples. In this article, we propose an ACP prediction model called ACP-DA (Data Augmentation), which uses data augmentation for insufficient samples to improve the prediction performance. In our method, to better exploit the information of peptide sequences, peptide sequences are represented by integrating binary profile features and AAindex features, and then the samples in the training set are augmented in the feature space. After data augmentation, the samples are used to train the machine learning model, which is used to predict ACPs. The performance of ACP-DA exceeds that of existing methods, and ACP-DA achieves better performance in the prediction of ACPs compared with a method without data augmentation. The proposed method is available at http://github.com/chenxgscuec/ACPDA.
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Affiliation(s)
- Xian-Gan Chen
- School of Biomedical Engineering, South-Central University for Nationalities, Wuhan, China.,Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis & Treatment, South-Central University for Nationalities, Wuhan, China.,Key Laboratory of Cognitive Science (South-Central University for Nationalities), State Ethnic Affairs Commission, Wuhan, China
| | - Wen Zhang
- College of Informatics, Huazhong Agricultural University, Wuhan, China.,Hubei Engineering Technology Research Center of Agricultural Big Data, Wuhan, China
| | - Xiaofei Yang
- School of Biomedical Engineering, South-Central University for Nationalities, Wuhan, China.,Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis & Treatment, South-Central University for Nationalities, Wuhan, China.,Key Laboratory of Cognitive Science (South-Central University for Nationalities), State Ethnic Affairs Commission, Wuhan, China
| | - Chenhong Li
- School of Biomedical Engineering, South-Central University for Nationalities, Wuhan, China.,Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis & Treatment, South-Central University for Nationalities, Wuhan, China.,Key Laboratory of Cognitive Science (South-Central University for Nationalities), State Ethnic Affairs Commission, Wuhan, China
| | - Hengling Chen
- School of Biomedical Engineering, South-Central University for Nationalities, Wuhan, China.,Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis & Treatment, South-Central University for Nationalities, Wuhan, China.,Key Laboratory of Cognitive Science (South-Central University for Nationalities), State Ethnic Affairs Commission, Wuhan, China
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90
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Yi HC, You ZH, Wang L, Su XR, Zhou X, Jiang TH. In silico drug repositioning using deep learning and comprehensive similarity measures. BMC Bioinformatics 2021; 22:293. [PMID: 34074242 PMCID: PMC8170943 DOI: 10.1186/s12859-020-03882-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 11/13/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Drug repositioning, meanings finding new uses for existing drugs, which can accelerate the processing of new drugs research and development. Various computational methods have been presented to predict novel drug-disease associations for drug repositioning based on similarity measures among drugs and diseases. However, there are some known associations between drugs and diseases that previous studies not utilized. METHODS In this work, we develop a deep gated recurrent units model to predict potential drug-disease interactions using comprehensive similarity measures and Gaussian interaction profile kernel. More specifically, the similarity measure is used to exploit discriminative feature for drugs based on their chemical fingerprints. Meanwhile, the Gaussian interactions profile kernel is employed to obtain efficient feature of diseases based on known disease-disease associations. Then, a deep gated recurrent units model is developed to predict potential drug-disease interactions. RESULTS The performance of the proposed model is evaluated on two benchmark datasets under tenfold cross-validation. And to further verify the predictive ability, case studies for predicting new potential indications of drugs were carried out. CONCLUSION The experimental results proved the proposed model is a useful tool for predicting new indications for drugs or new treatments for diseases, and can accelerate drug repositioning and related drug research and discovery.
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Affiliation(s)
- Hai-Cheng Yi
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhu-Hong You
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.
