1
|
Mera-Banguero C, Orduz S, Cardona P, Orrego A, Muñoz-Pérez J, Branch-Bedoya JW. AmpClass: an Antimicrobial Peptide Predictor Based on Supervised Machine Learning. AN ACAD BRAS CIENC 2024; 96:e20230756. [PMID: 39383429 DOI: 10.1590/0001-3765202420230756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 04/07/2024] [Indexed: 10/11/2024] Open
Abstract
In the last decades, antibiotic resistance has been considered a severe problem worldwide. Antimicrobial peptides (AMPs) are molecules that have shown potential for the development of new drugs against antibiotic-resistant bacteria. Nowadays, medicinal drug researchers use supervised learning methods to screen new peptides with antimicrobial potency to save time and resources. In this work, we consolidate a database with 15945 AMPs and 12535 non-AMPs taken as the base to train a pool of supervised learning models to recognize peptides with antimicrobial activity. Results show that the proposed tool (AmpClass) outperforms classical state-of-the-art prediction models and achieves similar results compared with deep learning models.
Collapse
Affiliation(s)
- Carlos Mera-Banguero
- Instituto Tecnológico Metropolitano, Departamento de Sistemas de Información, Facultad de Ingeniería, Calle 54A # 30-01, 050013, Medellín, Antioquia, Colombia
- Universidad de Antioquia, Departamento de Ingeniería de Sistemas, Facultad de Ingenierías, Calle 67 # 53 - 108, 050010, Medellín, Antioquia, Colombia
| | - Sergio Orduz
- Universidad Nacional de Colombia, sede Medellín, Departamento de Biociencias, Facultad de Ciencias, Carrera 65 # 59A - 110, 050034, Medellín, Antioquia, Colombia
| | - Pablo Cardona
- Universidad Nacional de Colombia, sede Medellín, Departamento de Biociencias, Facultad de Ciencias, Carrera 65 # 59A - 110, 050034, Medellín, Antioquia, Colombia
| | - Andrés Orrego
- Universidad Nacional de Colombia, sede Medellín, Departamento de Ciencias de la Computación y de la Decisión, Facultad de Minas, Av. 80 # 65 - 223, 050041, Medellín, Antioquia, Colombia
| | - Jorge Muñoz-Pérez
- Universidad Nacional de Colombia, sede Medellín, Departamento de Biociencias, Facultad de Ciencias, Carrera 65 # 59A - 110, 050034, Medellín, Antioquia, Colombia
| | - John W Branch-Bedoya
- Universidad Nacional de Colombia, sede Medellín, Departamento de Ciencias de la Computación y de la Decisión, Facultad de Minas, Av. 80 # 65 - 223, 050041, Medellín, Antioquia, Colombia
| |
Collapse
|
2
|
Guo X, Zheng Z, Cheong KH, Zou Q, Tiwari P, Ding Y. Sequence homology score-based deep fuzzy network for identifying therapeutic peptides. Neural Netw 2024; 178:106458. [PMID: 38901093 DOI: 10.1016/j.neunet.2024.106458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 05/29/2024] [Accepted: 06/09/2024] [Indexed: 06/22/2024]
Abstract
The detection of therapeutic peptides is a topic of immense interest in the biomedical field. Conventional biochemical experiment-based detection techniques are tedious and time-consuming. Computational biology has become a useful tool for improving the detection efficiency of therapeutic peptides. Most computational methods do not consider the deviation caused by noise. To improve the generalization performance of therapeutic peptide prediction methods, this work presents a sequence homology score-based deep fuzzy echo-state network with maximizing mixture correntropy (SHS-DFESN-MMC) model. Our method is compared with the existing methods on eight types of therapeutic peptide datasets. The model parameters are determined by 10 fold cross-validation on their training sets and verified by independent test sets. Across the 8 datasets, the average area under the receiver operating characteristic curve (AUC) values of SHS-DFESN-MMC are the highest on both the training (0.926) and independent sets (0.923).
Collapse
Affiliation(s)
- Xiaoyi Guo
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, PR China; Quzhou People's Hospital, Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, 324000, PR China; Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, S637371, Singapore.
| | - Ziyu Zheng
- Department of Mathematical Sciences, University of Nottingham Ningbo, Ningbo, 315100, PR China.
| | - Kang Hao Cheong
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, S637371, Singapore; College of Computing and Data Science, Nanyang Technological University, S639798, Singapore.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, PR China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, PR China.
| | - Prayag Tiwari
- School of Information Technology, Halmstad University, Sweden.
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, PR China.
| |
Collapse
|
3
|
Wang X, Wang S. ACP-PDAFF: Pretrained model and dual-channel attentional feature fusion for anticancer peptides prediction. Comput Biol Chem 2024; 112:108141. [PMID: 38996756 DOI: 10.1016/j.compbiolchem.2024.108141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 05/26/2024] [Accepted: 06/28/2024] [Indexed: 07/14/2024]
Abstract
Anticancer peptides(ACPs) have attracted significant interest as a novel method of treating cancer due to their ability to selectively kill cancer cells without damaging normal cells. Many artificial intelligence-based methods have demonstrated impressive performance in predicting ACPs. Nevertheless, the limitations of existing methods in feature engineering include handcrafted features driven by prior knowledge, insufficient feature extraction, and inefficient feature fusion. In this study, we propose a model based on a pretrained model, and dual-channel attentional feature fusion(DAFF), called ACP-PDAFF. Firstly, to reduce the heavy dependence on expert knowledge-based handcrafted features, binary profile features (BPF) and physicochemical properties features(PCPF) are used as inputs to the transformer model. Secondly, aimed at learning more diverse feature informations of ACPs, a pretrained model ProtBert is utilized. Thirdly, for better fusion of different feature channels, DAFF is employed. Finally, to evaluate the performance of the model, we compare it with other methods on five benchmark datasets, including ACP-Mixed-80 dataset, Main and Alternate datasets of AntiCP 2.0, LEE and Independet dataset, and ACPred-Fuse dataset. And the accuracies obtained by ACP-PDAFF are 0.86, 0.80, 0.94, 0.97 and 0.95 on five datasets, respectively, higher than existing methods by 1% to 12%. Therefore, by learning rich feature informations and effectively fusing different feature channels, ACD-PDAFF achieves outstanding performance. Our code and the datasets are available at https://github.com/wongsing/ACP-PDAFF.
Collapse
Affiliation(s)
- Xinyi Wang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China
| | - Shunfang Wang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China.
| |
Collapse
|
4
|
Zareei S, Khorsand B, Dantism A, Zareei N, Asgharzadeh F, Zahraee SS, Mashreghi Kashan S, Hekmatirad S, Amini S, Ghasemi F, Moradnia M, Vaghf A, Hemmatpour A, Hourfar H, Niknia S, Johari A, Salimi F, Fariborzi N, Shojaei Z, Asiaei E, Shabani H. PeptiHub: a curated repository of precisely annotated cancer-related peptides with advanced utilities for peptide exploration and discovery. Database (Oxford) 2024; 2024:baae092. [PMID: 39308247 PMCID: PMC11417155 DOI: 10.1093/database/baae092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 08/07/2024] [Accepted: 09/07/2024] [Indexed: 09/26/2024]
Abstract
Peptihub (https://bioinformaticscollege.ir/peptihub/) is a meticulously curated repository of cancer-related peptides (CRPs) that have been documented in scientific literature. A diverse collection of CRPs is included in the PeptiHub, showcasing a spectrum of effects and activities. While some peptides demonstrated significant anticancer efficacy, others exhibited no discernible impact, and some even possessed alternative non-drug functionalities, including drug carrier or carcinogenic attributes. Presently, Peptihub houses 874 CRPs, subjected to evaluation across 10 distinct organism categories, 26 organs, and 438 cell lines. Each entry in the database is accompanied by easily accessible 3D conformations, obtained either experimentally or through predictive methodology. Users are provided with three search frameworks offering basic, advanced, and BLAST sequence search options. Furthermore, precise annotations of peptides enable users to explore CRPs based on their specific activities (anticancer, no effect, insignificant effect, carcinogen, and others) and their effectiveness (rate and IC50) under cancer conditions, specifically within individual organs. This unique property facilitates the construction of robust training and testing datasets. Additionally, PeptiHub offers 1141 features with the convenience of selecting the most pertinent features to address their specific research questions. Features include aaindex1 (in six main subcategories: alpha propensities, beta propensity, composition indices, hydrophobicity, physicochemical properties, and other properties), amino acid composition (Amino acid Composition and Dipeptide Composition), and Grouped Amino Acid Composition (Grouped amino acid composition, Grouped dipeptide composition, and Conjoint triad) categories. These utilities not only speed up machine learning-based peptide design but also facilitate peptide classification. Database URL: https://bioinformaticscollege.ir/peptihub/.
Collapse
Affiliation(s)
- Sara Zareei
- Department of Cell & Molecular Biology, Faculty of Biological Sciences, Kharazmi University, South Mofateh Ave. , Tehran 15719-14911, Iran
| | - Babak Khorsand
- Department of Neurology, University of California, 200 S. Manchester Ave., Suite 206 Orange, Irvine, CA 92868-4280, USA
- Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Azadi Square , Mashhad 9177948974, Iran
| | - Alireza Dantism
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Jalal AlAhmad HWY, Tehran 14115-111, Iran
| | - Neda Zareei
- Transplant Research Center, Shiraz University of Medical Sciences, Khalili Str, Shiraz 7193711351, Iran
| | - Fereshteh Asgharzadeh
- Department of Medical Physiology, Faculty of Medicine, Mashhad University of Medical Sciences, Azadi Sq., Mashhad 9177948564, Iran
| | - Shadi Shams Zahraee
- Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Dr. Shahriari Sq., Tehran 1983969411, Iran
| | - Samane Mashreghi Kashan
- Department of Medicinal Biotechnology, Faculty of Advanced Technology in Medicine, Golestan University of Medical Sciences, Shast Kola Road, Gorgan 4918936316, Iran
| | - Shirin Hekmatirad
- Department of Toxicology and Pharmacology, Faculty of Pharmacy, Tehran University of Medical Sciences, 16 Azar Ave, Tehran 1416753955, Iran
| | - Shila Amini
- Department of Genetics, Faculty of Advanced Science and Technology, Medical Sciences Branch, Islamic Azad University, Shariati St., Tehran 19395/1495, Iran
| | - Fatemeh Ghasemi
- Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Azadi Square , Mashhad 9177948974, Iran
| | - Maryam Moradnia
- Division of Occupational and Environmental Medicine, Department of Laboratory Medicine, Faculty of Medicine, Lund University, Lund BOX 117,221 00, Sweden
| | - Atena Vaghf
- Department of Medical Biotechnology, Faculty of Advanced Technologies, Shahrekord University of Medical Science, Kashani BLVD., Shahrekord 8815713471, Iran
| | - Anahid Hemmatpour
- Department of Clinical Biochemistry, Faculty of Medicine, Shahid Sadoughi University of Medical Sciences and Health Services, Aalam Sq., Yazd 8915173149, Iran
| | - Hamdam Hourfar
- Bioprocess Engineering Research Group, Department of Industrial and Environmental Biotechnology, National Institute for Genetic Engineering and Biotechnology, Tehran-Karaj HWY, Tehran 14965/161, Iran
| | - Soudabeh Niknia
- Department of Biology, Kavian Institute of Higher Education, Elahiyeh Blv., Mashhad 91863-74915, Iran
| | - Ali Johari
- Department of Biology, Kavian Institute of Higher Education, Elahiyeh Blv., Mashhad 91863-74915, Iran
| | - Fatemeh Salimi
- Department of Clinical Science, Faculty of Veterinary Medicine, Razi University, Taq-e Bostan, Kermanshah 6714414971, Iran
| | - Neda Fariborzi
- Department of Biology and Biotechnology, Faculty of Molecular Biology and Genetics, University of Pavia, S.da Nuova, Pavia 65, 27100, Italy
| | - Zohreh Shojaei
- Department of Cell & Molecular Biology, Faculty of Biological Sciences, Kharazmi University, South Mofateh Ave. , Tehran 15719-14911, Iran
| | - Elaheh Asiaei
- Systems Biotechnology Research Group, Department of Industrial and Environmental Biotechnology, National Institute for Genetic Engineering and Biotechnology, Tehran-Karaj HWY., Tehran 14965/161, Iran
| | - Hossein Shabani
- Department of Biology, Faculty of Biosciences, Tehran North Branch, Islamic Azad University, Vafadar Blv., Tehran 1651153311, Iran
| |
Collapse
|
5
|
Kamble P, Nagar PR, Bhakhar KA, Garg P, Sobhia ME, Naidu S, Bharatam PV. Cancer pharmacoinformatics: Databases and analytical tools. Funct Integr Genomics 2024; 24:166. [PMID: 39294509 DOI: 10.1007/s10142-024-01445-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2024] [Revised: 08/26/2024] [Accepted: 09/03/2024] [Indexed: 09/20/2024]
Abstract
Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer.
