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Pham NT, Terrance AT, Jeon YJ, Rakkiyappan R, Manavalan B. ac4C-AFL: A high-precision identification of human mRNA N4-acetylcytidine sites based on adaptive feature representation learning. MOLECULAR THERAPY. NUCLEIC ACIDS 2024; 35:102192. [PMID: 38779332 PMCID: PMC11108997 DOI: 10.1016/j.omtn.2024.102192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 04/18/2024] [Indexed: 05/25/2024]
Abstract
RNA N4-acetylcytidine (ac4C) is a highly conserved RNA modification that plays a crucial role in controlling mRNA stability, processing, and translation. Consequently, accurate identification of ac4C sites across the genome is critical for understanding gene expression regulation mechanisms. In this study, we have developed ac4C-AFL, a bioinformatics tool that precisely identifies ac4C sites from primary RNA sequences. In ac4C-AFL, we identified the optimal sequence length for model building and implemented an adaptive feature representation strategy that is capable of extracting the most representative features from RNA. To identify the most relevant features, we proposed a novel ensemble feature importance scoring strategy to rank features effectively. We then used this information to conduct the sequential forward search, which individually determine the optimal feature set from the 16 sequence-derived feature descriptors. Utilizing these optimal feature descriptors, we constructed 176 baseline models using 11 popular classifiers. The most efficient baseline models were identified using the two-step feature selection approach, whose predicted scores were integrated and trained with the appropriate classifier to develop the final prediction model. Our rigorous cross-validations and independent tests demonstrate that ac4C-AFL surpasses contemporary tools in predicting ac4C sites. Moreover, we have developed a publicly accessible web server at https://balalab-skku.org/ac4C-AFL/.
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Affiliation(s)
- Nhat Truong Pham
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea
| | - Annie Terrina Terrance
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea
| | - Young-Jun Jeon
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea
| | - Rajan Rakkiyappan
- Department of Mathematics, Bharathiar University, Coimbatore, Tamil Nadu 641046, India
| | - Balachandran Manavalan
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea
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2
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Basith S, Pham NT, Manavalan B, Lee G. SEP-AlgPro: An efficient allergen prediction tool utilizing traditional machine learning and deep learning techniques with protein language model features. Int J Biol Macromol 2024; 273:133085. [PMID: 38871100 DOI: 10.1016/j.ijbiomac.2024.133085] [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: 11/24/2023] [Revised: 05/20/2024] [Accepted: 06/09/2024] [Indexed: 06/15/2024]
Abstract
Allergy is a hypersensitive condition in which individuals develop objective symptoms when exposed to harmless substances at a dose that would cause no harm to a "normal" person. Most current computational methods for allergen identification rely on homology or conventional machine learning using limited set of feature descriptors or validation on specific datasets, making them inefficient and inaccurate. Here, we propose SEP-AlgPro for the accurate identification of allergen protein from sequence information. We analyzed 10 conventional protein-based features and 14 different features derived from protein language models to gauge their effectiveness in differentiating allergens from non-allergens using 15 different classifiers. However, the final optimized model employs top 10 feature descriptors with top seven machine learning classifiers. Results show that the features derived from protein language models exhibit superior discriminative capabilities compared to traditional feature sets. This enabled us to select the most discriminatory baseline models, whose predicted outputs were aggregated and used as input to a deep neural network for the final allergen prediction. Extensive case studies showed that SEP-AlgPro outperforms state-of-the-art predictors in accurately identifying allergens. A user-friendly web server was developed and made freely available at https://balalab-skku.org/SEP-AlgPro/, making it a powerful tool for identifying potential allergens.
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Affiliation(s)
- Shaherin Basith
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Republic of Korea.
| | - Nhat Truong Pham
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Balachandran Manavalan
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
| | - Gwang Lee
- Department of Physiology, Ajou University School of Medicine, Suwon 16499, Republic of Korea; Department of Molecular Science and Technology, Ajou University, Suwon 16499, Republic of Korea.
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3
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Peng Q, Jiang L, Shen Y, Xu Y, Shen X, Zou L, Zhu Y, Shen Y. LC-MS metabolomics analysis of serum metabolites during neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Clin Transl Oncol 2024:10.1007/s12094-024-03537-x. [PMID: 38831193 DOI: 10.1007/s12094-024-03537-x] [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: 04/24/2024] [Accepted: 05/18/2024] [Indexed: 06/05/2024]
Abstract
BACKGROUND This study aimed to investigate the serum metabolite profiles during neoadjuvant chemoradiotherapy (NCRT) in locally advanced rectal cancer (LARC) using liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis. METHODS 60 serum samples were collected from 20 patients with LARC before, during, and after radiotherapy. LC-MS metabolomics analysis was performed to identify the metabolite variations. Functional annotation was applied to discover altered metabolic pathways. The key metabolites were screened and their ability to predict sensitivity to radiotherapy was calculated using random forests and ROC curves. RESULTS The results showed that NCRT led to significant changes in the serum metabolite profiles. The serum metabolic profiles showed an apparent separation between different time points and different sensitivity groups. Moreover, the functional annotation showed that the differential metabolites were associated with a series of important metabolic pathways. Pre-radiotherapy (3Z,6Z)-3,6-Nonadiena and pro-radiotherapy 1-Hydroxyibuprofen showed good predictive performance in discriminating the sensitive and non-sensitive group to NCRT, with an AUC of 0.812 and 0.75, respectively. Importantly, the combination of different metabolites significantly increased the predictive ability. CONCLUSION This study demonstrated the potential of LC-MS metabolomics for revealing the serum metabolite profiles during NCRT in LARC. The identified metabolites may serve as potential biomarkers and therapeutic targets for the management of this disease. Furthermore, the understanding of the affected metabolic pathways may help design more personalized therapeutic strategies for LARC patients.
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Affiliation(s)
- Qiliang Peng
- Department of Radiotherapy and Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, China
| | - Lili Jiang
- Department of Oncology, Nantong Haimen District People's Hospital, Jiangsu, China
| | - Yi Shen
- Department of Radiation Oncology, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, China
| | - Yao Xu
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Xinan Shen
- Department of Radiotherapy and Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China
| | - Li Zou
- Department of Radiotherapy and Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China
| | - Yaqun Zhu
- Department of Radiotherapy and Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
- Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China.
| | - Yuntian Shen
- Department of Radiotherapy and Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
- Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China.
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4
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Coelho LP, Santos-Júnior CD, de la Fuente-Nunez C. Challenges in computational discovery of bioactive peptides in 'omics data. Proteomics 2024; 24:e2300105. [PMID: 38458994 DOI: 10.1002/pmic.202300105] [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: 10/13/2023] [Revised: 02/06/2024] [Accepted: 02/06/2024] [Indexed: 03/10/2024]
Abstract
Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.
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Affiliation(s)
- Luis Pedro Coelho
- Centre for Microbiome Research, School of Biomedical Sciences, Queensland University of Technology, Woolloongabba, Queensland, Australia
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
| | - Célio Dias Santos-Júnior
- Institute of Science and Technology for Brain-Inspired Intelligence - ISTBI, Fudan University, Shanghai, China
- Laboratory of Microbial Processes & Biodiversity - LMPB, Hydrobiology Department, Federal University of São Carlos - UFSCar, São Paulo, Brazil
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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5
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Liao YH, Chen SZ, Bin YN, Zhao JP, Feng XL, Zheng CH. UsIL-6: An unbalanced learning strategy for identifying IL-6 inducing peptides by undersampling technique. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108176. [PMID: 38677081 DOI: 10.1016/j.cmpb.2024.108176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 03/26/2024] [Accepted: 04/11/2024] [Indexed: 04/29/2024]
Abstract
BACKGROUND AND OBJECTIVE Interleukin-6 (IL-6) is the critical factor of early warning, monitoring, and prognosis in the inflammatory storm of COVID-19 cases. IL-6 inducing peptides, which can induce cytokine IL-6 production, are very important for the development of diagnosis and immunotherapy. Although the existing methods have some success in predicting IL-6 inducing peptides, there is still room for improvement in the performance of these models in practical application. METHODS In this study, we proposed UsIL-6, a high-performance bioinformatics tool for identifying IL-6 inducing peptides. First, we extracted five groups of physicochemical properties and sequence structural information from IL-6 inducing peptide sequences, and obtained a 636-dimensional feature vector, we also employed NearMiss3 undersampling method and normalization method StandardScaler to process the data. Then, a 40-dimensional optimal feature vector was obtained by Boruta feature selection method. Finally, we combined this feature vector with extreme randomization tree classifier to build the final model UsIL-6. RESULTS The AUC value of UsIL-6 on the independent test dataset was 0.87, and the BACC value was 0.808, which indicated that UsIL-6 had better performance than the existing methods in IL-6 inducing peptide recognition. CONCLUSIONS The performance comparison on independent test dataset confirmed that UsIL-6 could achieve the highest performance, best robustness, and most excellent generalization ability. We hope that UsIL-6 will become a valuable method to identify, annotate and characterize new IL-6 inducing peptides.
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Affiliation(s)
- Yan-Hong Liao
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China
| | - Shou-Zhi Chen
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China
| | - Yan-Nan Bin
- School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China
| | - Jian-Ping Zhao
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China.
| | - Xin-Long Feng
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China.
| | - Chun-Hou Zheng
- School of Mathematics and System Science, Xinjiang University, Urumqi, Xinjiang 830017, China; School of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China
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6
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Li F, Xu B, Lu Z, Chen J, Fu Y, Huang J, Wang Y, Li X. Hollow CoFe Nanozymes Integrated with Oncolytic Peptides Designed via Machine-Learning for Tumor Therapy. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2311101. [PMID: 38234132 DOI: 10.1002/smll.202311101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 12/23/2023] [Indexed: 01/19/2024]
Abstract
Developing novel substances to synergize with nanozymes is a challenging yet indispensable task to enable the nanozyme-based therapeutics to tackle individual variations in tumor physicochemical properties. The advancement of machine learning (ML) has provided a useful tool to enhance the accuracy and efficiency in developing synergistic substances. In this study, ML models to mine low-cytotoxicity oncolytic peptides are applied. The filtering Pipeline is constructed using a traversal design and the Autogluon framework. Through the Pipeline, 37 novel peptides with high oncolytic activity against cancer cells and low cytotoxicity to normal cells are identified from a library of 25,740 sequences. Combining dataset testing with cytotoxicity experiments, an 80% accuracy rate is achieved, verifying the reliability of ML predictions. Peptide C2 is proven to possess membranolytic functions specifically for tumor cells as targeted by Pipeline. Then Peptide C2 with CoFe hollow hydroxide nanozyme (H-CF) to form the peptide/H-CF composite is integrated. The new composite exhibited acid-triggered membranolytic function and potent peroxidase-like (POD-like) activity, which induce ferroptosis to tumor cells and inhibits tumor growth. The study suggests that this novel ML-assisted design approach can offer an accurate and efficient paradigm for developing both oncolytic peptides and synergistic peptides for catalytic materials.