| | - Lei Wang
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
| | - Xiao-Rui Su
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xi Zhou
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
| | - Tong-Hai Jiang
- The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China
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91
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Zhao Y, Wang S, Fei W, Feng Y, Shen L, Yang X, Wang M, Wu M. Prediction of Anticancer Peptides with High Efficacy and Low Toxicity by Hybrid Model Based on 3D Structure of Peptides. Int J Mol Sci 2021; 22:5630. [PMID: 34073203 PMCID: PMC8198792 DOI: 10.3390/ijms22115630] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/30/2021] [Accepted: 05/19/2021] [Indexed: 02/07/2023] Open
Abstract
Recently, anticancer peptides (ACPs) have emerged as unique and promising therapeutic agents for cancer treatment compared with antibody and small molecule drugs. In addition to experimental methods of ACPs discovery, it is also necessary to develop accurate machine learning models for ACP prediction. In this study, features were extracted from the three-dimensional (3D) structure of peptides to develop the model, compared to most of the previous computational models, which are based on sequence information. In order to develop ACPs with more potency, more selectivity and less toxicity, the model for predicting ACPs, hemolytic peptides and toxic peptides were established by peptides 3D structure separately. Multiple datasets were collected according to whether the peptide sequence was chemically modified. After feature extraction and screening, diverse algorithms were used to build the model. Twelve models with excellent performance (Acc > 90%) in the ACPs mixed datasets were used to form a hybrid model to predict the candidate ACPs, and then the optimal model of hemolytic peptides (Acc = 73.68%) and toxic peptides (Acc = 85.5%) was used for safety prediction. Novel ACPs were found by using those models, and five peptides were randomly selected to determine their anticancer activity and toxic side effects in vitro experiments.
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Affiliation(s)
| | | | | | | | | | | | - Min Wang
- State Key Laboratory of Natural Medicines, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China; (Y.Z.); (S.W.); (W.F.); (Y.F.); (L.S.); (X.Y.)
| | - Min Wu
- State Key Laboratory of Natural Medicines, School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009, China; (Y.Z.); (S.W.); (W.F.); (Y.F.); (L.S.); (X.Y.)
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92
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Convolutional neural networks with image representation of amino acid sequences for protein function prediction. Comput Biol Chem 2021; 92:107494. [PMID: 33930742 DOI: 10.1016/j.compbiolchem.2021.107494] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 04/21/2021] [Indexed: 01/11/2023]
Abstract
Proteins are one of the most important molecules that govern the cellular processes in most of the living organisms. Various functions of the proteins are of paramount importance to understand the basics of life. Several supervised learning approaches are applied in this field to predict the functionality of proteins. In this paper, we propose a convolutional neural network based approach ProtConv to predict the functionality of proteins by converting the amino-acid sequences to a two dimensional image. We have used a protein embedding technique using transfer learning to generate the feature vector. Feature vector is then converted into a square sized single channel image to be fed into a convolutional network. The neural network architecture used here is a combination of convolutional filters and average pooling layers followed by dense fully connected layers to predict a binary function. We have performed experiments on standard benchmark datasets taken from two very important protein function prediction task: proinflammatory cytokines and anticancer peptides. Our experiments show that the proposed method, ProtConv achieves state-of-the-art performances on both of the datasets. All necessary details about implementation with source code and datasets are made available at: https://github.com/swakkhar/ProtConv.
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93
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Wang L, Niu D, Wang X, Khan J, Shen Q, Xue Y. A Novel Machine Learning Strategy for the Prediction of Antihypertensive Peptides Derived from Food with High Efficiency. Foods 2021; 10:foods10030550. [PMID: 33800877 PMCID: PMC7999667 DOI: 10.3390/foods10030550] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 03/01/2021] [Accepted: 03/03/2021] [Indexed: 12/22/2022] Open
Abstract
Strategies to screen antihypertensive peptides with high throughput and rapid speed will doubtlessly contribute to the treatment of hypertension. Food-derived antihypertensive peptides can reduce blood pressure without side effects. In the present study, a novel model based on the eXtreme Gradient Boosting (XGBoost) algorithm was developed and compared with the dominating machine learning models. To further reflect on the reliability of the method in a real situation, the optimized XGBoost model was utilized to predict the antihypertensive degree of the k-mer peptides cutting from six key proteins in bovine milk, and the peptide-protein docking technology was introduced to verify the findings. The results showed that the XGBoost model achieved outstanding performance, with an accuracy of 86.50% and area under the receiver operating characteristic curve of 94.11%, which were better than the other models. Using the XGBoost model, the prediction of antihypertensive peptides derived from milk protein was consistent with the peptide-protein docking results, and was more efficient. Our results indicate that using the XGBoost algorithm as a novel auxiliary tool is feasible to screen for antihypertensive peptides derived from food, with high throughput and high efficiency.