Collapse
Affiliation(s)
- Pradnya Kamble
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Prinsa R Nagar
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Kaushikkumar A Bhakhar
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Prabha Garg
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - M Elizabeth Sobhia
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India
| | - Srivatsava Naidu
- Center of Biomedical Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, India
| | - Prasad V Bharatam
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India.
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, S.A.S. Nagar, Punjab, India.
| |
Collapse
|
6
|
Sangaraju VK, Pham NT, Wei L, Yu X, Manavalan B. mACPpred 2.0: Stacked Deep Learning for Anticancer Peptide Prediction with Integrated Spatial and Probabilistic Feature Representations. J Mol Biol 2024; 436:168687. [PMID: 39237191 DOI: 10.1016/j.jmb.2024.168687] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 05/28/2024] [Accepted: 06/20/2024] [Indexed: 09/07/2024]
Abstract
Anticancer peptides (ACPs), naturally occurring molecules with remarkable potential to target and kill cancer cells. However, identifying ACPs based solely from their primary amino acid sequences remains a major hurdle in immunoinformatics. In the past, several web-based machine learning (ML) tools have been proposed to assist researchers in identifying potential ACPs for further testing. Notably, our meta-approach method, mACPpred, introduced in 2019, has significantly advanced the field of ACP research. Given the exponential growth in the number of characterized ACPs, there is now a pressing need to create an updated version of mACPpred. To develop mACPpred 2.0, we constructed an up-to-date benchmarking dataset by integrating all publicly available ACP datasets. We employed a large-scale of feature descriptors, encompassing both conventional feature descriptors and advanced pre-trained natural language processing (NLP)-based embeddings. We evaluated their ability to discriminate between ACPs and non-ACPs using eleven different classifiers. Subsequently, we employed a stacked deep learning (SDL) approach, incorporating 1D convolutional neural network (1D CNN) blocks and hybrid features. These features included the top seven performing NLP-based features and 90 probabilistic features, allowing us to identify hidden patterns within these diverse features and improve the accuracy of our ACP prediction model. This is the first study to integrate spatial and probabilistic feature representations for predicting ACPs. Rigorous cross-validation and independent tests conclusively demonstrated that mACPpred 2.0 not only surpassed its predecessor (mACPpred) but also outperformed the existing state-of-the-art predictors, highlighting the importance of advanced feature representation capabilities attained through SDL. To facilitate widespread use and accessibility, we have developed a user-friendly for mACPpred 2.0, available at https://balalab-skku.org/mACPpred2/.
Collapse
Affiliation(s)
- Vinoth Kumar Sangaraju
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea
| | - Nhat Truong Pham
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea
| | - Leyi Wei
- Faculty of Applied Sciences, Macao Polytechnic University, Macau
| | - Xue Yu
- Beidahuang Industry Group General Hospital, 150001 Harbin, China.
| | - Balachandran Manavalan
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea.
| |
Collapse
|
7
|
Yue J, Xu J, Li T, Li Y, Chen Z, Liang S, Liu Z, Wang Y. Discovery of potential antidiabetic peptides using deep learning. Comput Biol Med 2024; 180:109013. [PMID: 39137670 DOI: 10.1016/j.compbiomed.2024.109013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 07/01/2024] [Accepted: 08/08/2024] [Indexed: 08/15/2024]
Abstract
Antidiabetic peptides (ADPs), peptides with potential antidiabetic activity, hold significant importance in the treatment and control of diabetes. Despite their therapeutic potential, the discovery and prediction of ADPs remain challenging due to limited data, the complex nature of peptide functions, and the expensive and time-consuming nature of traditional wet lab experiments. This study aims to address these challenges by exploring methods for the discovery and prediction of ADPs using advanced deep learning techniques. Specifically, we developed two models: a single-channel CNN and a three-channel neural network (CNN + RNN + Bi-LSTM). ADPs were primarily gathered from the BioDADPep database, alongside thousands of non-ADPs sourced from anticancer, antibacterial, and antiviral peptide datasets. Subsequently, data preprocessing was performed with the evolutionary scale model (ESM-2), followed by model training and evaluation through 10-fold cross-validation. Furthermore, this work collected a series of newly published ADPs as an independent test set through literature review, and found that the CNN model achieved the highest accuracy (90.48 %) in predicting the independent test set, surpassing existing ADP prediction tools. Finally, the application of the model was considered. SeqGAN was used to generate new candidate ADPs, followed by screening with the constructed CNN model. Selected peptides were then evaluated using physicochemical property prediction and structural forecasts for pharmaceutical potential. In summary, this study not only established robust ADP prediction models but also employed these models to screen a batch of potential ADPs, addressing a critical need in the field of peptide-based antidiabetic research.
Collapse
Affiliation(s)
- Jianda Yue
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; Peptide and Small Molecule Drug R&D Plateform, Furong Laboratory, Hunan Normal University, Changsha, 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, 410081, China
| | - Jiawei Xu
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; Peptide and Small Molecule Drug R&D Plateform, Furong Laboratory, Hunan Normal University, Changsha, 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, 410081, China
| | - Tingting Li
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; Peptide and Small Molecule Drug R&D Plateform, Furong Laboratory, Hunan Normal University, Changsha, 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, 410081, China
| | - Yaqi Li
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; Peptide and Small Molecule Drug R&D Plateform, Furong Laboratory, Hunan Normal University, Changsha, 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, 410081, China
| | - Zihui Chen
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; Peptide and Small Molecule Drug R&D Plateform, Furong Laboratory, Hunan Normal University, Changsha, 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, 410081, China
| | - Songping Liang
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; Peptide and Small Molecule Drug R&D Plateform, Furong Laboratory, Hunan Normal University, Changsha, 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, 410081, China
| | - Zhonghua Liu
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; Peptide and Small Molecule Drug R&D Plateform, Furong Laboratory, Hunan Normal University, Changsha, 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, 410081, China.
| | - Ying Wang
- The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha, 410081, China; Peptide and Small Molecule Drug R&D Plateform, Furong Laboratory, Hunan Normal University, Changsha, 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha, 410081, China.
| |
Collapse
|
8
|
Garai S, Thomas J, Dey P, Das D. LGBM-ACp: an ensemble model for anticancer peptide prediction and in silico screening with potential drug targets. Mol Divers 2024; 28:1965-1981. [PMID: 36637711 DOI: 10.1007/s11030-023-10602-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/06/2023] [Indexed: 01/14/2023]
Abstract
Conventional cancer therapies are highly expensive and have serious complications. An alternative approach now emphasizes on the development of small, biologically active peptides without acute toxicity. Experimental screening to find curative anticancer peptides (ACP) often gives rise to multiple obstacles and is time dependent. Consequently, developing an effective computational technique to identify promising ACP candidates prior to preclinical research is in high demand. This study proposed a machine-learning framework that used the light gradient-boosting machine as a classifier and two compositional and two binary profile features as input. The ensemble model displayed an accuracy, MCC, and AUROC of 97.52%, 0.91, and 0.98, respectively, which outclassed most of the existing sequence-based computational tools. A distinct dataset of non-mutagenic, non-toxic, and non-inhibitory Cytochrome P-450 peptides was used to validate the hybrid model. The most relevant ACP in the alternative dataset was compared with two standard ACPs, beta defensin 2, and cecropin-A. Molecular docking of the predicted peptide revealed that it has a strong binding affinity with twenty-five anticancer drug targets, most notably phosphoenolpyruvate carboxykinase (- 7.2 kcal/mol). Additionally, molecular dynamics simulation and principal component analysis supported the stability of the peptide-receptor complex. Overall, the present findings will take a step forward in rational drug design through rapid identification and screening of therapeutic peptides.
Collapse
Affiliation(s)
- Swarnava Garai
- Department of Bioengineering, NIT Agartala, Tripura, 799046, India
| | - Juanit Thomas
- Department of Bioengineering, NIT Agartala, Tripura, 799046, India
| | - Palash Dey
- Civil Engineering Department, The ICFAI University, Tripura, 799210, India
| | - Deeplina Das
- Department of Bioengineering, NIT Agartala, Tripura, 799046, India.
| |
Collapse
|
9
|
Ullah F, Salam A, Nadeem M, Amin F, AlSalman H, Abrar M, Alfakih T. Extended dipeptide composition framework for accurate identification of anticancer peptides. Sci Rep 2024; 14:17381. [PMID: 39075193 PMCID: PMC11286958 DOI: 10.1038/s41598-024-68475-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Accepted: 07/24/2024] [Indexed: 07/31/2024] Open
Abstract
The identification of anticancer peptides (ACPs) is crucial, especially in the development of peptide-based cancer therapy. The classical models such as Split Amino Acid Composition (SAAC) and Pseudo Amino Acid Composition (PseAAC) lack the incorporation of feature representation. These advancements improve the predictive accuracy and efficiency of ACP identification. Thus, the effort of this research is to propose and develop an advanced framework based on feature extraction. Thus, to achieve this objective herein we propose an Extended Dipeptide Composition (EDPC) framework. The proposed EDPC framework extends the dipeptide composition by considering the local sequence environment information and reforming the CD-HIT framework to remove noise and redundancy. To measure the accuracy, we have performed several experiments. These experiments were employed using four famous machine learning (ML) algorithms named; Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K Nearest Neighbor (KNN). For comparisons, we have used accuracy, specificity, sensitivity, precision, recall, and F1-Score as evaluation criteria. The reliability of the proposed framework is further evaluated using statistical significance tests. As a result, the proposed EDPC framework exhibited enhanced performance than SAAC and PseAAC, where the SVM model delivered the highest accuracy of 96. 6% and significant enhancements in specificity, sensitivity, precision, and F1-score over multiple datasets. Due to the incorporation of enhanced feature representation and the incorporation of local and global sequence profiles proposed EDPC achieves higher classification performance. The proposed frameworks can deal with noise and also duplicating features. These are accompanied by a wide range of feature representations. Finally, our proposed framework can be used for clinical applications where ACP identification is essential. Future works will include extending to a larger variety of datasets, incorporating tertiary structural information, and using deep learning techniques to improve the proposed EDPC.
Collapse
Affiliation(s)
- Faizan Ullah
- Department of Computer Science, Bacha Khan University, Charsadda, 24420, Pakistan
| | - Abdu Salam
- Department of Computer Science, Abdul Wali Khan University, Mardan, 23200, Pakistan
| | - Muhammad Nadeem
- Department of Computer Science and Software Engineering, International Islamic University, Islamabad, 44000, Pakistan
| | - Farhan Amin
- School of Computer Science and Engineering, Yeungnam University, Gyeongsan, 38541, Korea.
| | - Hussain AlSalman
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, 11543, Riyadh, Saudi Arabia.
| | - Mohammad Abrar
- Faculty of Computer Studies, Arab Open University, Muscat, Oman
| | - Taha Alfakih
- Department of Information Systems, College of Computer and Information Sciences, King Saud University, 11543, Riyadh, Saudi Arabia
| |
Collapse
|
10
|
Arif M, Musleh S, Fida H, Alam T. PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation. Sci Rep 2024; 14:16992. [PMID: 39043738 PMCID: PMC11266708 DOI: 10.1038/s41598-024-67433-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Accepted: 07/11/2024] [Indexed: 07/25/2024] Open
Abstract
Anticancer peptides (ACPs) perform a promising role in discovering anti-cancer drugs. The growing research on ACPs as therapeutic agent is increasing due to its minimal side effects. However, identifying novel ACPs using wet-lab experiments are generally time-consuming, labor-intensive, and expensive. Leveraging computational methods for fast and accurate prediction of ACPs would harness the drug discovery process. Herein, a machine learning-based predictor, called PLMACPred, is developed for identifying ACPs from peptide sequence only. PLMACPred adopted a set of encoding schemes representing evolutionary-property, composition-property, and protein language model (PLM), i.e., evolutionary scale modeling (ESM-2)- and ProtT5-based embedding to encode peptides. Then, two-dimensional (2D) wavelet denoising (WD) was employed to remove the noise from extracted features. Finally, ensemble-based cascade deep forest (CDF) model was developed to identify ACP. PLMACPred model attained superior performance on all three benchmark datasets, namely, ACPmain, ACPAlter, and ACP740 over tenfold cross validation and independent dataset. PLMACPred outperformed the existing models and improved the prediction accuracy by 18.53%, 2.4%, 7.59% on ACPmain, ACPalter, ACP740 dataset, respectively. We showed that embedding from ProtT5 and ESM-2 was capable of capturing better contextual information from the entire sequence than the other encoding schemes for ACP prediction. For the explainability of proposed model, SHAP (SHapley Additive exPlanations) method was used to analyze the feature effect on the ACP prediction. A list of novel sequence motifs was proposed from the ACP sequence using MEME suites. We believe, PLMACPred will support in accelerating the discovery of novel ACPs as well as other activities of microbial peptides.