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Affiliation(s)
- Feiyu Li
- State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou, 310058, China
- ZJU-Hangzhou Global Science and Technology Innovation Center, Zhejiang University, Hangzhou, 311215, China
| | - Bocheng Xu
- ZJU-Hangzhou Global Science and Technology Innovation Center, Zhejiang University, Hangzhou, 311215, China
- Institute of Feed Science, College of Animal Science, Zhejiang University, Hangzhou, 310058, China
| | - Zijie Lu
- State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou, 310058, China
- ZJU-Hangzhou Global Science and Technology Innovation Center, Zhejiang University, Hangzhou, 311215, China
| | - Jiafei Chen
- Affiliated Hospital of Stomatology, Medical College, Zhejiang University, Hangzhou, 310000, China
| | - Yike Fu
- State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou, 310058, China
- ZJU-Hangzhou Global Science and Technology Innovation Center, Zhejiang University, Hangzhou, 311215, China
| | - Jie Huang
- Department of Mechanical Engineering, University College London, London, WC1E 7JE, UK
| | - Yizhen Wang
- ZJU-Hangzhou Global Science and Technology Innovation Center, Zhejiang University, Hangzhou, 311215, China
- Institute of Feed Science, College of Animal Science, Zhejiang University, Hangzhou, 310058, China
| | - Xiang Li
- State Key Laboratory of Silicon and Advanced Semiconductor Materials, School of Materials Science and Engineering, Zhejiang University, Hangzhou, 310058, China
- ZJU-Hangzhou Global Science and Technology Innovation Center, Zhejiang University, Hangzhou, 311215, China
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7
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Jain S, Gupta S, Patiyal S, Raghava GPS. THPdb2: compilation of FDA approved therapeutic peptides and proteins. Drug Discov Today 2024; 29:104047. [PMID: 38830503 DOI: 10.1016/j.drudis.2024.104047] [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: 02/01/2024] [Revised: 04/30/2024] [Accepted: 05/29/2024] [Indexed: 06/05/2024]
Abstract
During the past 20 years, there has been a significant increase in the number of protein-based drugs approved by the US Food and Drug Administration (FDA). This paper presents THPdb2, an updated version of the THPdb database, which holds information about all types of protein-based drugs, including peptides, antibodies, and biosimilar proteins. THPdb2 contains a total of 6,385 entries, providing comprehensive information about 894 FDA-approved therapeutic proteins, including 354 monoclonal antibodies and 85 peptides or polypeptides. Each entry includes the name of therapeutic molecule, the amino acid sequence, physical and chemical properties, and route of drug administration. The therapeutic molecules that are included in the database target a wide range of biological molecules, such as receptors, factors, and proteins, and have been approved for the treatment of various diseases, including cancers, infectious diseases, and immune disorders.
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Affiliation(s)
- Shipra Jain
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
| | - Srijanee Gupta
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India
| | - Sumeet Patiyal
- Cancer and Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi 110020, India.
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8
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Puszkarska AM, Taddese B, Revell J, Davies G, Field J, Hornigold DC, Buchanan A, Vaughan TJ, Colwell LJ. Machine learning designs new GCGR/GLP-1R dual agonists with enhanced biological potency. Nat Chem 2024:10.1038/s41557-024-01532-x. [PMID: 38755312 DOI: 10.1038/s41557-024-01532-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 04/08/2024] [Indexed: 05/18/2024]
Abstract
Several peptide dual agonists of the human glucagon receptor (GCGR) and the glucagon-like peptide-1 receptor (GLP-1R) are in development for the treatment of type 2 diabetes, obesity and their associated complications. Candidates must have high potency at both receptors, but it is unclear whether the limited experimental data available can be used to train models that accurately predict the activity at both receptors of new peptide variants. Here we use peptide sequence data labelled with in vitro potency at human GCGR and GLP-1R to train several models, including a deep multi-task neural-network model using multiple loss optimization. Model-guided sequence optimization was used to design three groups of peptide variants, with distinct ranges of predicted dual activity. We found that three of the model-designed sequences are potent dual agonists with superior biological activity. With our designs we were able to achieve up to sevenfold potency improvement at both receptors simultaneously compared to the best dual-agonist in the training set.
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Affiliation(s)
- Anna M Puszkarska
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
- Biologics Engineering, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Bruck Taddese
- Discovery Sciences, R&D, AstraZeneca, Cambridge, UK
- Biologics Center (NBC) at the Novartis Institute for BioMedical Research (NIBR), Basel, Switzerland
| | | | - Graeme Davies
- Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Joss Field
- Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - David C Hornigold
- Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Andrew Buchanan
- Biologics Engineering, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Tristan J Vaughan
- Biologics Engineering, Oncology R&D, AstraZeneca, Cambridge, UK
- Immunocore Ltd., Abingdon, UK
| | - Lucy J Colwell
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.
- Google DeepMind, Cambridge, MA, USA.
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9
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Niu Y, Li Z, Chen Z, Huang W, Tan J, Tian F, Yang T, Fan Y, Wei J, Mu J. Efficient screening of pharmacological broad-spectrum anti-cancer peptides utilizing advanced bidirectional Encoder representation from Transformers strategy. Heliyon 2024; 10:e30373. [PMID: 38765108 PMCID: PMC11101728 DOI: 10.1016/j.heliyon.2024.e30373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 04/24/2024] [Accepted: 04/24/2024] [Indexed: 05/21/2024] Open
Abstract
In the vanguard of oncological advancement, this investigation delineates the integration of deep learning paradigms to refine the screening process for Anticancer Peptides (ACPs), epitomizing a new frontier in broad-spectrum oncolytic therapeutics renowned for their targeted antitumor efficacy and specificity. Conventional methodologies for ACP identification are marred by prohibitive time and financial exigencies, representing a formidable impediment to the evolution of precision oncology. In response, our research heralds the development of a groundbreaking screening apparatus that marries Natural Language Processing (NLP) with the Pseudo Amino Acid Composition (PseAAC) technique, thereby inaugurating a comprehensive ACP compendium for the extraction of quintessential primary and secondary structural attributes. This innovative methodological approach is augmented by an optimized BERT model, meticulously calibrated for ACP detection, which conspicuously surpasses existing BERT variants and traditional machine learning algorithms in both accuracy and selectivity. Subjected to rigorous validation via five-fold cross-validation and external assessment, our model exhibited exemplary performance, boasting an average Area Under the Curve (AUC) of 0.9726 and an F1 score of 0.9385, with external validation further affirming its prowess (AUC of 0.9848 and F1 of 0.9371). These findings vividly underscore the method's unparalleled efficacy and prospective utility in the precise identification and prognostication of ACPs, significantly ameliorating the financial and temporal burdens traditionally associated with ACP research and development. Ergo, this pioneering screening paradigm promises to catalyze the discovery and clinical application of ACPs, constituting a seminal stride towards the realization of more efficacious and economically viable precision oncology interventions.
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Affiliation(s)
- Yupeng Niu
- College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China
- Artificial intelligence laboratory, Sichuan Agricultural University, Ya'an 625000, China
| | - Zhenghao Li
- College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China
- Artificial intelligence laboratory, Sichuan Agricultural University, Ya'an 625000, China
| | - Ziao Chen
- College of Law, Sichuan Agricultural University, Ya'an 625000, China
- Artificial intelligence laboratory, Sichuan Agricultural University, Ya'an 625000, China
| | - Wenyuan Huang
- College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China
- Artificial intelligence laboratory, Sichuan Agricultural University, Ya'an 625000, China
| | - Jingxuan Tan
- College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China
- Artificial intelligence laboratory, Sichuan Agricultural University, Ya'an 625000, China
| | - Fa Tian
- College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China
| | - Tao Yang
- College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China
- Artificial intelligence laboratory, Sichuan Agricultural University, Ya'an 625000, China
| | - Yamin Fan
- College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China
- Artificial intelligence laboratory, Sichuan Agricultural University, Ya'an 625000, China
| | - Jiangshu Wei
- College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China
| | - Jiong Mu
- College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China
- Artificial intelligence laboratory, Sichuan Agricultural University, Ya'an 625000, China
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10
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Dash R, Jabbari E. A Structure Independent Molecular Fragment Interfuse Model for Mesoscale Dissipative Particle Dynamics Simulation of Peptides. ACS OMEGA 2024; 9:18001-18022. [PMID: 38680324 PMCID: PMC11044228 DOI: 10.1021/acsomega.3c09534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/07/2024] [Accepted: 04/02/2024] [Indexed: 05/01/2024]
Abstract
There is a need to develop robust computational models for mesoscale simulation of the structure of peptides over large length scales toward the discovery of novel peptides for medical applications to address the issues of peptide aggregation, enzymatic degradation, and short half-life. The primary objective was to predict the structure and conformation of peptides whose native structures are not known. This work presents a new model for computation of interaction parameters between the beads in coarse-grained dissipative particle dynamics (DPD) simulation that is properly calibrated for amino acids, supports compressibility requirement of water molecules, and accounts for subtle differences in the structure of amino acids and the charge in the side chain of charged amino acids. This new model is referred to as Structure Independent Molecular Fragment Interfuse Model, abbreviated as SIMFIM, because it accounts for specific interactions between different beads, which represent molecular fragments of the amino acids, in calculating nonbonded interaction parameters in the absence of knowing the actual peptide structure. The electrostatic interactions are incorporated in this model by using a normal distribution of charges around the center of the beads to prevent the collapse of oppositely charged soft beads. The uniquely parameterized DPD force field in the SIMFIM model is optimized for a given peptide with respect to the degree of coarse-grained graining for simulating the peptide over long times and length scales. The SIMFIM model was tested in this work using four peptides, namely, TrpZip2, Rubrivinodin, Lihuanodin, and IC3-CB1/Gai peptides, whose structures were sourced from the Protein Data Bank. The SIMFIM model predicted radius of gyration (Rg) values for the peptides closer to the actual structures as compared to the conventional model, and there was less deviation between the predicted and actual structures of the peptides.
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Affiliation(s)
- Ricky
Anshuman Dash
- Biomimetic Materials and
Tissue Engineering Laboratory, Chemical Engineering Department, University of South Carolina, 301 Main Street, Columbia, South Carolina 29208, United States
| | - Esmaiel Jabbari
- Biomimetic Materials and
Tissue Engineering Laboratory, Chemical Engineering Department, University of South Carolina, 301 Main Street, Columbia, South Carolina 29208, United States
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11
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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.
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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
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12
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Scalzitti N, Miralavy I, Korenchan DE, Farrar CT, Gilad AA, Banzhaf W. Computational peptide discovery with a genetic programming approach. J Comput Aided Mol Des 2024; 38:17. [PMID: 38570405 DOI: 10.1007/s10822-024-00558-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/07/2024] [Indexed: 04/05/2024]
Abstract
The development of peptides for therapeutic targets or biomarkers for disease diagnosis is a challenging task in protein engineering. Current approaches are tedious, often time-consuming and require complex laboratory data due to the vast search spaces that need to be considered. In silico methods can accelerate research and substantially reduce costs. Evolutionary algorithms are a promising approach for exploring large search spaces and can facilitate the discovery of new peptides. This study presents the development and use of a new variant of the genetic-programming-based POET algorithm, called POETRegex , where individuals are represented by a list of regular expressions. This algorithm was trained on a small curated dataset and employed to generate new peptides improving the sensitivity of peptides in magnetic resonance imaging with chemical exchange saturation transfer (CEST). The resulting model achieves a performance gain of 20% over the initial POET models and is able to predict a candidate peptide with a 58% performance increase compared to the gold-standard peptide. By combining the power of genetic programming with the flexibility of regular expressions, new peptide targets were identified that improve the sensitivity of detection by CEST. This approach provides a promising research direction for the efficient identification of peptides with therapeutic or diagnostic potential.