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Affiliation(s)
- Liyang Wang
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China; (L.W.); (X.W.); (J.K.); (Q.S.)
| | - Dantong Niu
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;
| | - Xiaoya Wang
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China; (L.W.); (X.W.); (J.K.); (Q.S.)
| | - Jabir Khan
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China; (L.W.); (X.W.); (J.K.); (Q.S.)
| | - Qun Shen
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China; (L.W.); (X.W.); (J.K.); (Q.S.)
| | - Yong Xue
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China; (L.W.); (X.W.); (J.K.); (Q.S.)
- Correspondence:
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94
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Charoenkwan P, Chiangjong W, Lee VS, Nantasenamat C, Hasan MM, Shoombuatong W. Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method. Sci Rep 2021; 11:3017. [PMID: 33542286 PMCID: PMC7862624 DOI: 10.1038/s41598-021-82513-9] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 01/18/2021] [Indexed: 01/30/2023] Open
Abstract
As anticancer peptides (ACPs) have attracted great interest for cancer treatment, several approaches based on machine learning have been proposed for ACP identification. Although existing methods have afforded high prediction accuracies, however such models are using a large number of descriptors together with complex ensemble approaches that consequently leads to low interpretability and thus poses a challenge for biologists and biochemists. Therefore, it is desirable to develop a simple, interpretable and efficient predictor for accurate ACP identification as well as providing the means for the rational design of new anticancer peptides with promising potential for clinical application. Herein, we propose a novel flexible scoring card method (FSCM) making use of propensity scores of local and global sequential information for the development of a sequence-based ACP predictor (named iACP-FSCM) for improving the prediction accuracy and model interpretability. To the best of our knowledge, iACP-FSCM represents the first sequence-based ACP predictor for rationalizing an in-depth understanding into the molecular basis for the enhancement of anticancer activities of peptides via the use of FSCM-derived propensity scores. The independent testing results showed that the iACP-FSCM provided accuracies of 0.825 and 0.910 as evaluated on the main and alternative datasets, respectively. Results from comparative benchmarking demonstrated that iACP-FSCM could outperform seven other existing ACP predictors with marked improvements of 7% and 17% for accuracy and MCC, respectively, on the main dataset. Furthermore, the iACP-FSCM (0.910) achieved very comparable results to that of the state-of-the-art ensemble model AntiCP2.0 (0.920) as evaluated on the alternative dataset. Comparative results demonstrated that iACP-FSCM was the most suitable choice for ACP identification and characterization considering its simplicity, interpretability and generalizability. It is highly anticipated that the iACP-FSCM may be a robust tool for the rapid screening and identification of promising ACPs for clinical use.
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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
| | - Wararat Chiangjong
- Pediatric Translational Research Unit, Department of Pediatrics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, 10400, Thailand
| | - Vannajan Sanghiran Lee
- Department of Chemistry, Centre of Theoretical and Computational Physics, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan
| | - Watshara Shoombuatong
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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95
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Lv Z, Cui F, Zou Q, Zhang L, Xu L. Anticancer peptides prediction with deep representation learning features. Brief Bioinform 2021; 22:6126754. [PMID: 33529337 DOI: 10.1093/bib/bbab008] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/20/2020] [Accepted: 01/05/2021] [Indexed: 12/13/2022] Open
Abstract
Anticancer peptides constitute one of the most promising therapeutic agents for combating common human cancers. Using wet experiments to verify whether a peptide displays anticancer characteristics is time-consuming and costly. Hence, in this study, we proposed a computational method named identify anticancer peptides via deep representation learning features (iACP-DRLF) using light gradient boosting machine algorithm and deep representation learning features. Two kinds of sequence embedding technologies were used, namely soft symmetric alignment embedding and unified representation (UniRep) embedding, both of which involved deep neural network models based on long short-term memory networks and their derived networks. The results showed that the use of deep representation learning features greatly improved the capability of the models to discriminate anticancer peptides from other peptides. Also, UMAP (uniform manifold approximation and projection for dimension reduction) and SHAP (shapley additive explanations) analysis proved that UniRep have an advantage over other features for anticancer peptide identification. The python script and pretrained models could be downloaded from https://github.com/zhibinlv/iACP-DRLF or from http://public.aibiochem.net/iACP-DRLF/.