Collapse
Affiliation(s)
- Muhammad Arif
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Saleh Musleh
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Huma Fida
- Department of Microbiology, Abdul Wali Khan University, Mardan, KPK, Pakistan
| | - Tanvir Alam
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
| |
Collapse
|
11
|
Lin Z, Assaraf YG, Kwok HF. Peptides for microbe-induced cancers: latest therapeutic strategies and their advanced technologies. Cancer Metastasis Rev 2024:10.1007/s10555-024-10197-4. [PMID: 39008152 DOI: 10.1007/s10555-024-10197-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 06/14/2024] [Indexed: 07/16/2024]
Abstract
Cancer is a significant global health concern associated with multiple distinct factors, including microbial and viral infections. Numerous studies have elucidated the role of microorganisms, such as Helicobacter pylori (H. pylori), as well as viruses for example human papillomavirus (HPV), hepatitis B virus (HBV), and hepatitis C virus (HCV), in the development of human malignancies. Substantial attention has been focused on the treatment of these microorganism- and virus-associated cancers, with promising outcomes observed in studies employing peptide-based therapies. The current paper provides an overview of microbe- and virus-induced cancers and their underlying molecular mechanisms. We discuss an assortment of peptide-based therapies which are currently being developed, including tumor-targeting peptides and microbial/viral peptide-based vaccines. We describe the major technological advancements that have been made in the design, screening, and delivery of peptides as anticancer agents. The primary focus of the current review is to provide insight into the latest research and development in this field and to provide a realistic glimpse into the future of peptide-based therapies for microbe- and virus-induced neoplasms.
Collapse
Affiliation(s)
- Ziqi Lin
- Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida da Universidade, Taipa, Macau SAR
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Avenida da Universidade, Taipa, Macau SAR
| | - Yehuda G Assaraf
- The Fred Wyszkowski Cancer Research Lab, Faculty of Biology, Technion-Israel Instituteof Technology, Haifa, 3200003, Israel
| | - Hang Fai Kwok
- Cancer Centre, Faculty of Health Sciences, University of Macau, Avenida da Universidade, Taipa, Macau SAR.
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Avenida da Universidade, Taipa, Macau SAR.
- MoE Frontiers Science Center for Precision Oncology, University of Macau, Avenida de Universidade, Taipa, Macau SAR.
| |
Collapse
|
12
|
Weston M, Hu H, Li X. PSPI: A deep learning approach for prokaryotic small protein identification. Front Genet 2024; 15:1439423. [PMID: 39050248 PMCID: PMC11266045 DOI: 10.3389/fgene.2024.1439423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Accepted: 06/18/2024] [Indexed: 07/27/2024] Open
Abstract
Small Proteins (SPs) are pivotal in various cellular functions such as immunity, defense, and communication. Despite their significance, identifying them is still in its infancy. Existing computational tools are tailored to specific eukaryotic species, leaving only a few options for SP identification in prokaryotes. In addition, these existing tools still have suboptimal performance in SP identification. To fill this gap, we introduce PSPI, a deep learning-based approach designed specifically for predicting prokaryotic SPs. We showed that PSPI had a high accuracy in predicting generalized sets of prokaryotic SPs and sets specific to the human metagenome. Compared with three existing tools, PSPI was faster and showed greater precision, sensitivity, and specificity not only for prokaryotic SPs but also for eukaryotic ones. We also observed that the incorporation of (n, k)-mers greatly enhances the performance of PSPI, suggesting that many SPs may contain short linear motifs. The PSPI tool, which is freely available at https://www.cs.ucf.edu/∼xiaoman/tools/PSPI/, will be useful for studying SPs as a tool for identifying prokaryotic SPs and it can be trained to identify other types of SPs as well.
Collapse
Affiliation(s)
- Matthew Weston
- Department of Computer Science, University of Central Florida, Orlando, FL, United States
| | - Haiyan Hu
- Department of Computer Science, University of Central Florida, Orlando, FL, United States
| | - Xiaoman Li
- Burnett School of Biomedical Science, College of Medicine, University of Central Florida, Orlando, FL, United States
| |
Collapse
|
13
|
Bhattarai S, Tayara H, Chong KT. Advancing Peptide-Based Cancer Therapy with AI: In-Depth Analysis of State-of-the-Art AI Models. J Chem Inf Model 2024; 64:4941-4957. [PMID: 38874445 DOI: 10.1021/acs.jcim.4c00295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Anticancer peptides (ACPs) play a vital role in selectively targeting and eliminating cancer cells. Evaluating and comparing predictions from various machine learning (ML) and deep learning (DL) techniques is challenging but crucial for anticancer drug research. We conducted a comprehensive analysis of 15 ML and 10 DL models, including the models released after 2022, and found that support vector machines (SVMs) with feature combination and selection significantly enhance overall performance. DL models, especially convolutional neural networks (CNNs) with light gradient boosting machine (LGBM) based feature selection approaches, demonstrate improved characterization. Assessment using a new test data set (ACP10) identifies ACPred, MLACP 2.0, AI4ACP, mACPred, and AntiCP2.0_AAC as successive optimal predictors, showcasing robust performance. Our review underscores current prediction tool limitations and advocates for an omnidirectional ACP prediction framework to propel ongoing research.
Collapse
Affiliation(s)
- Sadik Bhattarai
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju-si, 54896 Jeollabuk-do, South Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju-si, 54896 Jeollabuk-do, South Korea
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju-si, 54896 Jeollabuk-do, South Korea
- Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju-si, 54896 Jeollabuk-do, South Korea
| |
Collapse
|
14
|
Yang S, Xu P. HemoDL: Hemolytic peptides prediction by double ensemble engines from Rich sequence-derived and transformer-enhanced information. Anal Biochem 2024; 690:115523. [PMID: 38552762 DOI: 10.1016/j.ab.2024.115523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/20/2024] [Accepted: 03/22/2024] [Indexed: 04/02/2024]
Abstract
Hemolytic peptides can trigger hemolysis by rupturing red blood cells' membranes and triggering cell disruption. Due to the labor-intensive and time-consuming in-lab identification process, accurate, high-throughput hemolytic peptide prediction is crucial for the growth of peptide sequence data in proteomics and peptidomics. In this study, we offer the HemoDL ensemble learning model, which learns the distinct distribution of sequence characteristics for predicting the hemolytic activity of peptides using a double LightGBM framework. To determine the most informative encoding features, we compare 17 widely used features across four benchmark datasets. Our investigation reveals that CTD, BPF, Charge, AAC, GDPC, ATC, QSO, and transformer-based features exhibit more positive contributions to detecting the hemolytic activity of peptides. Comparison with eight state-of-the-art methods demonstrates that HemoDL outperforms other models, attaining higher Matthews Correlation Coefficient values on four test datasets, ranging from 6.30% to 16.04%, 6.63%-11.26%, 4.76%-9.92%, and 7.41%-15.03%, respectively. Additionally, we provide the HemoDL with a user-friendly graphical interface available at https://github.com/abcair/HemoDL. In summary, the HemoDL model, leveraging CTD, BPF, Charge, AAC, GDPC, ATC, QSO and transformer-based encoding features within a double LightGBM learning framework, achieves high accuracy in predicting the hemolytic activity of peptides.
Collapse
Affiliation(s)
- Sen Yang
- School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software, Changzhou University, Changzhou, 213164, China; The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, 213164, China
| | - Piao Xu
- College of Economics and Management, Nanjing Forestry University, China.
| |
Collapse
|
15
|
Ghafoor H, Asim MN, Ibrahim MA, Ahmed S, Dengel A. CAPTURE: Comprehensive anti-cancer peptide predictor with a unique amino acid sequence encoder. Comput Biol Med 2024; 176:108538. [PMID: 38759585 DOI: 10.1016/j.compbiomed.2024.108538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/26/2024] [Accepted: 04/28/2024] [Indexed: 05/19/2024]
Abstract
Anticancer peptides (ACPs) key properties including bioactivity, high efficacy, low toxicity, and lack of drug resistance make them ideal candidates for cancer therapies. To deeply explore the potential of ACPs and accelerate development of cancer therapies, although 53 Artificial Intelligence supported computational predictors have been developed for ACPs and non ACPs classification but only one predictor has been developed for ACPs functional types annotations. Moreover, these predictors extract amino acids distribution patterns to transform peptides sequences into statistical vectors that are further fed to classifiers for discriminating peptides sequences and annotating peptides functional classes. Overall, these predictors remain fail in extracting diverse types of amino acids distribution patterns from peptide sequences. The paper in hand presents a unique CARE encoder that transforms peptides sequences into statistical vectors by extracting 4 different types of distribution patterns including correlation, distribution, composition, and transition. Across public benchmark dataset, proposed encoder potential is explored under two different evaluation settings namely; intrinsic and extrinsic. Extrinsic evaluation indicates that 12 different machine learning classifiers achieve superior performance with the proposed encoder as compared to 55 existing encoders. Furthermore, an intrinsic evaluation reveals that, unlike existing encoders, the proposed encoder generates more discriminative clusters for ACPs and non-ACPs classes. Across 8 public benchmark ACPs and non-ACPs classification datasets, proposed encoder and Adaboost classifier based CAPTURE predictor outperforms existing predictors with an average accuracy, recall and MCC score of 1%, 4%, and 2% respectively. In generalizeability evaluation case study, across 7 benchmark anti-microbial peptides classification datasets, CAPTURE surpasses existing predictors by an average AU-ROC of 2%. CAPTURE predictive pipeline along with label powerset method outperforms state-of-the-art ACPs functional types predictor by 5%, 5%, 5%, 6%, and 3% in terms of average accuracy, subset accuracy, precision, recall, and F1 respectively. CAPTURE web application is available at https://sds_genetic_analysis.opendfki.de/CAPTURE.
Collapse
Affiliation(s)
- Hina Ghafoor
- Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, 67663, Germany; German Research Center for Artificial Intelligence GmbH, Kaiserslautern, 67663, Germany
| | - Muhammad Nabeel Asim
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, 67663, Germany.
| | - Muhammad Ali Ibrahim
- Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, 67663, Germany; German Research Center for Artificial Intelligence GmbH, Kaiserslautern, 67663, Germany
| | - Sheraz Ahmed
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, 67663, Germany
| | - Andreas Dengel
- Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, 67663, Germany; German Research Center for Artificial Intelligence GmbH, Kaiserslautern, 67663, Germany
| |
Collapse
|
16
|
Song H, Lin X, Zhang H, Yin H. ACP-ESM2: The prediction of anticancer peptides based on pre-trained classifier. Comput Biol Chem 2024; 110:108091. [PMID: 38735271 DOI: 10.1016/j.compbiolchem.2024.108091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 04/07/2024] [Accepted: 04/29/2024] [Indexed: 05/14/2024]
Abstract
Anticancer peptides (ACPs) are a type of protein molecule that has anti-cancer activity and can inhibit cancer cell growth and survival. Traditional classification approaches for ACPs are expensive and time-consuming. This paper proposes a pre-trained classifier model, ESM2-GRU, for ACP prediction to make it easier to predict ACPs, gain a better understanding of the structural and functional differences of anti-cancer peptides, and optimize the design for the development of more effective anti-cancer treatment strategies. The model is made up of the ESM2 pre-trained model, a bidirectional GRU recurrent neural network, and a fully connected layer. ACP sequences are first fed into the ESM2 model, which then expands the dimensions before feeding the findings back into the bidirectional GRU recurrent neural network. Finally, the fully connected layer generates the ultimate output. Experimental validation demonstrates that the ESM2-GRU model greatly improves classification performance on the benchmark dataset ACP606, with AUC, ACC, and MCC values of 0.975, 0.852, and 0.738, respectively. This exceptional prediction potential helps to identify specific types of anti-cancer peptides, improving their targeting and selectivity and, therefore, furthering the development of tailored medicine and treatments.
Collapse
Affiliation(s)
- Huijia Song
- School of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Xiaozhu Lin
- School of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China.
| | - Huainian Zhang
- School of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Huijuan Yin
- School of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| |
Collapse
|
17
|
Liao YH, Chen SZ, Bin YN, Zhao JP, Feng XL, Zheng CH. UsIL-6: An unbalanced learning strategy for identifying IL-6 inducing peptides by undersampling technique. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108176. [PMID: 38677081 DOI: 10.1016/j.cmpb.2024.108176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 03/26/2024] [Accepted: 04/11/2024] [Indexed: 04/29/2024]
Abstract
BACKGROUND AND OBJECTIVE Interleukin-6 (IL-6) is the critical factor of early warning, monitoring, and prognosis in the inflammatory storm of COVID-19 cases. IL-6 inducing peptides, which can induce cytokine IL-6 production, are very important for the development of diagnosis and immunotherapy. Although the existing methods have some success in predicting IL-6 inducing peptides, there is still room for improvement in the performance of these models in practical application. METHODS In this study, we proposed UsIL-6, a high-performance bioinformatics tool for identifying IL-6 inducing peptides. First, we extracted five groups of physicochemical properties and sequence structural information from IL-6 inducing peptide sequences, and obtained a 636-dimensional feature vector, we also employed NearMiss3 undersampling method and normalization method StandardScaler to process the data. Then, a 40-dimensional optimal feature vector was obtained by Boruta feature selection method. Finally, we combined this feature vector with extreme randomization tree classifier to build the final model UsIL-6. RESULTS The AUC value of UsIL-6 on the independent test dataset was 0.87, and the BACC value was 0.808, which indicated that UsIL-6 had better performance than the existing methods in IL-6 inducing peptide recognition. CONCLUSIONS The performance comparison on independent test dataset confirmed that UsIL-6 could achieve the highest performance, best robustness, and most excellent generalization ability. We hope that UsIL-6 will become a valuable method to identify, annotate and characterize new IL-6 inducing peptides.