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Affiliation(s)
- Nicolas Scalzitti
- BEACON Center of Evolution in Action, Michigan State University, East Lansing, MI, USA
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Iliya Miralavy
- BEACON Center of Evolution in Action, Michigan State University, East Lansing, MI, USA
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - David E Korenchan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Christian T Farrar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Assaf A Gilad
- BEACON Center of Evolution in Action, Michigan State University, East Lansing, MI, USA.
- Department of Chemical Engineering, Michigan State University, East Lansing, MI, USA.
- Department of Radiology, Michigan State University, East Lansing, MI, USA.
| | - Wolfgang Banzhaf
- BEACON Center of Evolution in Action, Michigan State University, East Lansing, MI, USA.
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA.
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13
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Balaji PD, Selvam S, Sohn H, Madhavan T. MLASM: Machine learning based prediction of anticancer small molecules. Mol Divers 2024:10.1007/s11030-024-10823-x. [PMID: 38554168 DOI: 10.1007/s11030-024-10823-x] [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: 01/03/2024] [Accepted: 02/10/2024] [Indexed: 04/01/2024]
Abstract
Cancer, being the second leading cause of death globally. So, the development of effective anticancer treatments is crucial in the field of medicine. Anticancer peptides (ACPs) have shown promising therapeutic potential in cancer treatment compared to traditional methods. However, the process of identifying ACPs through experimental means is often time-intensive and expensive. To overcome this issue, we employed a machine learning-based approach for the first time to develop an anticancer model using small molecules. Anticancer small molecules (ACSMs) are compounds that have been developed to target and inhibit cancer cells. In this study, we used 10,000 compounds to develop the machine learning models using five algorithms such as, Random Forest (RF), Light gradient boosting machine (LightGBM), K-nearest neighbors (KNN), Decision tree (DT) and Extreme Gradient Boosting (XGB). The developed models were evaluated using the test set and top three models were identified (RF, LightGBM and XGB). Furthermore, to validate the predictive performance of our models, we have performed external validation using an FDA approved anticancer compounds/drugs. Following this analysis, we found that our LightGBM model correctly predicted 9 compounds as active. However, RF and XGB exhibited some limitations by predicting 8 and 7 compounds as active out of 10, respectively. These results demonstrate that, when compared to RF and XGB, the LightGBM model showcase robust prediction capabilities, achieving a superior accuracy of 79% with an AUC of 0.88. These findings provide promising insights into the potential of our approach for predicting anticancer small molecules, highlighting the role of machine learning in advancing cancer treatment research.
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Affiliation(s)
- Priya Dharshini Balaji
- Computational Biology Laboratory, Department of Genetic Engineering, School of Bio-Engineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, 603203, India
| | - Subathra Selvam
- Computational Biology Laboratory, Department of Genetic Engineering, School of Bio-Engineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, 603203, India
| | - Honglae Sohn
- Department of Chemistry, Department of Carbon Materials, Chosun University, Gwangju, South Korea
| | - Thirumurthy Madhavan
- Computational Biology Laboratory, Department of Genetic Engineering, School of Bio-Engineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, 603203, India.
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14
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Zhao Z, Laps S, Gichtin JS, Metanis N. Selenium chemistry for spatio-selective peptide and protein functionalization. Nat Rev Chem 2024; 8:211-229. [PMID: 38388838 DOI: 10.1038/s41570-024-00579-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/15/2024] [Indexed: 02/24/2024]
Abstract
The ability to construct a peptide or protein in a spatio-specific manner is of great interest for therapeutic and biochemical research. However, the various functional groups present in peptide sequences and the need to perform chemistry under mild and aqueous conditions make selective protein functionalization one of the greatest synthetic challenges. The fascinating paradox of selenium (Se) - being found in both toxic compounds and also harnessed by nature for essential biochemical processes - has inspired the recent exploration of selenium chemistry for site-selective functionalization of peptides and proteins. In this Review, we discuss such approaches, including metal-free and metal-catalysed transformations, as well as traceless chemical modifications. We report their advantages, limitations and applications, as well as future research avenues.
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Affiliation(s)
- Zhenguang Zhao
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Shay Laps
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Jacob S Gichtin
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Norman Metanis
- Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel.
- Casali Center for Applied Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel.
- The Center for Nanoscience and Nanotechnology, The Hebrew University of Jerusalem, Jerusalem, Israel.
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15
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Almeida JR. The Century-Long Journey of Peptide-Based Drugs. Antibiotics (Basel) 2024; 13:196. [PMID: 38534631 DOI: 10.3390/antibiotics13030196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 02/17/2024] [Indexed: 03/28/2024] Open
Abstract
The pioneering medical application of peptides as therapeutics began approximately a century ago; however, they remain clinically relevant candidates garnering more attention on the drug development agenda [...].
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Affiliation(s)
- José R Almeida
- Biomolecules Discovery Group, Universidad Regional Amazónica Ikiam, Km 7 Via Muyuna, Tena 150101, Ecuador
- School of Pharmacy, University of Reading, Reading RG6 6UB, UK
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16
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Pan X, Li Y, Huang P, Staecker H, He M. Extracellular vesicles for developing targeted hearing loss therapy. J Control Release 2024; 366:460-478. [PMID: 38182057 DOI: 10.1016/j.jconrel.2023.12.050] [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: 10/12/2023] [Revised: 12/19/2023] [Accepted: 12/28/2023] [Indexed: 01/07/2024]
Abstract
Substantial efforts have been made for local administration of small molecules or biologics in treating hearing loss diseases caused by either trauma, genetic mutations, or drug ototoxicity. Recently, extracellular vesicles (EVs) naturally secreted from cells have drawn increasing attention on attenuating hearing impairment from both preclinical studies and clinical studies. Highly emerging field utilizing diverse bioengineering technologies for developing EVs as the bioderived therapeutic materials, along with artificial intelligence (AI)-based targeting toolkits, shed the light on the unique properties of EVs specific to inner ear delivery. This review will illuminate such exciting research field from fundamentals of hearing protective functions of EVs to biotechnology advancement and potential clinical translation of functionalized EVs. Specifically, the advancements in assessing targeting ligands using AI algorithms are systematically discussed. The overall translational potential of EVs is reviewed in the context of auditory sensing system for developing next generation gene therapy.
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Affiliation(s)
- Xiaoshu Pan
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, Florida 32610, United States
| | - Yanjun Li
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, Florida 32610, United States
| | - Peixin Huang
- Department of Otolaryngology, Head and Neck Surgery, University of Kansas School of Medicine, Kansas City, Kansas 66160, United States
| | - Hinrich Staecker
- Department of Otolaryngology, Head and Neck Surgery, University of Kansas School of Medicine, Kansas City, Kansas 66160, United States.
| | - Mei He
- Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, Florida 32610, United States.
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17
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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.
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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.
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18
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Feijoo-Coronel ML, Mendes B, Ramírez D, Peña-Varas C, de los Monteros-Silva NQE, Proaño-Bolaños C, de Oliveira LC, Lívio DF, da Silva JA, da Silva JMSF, Pereira MGAG, Rodrigues MQRB, Teixeira MM, Granjeiro PA, Patel K, Vaiyapuri S, Almeida JR. Antibacterial and Antiviral Properties of Chenopodin-Derived Synthetic Peptides. Antibiotics (Basel) 2024; 13:78. [PMID: 38247637 PMCID: PMC10812719 DOI: 10.3390/antibiotics13010078] [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: 11/29/2023] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 01/23/2024] Open
Abstract
Antimicrobial peptides have been developed based on plant-derived molecular scaffolds for the treatment of infectious diseases. Chenopodin is an abundant seed storage protein in quinoa, an Andean plant with high nutritional and therapeutic properties. Here, we used computer- and physicochemical-based strategies and designed four peptides derived from the primary structure of Chenopodin. Two peptides reproduce natural fragments of 14 amino acids from Chenopodin, named Chen1 and Chen2, and two engineered peptides of the same length were designed based on the Chen1 sequence. The two amino acids of Chen1 containing amide side chains were replaced by arginine (ChenR) or tryptophan (ChenW) to generate engineered cationic and hydrophobic peptides. The evaluation of these 14-mer peptides on Staphylococcus aureus and Escherichia coli showed that Chen1 does not have antibacterial activity up to 512 µM against these strains, while other peptides exhibited antibacterial effects at lower concentrations. The chemical substitutions of glutamine and asparagine by amino acids with cationic or aromatic side chains significantly favoured their antibacterial effects. These peptides did not show significant hemolytic activity. The fluorescence microscopy analysis highlighted the membranolytic nature of Chenopodin-derived peptides. Using molecular dynamic simulations, we found that a pore is formed when multiple peptides are assembled in the membrane. Whereas, some of them form secondary structures when interacting with the membrane, allowing water translocations during the simulations. Finally, Chen2 and ChenR significantly reduced SARS-CoV-2 infection. These findings demonstrate that Chenopodin is a highly useful template for the design, engineering, and manufacturing of non-toxic, antibacterial, and antiviral peptides.