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Affiliation(s)
- Zhibin Lv
- University of Electronic Science and Technology of China
| | - Feifei Cui
- University of Electronic Science and Technology of China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences at University of Electronic Science and Technology of China
| | - Lichao Zhang
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic
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96
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DeepACPpred: A Novel Hybrid CNN-RNN Architecture for Predicting Anti-Cancer Peptides. ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2021. [DOI: 10.1007/978-3-030-54568-0_7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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97
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Cai L, Wang L, Fu X, Xia C, Zeng X, Zou Q. ITP-Pred: an interpretable method for predicting, therapeutic peptides with fused features low-dimension representation. Brief Bioinform 2020; 22:6032630. [PMID: 33313672 DOI: 10.1093/bib/bbaa367] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 11/04/2020] [Accepted: 11/16/2020] [Indexed: 12/14/2022] Open
Abstract
The peptide therapeutics market is providing new opportunities for the biotechnology and pharmaceutical industries. Therefore, identifying therapeutic peptides and exploring their properties are important. Although several studies have proposed different machine learning methods to predict peptides as being therapeutic peptides, most do not explain the decision factors of model in detail. In this work, an Interpretable Therapeutic Peptide Prediction (ITP-Pred) model based on efficient feature fusion was developed. First, we proposed three kinds of feature descriptors based on sequence and physicochemical property encoded, namely amino acid composition (AAC), group AAC and coding autocorrelation, and concatenated them to obtain the feature representation of therapeutic peptide. Then, we input it into the CNN-Bi-directional Long Short-Term Memory (BiLSTM) model to automatically learn recognition of therapeutic peptides. The cross-validation and independent verification experiments results indicated that ITP-Pred has a higher prediction performance on the benchmark dataset than other comparison methods. Finally, we analyzed the output of the model from two aspects: sequence order and physical and chemical properties, mining important features as guidance for the design of better models that can complement existing methods.
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Affiliation(s)
| | | | | | | | | | - Quan Zou
- University of Electronic Science and Technology of China
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98
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Puentes PR, Henao MC, Torres CE, Gómez SC, Gómez LA, Burgos JC, Arbeláez P, Osma JF, Muñoz-Camargo C, Reyes LH, Cruz JC. Design, Screening, and Testing of Non-Rational Peptide Libraries with Antimicrobial Activity: In Silico and Experimental Approaches. Antibiotics (Basel) 2020; 9:E854. [PMID: 33265897 PMCID: PMC7759991 DOI: 10.3390/antibiotics9120854] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 11/20/2020] [Accepted: 11/23/2020] [Indexed: 12/13/2022] Open
Abstract
One of the challenges of modern biotechnology is to find new routes to mitigate the resistance to conventional antibiotics. Antimicrobial peptides (AMPs) are an alternative type of biomolecules, naturally present in a wide variety of organisms, with the capacity to overcome the current microorganism resistance threat. Here, we reviewed our recent efforts to develop a new library of non-rationally produced AMPs that relies on bacterial genome inherent diversity and compared it with rationally designed libraries. Our approach is based on a four-stage workflow process that incorporates the interplay of recent developments in four major emerging technologies: artificial intelligence, molecular dynamics, surface-display in microorganisms, and microfluidics. Implementing this framework is challenging because to obtain reliable results, the in silico algorithms to search for candidate AMPs need to overcome issues of the state-of-the-art approaches that limit the possibilities for multi-space data distribution analyses in extremely large databases. We expect to tackle this challenge by using a recently developed classification algorithm based on deep learning models that rely on convolutional layers and gated recurrent units. This will be complemented by carefully tailored molecular dynamics simulations to elucidate specific interactions with lipid bilayers. Candidate AMPs will be recombinantly-expressed on the surface of microorganisms for further screening via different droplet-based microfluidic-based strategies to identify AMPs with the desired lytic abilities. We believe that the proposed approach opens opportunities for searching and screening bioactive peptides for other applications.