Collapse
Affiliation(s)
- Yan-Hong Liao
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China
| | - Shou-Zhi Chen
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China
| | - Yan-Nan Bin
- School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China
| | - Jian-Ping Zhao
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China.
| | - Xin-Long Feng
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China.
| | - Chun-Hou Zheng
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China; School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China
| |
Collapse
|
18
|
Chen Z, Wang R, Guo J, Wang X. The role and future prospects of artificial intelligence algorithms in peptide drug development. Biomed Pharmacother 2024; 175:116709. [PMID: 38713945 DOI: 10.1016/j.biopha.2024.116709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 05/01/2024] [Accepted: 05/02/2024] [Indexed: 05/09/2024] Open
Abstract
Peptide medications have been more well-known in recent years due to their many benefits, including low side effects, high biological activity, specificity, effectiveness, and so on. Over 100 peptide medications have been introduced to the market to treat a variety of illnesses. Most of these peptide medications are developed on the basis of endogenous peptides or natural peptides, which frequently required expensive, time-consuming, and extensive tests to confirm. As artificial intelligence advances quickly, it is now possible to build machine learning or deep learning models that screen a large number of candidate sequences for therapeutic peptides. Therapeutic peptides, such as those with antibacterial or anticancer properties, have been developed by the application of artificial intelligence algorithms.The process of finding and developing peptide drugs is outlined in this review, along with a few related cases that were helped by AI and conventional methods. These resources will open up new avenues for peptide drug development and discovery, helping to meet the pressing needs of clinical patients for disease treatment. Although peptide drugs are a new class of biopharmaceuticals that distinguish them from chemical and small molecule drugs, their clinical purpose and value cannot be ignored. However, the traditional peptide drug research and development has a long development cycle and high investment, and the creation of peptide medications will be substantially hastened by the AI-assisted (AI+) mode, offering a new boost for combating diseases.
Collapse
Affiliation(s)
- Zhiheng Chen
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Ruoxi Wang
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Junqi Guo
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China.
| | - Xiaogang Wang
- Guangdong Provincial Key Laboratory of Bone and Joint Degenerative Diseases, The Third Affiliated Hospital of Southern Medical University, Guangzhou, Guangdong 510630, China.
| |
Collapse
|
19
|
Arif R, Kanwal S, Ahmed S, Kabir M. A Computational Predictor for Accurate Identification of Tumor Homing Peptides by Integrating Sequential and Deep BiLSTM Features. Interdiscip Sci 2024; 16:503-518. [PMID: 38733473 DOI: 10.1007/s12539-024-00628-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 03/16/2024] [Accepted: 03/27/2024] [Indexed: 05/13/2024]
Abstract
Cancer remains a severe illness, and current research indicates that tumor homing peptides (THPs) play an important part in cancer therapy. The identification of THPs can provide crucial insights for drug-discovery and pharmaceutical industries as they allow for tailored medication delivery towards cancer cells. These peptides have a high affinity enabling particular receptors present upon tumor surfaces, allowing for the creation of precision medications that reduce off-target consequences and enhance cancer patient treatment results. Wet-lab techniques are considered essential tools for studying THPs; however, they're labor-extensive and time-consuming, therefore making prediction of THPs a challenging task for the researchers. Computational-techniques, on the other hand, are considered significant tools in identifying THPs according to the sequence data. Despite many strategies have been presented to predict new THP, there is still a need to develop a robust method with higher rates of success. In this paper, we developed a novel framework, THP-DF, for accurately identifying THPs on a large-scale. Firstly, the peptide sequences are encoded through various sequential features. Secondly, each feature is passed to BiLSTM and attention layers to extract simplified deep features. Finally, an ensemble-framework is formed via integrating sequential- and deep features which are fed to a support vector machine which with 10-fold cross-validation to carry to validate the efficiency. The experimental results showed that THP-DF worked better on both [Formula: see text] and [Formula: see text] datasets by achieving accuracy of > 95% which are higher than existing predictors both datasets. This indicates that the proposed predictor could be a beneficial tool to precisely and rapidly identify THPs and will contribute to the cutting-edge cancer treatment strategies and pharmaceuticals.
Collapse
Affiliation(s)
- Roha Arif
- School of Systems and Technology, University of Management and Technology, Lahore, 54782, Pakistan
| | - Sameera Kanwal
- School of Systems and Technology, University of Management and Technology, Lahore, 54782, Pakistan
| | - Saeed Ahmed
- School of Systems and Technology, University of Management and Technology, Lahore, 54782, Pakistan
| | - Muhammad Kabir
- School of Systems and Technology, University of Management and Technology, Lahore, 54782, Pakistan.
| |
Collapse
|
20
|
Lin L, Li C, Zhang T, Xia C, Bai Q, Jin L, Shen Y. An in silico scheme for optimizing the enzymatic acquisition of natural biologically active peptides based on machine learning and virtual digestion. Anal Chim Acta 2024; 1298:342419. [PMID: 38462343 DOI: 10.1016/j.aca.2024.342419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 12/23/2023] [Accepted: 02/26/2024] [Indexed: 03/12/2024]
Abstract
BACKGROUND As a potential natural active substance, natural biologically active peptides (NBAPs) are recently attracting increasing attention. The traditional proteolysis methods of obtaining effective NBAPs are considerably vexing, especially since multiple proteases can be used, which blocks the exploration of available NBAPs. Although the development of virtual digesting brings some degree of convenience, the activity of the obtained peptides remains unclear, which would still not allow efficient access to the NBAPs. It is necessary to develop an efficient and accurate strategy for acquiring NBAPs. RESULTS A new in silico scheme named SSA-LSTM-VD, which combines a sparrow search algorithm-long short-term memory (SSA-LSTM) deep learning and virtually digested, was presented to optimize the proteolysis acquisition of NBAPs. Therein, SSA-LSTM reached the highest Efficiency value reached 98.00 % compared to traditional machine learning algorithms, and basic LSTM algorithm. SSA-LSTM was trained to predict the activity of peptides in the proteins virtually digested results, obtain the percentage of target active peptide, and select the appropriate protease for the actual experiment. As an application, SSA-LSTM was employed to predict the percentage of neuroprotective peptides in the virtual digested result of walnut protein, and trypsin was ultimately found to possess the highest value (85.29 %). The walnut protein was digested by trypsin (WPTrH) and the peptide sequence obtained was analyzed closely matches the theoretical neuroprotective peptide. More importantly, the neuroprotective effects of WPTrH had been demonstrated in nerve damage mouse models. SIGNIFICANCE The proposed SSA-LSTM-VD in this paper makes the acquisition of NBAPs efficient and accurate. The approach combines deep learning and virtually digested skillfully. Utilizing the SSA-LSTM-VD based strategy holds promise for discovering and developing peptides with neuroprotective properties or other desired biological activities.
Collapse
Affiliation(s)
- Like Lin
- Key Laboratory of Synthetic and Natural Functional Molecule of Ministry of Education, College of Chemistry and Materials Science, National Demonstration Center for Experimental Chemistry Education, Northwest University, Xi'an, Shaanxi, 710127, People's Republic of China
| | - Cong Li
- Key Laboratory of Synthetic and Natural Functional Molecule of Ministry of Education, College of Chemistry and Materials Science, National Demonstration Center for Experimental Chemistry Education, Northwest University, Xi'an, Shaanxi, 710127, People's Republic of China.
| | - Tianlong Zhang
- Key Laboratory of Synthetic and Natural Functional Molecule of Ministry of Education, College of Chemistry and Materials Science, National Demonstration Center for Experimental Chemistry Education, Northwest University, Xi'an, Shaanxi, 710127, People's Republic of China
| | - Chaoshuang Xia
- Center for Biomedical Mass Spectrometry, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, 02118, United States
| | - Qiuhong Bai
- Key Laboratory of Synthetic and Natural Functional Molecule of Ministry of Education, College of Chemistry and Materials Science, National Demonstration Center for Experimental Chemistry Education, Northwest University, Xi'an, Shaanxi, 710127, People's Republic of China
| | - Lihua Jin
- Key Laboratory of Synthetic and Natural Functional Molecule of Ministry of Education, College of Chemistry and Materials Science, National Demonstration Center for Experimental Chemistry Education, Northwest University, Xi'an, Shaanxi, 710127, People's Republic of China
| | - Yehua Shen
- Key Laboratory of Synthetic and Natural Functional Molecule of Ministry of Education, College of Chemistry and Materials Science, National Demonstration Center for Experimental Chemistry Education, Northwest University, Xi'an, Shaanxi, 710127, People's Republic of China.
| |
Collapse
|
21
|
Xu M, Pang J, Ye Y, Zhang Z. Integrating Traditional Machine Learning and Deep Learning for Precision Screening of Anticancer Peptides: A Novel Approach for Efficient Drug Discovery. ACS OMEGA 2024; 9:16820-16831. [PMID: 38617603 PMCID: PMC11007766 DOI: 10.1021/acsomega.4c01374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 03/03/2024] [Accepted: 03/22/2024] [Indexed: 04/16/2024]
Abstract
The rapid and effective identification of anticancer peptides (ACPs) by computer technology provides a new perspective for cancer treatment. In the identification process of ACPs, accurate sequence encoding and effective classification models are crucial for predicting their biological activity. Traditional machine learning methods have been widely applied in sequence analysis, but deep learning provides a new approach to capture sequence complexity. In this study, a two-stage ACPs classification model was innovatively proposed. Three novel coding strategies were explored; two mainstream Natural Language Processing (NLP) models and 11 machine learning models were fused to identify ACPs, which significantly improved the prediction accuracy of ACPs. We analyzed the correlation between peptide chain amino acids and evaluated the relevant performance of the model by the ROC curve and t-SNE dimensionality reduction technique. The results indicated that the deep learning and machine learning fusion models of M3E-base and KNeighborsDist models, especially when considering the semantic information on amino acid sequences, achieved the highest average accuracy (AvgAcc) of 0.939, with an AUC value as high as 0.97. Then, in vitro cell experiments were used to verify that the two ACPs predicted by the model had antitumor efficacy. This study provides a convenient and effective method for screening ACPs. With further optimization and testing, these strategies have the potential to play an important role in drug discovery and design.
Collapse
Affiliation(s)
- Meiqi Xu
- Key
Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang
Province, School of Medicine, Hangzhou City
University, Hangzhou 310015, Zhejiang, China
| | - Jiefu Pang
- School
of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China
| | - Yangyang Ye
- Key
Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang
Province, School of Medicine, Hangzhou City
University, Hangzhou 310015, Zhejiang, China
| | - Ziyi Zhang
- Key
Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang
Province, School of Medicine, Hangzhou City
University, Hangzhou 310015, Zhejiang, China
| |
Collapse
|
22
|
Yang X, Jin J, Wang R, Li Z, Wang Y, Wei L. CACPP: A Contrastive Learning-Based Siamese Network to Identify Anticancer Peptides Based on Sequence Only. J Chem Inf Model 2024; 64:2807-2816. [PMID: 37252890 DOI: 10.1021/acs.jcim.3c00297] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Anticancer peptides (ACPs) recently have been receiving increasing attention in cancer therapy due to their low consumption, few adverse side effects, and easy accessibility. However, it remains a great challenge to identify anticancer peptides via experimental approaches, requiring expensive and time-consuming experimental studies. In addition, traditional machine-learning-based methods are proposed for ACP prediction mainly depending on hand-crafted feature engineering, which normally achieves low prediction performance. In this study, we propose CACPP (Contrastive ACP Predictor), a deep learning framework based on the convolutional neural network (CNN) and contrastive learning for accurately predicting anticancer peptides. In particular, we introduce the TextCNN model to extract the high-latent features based on the peptide sequences only and exploit the contrastive learning module to learn more distinguishable feature representations to make better predictions. Comparative results on the benchmark data sets indicate that CACPP outperforms all the state-of-the-art methods in the prediction of anticancer peptides. Moreover, to intuitively show that our model has good classification ability, we visualize the dimension reduction of the features from our model and explore the relationship between ACP sequences and anticancer functions. Furthermore, we also discuss the influence of data set construction on model prediction and explore our model performance on the data sets with verified negative samples.