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Affiliation(s)
- Marcia L. Feijoo-Coronel
- Biomolecules Discovery Group, Universidad Regional Amazónica Ikiam, Km 7 Via Muyuna, Tena 150101, Ecuador
| | - Bruno Mendes
- Biomolecules Discovery Group, Universidad Regional Amazónica Ikiam, Km 7 Via Muyuna, Tena 150101, Ecuador
| | - David Ramírez
- Departamento de Farmacología, Facultad de Ciencias Biológicas, Universidad de Concepción, Concepción 4030000, Chile
| | - Carlos Peña-Varas
- Departamento de Farmacología, Facultad de Ciencias Biológicas, Universidad de Concepción, Concepción 4030000, Chile
| | | | - Carolina Proaño-Bolaños
- Biomolecules Discovery Group, Universidad Regional Amazónica Ikiam, Km 7 Via Muyuna, Tena 150101, Ecuador
| | - Leonardo Camilo de Oliveira
- Centro de Pesquisa e Desenvolvimento de Fármacos, Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Federal University of Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Diego Fernandes Lívio
- Campus Centro Oeste, Federal University of São João Del-Rei, Rua Sebastião Gonçalves Filho, n 400, Chanadour, Divinópolis 35501-296, Brazil
| | - José Antônio da Silva
- Campus Centro Oeste, Federal University of São João Del-Rei, Rua Sebastião Gonçalves Filho, n 400, Chanadour, Divinópolis 35501-296, Brazil
| | - José Maurício S. F. da Silva
- Departamento de Bioquímica, Centro de Ciências Biomédicas, Federal University of Alfenas, Rua Gabriel Monteiro da Silva, 700, Sala E209, Alfenas 37130-001, Brazil
| | - Marília Gabriella A. G. Pereira
- Departamento de Bioquímica, Centro de Ciências Biomédicas, Federal University of Alfenas, Rua Gabriel Monteiro da Silva, 700, Sala E209, Alfenas 37130-001, Brazil
| | - Marina Q. R. B. Rodrigues
- Departamento de Bioquímica, Centro de Ciências Biomédicas, Federal University of Alfenas, Rua Gabriel Monteiro da Silva, 700, Sala E209, Alfenas 37130-001, Brazil
- Departamento de Engenharia de Biossistemas, Campus Dom Bosco, Federal University of São João Del-Rei, Praça Dom Helvécio, 74, Fábricas, São João del-Rei 36301-160, Brazil
| | - Mauro M. Teixeira
- Centro de Pesquisa e Desenvolvimento de Fármacos, Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Federal University of Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Paulo Afonso Granjeiro
- Campus Centro Oeste, Federal University of São João Del-Rei, Rua Sebastião Gonçalves Filho, n 400, Chanadour, Divinópolis 35501-296, Brazil
| | - Ketan Patel
- School of Biological Sciences, University of Reading, Reading RG6 6UB, UK
| | | | - José R. Almeida
- Biomolecules Discovery Group, Universidad Regional Amazónica Ikiam, Km 7 Via Muyuna, Tena 150101, Ecuador
- School of Pharmacy, University of Reading, Reading RG6 6UB, UK
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19
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Peng Q, Tao J, Xu Y, Shen Y, Wang Y, Jiao Y, Mao Y, Zhu Y, Liu Y, Tian Y. Lipid metabolism-associated genes serve as potential predictive biomarkers in neoadjuvant chemoradiotherapy combined with immunotherapy in rectal cancer. Transl Oncol 2024; 39:101828. [PMID: 38000147 DOI: 10.1016/j.tranon.2023.101828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 10/26/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND The aim of this study was to investigate the potential role of lipid metabolism-associated genes (LMAGs) in neoadjuvant chemoradiotherapy (nCRT) and immunotherapy for rectal cancer. METHODS Differential LMAGs were characterized and functional enrichment analysis was performed. Multiple machine learning algorithms were combined to explore candidate LMAGs. ROC analysis was performed to evaluate the predicting accuracy of candidate LMAGs. The expression patterns, prognostic value, genetic alterations, and immune cell infiltration of the top-ranked LMAGs were investigated. RESULTS We identified 45 LMAGs that were differentially expressed in tumor samples of nCRT responders and non-responders. These LMAGs were closely associated with lipid metabolism-related biological processes and pathways. ROC analysis revealed that the SREBF2 gene, an important transcription factor in regulating lipid metabolism, was the highest predictor of nCRT in rectal cancer. SREBF2 was highly expressed in rectal cancer tissues and high expression of SREBF2 was associated with favorable prognosis. Multivariate analysis showed that SREBF2 was an independent prognostic factor, and we integrated it with other clinical factors to establish an effective prognostic nomogram. SREBF2 also played a synergistic role with its co-expressed genes in the prognostic process of rectal cancer. Furthermore, SREBF2 was demonstrated to be closely associated with multiple immune infiltrating cells, and immunotherapy-related genes and may be used to predict the response to immunotherapy. CONCLUSION Our study suggests that LMAGs may serve as promising biomarkers in nCRT combined with immunotherapy for rectal cancer. However, large-scale clinical trials and biological experiments are necessary to demonstrate the efficacy and underlying mechanisms.
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Affiliation(s)
- Qiliang Peng
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China; Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China; State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, China
| | - Jialong Tao
- Department of Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yingjie Xu
- Department of Cardiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yi Shen
- Department of Radiation Oncology, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, China
| | - Yong Wang
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China; Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China
| | - Yang Jiao
- Re-Stem Biotechnology Co., Ltd, Suzhou, China
| | - Yiheng Mao
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China; Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China
| | - Yaqun Zhu
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China; Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China.
| | - Yulong Liu
- Department of Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China; State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Medical College of Soochow University, Suzhou, China.
| | - Ye Tian
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, China; Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China.
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20
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Feng H, Wang F, Li N, Xu Q, Zheng G, Sun X, Hu M, Li X, Xing G, Zhang G. Use of tree-based machine learning methods to screen affinitive peptides based on docking data. Mol Inform 2023; 42:e202300143. [PMID: 37696773 DOI: 10.1002/minf.202300143] [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: 06/13/2023] [Revised: 09/03/2023] [Accepted: 09/11/2023] [Indexed: 09/13/2023]
Abstract
Screening peptides with good affinity is an important step in peptide-drug discovery. Recent advancement in computer and data science have made machine learning a useful tool in accurately affinitive-peptide screening. In current study, four different tree-based algorithms, including Classification and regression trees (CART), C5.0 decision tree (C50), Bagged CART (BAG) and Random Forest (RF), were employed to explore the relationship between experimental peptide affinities and virtual docking data, and the performance of each model was also compared in parallel. All four algorithms showed better performances on dataset pre-scaled, -centered and -PCA than other pre-processed dataset. After model re-built and hyperparameter optimization, the optimal C50 model (C50O) showed the best performances in terms of Accuracy, Kappa, Sensitivity, Specificity, F1, MCC and AUC when validated on test data and an unknown PEDV datasets evaluation (Accuracy=80.4 %). BAG and RFO (the optimal RF), as two best models during training process, did not performed as expecting during in testing and unknown dataset validations. Furthermore, the high correlation of the predictions of RFO and BAG to C50O implied the high stability and robustness of their prediction. Whereas although the good performance on unknown dataset, the poor performance in test data validation and correlation analysis indicated CARTO could not be used for future data prediction. To accurately evaluate the peptide affinity, the current study firstly gave a tree-model competition on affinitive peptide prediction by using virtual docking data, which would expand the application of machine learning algorithms in studying PepPIs and benefit the development of peptide therapeutics.
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Affiliation(s)
- Hua Feng
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Fangyu Wang
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Ning Li
- College of Food Science and Technology, Henan Agricultural University, Zhengzhou, China
| | - Qian Xu
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Guanming Zheng
- Public Health and Preventive Medicine Teaching and Research Center, Henan University of Chinese Medicine, Zhengzhou, Henan, China
| | - Xuefeng Sun
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Man Hu
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Xuewu Li
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Guangxu Xing
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
| | - Gaiping Zhang
- Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China
- Longhu Modern Immunology Laboratory, Zhengzhou, China
- School of Advanced Agricultural sciences, Peking University, Beijing, China
- Jiangsu Co-Innovation Center for the Prevention and Control of Important Animal Infectious Diseases and Zoonoses, Yangzhou University, Yangzhou, Jiangsu, China
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21
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Pham NT, Rakkiyapan R, Park J, Malik A, Manavalan B. H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA. Brief Bioinform 2023; 25:bbad476. [PMID: 38180830 PMCID: PMC10768780 DOI: 10.1093/bib/bbad476] [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: 09/30/2023] [Revised: 11/22/2023] [Accepted: 11/28/2023] [Indexed: 01/07/2024] Open
Abstract
2'-O-methylation (2OM) is the most common post-transcriptional modification of RNA. It plays a crucial role in RNA splicing, RNA stability and innate immunity. Despite advances in high-throughput detection, the chemical stability of 2OM makes it difficult to detect and map in messenger RNA. Therefore, bioinformatics tools have been developed using machine learning (ML) algorithms to identify 2OM sites. These tools have made significant progress, but their performances remain unsatisfactory and need further improvement. In this study, we introduced H2Opred, a novel hybrid deep learning (HDL) model for accurately identifying 2OM sites in human RNA. Notably, this is the first application of HDL in developing four nucleotide-specific models [adenine (A2OM), cytosine (C2OM), guanine (G2OM) and uracil (U2OM)] as well as a generic model (N2OM). H2Opred incorporated both stacked 1D convolutional neural network (1D-CNN) blocks and stacked attention-based bidirectional gated recurrent unit (Bi-GRU-Att) blocks. 1D-CNN blocks learned effective feature representations from 14 conventional descriptors, while Bi-GRU-Att blocks learned feature representations from five natural language processing-based embeddings extracted from RNA sequences. H2Opred integrated these feature representations to make the final prediction. Rigorous cross-validation analysis demonstrated that H2Opred consistently outperforms conventional ML-based single-feature models on five different datasets. Moreover, the generic model of H2Opred demonstrated a remarkable performance on both training and testing datasets, significantly outperforming the existing predictor and other four nucleotide-specific H2Opred models. To enhance accessibility and usability, we have deployed a user-friendly web server for H2Opred, accessible at https://balalab-skku.org/H2Opred/. This platform will serve as an invaluable tool for accurately predicting 2OM sites within human RNA, thereby facilitating broader applications in relevant research endeavors.
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Affiliation(s)
- Nhat Truong Pham
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Rajan Rakkiyapan
- Department of Mathematics, Bharathiar University, Coimbatore - 641046, Tamil Nadu, India
| | - Jongsun Park
- InfoBoss inc. and InfoBoss Research Center, Gangnam-gu, Seoul 06278, Republic of Korea
| | - Adeel Malik
- Institute of Intelligence Informatics Technology, Sangmyung University, Seoul, 03016, Republic of Korea
| | - Balachandran Manavalan
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea
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22
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Yu S, Liao B, Zhu W, Peng D, Wu F. Accurate prediction and key protein sequence feature identification of cyclins. Brief Funct Genomics 2023; 22:411-419. [PMID: 37118891 DOI: 10.1093/bfgp/elad014] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 03/03/2023] [Accepted: 03/17/2023] [Indexed: 04/30/2023] Open
Abstract
Cyclin proteins are a group of proteins that activate the cell cycle by forming complexes with cyclin-dependent kinases. Identifying cyclins correctly can provide key clues to understanding the function of cyclins. However, due to the low similarity between cyclin protein sequences, the advancement of a machine learning-based approach to identify cycles is urgently needed. In this study, cyclin protein sequence features were extracted using the profile-based auto-cross covariance method. Then the features were ranked and selected with maximum relevance-maximum distance (MRMD) 1.0 and MRMD2.0. Finally, the prediction model was assessed through 10-fold cross-validation. The computational experiments showed that the best protein sequence features generated by MRMD1.0 could correctly predict 98.2% of cyclins using the random forest (RF) classifier, whereas seven-dimensional key protein sequence features identified with MRMD2.0 could correctly predict 96.1% of cyclins, which was superior to previous studies on the same dataset both in terms of dimensionality and performance comparisons. Therefore, our work provided a valuable tool for identifying cyclins. The model data can be downloaded from https://github.com/YUshunL/cyclin.
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Affiliation(s)
- Shaoyou Yu
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Bo Liao
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Wen Zhu
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Dejun Peng
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Fangxiang Wu
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
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23
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Ferreira R, Amado F, Vitorino R. Empowering peptidomics: utilizing computational tools and approaches. Bioanalysis 2023; 15:1315-1325. [PMID: 37737150 DOI: 10.4155/bio-2023-0102] [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] [Indexed: 09/23/2023] Open
Abstract
Bioinformatics plays a critical role in the advancement of peptidomics by providing powerful tools for data analysis, interpretation and integration. Peptidomics is concerned with the study of peptides, short chains of amino acids with diverse biological functions. This area includes peptide identification and characterization, database construction, de novo sequencing, functional annotation, omics data integration and systems biology. Artificial intelligence techniques, such as machine learning and natural language processing, aid in the interpretation of peptide sequence data and the generation of biological insights. By using bioinformatics approaches, peptidomics researchers can accelerate peptide discovery, understand their functions and gain insights into complex molecular interactions.