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Affiliation(s)
- Paola Ruiz Puentes
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogota DC 111711, Colombia; (P.R.P.); (P.A.)
- Department of Biomedical Engineering, Universidad de los Andes, Bogota DC 111711, Colombia; (C.E.T.); (S.C.G.); (L.A.G.); (C.M.-C.)
| | - María C. Henao
- Grupo de Diseño de Productos y Procesos, Department of Chemical and Food Engineering, Universidad de los Andes, Bogota DC 111711, Colombia;
| | - Carlos E. Torres
- Department of Biomedical Engineering, Universidad de los Andes, Bogota DC 111711, Colombia; (C.E.T.); (S.C.G.); (L.A.G.); (C.M.-C.)
| | - Saúl C. Gómez
- Department of Biomedical Engineering, Universidad de los Andes, Bogota DC 111711, Colombia; (C.E.T.); (S.C.G.); (L.A.G.); (C.M.-C.)
| | - Laura A. Gómez
- Department of Biomedical Engineering, Universidad de los Andes, Bogota DC 111711, Colombia; (C.E.T.); (S.C.G.); (L.A.G.); (C.M.-C.)
| | - Juan C. Burgos
- Chemical Engineering Program, Universidad de Cartagena, Cartagena 130015, Colombia;
| | - Pablo Arbeláez
- Center for Research and Formation in Artificial Intelligence, Universidad de los Andes, Bogota DC 111711, Colombia; (P.R.P.); (P.A.)
- Department of Biomedical Engineering, Universidad de los Andes, Bogota DC 111711, Colombia; (C.E.T.); (S.C.G.); (L.A.G.); (C.M.-C.)
| | - Johann F. Osma
- Department of Electrical and Electronic Engineering, Universidad de los Andes, Bogota DC 111711, Colombia;
| | - Carolina Muñoz-Camargo
- Department of Biomedical Engineering, Universidad de los Andes, Bogota DC 111711, Colombia; (C.E.T.); (S.C.G.); (L.A.G.); (C.M.-C.)
| | - Luis H. Reyes
- Grupo de Diseño de Productos y Procesos, Department of Chemical and Food Engineering, Universidad de los Andes, Bogota DC 111711, Colombia;
| | - Juan C. Cruz
- Department of Biomedical Engineering, Universidad de los Andes, Bogota DC 111711, Colombia; (C.E.T.); (S.C.G.); (L.A.G.); (C.M.-C.)
- School of Chemical Engineering and Advanced Materials, The University of Adelaide, Adelaide 5005, Australia
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99
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Timmons PB, Hewage CM. ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides. Biomed Pharmacother 2020; 133:111051. [PMID: 33254015 DOI: 10.1016/j.biopha.2020.111051] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/08/2020] [Accepted: 11/19/2020] [Indexed: 12/12/2022] Open
Abstract
The prevalence of cancer as a threat to human life, responsible for 9.6 million deaths worldwide in 2018, motivates the search for new anticancer agents. While many options are currently available for treatment, these are often expensive and impact the human body unfavourably. Anticancer peptides represent a promising emerging field of anticancer therapeutics, which are characterized by favourable toxicity profile. The development of accurate in silico methods for anticancer peptide prediction is of paramount importance, as the amount of available sequence data is growing each year. This study leverages advances in machine learning research to produce a novel sequence-based deep neural network classifier for anticancer peptide activity. The classifier achieves performance comparable to the best-in-class, with a cross-validated accuracy of 98.3%, Matthews correlation coefficient of 0.91 and an Area Under the Curve of 0.95. This innovative classifier is available as a web server at https://research.timmons.eu/ennaact, facilitating in silico screening and design of new anticancer peptide chemotherapeutics by the 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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100
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Enhanced prediction of anti-tubercular peptides from sequence information using divergence measure-based intuitionistic fuzzy-rough feature selection. Soft comput 2020. [DOI: 10.1007/s00500-020-05363-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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