Collapse
Affiliation(s)
- Xuetong Yang
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| | - Junru Jin
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| | - Ruheng Wang
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| | - Zhongshen Li
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| | - Yu Wang
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| | - Leyi Wei
- School of Software, Shandong University, Jinan 250101, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China
| |
Collapse
|
23
|
Liang X, Zhao H, Wang J. MA-PEP: A novel anticancer peptide prediction framework with multimodal feature fusion based on attention mechanism. Protein Sci 2024; 33:e4966. [PMID: 38532681 DOI: 10.1002/pro.4966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 01/30/2024] [Accepted: 03/06/2024] [Indexed: 03/28/2024]
Abstract
AntiCancer Peptides (ACPs) have emerged as promising therapeutic agents for cancer treatment. The time-consuming and costly nature of wet-lab discriminatory methods has spurred the development of various machine learning and deep learning-based ACP classification methods. Nonetheless, current methods encountered challenges in efficiently integrating features from various peptide modalities, thereby limiting a more comprehensive understanding of ACPs and further restricting the improvement of prediction model performance. In this study, we introduce a novel ACP prediction method, MA-PEP, which leverages multiple attention mechanisms for feature enhancement and fusion to improve ACP prediction. By integrating the enhanced molecular-level chemical features and sequence information of peptides, MA-PEP demonstrates superior prediction performance across several benchmark datasets, highlighting its efficacy in ACP prediction. Moreover, the visual analysis and case studies further demonstrate MA-PEP's reliable feature extraction capability and its promise in the realm of ACP exploration. The code and datasets for MA-PEP are available at https://github.com/liangxiaodata/MA-PEP.
Collapse
Affiliation(s)
- Xiao Liang
- School of Computer Science and Engineering, Central South University, Changsha, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, China
| | - Haochen Zhao
- School of Computer Science and Engineering, Central South University, Changsha, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, China
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha, China
- Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, China
| |
Collapse
|
24
|
Lee B, Shin D. Contrastive learning for enhancing feature extraction in anticancer peptides. Brief Bioinform 2024; 25:bbae220. [PMID: 38725157 PMCID: PMC11082072 DOI: 10.1093/bib/bbae220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 03/28/2024] [Accepted: 04/21/2024] [Indexed: 05/13/2024] Open
Abstract
Cancer, recognized as a primary cause of death worldwide, has profound health implications and incurs a substantial social burden. Numerous efforts have been made to develop cancer treatments, among which anticancer peptides (ACPs) are garnering recognition for their potential applications. While ACP screening is time-consuming and costly, in silico prediction tools provide a way to overcome these challenges. Herein, we present a deep learning model designed to screen ACPs using peptide sequences only. A contrastive learning technique was applied to enhance model performance, yielding better results than a model trained solely on binary classification loss. Furthermore, two independent encoders were employed as a replacement for data augmentation, a technique commonly used in contrastive learning. Our model achieved superior performance on five of six benchmark datasets against previous state-of-the-art models. As prediction tools advance, the potential in peptide-based cancer therapeutics increases, promising a brighter future for oncology research and patient care.
Collapse
Affiliation(s)
- Byungjo Lee
- Research Institute, National Cancer Center, 323, Ilsan-ro, Ilsandong-gu, Goyang, 10408, Republic of Korea
| | - Dongkwan Shin
- Research Institute, National Cancer Center, 323, Ilsan-ro, Ilsandong-gu, Goyang, 10408, Republic of Korea
- Department of Cancer Biomedical Science, National Cancer Center Graduate School of Cancer Science and Policy, 323, Ilsan-ro, Ilsandong-gu, Goyang, 10408, Republic of Korea
| |
Collapse
|
25
|
Sun B, Zhang L, Li M, Wang X, Wang W. Applications of peptide-based nanomaterials in targeting cancer therapy. Biomater Sci 2024; 12:1630-1642. [PMID: 38404259 DOI: 10.1039/d3bm02026f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
To meet the demand for precision medicine, researchers are committed to developing novel strategies to reduce systemic toxicity and side effects in cancer treatment. Targeting peptides are widely applied due to their affinity and specificity, and their ability to be high-throughput screened, chemically synthesized and modified. More importantly, peptides can form ordered self-assembled structures through non-covalent supramolecular interactions, which can form nanostructures with different morphologies and functions, playing crucial roles in targeted diagnosis and treatment. Among them, in targeted immunotherapy, utilizing targeting peptides to block the binding between immune checkpoints and ligands, thereby activating the immune system to eliminate cancer cells, is an advanced therapeutic strategy. In this mini-review, we summarize the screening, self-assembly, and biomedical applications of targeting peptide-based nanomaterials. Furthermore, this mini-review summarizes the potential and optimization strategies of targeting peptides.
Collapse
Affiliation(s)
- Beilei Sun
- Key Laboratory of Medical Molecule Science and Pharmaceutics Engineering, Ministry of Industry and Information Technology, Key Laboratory of Cluster Science of Ministry of Education, Beijing Key Laboratory of Photoelectronic/Electro-photonic Conversion Materials, School of Medical Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, P. R. China.
| | - Limin Zhang
- Key Laboratory of Medical Molecule Science and Pharmaceutics Engineering, Ministry of Industry and Information Technology, Key Laboratory of Cluster Science of Ministry of Education, Beijing Key Laboratory of Photoelectronic/Electro-photonic Conversion Materials, School of Medical Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, P. R. China.
| | - Mengzhen Li
- Key Laboratory of Medical Molecule Science and Pharmaceutics Engineering, Ministry of Industry and Information Technology, Key Laboratory of Cluster Science of Ministry of Education, Beijing Key Laboratory of Photoelectronic/Electro-photonic Conversion Materials, School of Medical Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, P. R. China.
| | - Xin Wang
- Key Laboratory of Medical Molecule Science and Pharmaceutics Engineering, Ministry of Industry and Information Technology, Key Laboratory of Cluster Science of Ministry of Education, Beijing Key Laboratory of Photoelectronic/Electro-photonic Conversion Materials, School of Medical Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, P. R. China.
| | - Weizhi Wang
- Key Laboratory of Medical Molecule Science and Pharmaceutics Engineering, Ministry of Industry and Information Technology, Key Laboratory of Cluster Science of Ministry of Education, Beijing Key Laboratory of Photoelectronic/Electro-photonic Conversion Materials, School of Medical Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology, Beijing 100081, P. R. China.
| |
Collapse
|
26
|
Bian J, Liu X, Dong G, Hou C, Huang S, Zhang D. ACP-ML: A sequence-based method for anticancer peptide prediction. Comput Biol Med 2024; 170:108063. [PMID: 38301519 DOI: 10.1016/j.compbiomed.2024.108063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/08/2024] [Accepted: 01/27/2024] [Indexed: 02/03/2024]
Abstract
Cancer is a serious malignant tumor and is difficult to cure. Chemotherapy, as a primary treatment for cancer, causes significant harm to normal cells in the body and is often accompanied by serious side effects. Recently, anti-cancer peptides (ACPs) as a type of protein for treating cancers dominated research into the development of new anti-tumor drugs because of their ability to specifically target and destroy cancer cells. The screening of proteins with cancer-inhibiting properties from a large pool of proteins is key to the development of anti-tumor drugs. However, it is expensive and inefficient to accurately identify protein functions only through biological experiments due to their complex structure. Therefore, we propose a new prediction model ACP-ML to effectively predict ACPs. In terms of feature extraction, DPC, PseAAC, CTDC, CTDT and CS-Pse-PSSM features were used and the most optimal feature set was selected by comparing combinations of these features. Then, a two-step feature selection process using MRMD and RFE algorithms was performed to determine the most crucial features from the most optimal feature set for identifying ACPs. Furthermore, we assessed the classification accuracy of single learning models and different strategies-based ensemble models through ten-fold cross-validation. Ultimately, a voting-based ensemble learning method is developed to predict ACPs. To validate its effectiveness, two independent test sets were used to perform tests, achieving accuracy of 90.891 % and 92.578 % respectively. Compared with existing anticancer peptide prediction algorithms, the proposed feature processing method is more effective, and the proposed ensemble model ACP-ML exhibits stronger generalization capability and higher accuracy.
Collapse
Affiliation(s)
- Jilong Bian
- Northeast Forestry University, College of Computer and Control Engineering, Harbin, Heilongjiang, China.
| | - Xuan Liu
- Northeast Forestry University, College of Computer and Control Engineering, Harbin, Heilongjiang, China
| | - Guanghui Dong
- Northeast Forestry University, College of Computer and Control Engineering, Harbin, Heilongjiang, China
| | - Chang Hou
- Northeast Forestry University, College of Computer and Control Engineering, Harbin, Heilongjiang, China
| | - Shan Huang
- Department of Neurology, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, China.
| | - Dandan Zhang
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
| |
Collapse
|
27
|
Liu M, Wu T, Li X, Zhu Y, Chen S, Huang J, Zhou F, Liu H. ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization. Front Genet 2024; 15:1352504. [PMID: 38487252 PMCID: PMC10937565 DOI: 10.3389/fgene.2024.1352504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 02/19/2024] [Indexed: 03/17/2024] Open
Abstract
Background: Cancer is a significant global health problem that continues to cause a high number of deaths worldwide. Traditional cancer treatments often come with risks that can compromise the functionality of vital organs. As a potential alternative to these conventional therapies, Anticancer peptides (ACPs) have garnered attention for their small size, high specificity, and reduced toxicity, making them as a promising option for cancer treatments. Methods: However, the process of identifying effective ACPs through wet-lab screening experiments is time-consuming and requires a lot of labor. To overcome this challenge, a deep ensemble learning method is constructed to predict anticancer peptides (ACPs) in this study. To evaluate the reliability of the framework, four different datasets are used in this study for training and testing. During the training process of the model, integration of feature selection methods, feature dimensionality reduction measures, and optimization of the deep ensemble model are carried out. Finally, we explored the interpretability of features that affected the final prediction results and built a web server platform to facilitate anticancer peptides prediction, which can be used by all researchers for further studies. This web server can be accessed at http://lmylab.online:5001/. Results: The result of this study achieves an accuracy rate of 98.53% and an AUC (Area under Curve) value of 0.9972 on the ACPfel dataset, it has improvements on other datasets as well.
Collapse
Affiliation(s)
- Mingyou Liu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
| | - Tao Wu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
| | - Xue Li
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
| | - Yingxue Zhu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
| | - Sen Chen
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
| | - Jian Huang
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Fengfeng Zhou
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Hongmei Liu
- School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China
- Engineering Research Center of Health Medicine Biotechnology of Guizhou Province, Guizhou Medical University, Guiyang, China
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| |
Collapse
|
28
|
Karim T, Shaon MSH, Sultan MF, Hasan MZ, Kafy AA. ANNprob-ACPs: A novel anticancer peptide identifier based on probabilistic feature fusion approach. Comput Biol Med 2024; 169:107915. [PMID: 38171261 DOI: 10.1016/j.compbiomed.2023.107915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 12/28/2023] [Accepted: 12/29/2023] [Indexed: 01/05/2024]
Abstract
Anticancer Peptides (ACPs) offer significant potential as cancer treatment drugs in this modern era. Quickly identifying active compounds from protein sequences is crucial for healthcare and cancer treatment. In this paper ANNprob-ACPs, a novel and effective model for detecting ACPs has been implemented based on nine feature encoding techniques, including AAC, CC, W2V, DPC, PAAC, QSO, CTDC, CTDT, and CKSAAGP. After analyzing the performance of several machine learning models, the six best models were selected based on their overall performances in every evaluation metric. The probability scores of each model were subsequently aggregated and used as input of our meta- model, called ANNprob-ACPs. Our model outperformed all others and its potential to lead to phenomenal identification of ACPs. The results of this study showed notable improvement in 10-fold cross-validation and independent test, with accuracy of 93.72% and 90.62%, respectively. Our proposed model, ANNprob-ACPs outperformed existing approaches in terms of accuracy and effectiveness in discovering ACPs. By using SHAP, this study obtained the physicochemical properties of QSO, and compositional properties of DPC, AAC, and PAAC are more impactful for our model's performances, which have a major impact on a drug's interactions and future discoveries. Consequently, this model is crucial for the future and has a high probability of detecting ACPs more frequently. We developed a web server of ANNprob-ACPs, which is accessible at ANNprob-ACPs webserver.
Collapse
Affiliation(s)
- Tasmin Karim
- Department of Computer Science & Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh; Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh.
| | - Md Shazzad Hossain Shaon
- Department of Computer Science & Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh; Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh.
| | - Md Fahim Sultan
- Department of Computer Science & Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh; Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh.
| | - Md Zahid Hasan
- Department of Computer Science & Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh; Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh.
| | - Abdulla-Al Kafy
- Department of Urban & Regional Planning, Rajshahi University of Engineering & Technology (RUET), Rajshahi, 6204, Bangladesh.
| |
Collapse
|
29
|
Li C, Jin K. Chemical Strategies towards the Development of Effective Anticancer Peptides. Curr Med Chem 2024; 31:1839-1873. [PMID: 37170992 DOI: 10.2174/0929867330666230426111157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 01/28/2023] [Accepted: 02/24/2023] [Indexed: 05/13/2023]
Abstract
Cancer is increasingly recognized as one of the primary causes of death and has become a multifaceted global health issue. Modern medical science has made significant advancements in the diagnosis and therapy of cancer over the past decade. The detrimental side effects, lack of efficacy, and multidrug resistance of conventional cancer therapies have created an urgent need for novel anticancer therapeutics or treatments with low cytotoxicity and drug resistance. The pharmaceutical groups have recognized the crucial role that peptide therapeutic agents can play in addressing unsatisfied healthcare demands and how these become great supplements or even preferable alternatives to biological therapies and small molecules. Anticancer peptides, as a vibrant therapeutic strategy against various cancer cells, have demonstrated incredible anticancer potential due to high specificity and selectivity, low toxicity, and the ability to target the surface of traditional "undruggable" proteins. This review will provide the research progression of anticancer peptides, mainly focusing on the discovery and modifications along with the optimization and application of these peptides in clinical practice.