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Affiliation(s)
- Rita Ferreira
- LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Francisco Amado
- LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Rui Vitorino
- LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal
- Unidade de Investigação Cardiovascular, Departamento de Cirurgia e Fisiologia, Universidade do Porto, Porto, Portugal
- iBiMED, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
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24
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Lv H, Yan K, Liu B. TPpred-LE: therapeutic peptide function prediction based on label embedding. BMC Biol 2023; 21:238. [PMID: 37904157 PMCID: PMC10617231 DOI: 10.1186/s12915-023-01740-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 10/17/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND Therapeutic peptides play an essential role in human physiology, treatment paradigms and bio-pharmacy. Several computational methods have been developed to identify the functions of therapeutic peptides based on binary classification and multi-label classification. However, these methods fail to explicitly exploit the relationship information among different functions, preventing the further improvement of the prediction performance. Besides, with the development of peptide detection technology, peptide functions will be more comprehensively discovered. Therefore, it is necessary to explore computational methods for detecting therapeutic peptide functions with limited labeled data. RESULTS In this study, a novel method called TPpred-LE based on Transformer framework was proposed for predicting therapeutic peptide multiple functions, which can explicitly extract the function correlation information by using label embedding methodology and exploit the specificity information based on function-specific classifiers. Besides, we incorporated the multi-label classifier retraining approach (MCRT) into TPpred-LE to detect the new therapeutic functions with limited labeled data. Experimental results demonstrate that TPpred-LE outperforms the other state-of-the-art methods, and TPpred-LE with MCRT is robust for the limited labeled data. CONCLUSIONS In summary, TPpred-LE is a function-specific classifier for accurate therapeutic peptide function prediction, demonstrating the importance of the relationship information for therapeutic peptide function prediction. MCRT is a simple but effective strategy to detect functions with limited labeled data.
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Affiliation(s)
- Hongwu Lv
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Ke Yan
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, No. 5, South Zhongguancun Street, Haidian District, Beijing, 100081, China.
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25
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Guan C, Luo J, Li S, Tan ZL, Wang Y, Chen H, Yamamoto N, Zhang C, Lu Y, Chen J, Xing XH. Exploration of DPP-IV Inhibitory Peptide Design Rules Assisted by the Deep Learning Pipeline That Identifies the Restriction Enzyme Cutting Site. ACS OMEGA 2023; 8:39662-39672. [PMID: 37901493 PMCID: PMC10601436 DOI: 10.1021/acsomega.3c05571] [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: 07/30/2023] [Accepted: 09/27/2023] [Indexed: 10/31/2023]
Abstract
The mining of antidiabetic dipeptidyl peptidase IV (DPP-IV) inhibitory peptides (DPP-IV-IPs) is currently a costly and laborious process. Due to the absence of rational peptide design rules, it relies on cumbersome screening of unknown enzyme hydrolysates. Here, we present an enhanced deep learning model called bidirectional encoder representation (BERT)-DPPIV, specifically designed to classify DPP-IV-IPs and explore their design rules to discover potent candidates. The end-to-end model utilizes a fine-tuned BERT architecture to extract structural/functional information from input peptides and accurately identify DPP-IV-Ips from input peptides. Experimental results in the benchmark data set showed BERT-DPPIV yielded state-of-the-art accuracy and MCC of 0.894 and 0.790, surpassing the 0.797 and 0.594 obtained by the sequence-feature model. Furthermore, we leveraged the attention mechanism to uncover that our model could recognize the restriction enzyme cutting site and specific residues that contribute to the inhibition of DPP-IV. Moreover, guided by BERT-DPPIV, proposed design rules for DPP-IV inhibitory tripeptides and pentapeptides were validated, and they can be used to screen potent DPP-IV-IPs.
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Affiliation(s)
- Changge Guan
- Key
Laboratory for Industrial Biocatalysis, Ministry of Education of China,
Department of Chemical Engineering, Tsinghua
University, Beijing 100084, China
| | - Jiawei Luo
- Department
of Computer Science and Technology, Harbin
Institute of Technology, Shenzhen 518055, China
| | - Shucheng Li
- Key
Laboratory for Industrial Biocatalysis, Ministry of Education of China,
Department of Chemical Engineering, Tsinghua
University, Beijing 100084, China
| | - Zheng Lin Tan
- School
of Life Science and Technology, Tokyo Institute
of Technology, 4259 Nagatsutacho, Midori Ward, Yokohama,
Kanagawa Prefecture 226-0026, Japan
| | - Yi Wang
- Key
Laboratory for Industrial Biocatalysis, Ministry of Education of China,
Department of Chemical Engineering, Tsinghua
University, Beijing 100084, China
| | - Haihong Chen
- Institute
of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China
- Institute
of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518118, China
| | - Naoyuki Yamamoto
- School
of Life Science and Technology, Tokyo Institute
of Technology, 4259 Nagatsutacho, Midori Ward, Yokohama,
Kanagawa Prefecture 226-0026, Japan
| | - Chong Zhang
- Key
Laboratory for Industrial Biocatalysis, Ministry of Education of China,
Department of Chemical Engineering, Tsinghua
University, Beijing 100084, China
- Center
for Synthetic and Systems Biology, Tsinghua
University, Beijing 100084, China
| | - Yuan Lu
- Key
Laboratory for Industrial Biocatalysis, Ministry of Education of China,
Department of Chemical Engineering, Tsinghua
University, Beijing 100084, China
| | - Junjie Chen
- Department
of Computer Science and Technology, Harbin
Institute of Technology, Shenzhen 518055, China
| | - Xin-Hui Xing
- Key
Laboratory for Industrial Biocatalysis, Ministry of Education of China,
Department of Chemical Engineering, Tsinghua
University, Beijing 100084, China
- Institute
of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China
- Institute
of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518118, China
- Center
for Synthetic and Systems Biology, Tsinghua
University, Beijing 100084, China
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26
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Basith S, Pham NT, Song M, Lee G, Manavalan B. ADP-Fuse: A novel two-layer machine learning predictor to identify antidiabetic peptides and diabetes types using multiview information. Comput Biol Med 2023; 165:107386. [PMID: 37619323 DOI: 10.1016/j.compbiomed.2023.107386] [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: 06/22/2023] [Revised: 08/03/2023] [Accepted: 08/14/2023] [Indexed: 08/26/2023]
Abstract
Diabetes mellitus has become a major public health concern associated with high mortality and reduced life expectancy and can cause blindness, heart attacks, kidney failure, lower limb amputations, and strokes. A new generation of antidiabetic peptides (ADPs) that act on β-cells or T-cells to regulate insulin production is being developed to alleviate the effects of diabetes. However, the lack of effective peptide-mining tools has hampered the discovery of these promising drugs. Hence, novel computational tools need to be developed urgently. In this study, we present ADP-Fuse, a novel two-layer prediction framework capable of accurately identifying ADPs or non-ADPs and categorizing them into type 1 and type 2 ADPs. First, we comprehensively evaluated 22 peptide sequence-derived features coupled with eight notable machine learning algorithms. Subsequently, the most suitable feature descriptors and classifiers for both layers were identified. The output of these single-feature models, embedded with multiview information, was trained with an appropriate classifier to provide the final prediction. Comprehensive cross-validation and independent tests substantiate that ADP-Fuse surpasses single-feature models and the feature fusion approach for the prediction of ADPs and their types. In addition, the SHapley Additive exPlanation method was used to elucidate the contributions of individual features to the prediction of ADPs and their types. Finally, a user-friendly web server for ADP-Fuse was developed and made publicly accessible (https://balalab-skku.org/ADP-Fuse), enabling the swift screening and identification of novel ADPs and their types. This framework is expected to contribute significantly to antidiabetic peptide identification.
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Affiliation(s)
- Shaherin Basith
- Department of Physiology, Ajou University School of Medicine, Suwon, 16499, Republic of Korea
| | - Nhat Truong Pham
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Minkyung Song
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea; Department of Biopharmaceutical Convergence, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
| | - Gwang Lee
- Department of Physiology, Ajou University School of Medicine, Suwon, 16499, Republic of Korea; Department of Molecular Science and Technology, Ajou University, Suwon, 16499, Republic of Korea.
| | - Balachandran Manavalan
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
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27
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Charoenkwan P, Kongsompong S, Schaduangrat N, Chumnanpuen P, Shoombuatong W. TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides. BMC Bioinformatics 2023; 24:356. [PMID: 37735626 PMCID: PMC10512532 DOI: 10.1186/s12859-023-05463-1] [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: 03/30/2023] [Accepted: 09/01/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Tyrosinase is an enzyme involved in melanin production in the skin. Several hyperpigmentation disorders involve the overproduction of melanin and instability of tyrosinase activity resulting in darker, discolored patches on the skin. Therefore, discovering tyrosinase inhibitory peptides (TIPs) is of great significance for basic research and clinical treatments. However, the identification of TIPs using experimental methods is generally cost-ineffective and time-consuming. RESULTS Herein, a stacked ensemble learning approach, called TIPred, is proposed for the accurate and quick identification of TIPs by using sequence information. TIPred explored a comprehensive set of various baseline models derived from well-known machine learning (ML) algorithms and heterogeneous feature encoding schemes from multiple perspectives, such as chemical structure properties, physicochemical properties, and composition information. Subsequently, 130 baseline models were trained and optimized to create new probabilistic features. Finally, the feature selection approach was utilized to determine the optimal feature vector for developing TIPred. Both tenfold cross-validation and independent test methods were employed to assess the predictive capability of TIPred by using the stacking strategy. Experimental results showed that TIPred significantly outperformed the state-of-the-art method in terms of the independent test, with an accuracy of 0.923, MCC of 0.757 and an AUC of 0.977. CONCLUSIONS The proposed TIPred approach could be a valuable tool for rapidly discovering novel TIPs and effectively identifying potential TIP candidates for follow-up experimental validation. Moreover, an online webserver of TIPred is publicly available at http://pmlabstack.pythonanywhere.com/TIPred .
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Sasikarn Kongsompong
- Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand
| | - Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Pramote Chumnanpuen
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand.
- Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok, 10900, Thailand.
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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28
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Biala G, Kedzierska E, Kruk-Slomka M, Orzelska-Gorka J, Hmaidan S, Skrok A, Kaminski J, Havrankova E, Nadaska D, Malik I. Research in the Field of Drug Design and Development. Pharmaceuticals (Basel) 2023; 16:1283. [PMID: 37765091 PMCID: PMC10536713 DOI: 10.3390/ph16091283] [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: 08/04/2023] [Revised: 08/28/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
The processes used by academic and industrial scientists to discover new drugs have recently experienced a true renaissance, with many new and exciting techniques being developed over the past 5-10 years alone. Drug design and discovery, and the search for new safe and well-tolerated compounds, as well as the ineffectiveness of existing therapies, and society's insufficient knowledge concerning the prophylactics and pharmacotherapy of the most common diseases today, comprise a serious challenge. This can influence not only the quality of human life, but also the health of whole societies, which became evident during the COVID-19 pandemic. In general, the process of drug development consists of three main stages: drug discovery, preclinical development using cell-based and animal models/tests, clinical trials on humans and, finally, forward moving toward the step of obtaining regulatory approval, in order to market the potential drug. In this review, we will attempt to outline the first three most important consecutive phases in drug design and development, based on the experience of three cooperating and complementary academic centers of the Visegrád group; i.e., Medical University of Lublin, Poland, Masaryk University of Brno, Czech Republic, and Comenius University Bratislava, Slovak Republic.