Collapse
Affiliation(s)
- Cuicui Li
- Key Laboratory of Chemical Biology (Ministry of Education), Department of Medicinal Chemistry, School of Pharmacy, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
| | - Kang Jin
- Key Laboratory of Chemical Biology (Ministry of Education), Department of Medicinal Chemistry, School of Pharmacy, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
| |
Collapse
|
30
|
Wu Z, Wu Y, Zhu C, Wu X, Zhai S, Wang X, Su Z, Duan H. Efficient Computational Framework for Target-Specific Active Peptide Discovery: A Case Study on IL-17C Targeting Cyclic Peptides. J Chem Inf Model 2023; 63:7655-7668. [PMID: 38049371 DOI: 10.1021/acs.jcim.3c01385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/06/2023]
Abstract
The development of potentially active peptides for specific targets is critical for the modern pharmaceutical industry's growth. In this study, we present an efficient computational framework for the discovery of active peptides targeting a specific pharmacological target, which combines a conditional variational autoencoder (CVAE) and a classifier named TCPP based on the Transformer and convolutional neural network. In our example scenario, we constructed an active cyclic peptide library targeting interleukin-17C (IL-17C) through a library-based in vitro selection strategy. The CVAE model is trained on the preprocessed peptide data sets to generate potentially active peptides and the TCPP further screens the generated peptides. Ultimately, six candidate peptides predicted by the model were synthesized and assayed for their activity, and four of them exhibited promising binding affinity to IL-17C. Our study provides a one-stop-shop for target-specific active peptide discovery, which is expected to boost up the process of peptide drug development.
Collapse
Affiliation(s)
- Zhipeng Wu
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Yejian Wu
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Cheng Zhu
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Xinyi Wu
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Silong Zhai
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Xinqiao Wang
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Zhihao Su
- Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China
| | - Hongliang Duan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
| |
Collapse
|
31
|
La Paglia L, Vazzana M, Mauro M, Urso A, Arizza V, Vizzini A. Bioactive Molecules from the Innate Immunity of Ascidians and Innovative Methods of Drug Discovery: A Computational Approach Based on Artificial Intelligence. Mar Drugs 2023; 22:6. [PMID: 38276644 PMCID: PMC10817596 DOI: 10.3390/md22010006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/12/2023] [Accepted: 12/17/2023] [Indexed: 01/27/2024] Open
Abstract
The study of bioactive molecules of marine origin has created an important bridge between biological knowledge and its applications in biotechnology and biomedicine. Current studies in different research fields, such as biomedicine, aim to discover marine molecules characterized by biological activities that can be used to produce potential drugs for human use. In recent decades, increasing attention has been paid to a particular group of marine invertebrates, the Ascidians, as they are a source of bioactive products. We describe omics data and computational methods relevant to identifying the mechanisms and processes of innate immunity underlying the biosynthesis of bioactive molecules, focusing on innovative computational approaches based on Artificial Intelligence. Since there is increasing attention on finding new solutions for a sustainable supply of bioactive compounds, we propose that a possible improvement in the biodiscovery pipeline might also come from the study and utilization of marine invertebrates' innate immunity.
Collapse
Affiliation(s)
- Laura La Paglia
- Istituto di Calcolo e Reti ad Alte Prestazioni–Consiglio Nazionale delle Ricerche, Via Ugo La Malfa 153, 90146 Palermo, Italy; (L.L.P.); (A.U.)
| | - Mirella Vazzana
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
| | - Manuela Mauro
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
| | - Alfonso Urso
- Istituto di Calcolo e Reti ad Alte Prestazioni–Consiglio Nazionale delle Ricerche, Via Ugo La Malfa 153, 90146 Palermo, Italy; (L.L.P.); (A.U.)
| | - Vincenzo Arizza
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
| | - Aiti Vizzini
- Dipartimento di Scienze e Tecnologie Biologiche, Chimiche e Farmaceutiche–Università di Palermo, Via Archirafi 18, 90100 Palermo, Italy; (M.V.); (M.M.); (V.A.)
| |
Collapse
|
32
|
Xu K, Zhao X, Tan Y, Wu J, Cai Y, Zhou J, Wang X. A systematical review on antimicrobial peptides and their food applications. BIOMATERIALS ADVANCES 2023; 155:213684. [PMID: 37976831 DOI: 10.1016/j.bioadv.2023.213684] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 10/29/2023] [Accepted: 11/02/2023] [Indexed: 11/19/2023]
Abstract
Food safety issues are a major concern in food processing and packaging industries. Food spoilage is caused by microbial contamination, where antimicrobial peptides (APs) provide solutions by eliminating microorganisms. APs such as nisin have been successfully and commonly used in food processing and preservation. Here, we discuss all aspects of the functionalization of APs in food applications. We briefly review the natural sources of APs and their native functions. Recombinant expression of APs in microorganisms and their yields are described. The molecular mechanisms of AP antibacterial action are explained, and this knowledge can further benefit the design of functional APs. We highlight current utilities and challenges for the application of APs in the food industry, and address rational methods for AP design that may overcome current limitations.
Collapse
Affiliation(s)
- Kangjie Xu
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - XinYi Zhao
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Yameng Tan
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Junheng Wu
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Yiqing Cai
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China
| | - Jingwen Zhou
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Jiangsu Province Engineering Research Center of Food Synthetic Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China..
| | - Xinglong Wang
- Engineering Research Center of Ministry of Education on Food Synthetic Biotechnology and School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China; Science Center for Future Foods, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China.
| |
Collapse
|
33
|
Sun M, Hu H, Pang W, Zhou Y. ACP-BC: A Model for Accurate Identification of Anticancer Peptides Based on Fusion Features of Bidirectional Long Short-Term Memory and Chemically Derived Information. Int J Mol Sci 2023; 24:15447. [PMID: 37895128 PMCID: PMC10607064 DOI: 10.3390/ijms242015447] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 09/10/2023] [Accepted: 10/20/2023] [Indexed: 10/29/2023] Open
Abstract
Anticancer peptides (ACPs) have been proven to possess potent anticancer activities. Although computational methods have emerged for rapid ACPs identification, their accuracy still needs improvement. In this study, we propose a model called ACP-BC, a three-channel end-to-end model that utilizes various combinations of data augmentation techniques. In the first channel, features are extracted from the raw sequence using a bidirectional long short-term memory network. In the second channel, the entire sequence is converted into a chemical molecular formula, which is further simplified using Simplified Molecular Input Line Entry System notation to obtain deep abstract features through a bidirectional encoder representation transformer (BERT). In the third channel, we manually selected four effective features according to dipeptide composition, binary profile feature, k-mer sparse matrix, and pseudo amino acid composition. Notably, the application of chemical BERT in predicting ACPs is novel and successfully integrated into our model. To validate the performance of our model, we selected two benchmark datasets, ACPs740 and ACPs240. ACP-BC achieved prediction accuracy with 87% and 90% on these two datasets, respectively, representing improvements of 1.3% and 7% compared to existing state-of-the-art methods on these datasets. Therefore, systematic comparative experiments have shown that the ACP-BC can effectively identify anticancer peptides.
Collapse
Affiliation(s)
- Mingwei Sun
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (M.S.); (H.H.)
| | - Haoyuan Hu
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (M.S.); (H.H.)
| | - Wei Pang
- School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK;
| | - You Zhou
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (M.S.); (H.H.)
- College of Software, Jilin University, Changchun 130012, China
| |
Collapse
|
34
|
Tao H, Shan S, Fu H, Zhu C, Liu B. An Augmented Sample Selection Framework for Prediction of Anticancer Peptides. Molecules 2023; 28:6680. [PMID: 37764455 PMCID: PMC10535447 DOI: 10.3390/molecules28186680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 09/14/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023] Open
Abstract
Anticancer peptides (ACPs) have promising prospects for cancer treatment. Traditional ACP identification experiments have the limitations of low efficiency and high cost. In recent years, data-driven deep learning techniques have shown significant potential for ACP prediction. However, data-driven prediction models rely heavily on extensive training data. Furthermore, the current publicly accessible ACP dataset is limited in size, leading to inadequate model generalization. While data augmentation effectively expands dataset size, existing techniques for augmenting ACP data often generate noisy samples, adversely affecting prediction performance. Therefore, this paper proposes a novel augmented sample selection framework for the prediction of anticancer peptides (ACPs-ASSF). First, the prediction model is trained using raw data. Then, the augmented samples generated using the data augmentation technique are fed into the trained model to compute pseudo-labels and estimate the uncertainty of the model prediction. Finally, samples with low uncertainty, high confidence, and pseudo-labels consistent with the original labels are selected and incorporated into the training set to retrain the model. The evaluation results for the ACP240 and ACP740 datasets show that ACPs-ASSF achieved accuracy improvements of up to 5.41% and 5.68%, respectively, compared to the traditional data augmentation method.
Collapse
Affiliation(s)
- Huawei Tao
- Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; (H.T.); (S.S.); (H.F.); (C.Z.)
- Henan Engineering Laboratory of Grain IOT Technology, Henan University of Technology, Zhengzhou 450001, China
| | - Shuai Shan
- Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; (H.T.); (S.S.); (H.F.); (C.Z.)
- Henan Engineering Laboratory of Grain IOT Technology, Henan University of Technology, Zhengzhou 450001, China
| | - Hongliang Fu
- Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; (H.T.); (S.S.); (H.F.); (C.Z.)
- Henan Engineering Laboratory of Grain IOT Technology, Henan University of Technology, Zhengzhou 450001, China
| | - Chunhua Zhu
- Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China; (H.T.); (S.S.); (H.F.); (C.Z.)
- Henan Engineering Laboratory of Grain IOT Technology, Henan University of Technology, Zhengzhou 450001, China
| | - Boye Liu
- College of Food Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
| |
Collapse
|
35
|
Han J, Zhang S, Liu J. Protocol for predicting peptides with anticancer and antimicrobial properties by a tri-fusion neural network. STAR Protoc 2023; 4:102541. [PMID: 37660298 PMCID: PMC10491854 DOI: 10.1016/j.xpro.2023.102541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/11/2023] [Accepted: 08/04/2023] [Indexed: 09/05/2023] Open
Abstract
Here, we describe the use of TriNet to predict peptides with anticancer and antimicrobial properties by a tri-fusion neural network. We detail the use of TriNet for both the offline Python script version and the online service, thereby demonstrating its convenience for users. In addition, we provide a detailed explanation of the training process of TriNet to enhance the understanding of researchers seeking to leverage deep learning techniques for peptide classification. For complete details on the use and execution of this protocol, please refer to Zhou et al.1.
Collapse
Affiliation(s)
- Jiyun Han
- School of Mathematics and Statistics, Shandong University, Weihai 264209, China
| | - Shizhuo Zhang
- School of Mathematics and Statistics, Shandong University, Weihai 264209, China
| | - Juntao Liu
- School of Mathematics and Statistics, Shandong University, Weihai 264209, China.
| |
Collapse
|
36
|
Chen S, Liao Y, Zhao J, Bin Y, Zheng C. PACVP: Prediction of Anti-Coronavirus Peptides Using a Stacking Learning Strategy With Effective Feature Representation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3106-3116. [PMID: 37022025 DOI: 10.1109/tcbb.2023.3238370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Due to the global outbreak of COVID-19 and its variants, antiviral peptides with anti-coronavirus activity (ACVPs) represent a promising new drug candidate for the treatment of coronavirus infection. At present, several computational tools have been developed to identify ACVPs, but the overall prediction performance is still not enough to meet the actual therapeutic application. In this study, we constructed an efficient and reliable prediction model PACVP (Prediction of Anti-CoronaVirus Peptides) for identifying ACVPs based on effective feature representation and a two-layer stacking learning framework. In the first layer, we use nine feature encoding methods with different feature representation angles to characterize the rich sequence information and fuse them into a feature matrix. Secondly, data normalization and unbalanced data processing are carried out. Next, 12 baseline models are constructed by combining three feature selection methods and four machine learning classification algorithms. In the second layer, we input the optimal probability features into the logistic regression algorithm (LR) to train the final model PACVP. The experiments show that PACVP achieves favorable prediction performance on independent test dataset, with ACC of 0.9208 and AUC of 0.9465. We hope that PACVP will become a useful method for identifying, annotating and characterizing novel ACVPs.