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Affiliation(s)
- Grazyna Biala
- Chair and Department of Pharmacology with Pharmacodynamics, Medical University of Lublin, Chodźki 4A, 20-093 Lublin, Poland; (E.K.); (M.K.-S.); (J.O.-G.)
| | - Ewa Kedzierska
- Chair and Department of Pharmacology with Pharmacodynamics, Medical University of Lublin, Chodźki 4A, 20-093 Lublin, Poland; (E.K.); (M.K.-S.); (J.O.-G.)
| | - Marta Kruk-Slomka
- Chair and Department of Pharmacology with Pharmacodynamics, Medical University of Lublin, Chodźki 4A, 20-093 Lublin, Poland; (E.K.); (M.K.-S.); (J.O.-G.)
| | - Jolanta Orzelska-Gorka
- Chair and Department of Pharmacology with Pharmacodynamics, Medical University of Lublin, Chodźki 4A, 20-093 Lublin, Poland; (E.K.); (M.K.-S.); (J.O.-G.)
| | - Sara Hmaidan
- Chair and Department of Pharmacology with Pharmacodynamics, Medical University of Lublin, Chodźki 4A, 20-093 Lublin, Poland; (E.K.); (M.K.-S.); (J.O.-G.)
| | - Aleksandra Skrok
- Chair and Department of Pharmacology with Pharmacodynamics, Medical University of Lublin, Chodźki 4A, 20-093 Lublin, Poland; (E.K.); (M.K.-S.); (J.O.-G.)
| | - Jakub Kaminski
- Chair and Department of Pharmacology with Pharmacodynamics, Medical University of Lublin, Chodźki 4A, 20-093 Lublin, Poland; (E.K.); (M.K.-S.); (J.O.-G.)
| | - Eva Havrankova
- Department of Chemical Drugs, Faculty of Pharmacy, Masaryk University of Brno, 601 77 Brno, Czech Republic;
| | - Dominika Nadaska
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Comenius University Bratislava, 832 32 Bratislava, Slovakia (I.M.)
| | - Ivan Malik
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Comenius University Bratislava, 832 32 Bratislava, Slovakia (I.M.)
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29
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Cui Z, Wang SG, He Y, Chen ZH, Zhang QH. DeepTPpred: A Deep Learning Approach With Matrix Factorization for Predicting Therapeutic Peptides by Integrating Length Information. IEEE J Biomed Health Inform 2023; 27:4611-4622. [PMID: 37368803 DOI: 10.1109/jbhi.2023.3290014] [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: 06/29/2023]
Abstract
The abuse of traditional antibiotics has led to increased resistance of bacteria and viruses. Efficient therapeutic peptide prediction is critical for peptide drug discovery. However, most of the existing methods only make effective predictions for one class of therapeutic peptides. It is worth noting that currently no predictive method considers sequence length information as a distinct feature of therapeutic peptides. In this article, a novel deep learning approach with matrix factorization for predicting therapeutic peptides (DeepTPpred) by integrating length information are proposed. The matrix factorization layer can learn the potential features of the encoded sequence through the mechanism of first compression and then restoration. And the length features of the sequence of therapeutic peptides are embedded with encoded amino acid sequences. To automatically learn therapeutic peptide predictions, these latent features are input into the neural networks with self-attention mechanism. On eight therapeutic peptide datasets, DeepTPpred achieved excellent prediction results. Based on these datasets, we first integrated eight datasets to obtain a full therapeutic peptide integration dataset. Then, we obtained two functional integration datasets based on the functional similarity of the peptides. Finally, we also conduct experiments on the latest versions of the ACP and CPP datasets. Overall, the experimental results show that our work is effective for the identification of therapeutic peptides.
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30
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Chen S, Liao Y, Zhao J, Bin Y, Zheng C. PACVP: Prediction of Anti-Coronavirus Peptides Using a Stacking Learning Strategy With Effective Feature Representation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3106-3116. [PMID: 37022025 DOI: 10.1109/tcbb.2023.3238370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Due to the global outbreak of COVID-19 and its variants, antiviral peptides with anti-coronavirus activity (ACVPs) represent a promising new drug candidate for the treatment of coronavirus infection. At present, several computational tools have been developed to identify ACVPs, but the overall prediction performance is still not enough to meet the actual therapeutic application. In this study, we constructed an efficient and reliable prediction model PACVP (Prediction of Anti-CoronaVirus Peptides) for identifying ACVPs based on effective feature representation and a two-layer stacking learning framework. In the first layer, we use nine feature encoding methods with different feature representation angles to characterize the rich sequence information and fuse them into a feature matrix. Secondly, data normalization and unbalanced data processing are carried out. Next, 12 baseline models are constructed by combining three feature selection methods and four machine learning classification algorithms. In the second layer, we input the optimal probability features into the logistic regression algorithm (LR) to train the final model PACVP. The experiments show that PACVP achieves favorable prediction performance on independent test dataset, with ACC of 0.9208 and AUC of 0.9465. We hope that PACVP will become a useful method for identifying, annotating and characterizing novel ACVPs.
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31
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Charoenkwan P, Schaduangrat N, Shoombuatong W. StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens. BMC Bioinformatics 2023; 24:301. [PMID: 37507654 PMCID: PMC10386778 DOI: 10.1186/s12859-023-05421-x] [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: 04/11/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND The identification of tumor T cell antigens (TTCAs) is crucial for providing insights into their functional mechanisms and utilizing their potential in anticancer vaccines development. In this context, TTCAs are highly promising. Meanwhile, experimental technologies for discovering and characterizing new TTCAs are expensive and time-consuming. Although many machine learning (ML)-based models have been proposed for identifying new TTCAs, there is still a need to develop a robust model that can achieve higher rates of accuracy and precision. RESULTS In this study, we propose a new stacking ensemble learning-based framework, termed StackTTCA, for accurate and large-scale identification of TTCAs. Firstly, we constructed 156 different baseline models by using 12 different feature encoding schemes and 13 popular ML algorithms. Secondly, these baseline models were trained and employed to create a new probabilistic feature vector. Finally, the optimal probabilistic feature vector was determined based the feature selection strategy and then used for the construction of our stacked model. Comparative benchmarking experiments indicated that StackTTCA clearly outperformed several ML classifiers and the existing methods in terms of the independent test, with an accuracy of 0.932 and Matthew's correlation coefficient of 0.866. CONCLUSIONS In summary, the proposed stacking ensemble learning-based framework of StackTTCA could help to precisely and rapidly identify true TTCAs for follow-up experimental verification. In addition, we developed an online web server ( http://2pmlab.camt.cmu.ac.th/StackTTCA ) to maximize user convenience for high-throughput screening of novel TTCAs.
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Affiliation(s)
- Phasit Charoenkwan
- Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand
| | - Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
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Fetse J, Kandel S, Mamani UF, Cheng K. Recent advances in the development of therapeutic peptides. Trends Pharmacol Sci 2023; 44:425-441. [PMID: 37246037 PMCID: PMC10330351 DOI: 10.1016/j.tips.2023.04.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 04/14/2023] [Accepted: 04/18/2023] [Indexed: 05/30/2023]
Abstract
Peptides have unique characteristics that make them highly desirable as therapeutic agents. The physicochemical and proteolytic stability profiles determine the therapeutic potential of peptides. Multiple strategies to enhance the therapeutic profile of peptides have emerged. They include chemical modifications, such as cyclization, substitution with d-amino acids, peptoid formation, N-methylation, and side-chain halogenation, and incorporation in delivery systems. There have been recent advances in approaches to discover peptides having these modifications to attain desirable therapeutic properties. We critically review these recent advancements in therapeutic peptide development.
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Affiliation(s)
- John Fetse
- Division of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, 2464 Charlotte Street, Kansas City, MO 64108, USA
| | - Sashi Kandel
- Division of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, 2464 Charlotte Street, Kansas City, MO 64108, USA
| | - Umar-Farouk Mamani
- Division of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, 2464 Charlotte Street, Kansas City, MO 64108, USA
| | - Kun Cheng
- Division of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, 2464 Charlotte Street, Kansas City, MO 64108, USA.
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Zhang G, Tang Q, Feng P, Chen W. IPs-GRUAtt: An attention-based bidirectional gated recurrent unit network for predicting phosphorylation sites of SARS-CoV-2 infection. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 32:28-35. [PMID: 36908648 PMCID: PMC9968446 DOI: 10.1016/j.omtn.2023.02.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 02/22/2023] [Indexed: 02/27/2023]
Abstract
The global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has generated tremendous concern and poses a serious threat to international public health. Phosphorylation is a common post-translational modification affecting many essential cellular processes and is inextricably linked to SARS-CoV-2 infection. Hence, accurate identification of phosphorylation sites will be helpful to understand the mechanisms of SARS-CoV-2 infection and mitigate the ongoing COVID-19 pandemic. In the present study, an attention-based bidirectional gated recurrent unit network, called IPs-GRUAtt, was proposed to identify phosphorylation sites in SARS-CoV-2-infected host cells. Comparative results demonstrated that IPs-GRUAtt surpassed both state-of-the-art machine-learning methods and existing models for identifying phosphorylation sites. Moreover, the attention mechanism made IPs-GRUAtt able to extract the key features from protein sequences. These results demonstrated that the IPs-GRUAtt is a powerful tool for identifying phosphorylation sites. For facilitating its academic use, a freely available online web server for IPs-GRUAtt is provided at http://cbcb.cdutcm.edu.cn/phosphory/.
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Affiliation(s)
- Guiyang Zhang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Chengdu University of Traditional Chinese Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Qiang Tang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Chengdu University of Traditional Chinese Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Pengmian Feng
- State Key Laboratory of Southwestern Chinese Medicine Resources, School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Wei Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Chengdu University of Traditional Chinese Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,State Key Laboratory of Southwestern Chinese Medicine Resources, School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
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Di Stasi R, De Rosa L, D'Andrea LD. Structure-Based Design of Peptides Targeting VEGF/VEGFRs. Pharmaceuticals (Basel) 2023; 16:851. [PMID: 37375798 DOI: 10.3390/ph16060851] [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: 03/14/2023] [Revised: 05/03/2023] [Accepted: 06/04/2023] [Indexed: 06/29/2023] Open
Abstract
Vascular endothelial growth factor (VEGF) and its receptors (VEGFRs) play a main role in the regulation of angiogenesis and lymphangiogenesis. Furthermore, they are implicated in the onset of several diseases such as rheumatoid arthritis, degenerative eye conditions, tumor growth, ulcers and ischemia. Therefore, molecules able to target the VEGF and its receptors are of great pharmaceutical interest. Several types of molecules have been reported so far. In this review, we focus on the structure-based design of peptides mimicking VEGF/VEGFR binding epitopes. The binding interface of the complex has been dissected and the different regions challenged for peptide design. All these trials furnished a better understanding of the molecular recognition process and provide us with a wealth of molecules that could be optimized to be exploited for pharmaceutical applications.
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Affiliation(s)
| | - Lucia De Rosa
- Istituto di Biostrutture e Bioimmagini, CNR, 80131 Napoli, Italy
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35
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Su W, Qian X, Yang K, Ding H, Huang C, Zhang Z. Recognition of outer membrane proteins using multiple feature fusion. Front Genet 2023; 14:1211020. [PMID: 37351347 PMCID: PMC10284346 DOI: 10.3389/fgene.2023.1211020] [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: 04/24/2023] [Accepted: 05/24/2023] [Indexed: 06/24/2023] Open
Abstract
Introduction: Outer membrane proteins are crucial in maintaining the structural stability and permeability of the outer membrane. Outer membrane proteins exhibit several functions such as antigenicity and strong immunogenicity, which have potential applications in clinical diagnosis and disease prevention. However, wet experiments for studying OMPs are time and capital-intensive, thereby necessitating the use of computational methods for their identification. Methods: In this study, we developed a computational model to predict outer membrane proteins. The non-redundant dataset consists of a positive set of 208 outer membrane proteins and a negative set of 876 non-outer membrane proteins. In this study, we employed the pseudo amino acid composition method to extract feature vectors and subsequently utilized the support vector machine for prediction. Results and Discussion: In the Jackknife cross-validation, the overall accuracy and the area under receiver operating characteristic curve were observed to be 93.19% and 0.966, respectively. These results demonstrate that our model can produce accurate predictions, and could serve as a valuable guide for experimental research on outer membrane proteins.
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Affiliation(s)
- Wenxia Su
- College of Science, Inner Mongolia Agriculture University, Hohhot, China
| | - Xiaojun Qian
- School of Life Science and Technology, Center for Information Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Keli Yang
- Nonlinear Research Institute, Baoji University of Arts and Sciences, Baoji, China
| | - Hui Ding
- School of Life Science and Technology, Center for Information Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chengbing Huang
- School of Computer Science and Technology, Aba Teachers University, Aba, China
| | - Zhaoyue Zhang
- School of Life Science and Technology, Center for Information Biology, University of Electronic Science and Technology of China, Chengdu, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
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36
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Kazmirchuk TDD, Bradbury-Jost C, Withey TA, Gessese T, Azad T, Samanfar B, Dehne F, Golshani A. Peptides of a Feather: How Computation Is Taking Peptide Therapeutics under Its Wing. Genes (Basel) 2023; 14:1194. [PMID: 37372372 DOI: 10.3390/genes14061194] [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: 03/30/2023] [Revised: 05/24/2023] [Accepted: 05/26/2023] [Indexed: 06/29/2023] Open
Abstract
Leveraging computation in the development of peptide therapeutics has garnered increasing recognition as a valuable tool to generate novel therapeutics for disease-related targets. To this end, computation has transformed the field of peptide design through identifying novel therapeutics that exhibit enhanced pharmacokinetic properties and reduced toxicity. The process of in-silico peptide design involves the application of molecular docking, molecular dynamics simulations, and machine learning algorithms. Three primary approaches for peptide therapeutic design including structural-based, protein mimicry, and short motif design have been predominantly adopted. Despite the ongoing progress made in this field, there are still significant challenges pertaining to peptide design including: enhancing the accuracy of computational methods; improving the success rate of preclinical and clinical trials; and developing better strategies to predict pharmacokinetics and toxicity. In this review, we discuss past and present research pertaining to the design and development of in-silico peptide therapeutics in addition to highlighting the potential of computation and artificial intelligence in the future of disease therapeutics.
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Affiliation(s)
- Thomas David Daniel Kazmirchuk
- Department of Biology, and the Ottawa Institute of Systems Biology (OISB), Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Calvin Bradbury-Jost
- Department of Biology, and the Ottawa Institute of Systems Biology (OISB), Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Taylor Ann Withey
- Department of Biology, and the Ottawa Institute of Systems Biology (OISB), Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Tadesse Gessese
- Department of Biology, and the Ottawa Institute of Systems Biology (OISB), Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Taha Azad
- Department of Microbiology and Infectious Diseases, Université de Sherbrooke, Sherbrooke, QC J1E 4K8, Canada
- Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke (CHUS), Sherbrooke, QC J1H 5N4, Canada
| | - Bahram Samanfar
- Department of Biology, and the Ottawa Institute of Systems Biology (OISB), Carleton University, Ottawa, ON K1S 5B6, Canada
- Agriculture and Agri-Food Canada, Ottawa Research and Development Centre (ORDC), Ottawa, ON K1A 0C6, Canada
| | - Frank Dehne
- School of Computer Science, Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Ashkan Golshani
- Department of Biology, and the Ottawa Institute of Systems Biology (OISB), Carleton University, Ottawa, ON K1S 5B6, Canada
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37
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Kim SG, George NP, Hwang JS, Park S, Kim MO, Lee SH, Lee G. Human Bone Marrow-Derived Mesenchymal Stem Cell Applications in Neurodegenerative Disease Treatment and Integrated Omics Analysis for Successful Stem Cell Therapy. Bioengineering (Basel) 2023; 10:bioengineering10050621. [PMID: 37237691 DOI: 10.3390/bioengineering10050621] [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: 04/27/2023] [Revised: 05/16/2023] [Accepted: 05/18/2023] [Indexed: 05/28/2023] Open
Abstract
Neurodegenerative diseases (NDDs), which are chronic and progressive diseases, are a growing health concern. Among the therapeutic methods, stem-cell-based therapy is an attractive approach to NDD treatment owing to stem cells' characteristics such as their angiogenic ability, anti-inflammatory, paracrine, and anti-apoptotic effects, and homing ability to the damaged brain region. Human bone-marrow-derived mesenchymal stem cells (hBM-MSCs) are attractive NDD therapeutic agents owing to their widespread availability, easy attainability and in vitro manipulation and the lack of ethical issues. Ex vivo hBM-MSC expansion before transplantation is essential because of the low cell numbers in bone marrow aspirates. However, hBM-MSC quality decreases over time after detachment from culture dishes, and the ability of hBM-MSCs to differentiate after detachment from culture dishes remains poorly understood. Conventional analysis of hBM-MSCs characteristics before transplantation into the brain has several limitations. However, omics analyses provide more comprehensive molecular profiling of multifactorial biological systems. Omics and machine learning approaches can handle big data and provide more detailed characterization of hBM-MSCs. Here, we provide a brief review on the application of hBM-MSCs in the treatment of NDDs and an overview of integrated omics analysis of the quality and differentiation ability of hBM-MSCs detached from culture dishes for successful stem cell therapy.
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Affiliation(s)
- Seok Gi Kim
- Department of Molecular Science and Technology, Ajou University, 206 World Cup-ro, Suwon 16499, Republic of Korea
| | - Nimisha Pradeep George
- Department of Molecular Science and Technology, Ajou University, 206 World Cup-ro, Suwon 16499, Republic of Korea
| | - Ji Su Hwang
- Department of Molecular Science and Technology, Ajou University, 206 World Cup-ro, Suwon 16499, Republic of Korea
| | - Seokho Park
- Department of Physiology, Ajou University School of Medicine, 206 World Cup-ro, Suwon 16499, Republic of Korea
- Department of Biomedical Science, Graduate School of Ajou University, 206 World Cup-ro, Suwon 16499, Republic of Korea
| | - Myeong Ok Kim
- Division of Life Science and Applied Life Science (BK21 FOUR), College of Natural Sciences, Gyeongsang National University, Jinju 52828, Republic of Korea
| | - Soo Hwan Lee
- Department of Physiology, Ajou University School of Medicine, 206 World Cup-ro, Suwon 16499, Republic of Korea
- Department of Biomedical Science, Graduate School of Ajou University, 206 World Cup-ro, Suwon 16499, Republic of Korea
| | - Gwang Lee
- Department of Molecular Science and Technology, Ajou University, 206 World Cup-ro, Suwon 16499, Republic of Korea
- Department of Physiology, Ajou University School of Medicine, 206 World Cup-ro, Suwon 16499, Republic of Korea
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38
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Hsueh HT, Chou RT, Rai U, Liyanage W, Kim YC, Appell MB, Pejavar J, Leo KT, Davison C, Kolodziejski P, Mozzer A, Kwon H, Sista M, Anders NM, Hemingway A, Rompicharla SVK, Edwards M, Pitha I, Hanes J, Cummings MP, Ensign LM. Machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery. Nat Commun 2023; 14:2509. [PMID: 37130851 PMCID: PMC10154330 DOI: 10.1038/s41467-023-38056-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 04/12/2023] [Indexed: 05/04/2023] Open
Abstract
Sustained drug delivery strategies have many potential benefits for treating a range of diseases, particularly chronic diseases that require treatment for years. For many chronic ocular diseases, patient adherence to eye drop dosing regimens and the need for frequent intraocular injections are significant barriers to effective disease management. Here, we utilize peptide engineering to impart melanin binding properties to peptide-drug conjugates to act as a sustained-release depot in the eye. We develop a super learning-based methodology to engineer multifunctional peptides that efficiently enter cells, bind to melanin, and have low cytotoxicity. When the lead multifunctional peptide (HR97) is conjugated to brimonidine, an intraocular pressure lowering drug that is prescribed for three times per day topical dosing, intraocular pressure reduction is observed for up to 18 days after a single intracameral injection in rabbits. Further, the cumulative intraocular pressure lowering effect increases ~17-fold compared to free brimonidine injection. Engineered multifunctional peptide-drug conjugates are a promising approach for providing sustained therapeutic delivery in the eye and beyond.
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Affiliation(s)
- Henry T Hsueh
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Renee Ti Chou
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA
| | - Usha Rai
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Wathsala Liyanage
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yoo Chun Kim
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Matthew B Appell
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Jahnavi Pejavar
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Kirby T Leo
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Charlotte Davison
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Patricia Kolodziejski
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Ann Mozzer
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - HyeYoung Kwon
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Maanasa Sista
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Nicole M Anders
- The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, MD, USA
| | - Avelina Hemingway
- The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, MD, USA
| | - Sri Vishnu Kiran Rompicharla
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Malia Edwards
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ian Pitha
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Justin Hanes
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, MD, USA
| | - Michael P Cummings
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, College Park, MD, USA.
| | - Laura M Ensign
- Center for Nanomedicine at the Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Pharmacology and Molecular Sciences, Johns Hopkins University, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, MD, USA.
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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.
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MSINGB: A Novel Computational Method Based on NGBoost for Identifying Microsatellite Instability Status from Tumor Mutation Annotation Data. Interdiscip Sci 2023; 15:100-110. [PMID: 36350503 DOI: 10.1007/s12539-022-00544-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 10/19/2022] [Accepted: 10/22/2022] [Indexed: 11/11/2022]
Abstract
Microsatellite instability (MSI), a vital mutator phenotype caused by DNA mismatch repair deficiency, is frequently observed in several tumors. MSI is recognized as a critical molecular biomarker for diagnosis, prognosis, and therapeutic selection in several cancers. Identifying MSI status for current gold standard methods based on experimental analysis is laborious, time-consuming, and costly. Although several computational methods based on machine learning have been proposed to identify MSI status, we need to further understand which machine learning model would favor identification for MSI and which feature subset is strongly related to MSI. On this basis, more effective machine learning-based methods can be developed to improve the performance of MSI status identification. In this work, we present MSINGB, an NGBoost-based method for identifying MSI status from tumor somatic mutation annotation data. MSINGB first evaluates the prediction performance of 11 popular machine learning algorithms and 9 deep learning models to identify MSI. Among 20 models, NGBoost, a novel natural gradient boosting method, achieves the overall best performance. MSINGB then introduces two feature selection strategies to find the compact feature subset, which is strongly related to MSI, and employs the SHAP approach to interpreting how selected features impact the model prediction. MSINGB achieves a better prediction performance on both the tenfold cross-validation test and independent test compared with state-of-the-art methods.