Collapse
|
37
|
Xu J, Li F, Li C, Guo X, Landersdorfer C, Shen HH, Peleg AY, Li J, Imoto S, Yao J, Akutsu T, Song J. iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities. Brief Bioinform 2023; 24:bbad240. [PMID: 37369638 PMCID: PMC10359087 DOI: 10.1093/bib/bbad240] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 05/30/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
Antimicrobial peptides (AMPs) are short peptides that play crucial roles in diverse biological processes and have various functional activities against target organisms. Due to the abuse of chemical antibiotics and microbial pathogens' increasing resistance to antibiotics, AMPs have the potential to be alternatives to antibiotics. As such, the identification of AMPs has become a widely discussed topic. A variety of computational approaches have been developed to identify AMPs based on machine learning algorithms. However, most of them are not capable of predicting the functional activities of AMPs, and those predictors that can specify activities only focus on a few of them. In this study, we first surveyed 10 predictors that can identify AMPs and their functional activities in terms of the features they employed and the algorithms they utilized. Then, we constructed comprehensive AMP datasets and proposed a new deep learning-based framework, iAMPCN (identification of AMPs based on CNNs), to identify AMPs and their related 22 functional activities. Our experiments demonstrate that iAMPCN significantly improved the prediction performance of AMPs and their corresponding functional activities based on four types of sequence features. Benchmarking experiments on the independent test datasets showed that iAMPCN outperformed a number of state-of-the-art approaches for predicting AMPs and their functional activities. Furthermore, we analyzed the amino acid preferences of different AMP activities and evaluated the model on datasets of varying sequence redundancy thresholds. To facilitate the community-wide identification of AMPs and their corresponding functional types, we have made the source codes of iAMPCN publicly available at https://github.com/joy50706/iAMPCN/tree/master. We anticipate that iAMPCN can be explored as a valuable tool for identifying potential AMPs with specific functional activities for further experimental validation.
Collapse
Affiliation(s)
- Jing Xu
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Fuyi Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- College of Information Engineering, Northwest A&F University, Shaanxi 712100, China
- The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC 3800, Australia
| | - Chen Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Xudong Guo
- College of Information Engineering, Northwest A&F University, Shaanxi 712100, China
| | - Cornelia Landersdorfer
- Monash Institute of Pharmaceutical Sciences, Monash University, Melbourne, VIC 3800, Australia
| | - Hsin-Hui Shen
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Department of Materials Science and Engineering, Faculty of Engineering, Monash University, Clayton, VIC, 3800, Australia
| | - Anton Y Peleg
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Department of Infectious Diseases, Alfred Hospital, Alfred Health, Melbourne, Victoria, Australia
| | - Jian Li
- Monash Biomedicine Discovery Institute and Department of Microbiology, Monash University, Melbourne, VIC 3800, Australia
| | - Seiya Imoto
- Division of Health Medical Intelligence, Human Genome Center, Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan
- Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | | | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan
| |
Collapse
|
38
|
Dou B, Zhu Z, Merkurjev E, Ke L, Chen L, Jiang J, Zhu Y, Liu J, Zhang B, Wei GW. Machine Learning Methods for Small Data Challenges in Molecular Science. Chem Rev 2023; 123:8736-8780. [PMID: 37384816 PMCID: PMC10999174 DOI: 10.1021/acs.chemrev.3c00189] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.
Collapse
Affiliation(s)
- Bozheng Dou
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Zailiang Zhu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Ekaterina Merkurjev
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Lu Ke
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Long Chen
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Jian Jiang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yueying Zhu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Jie Liu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Bengong Zhang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
| |
Collapse
|
39
|
Guan J, Yao L, Chung CR, Chiang YC, Lee TY. StackTHPred: Identifying Tumor-Homing Peptides through GBDT-Based Feature Selection with Stacking Ensemble Architecture. Int J Mol Sci 2023; 24:10348. [PMID: 37373494 DOI: 10.3390/ijms241210348] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 05/31/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023] Open
Abstract
One of the major challenges in cancer therapy lies in the limited targeting specificity exhibited by existing anti-cancer drugs. Tumor-homing peptides (THPs) have emerged as a promising solution to this issue, due to their capability to specifically bind to and accumulate in tumor tissues while minimally impacting healthy tissues. THPs are short oligopeptides that offer a superior biological safety profile, with minimal antigenicity, and faster incorporation rates into target cells/tissues. However, identifying THPs experimentally, using methods such as phage display or in vivo screening, is a complex, time-consuming task, hence the need for computational methods. In this study, we proposed StackTHPred, a novel machine learning-based framework that predicts THPs using optimal features and a stacking architecture. With an effective feature selection algorithm and three tree-based machine learning algorithms, StackTHPred has demonstrated advanced performance, surpassing existing THP prediction methods. It achieved an accuracy of 0.915 and a 0.831 Matthews Correlation Coefficient (MCC) score on the main dataset, and an accuracy of 0.883 and a 0.767 MCC score on the small dataset. StackTHPred also offers favorable interpretability, enabling researchers to better understand the intrinsic characteristics of THPs. Overall, StackTHPred is beneficial for both the exploration and identification of THPs and facilitates the development of innovative cancer therapies.
Collapse
Affiliation(s)
- Jiahui Guan
- School of Medicine, The Chinese University of Hong Kong (Shenzhen) 2001 Longxiang Road, Shenzhen 518172, China
| | - Lantian Yao
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
- School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Chia-Ru Chung
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Ying-Chih Chiang
- School of Medicine, The Chinese University of Hong Kong (Shenzhen) 2001 Longxiang Road, Shenzhen 518172, China
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| |
Collapse
|
40
|
Wong L, Wang L, You ZH, Yuan CA, Huang YA, Cao MY. GKLOMLI: a link prediction model for inferring miRNA-lncRNA interactions by using Gaussian kernel-based method on network profile and linear optimization algorithm. BMC Bioinformatics 2023; 24:188. [PMID: 37158823 PMCID: PMC10169329 DOI: 10.1186/s12859-023-05309-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 04/27/2023] [Indexed: 05/10/2023] Open
Abstract
BACKGROUND The limited knowledge of miRNA-lncRNA interactions is considered as an obstruction of revealing the regulatory mechanism. Accumulating evidence on Human diseases indicates that the modulation of gene expression has a great relationship with the interactions between miRNAs and lncRNAs. However, such interaction validation via crosslinking-immunoprecipitation and high-throughput sequencing (CLIP-seq) experiments that inevitably costs too much money and time but with unsatisfactory results. Therefore, more and more computational prediction tools have been developed to offer many reliable candidates for a better design of further bio-experiments. METHODS In this work, we proposed a novel link prediction model based on Gaussian kernel-based method and linear optimization algorithm for inferring miRNA-lncRNA interactions (GKLOMLI). Given an observed miRNA-lncRNA interaction network, the Gaussian kernel-based method was employed to output two similarity matrixes of miRNAs and lncRNAs. Based on the integrated matrix combined with similarity matrixes and the observed interaction network, a linear optimization-based link prediction model was trained for inferring miRNA-lncRNA interactions. RESULTS To evaluate the performance of our proposed method, k-fold cross-validation (CV) and leave-one-out CV were implemented, in which each CV experiment was carried out 100 times on a training set generated randomly. The high area under the curves (AUCs) at 0.8623 ± 0.0027 (2-fold CV), 0.9053 ± 0.0017 (5-fold CV), 0.9151 ± 0.0013 (10-fold CV), and 0.9236 (LOO-CV), illustrated the precision and reliability of our proposed method. CONCLUSION GKLOMLI with high performance is anticipated to be used to reveal underlying interactions between miRNA and their target lncRNAs, and deciphers the potential mechanisms of the complex diseases.
Collapse
Affiliation(s)
- Leon Wong
- Guangxi Key Lab of Human-machine Interaction and Intelligent Decision, Guangxi Academy of Sciences, Nanning, 530007, China
- Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, 200092, Shanghai, China
| | - Lei Wang
- Guangxi Key Lab of Human-machine Interaction and Intelligent Decision, Guangxi Academy of Sciences, Nanning, 530007, China.
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277160, China.
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710139, China.
| | - Chang-An Yuan
- Guangxi Key Lab of Human-machine Interaction and Intelligent Decision, Guangxi Academy of Sciences, Nanning, 530007, China
| | - Yu-An Huang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710139, China
| | - Mei-Yuan Cao
- School of Electrical and Electronic Engineering, Guangdong Technology College, Zhaoqing, 526100, China
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor, Malaysia
| |
Collapse
|
41
|
Jing Y, Zhang S, Wang H. DapNet-HLA: Adaptive dual-attention mechanism network based on deep learning to predict non-classical HLA binding sites. Anal Biochem 2023; 666:115075. [PMID: 36740003 DOI: 10.1016/j.ab.2023.115075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/30/2023] [Accepted: 02/02/2023] [Indexed: 02/05/2023]
Abstract
Human leukocyte antigen (HLA) plays a vital role in immunomodulatory function. Studies have shown that immunotherapy based on non-classical HLA has essential applications in cancer, COVID-19, and allergic diseases. However, there are few deep learning methods to predict non-classical HLA alleles. In this work, an adaptive dual-attention network named DapNet-HLA is established based on existing datasets. Firstly, amino acid sequences are transformed into digital vectors by looking up the table. To overcome the feature sparsity problem caused by unique one-hot encoding, the fused word embedding method is used to map each amino acid to a low-dimensional word vector optimized with the training of the classifier. Then, we use the GCB (group convolution block), SENet attention (squeeze-and-excitation networks), BiLSTM (bidirectional long short-term memory network), and Bahdanau attention mechanism to construct the classifier. The use of SENet can make the weight of the effective feature map high, so that the model can be trained to achieve better results. Attention mechanism is an Encoder-Decoder model used to improve the effectiveness of RNN, LSTM or GRU (gated recurrent neural network). The ablation experiment shows that DapNet-HLA has the best adaptability for five datasets. On the five test datasets, the ACC index and MCC index of DapNet-HLA are 4.89% and 0.0933 higher than the comparison method, respectively. According to the ROC curve and PR curve verified by the 5-fold cross-validation, the AUC value of each fold has a slight fluctuation, which proves the robustness of the DapNet-HLA. The codes and datasets are accessible at https://github.com/JYY625/DapNet-HLA.
Collapse
Affiliation(s)
- Yuanyuan Jing
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
| | - Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China.
| | - Houqiang Wang
- School of Mathematics and Statistics, Xidian University, Xi'an, 710071, PR China
| |
Collapse
|
42
|
Deng H, Ding M, Wang Y, Li W, Liu G, Tang Y. ACP-MLC: A two-level prediction engine for identification of anticancer peptides and multi-label classification of their functional types. Comput Biol Med 2023; 158:106844. [PMID: 37058760 DOI: 10.1016/j.compbiomed.2023.106844] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 03/09/2023] [Accepted: 03/30/2023] [Indexed: 04/07/2023]
Abstract
Anticancer peptides (ACPs), a series of short bioactive peptides, are promising candidates in fighting against cancer due to their high activity, low toxicity, and not likely cause drug resistance. The accurate identification of ACPs and classification of their functional types is of great importance for investigating their mechanisms of action and developing peptide-based anticancer therapies. Here, we provided a computational tool, called ACP-MLC, to address binary classification and multi-label classification of ACPs for a given peptide sequence. Briefly, ACP-MLC is a two-level prediction engine, in which the 1st-level model predicts whether a query sequence is an ACP or not by random forest algorithm, and the 2nd-level model predicts which tissue types the sequence might target by the binary relevance algorithm. Development and evaluation by high-quality datasets, our ACP-MLC yielded an area under the receiver operating characteristic curve (AUC) of 0.888 on the independent test set for the 1st-level prediction, and obtained 0.157 hamming loss, 0.577 subset accuracy, 0.802 F1-scoremacro, and 0.826 F1-scoremicro on the independent test set for the 2nd-level prediction. A systematic comparison demonstrated that ACP-MLC outperformed existing binary classifiers and other multi-label learning classifiers for ACP prediction. Finally, we interpreted the important features of ACP-MLC by the SHAP method. User-friendly software and the datasets are available at https://github.com/Nicole-DH/ACP-MLC. We believe that the ACP-MLC would be a powerful tool in ACP discovery.
Collapse
|
43
|
Zhou W, Liu Y, Li Y, Kong S, Wang W, Ding B, Han J, Mou C, Gao X, Liu J. TriNet: A tri-fusion neural network for the prediction of anticancer and antimicrobial peptides. PATTERNS (NEW YORK, N.Y.) 2023; 4:100702. [PMID: 36960450 PMCID: PMC10028424 DOI: 10.1016/j.patter.2023.100702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 12/20/2022] [Accepted: 02/03/2023] [Indexed: 03/04/2023]
Abstract
The accurate identification of anticancer peptides (ACPs) and antimicrobial peptides (AMPs) remains a computational challenge. We propose a tri-fusion neural network termed TriNet for the accurate prediction of both ACPs and AMPs. The framework first defines three kinds of features to capture the peptide information contained in serial fingerprints, sequence evolutions, and physicochemical properties, which are then fed into three parallel modules: a convolutional neural network module enhanced by channel attention, a bidirectional long short-term memory module, and an encoder module for training and final classification. To achieve a better training effect, TriNet is trained via a training approach using iterative interactions between the samples in the training and validation datasets. TriNet is tested on multiple challenging ACP and AMP datasets and exhibits significant improvements over various state-of-the-art methods. The web server and source code of TriNet are respectively available at http://liulab.top/TriNet/server and https://github.com/wanyunzh/TriNet.