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41
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Nithiyanandam S, Sangaraju VK, Manavalan B, Lee G. Computational prediction of protein folding rate using structural parameters and network centrality measures. Comput Biol Med 2023; 155:106436. [PMID: 36848800 DOI: 10.1016/j.compbiomed.2022.106436] [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: 08/17/2022] [Revised: 11/28/2022] [Accepted: 12/13/2022] [Indexed: 02/17/2023]
Abstract
Protein folding is a complex physicochemical process whereby a polymer of amino acids samples numerous conformations in its unfolded state before settling on an essentially unique native three-dimensional (3D) structure. To understand this process, several theoretical studies have used a set of 3D structures, identified different structural parameters, and analyzed their relationships using the natural logarithmic protein folding rate (ln(kf)). Unfortunately, these structural parameters are specific to a small set of proteins that are not capable of accurately predicting ln(kf) for both two-state (TS) and non-two-state (NTS) proteins. To overcome the limitations of the statistical approach, a few machine learning (ML)-based models have been proposed using limited training data. However, none of these methods can explain plausible folding mechanisms. In this study, we evaluated the predictive capabilities of ten different ML algorithms using eight different structural parameters and five different network centrality measures based on newly constructed datasets. In comparison to the other nine regressors, support vector machine was found to be the most appropriate for predicting ln(kf) with mean absolute differences of 1.856, 1.55, and 1.745 for the TS, NTS, and combined datasets, respectively. Furthermore, combining structural parameters and network centrality measures improves the prediction performance compared to individual parameters, indicating that multiple factors are involved in the folding process.
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Affiliation(s)
- Saraswathy Nithiyanandam
- Department of Molecular Science and Technology, Ajou University, 206 World Cup-ro, Suwon, 16499, South Korea
| | - Vinoth Kumar Sangaraju
- Department of Physiology, Ajou University School of Medicine, 206 World Cup-ro, Suwon, 16499, South Korea
| | - Balachandran Manavalan
- Department of Physiology, Ajou University School of Medicine, 206 World Cup-ro, Suwon, 16499, South Korea.
| | - Gwang Lee
- Department of Molecular Science and Technology, Ajou University, 206 World Cup-ro, Suwon, 16499, South Korea; Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, South Korea.
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Yan K, Lv H, Wen J, Guo Y, Xu Y, Liu B. PreTP-Stack: Prediction of Therapeutic Peptides Based on the Stacked Ensemble Learing. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1337-1344. [PMID: 35700248 DOI: 10.1109/tcbb.2022.3183018] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Therapeutic peptide prediction is critical for drug development and therapeutic therapy. Researchers have developed several computational methods to identify different therapeutic peptide types. However, most computational methods focus on identifying the specific type of therapeutic peptides and fail to accurately predict all types of therapeutic peptides. Moreover, it is still challenging to utilize different properties features to predict the therapeutic peptides. In this study, a novel stacking framework PreTP-Stack is proposed for predicting different types of therapeutic peptide. PreTP-Stack is constructed based on ten different features and four predictors (Random Forest, Linear Discriminant Analysis, XGBoost and Support Vector Machine). Then the proposed method constructs an auto-weighted multi-view learning model as a final meta-classifier to enhance the performance of the basic models. Experimental results showed that the proposed method achieved better or highly comparable performance with the state-of-the-art methods for predicting eight types of therapeutic peptides A user-friendly web-server predictor is available at http://bliulab.net/PreTP-Stack.
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43
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Chakrobarty S, Garai S, Ghosh A, Mukerjee N, Das D. Bioactive plantaricins as potent anti-cancer drug candidates: double docking, molecular dynamics simulation and in vitro cytotoxicity analysis. J Biomol Struct Dyn 2023; 41:13605-13615. [PMID: 36775653 DOI: 10.1080/07391102.2023.2177732] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 02/02/2023] [Indexed: 02/14/2023]
Abstract
The medical community is desperate for a reliable source of medications to alleviate the severity of conventional cancer treatments and prevent secondary microbial infections in oncological patients. In this regard, plantaricins from lactic acid bacteria were explored as prospective drug candidates against known anti-cancer drug targets. Three plantaricins, JLA-9, GZ1-27 and BN, have a binding affinity of -8.8, -8.6 and -7.2 kcal/mol, respectively, with squalene synthase (SQS), a key molecule in lung cancer metastasis. All three plantaricins displayed analogous binding patterns as SQS inhibitors and generated hydrogen and hydrophobic interactions with ARG 47, ARG 188, PHE24, LEU183 and PRO292. Structural stability of docked complexes was validated using molecular dynamics simulation derived parameters such as RMSD, RMSF and radius of gyration. Based on MD simulation results, conformational changes and stabilities of docked SQS/plantaricin complexes with respect to the time frame were evaluated using machine learning (logistic regression algorithm). Double docking with SQS/matrix metalloproteinase MMP1 and PCA analysis revealed the potential of plantaricin JLA-9 as a multi-substrate inhibitor. Further, plantaricin JLA-9 induced a significant cytotoxic response against the lung carcinoma cell line (A549) in a dose and time dependent manner with inhibition concentration (IC50) of 0.082 µg/ml after 48 h. However, plantaricin JLA-9 did not induce cytotoxicity in normal lung cells (L-132), as the IC50 value was not obtained even at a higher dose of 0.8 µg/ml. In silico pharmacokinetic (ADMET) profile implies that plantaricin JLA-9 could be developed as new age anti-cancer therapeutic with a preference for parenteral administration.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | - Swarnava Garai
- Department of Bioengineering, NIT Agartala, Agartala, India
| | - Arabinda Ghosh
- Department of Botany, Gauhati University, Guwahati, Assam, India
| | - Nobendu Mukerjee
- Department of Microbiology, West Bengal State University, Barasat, Kolkata, India
| | - Deeplina Das
- Department of Bioengineering, NIT Agartala, Agartala, India
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44
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Scott-Fordsmand JJ, Amorim MJB. Using Machine Learning to make nanomaterials sustainable. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 859:160303. [PMID: 36410486 DOI: 10.1016/j.scitotenv.2022.160303] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/06/2022] [Accepted: 11/15/2022] [Indexed: 06/16/2023]
Abstract
Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.
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Affiliation(s)
| | - Mónica J B Amorim
- Department of Biology & CESAM, University of Aveiro, 3810-193 Aveiro, Portugal.
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45
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Kumar M, Nguyen TPN, Kaur J, Singh TG, Soni D, Singh R, Kumar P. Opportunities and challenges in application of artificial intelligence in pharmacology. Pharmacol Rep 2023; 75:3-18. [PMID: 36624355 PMCID: PMC9838466 DOI: 10.1007/s43440-022-00445-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 12/23/2022] [Accepted: 12/25/2022] [Indexed: 01/11/2023]
Abstract
Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.
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Affiliation(s)
- Mandeep Kumar
- Department of Pharmacy, Unit of Pharmacology and Toxicology, University of Genoa, Genoa, Italy
| | - T P Nhung Nguyen
- Department of Pharmacy, Unit of Pharmacology and Toxicology, University of Genoa, Genoa, Italy
- Department of Pharmacy, Da Nang University of Medical Technology and Pharmacy, Da Nang, Vietnam
| | - Jasleen Kaur
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Lucknow, Uttar Pradesh, 226002, India
| | | | - Divya Soni
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India
| | - Randhir Singh
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India
| | - Puneet Kumar
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India.
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46
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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.
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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:
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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 2023:10.1007/s11030-023-10602-0. [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] [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.
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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.
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48
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Bupi N, Sangaraju VK, Phan LT, Lal A, Vo TTB, Ho PT, Qureshi MA, Tabassum M, Lee S, Manavalan B. An Effective Integrated Machine Learning Framework for Identifying Severity of Tomato Yellow Leaf Curl Virus and Their Experimental Validation. RESEARCH (WASHINGTON, D.C.) 2023; 6:0016. [PMID: 36930763 PMCID: PMC10013792 DOI: 10.34133/research.0016] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 11/07/2022] [Indexed: 01/13/2023]
Abstract
Tomato yellow leaf curl virus (TYLCV) dispersed across different countries, specifically to subtropical regions, associated with more severe symptoms. Since TYLCV was first isolated in 1931, it has been a menace to tomato industrial production worldwide over the past century. Three groups were newly isolated from TYLCV-resistant tomatoes in 2022; however, their functions are unknown. The development of machine learning (ML)-based models using characterized sequences and evaluating blind predictions is one of the major challenges in interdisciplinary research. The purpose of this study was to develop an integrated computational framework for the accurate identification of symptoms (mild or severe) based on TYLCV sequences (isolated in Korea). For the development of the framework, we first extracted 11 different feature encodings and hybrid features from the training data and then explored 8 different classifiers and developed their respective prediction models by using randomized 10-fold cross-validation. Subsequently, we carried out a systematic evaluation of these 96 developed models and selected the top 90 models, whose predicted class labels were combined and considered as reduced features. On the basis of these features, a multilayer perceptron was applied and developed the final prediction model (IML-TYLCVs). We conducted blind prediction on 3 groups using IML-TYLCVs, and the results indicated that 2 groups were severe and 1 group was mild. Furthermore, we confirmed the prediction with virus-challenging experiments of tomato plant phenotypes using infectious clones from 3 groups. Plant virologists and plant breeding professionals can access the user-friendly online IML-TYLCVs web server at https://balalab-skku.org/IML-TYLCVs, which can guide them in developing new protection strategies for newly emerging viruses.
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Affiliation(s)
- Nattanong Bupi
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| | - Vinoth Kumar Sangaraju
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| | - Le Thi Phan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| | - Aamir Lal
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| | - Thuy Thi Bich Vo
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| | - Phuong Thi Ho
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| | - Muhammad Amir Qureshi
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| | - Marjia Tabassum
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| | - Sukchan Lee
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| | - Balachandran Manavalan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
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49
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Zhou C, Peng D, Liao B, Jia R, Wu F. ACP_MS: prediction of anticancer peptides based on feature extraction. Brief Bioinform 2022; 23:6793775. [PMID: 36326080 DOI: 10.1093/bib/bbac462] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/10/2022] [Accepted: 09/27/2022] [Indexed: 11/06/2022] Open
Abstract
Anticancer peptides (ACPs) are bioactive peptides with antitumor activity and have become the most promising drugs in the treatment of cancer. Therefore, the accurate prediction of ACPs is of great significance to the research of cancer diseases. In the paper, we developed a more efficient prediction model called ACP_MS. Firstly, the monoMonoKGap method is used to extract the characteristic of anticancer peptide sequences and form the digital features. Then, the AdaBoost model is used to select the most discriminating features from the digital features. Finally, a stochastic gradient descent algorithm is introduced to identify anticancer peptide sequences. We adopt 7-fold cross-validation and independent test set validation, and the final accuracy of the main dataset reached 92.653% and 91.597%, respectively. The accuracy of the alternate dataset reached 98.678% and 98.317%, respectively. Compared with other advanced prediction models, the ACP_MS model improves the identification ability of anticancer peptide sequences. The data of this model can be downloaded from the public website for free https://github.com/Zhoucaimao1998/Zc.
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Affiliation(s)
- Caimao Zhou
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China.,School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Dejun Peng
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China.,School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Bo Liao
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China.,School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Ranran Jia
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China.,School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Fangxiang Wu
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China.,School of Mathematics and Statistics, Hainan Normal University, Haikou, China
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50
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The dynamic landscape of peptide activity prediction. Comput Struct Biotechnol J 2022; 20:6526-6533. [DOI: 10.1016/j.csbj.2022.11.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/21/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022] Open
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