Collapse
Affiliation(s)
- Wanyun Zhou
- SDU-ANU Joint Science College, Shandong University (Weihai), Weihai 264209, China
| | - Yufei Liu
- SDU-ANU Joint Science College, Shandong University (Weihai), Weihai 264209, China
| | - Yingxin Li
- School of Mechanical, Electrical & Information Engineering, Shandong University (Weihai), Weihai 264209, China
| | - Siqi Kong
- SDU-ANU Joint Science College, Shandong University (Weihai), Weihai 264209, China
| | - Weilin Wang
- SDU-ANU Joint Science College, Shandong University (Weihai), Weihai 264209, China
| | - Boyun Ding
- SDU-ANU Joint Science College, Shandong University (Weihai), Weihai 264209, China
| | - Jiyun Han
- School of Mathematics and Statistics, Shandong University (Weihai), Weihai 264209, China
| | - Chaozhou Mou
- School of Mathematics and Statistics, Shandong University (Weihai), Weihai 264209, China
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Juntao Liu
- School of Mathematics and Statistics, Shandong University (Weihai), Weihai 264209, China
| |
Collapse
|
44
|
Yao L, Li W, Zhang Y, Deng J, Pang Y, Huang Y, Chung CR, Yu J, Chiang YC, Lee TY. Accelerating the Discovery of Anticancer Peptides through Deep Forest Architecture with Deep Graphical Representation. Int J Mol Sci 2023; 24:ijms24054328. [PMID: 36901759 PMCID: PMC10001941 DOI: 10.3390/ijms24054328] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 02/02/2023] [Accepted: 02/07/2023] [Indexed: 02/24/2023] Open
Abstract
Cancer is one of the leading diseases threatening human life and health worldwide. Peptide-based therapies have attracted much attention in recent years. Therefore, the precise prediction of anticancer peptides (ACPs) is crucial for discovering and designing novel cancer treatments. In this study, we proposed a novel machine learning framework (GRDF) that incorporates deep graphical representation and deep forest architecture for identifying ACPs. Specifically, GRDF extracts graphical features based on the physicochemical properties of peptides and integrates their evolutionary information along with binary profiles for constructing models. Moreover, we employ the deep forest algorithm, which adopts a layer-by-layer cascade architecture similar to deep neural networks, enabling excellent performance on small datasets but without complicated tuning of hyperparameters. The experiment shows GRDF exhibits state-of-the-art performance on two elaborate datasets (Set 1 and Set 2), achieving 77.12% accuracy and 77.54% F1-score on Set 1, as well as 94.10% accuracy and 94.15% F1-score on Set 2, exceeding existing ACP prediction methods. Our models exhibit greater robustness than the baseline algorithms commonly used for other sequence analysis tasks. In addition, GRDF is well-interpretable, enabling researchers to better understand the features of peptide sequences. The promising results demonstrate that GRDF is remarkably effective in identifying ACPs. Therefore, the framework presented in this study could assist researchers in facilitating the discovery of anticancer peptides and contribute to developing novel cancer treatments.
Collapse
Affiliation(s)
- Lantian Yao
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
- School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Wenshuo Li
- School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Yuntian Zhang
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
- School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Junyang Deng
- School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Yuxuan Pang
- School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Yixian Huang
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
- School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Chia-Ru Chung
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Jinhan Yu
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
- School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
| | - Ying-Chih Chiang
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
- Correspondence: (Y.-C.C.); (T.-Y.L.)
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Road, Shenzhen 518172, China
- Correspondence: (Y.-C.C.); (T.-Y.L.)
| |
Collapse
|
45
|
Ghaly G, Tallima H, Dabbish E, Badr ElDin N, Abd El-Rahman MK, Ibrahim MAA, Shoeib T. Anti-Cancer Peptides: Status and Future Prospects. Molecules 2023; 28:molecules28031148. [PMID: 36770815 PMCID: PMC9920184 DOI: 10.3390/molecules28031148] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 12/26/2022] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
The dramatic rise in cancer incidence, alongside treatment deficiencies, has elevated cancer to the second-leading cause of death globally. The increasing morbidity and mortality of this disease can be traced back to a number of causes, including treatment-related side effects, drug resistance, inadequate curative treatment and tumor relapse. Recently, anti-cancer bioactive peptides (ACPs) have emerged as a potential therapeutic choice within the pharmaceutical arsenal due to their high penetration, specificity and fewer side effects. In this contribution, we present a general overview of the literature concerning the conformational structures, modes of action and membrane interaction mechanisms of ACPs, as well as provide recent examples of their successful employment as targeting ligands in cancer treatment. The use of ACPs as a diagnostic tool is summarized, and their advantages in these applications are highlighted. This review expounds on the main approaches for peptide synthesis along with their reconstruction and modification needed to enhance their therapeutic effect. Computational approaches that could predict therapeutic efficacy and suggest ACP candidates for experimental studies are discussed. Future research prospects in this rapidly expanding area are also offered.
Collapse
Affiliation(s)
- Gehane Ghaly
- Department of Chemistry, The American University in Cairo, New Cairo 11835, Egypt
| | - Hatem Tallima
- Department of Chemistry, The American University in Cairo, New Cairo 11835, Egypt
| | - Eslam Dabbish
- Department of Chemistry, The American University in Cairo, New Cairo 11835, Egypt
| | - Norhan Badr ElDin
- Analytical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr-El Aini Street, Cairo 11562, Egypt
| | - Mohamed K. Abd El-Rahman
- Analytical Chemistry Department, Faculty of Pharmacy, Cairo University, Kasr-El Aini Street, Cairo 11562, Egypt
- Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, MA 02138, USA
| | - Mahmoud A. A. Ibrahim
- Computational Chemistry Laboratory, Chemistry Department, Faculty of Science, Minia University, Minia 61519, Egypt
- School of Health Sciences, University of Kwa-Zulu-Natal, Westville, Durban 4000, South Africa
| | - Tamer Shoeib
- Department of Chemistry, The American University in Cairo, New Cairo 11835, Egypt
- Correspondence:
| |
Collapse
|
46
|
Yuan Q, Chen K, Yu Y, Le NQK, Chua MCH. Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding. Brief Bioinform 2023; 24:6987656. [PMID: 36642410 DOI: 10.1093/bib/bbac630] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 12/01/2022] [Accepted: 12/28/2022] [Indexed: 01/17/2023] Open
Abstract
Anticancer peptides (ACPs) are the types of peptides that have been demonstrated to have anticancer activities. Using ACPs to prevent cancer could be a viable alternative to conventional cancer treatments because they are safer and display higher selectivity. Due to ACP identification being highly lab-limited, expensive and lengthy, a computational method is proposed to predict ACPs from sequence information in this study. The process includes the input of the peptide sequences, feature extraction in terms of ordinal encoding with positional information and handcrafted features, and finally feature selection. The whole model comprises of two modules, including deep learning and machine learning algorithms. The deep learning module contained two channels: bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN). Light Gradient Boosting Machine (LightGBM) was used in the machine learning module. Finally, this study voted the three models' classification results for the three paths resulting in the model ensemble layer. This study provides insights into ACP prediction utilizing a novel method and presented a promising performance. It used a benchmark dataset for further exploration and improvement compared with previous studies. Our final model has an accuracy of 0.7895, sensitivity of 0.8153 and specificity of 0.7676, and it was increased by at least 2% compared with the state-of-the-art studies in all metrics. Hence, this paper presents a novel method that can potentially predict ACPs more effectively and efficiently. The work and source codes are made available to the community of researchers and developers at https://github.com/khanhlee/acp-ope/.
Collapse
Affiliation(s)
- Qitong Yuan
- Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, 119615, Singapore, Singapore
| | - Keyi Chen
- Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, 119615, Singapore, Singapore
| | - Yimin Yu
- Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, 119615, Singapore, Singapore
| | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, 250 Wuxing St, 106, Taipei, Taiwan.,Research Center for Artificial Intelligence in Medicine, Taipei Medical University, 250 Wuxing St, 106, Taipei, Taiwan.,Translational Imaging Research Center, Taipei Medical University Hospital, 252 Wuxing St, 110, Taipei, Taiwan
| | - Matthew Chin Heng Chua
- Institute of Systems Science, National University of Singapore, 25 Heng Mui Keng Terrace, 119615, Singapore, Singapore
| |
Collapse
|
47
|
Guo X, Tiwari P, Zou Q, Ding Y. Subspace projection-based weighted echo state networks for predicting therapeutic peptides. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
|
48
|
Liang Y, Ma X. iACP-GE: accurate identification of anticancer peptides by using gradient boosting decision tree and extra tree. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2023; 34:1-19. [PMID: 36562289 DOI: 10.1080/1062936x.2022.2160011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
Cancer is one of the main diseases threatening human life, accounting for millions of deaths around the world each year. Traditional physical and chemical methods for cancer treatment are extremely time-consuming, lab-intensive, expensive, inefficient and difficult to be applied in a high-throughput way. Hence, it is an urgent task to develop automated computational methods to enable fast and accurate identification of anticancer peptides (ACPs). In this paper, we develop a novel model named iACP-GE to identify ACPs. Multi-features are extracted by using binary encoding, enhanced grouped amino acid composition and BLOSUM62 encoding based on the N5C5 sequence, as well as detrended forward moving-average auto-cross correlation analysis based on physicochemical properties of 20 natural amino acids. Thus, 835 features are obtained for each sample, in order to avoid information redundancy, gradient boosting decision tree was adopted as the feature selection strategy. Then, the optimal feature subset is input to the extra tree classifier. The accuracies of ACP740 and ACP240 datasets with the 5-fold cross-validation were 90.54% and 91.25%, respectively. Experimental results indicate that iACP-GE significantly outperforms several existing models on ACP740 and ACP240 datasets and can be used as an effective tool for the identification of ACPs. The datasets and source codes for iACP-GE are available at https://github.com/yunyunliang88/iACP-GE.
Collapse
Affiliation(s)
- Y Liang
- School of Science, Xi'an Polytechnic University, Xi'an, P. R. China
| | - X Ma
- School of Science, Xi'an Polytechnic University, Xi'an, P. R. China
| |
Collapse
|
49
|
Singh DP, Kaushik B. A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques. Chem Biol Drug Des 2023; 101:175-194. [PMID: 36303299 DOI: 10.1111/cbdd.14164] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/13/2022] [Accepted: 10/24/2022] [Indexed: 12/24/2022]
Abstract
Computational methods have gained prominence in healthcare research. The accessibility of healthcare data has greatly incited academicians and researchers to develop executions that help in prognosis of cancer drug response. Among various computational methods, machine-learning (ML) and deep-learning (DL) methods provide the most consistent and effectual approaches to handle the serious aftermaths of the deadly disease and drug administered to the patients. Hence, this systematic literature review has reviewed researches that have investigated drug discovery and prognosis of anticancer drug response using ML and DL algorithms. Fot this purpose, PRISMA guidelines have been followed to choose research papers from Google Scholar, PubMed, and Sciencedirect websites. A total count of 105 papers that align with the context of this review were chosen. Further, the review also presents accuracy of the existing ML and DL methods in the prediction of anticancer drug response. It has been found from the review that, amidst the availability of various studies, there are certain challenges associated with each method. Thus, future researchers can consider these limitations and challenges to develop a prominent anticancer drug response prediction method, and it would be greatly beneficial to the medical professionals in administering non-invasive treatment to the patients.
Collapse
Affiliation(s)
- Davinder Paul Singh
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
| | - Baijnath Kaushik
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
| |
Collapse
|
50
|
ACPred-BMF: bidirectional LSTM with multiple feature representations for explainable anticancer peptide prediction. Sci Rep 2022; 12:21915. [PMID: 36535969 PMCID: PMC9763336 DOI: 10.1038/s41598-022-24404-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 11/15/2022] [Indexed: 12/24/2022] Open
Abstract
Cancer has become a major factor threatening human life and health. Under the circumstance that traditional treatment methods such as chemotherapy and radiotherapy are not highly specific and often cause severe side effects and toxicity, new treatment methods are urgently needed. Anticancer peptide drugs have low toxicity, stronger efficacy and specificity, and have emerged as a new type of cancer treatment drugs. However, experimental identification of anticancer peptides is time-consuming and expensive, and difficult to perform in a high-throughput manner. Computational identification of anticancer peptides can make up for the shortcomings of experimental identification. In this study, a deep learning-based predictor named ACPred-BMF is proposed for the prediction of anticancer peptides. This method uses the quantitative and qualitative properties of amino acids, binary profile feature to numerical representation for the peptide sequences. The Bidirectional LSTM network architecture is used in the model, and the attention mechanism is also considered. To alleviate the black-box problem of deep learning model prediction, we visualized the automatically extracted features and used the Shapley additive explanations algorithm to determine the importance of features to further understand the anticancer peptide mechanism. The results show that our method is one of the state-of-the-art anticancer peptide predictors. A web server as the implementation of ACPred-BMF that can be accessed via: http://mialab.ruc.edu.cn/ACPredBMFServer/ .
Collapse
|