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Hassan MT, Tayara H, Chong KT. NaII-Pred: An ensemble-learning framework for the identification and interpretation of sodium ion inhibitors by fusing multiple feature representation. Comput Biol Med 2024; 178:108737. [PMID: 38879934 DOI: 10.1016/j.compbiomed.2024.108737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 04/21/2024] [Accepted: 06/08/2024] [Indexed: 06/18/2024]
Abstract
High-affinity ligand peptides for ion channels are essential for controlling the flow of ions across the plasma membrane. These peptides are now being investigated as possible therapeutic possibilities for a variety of illnesses, including cancer and cardiovascular disease. So, the identification and interpretation of ligand peptide inhibitors to control ion flow across cells become pivotal for exploration. In this work, we developed an ensemble-based model, NaII-Pred, for the identification of sodium ion inhibitors. The ensemble model was trained, tested, and evaluated on three different datasets. The NaII-Pred method employs six different descriptors and a hybrid feature set in conjunction with five conventional machine learning classifiers to create 35 baseline models. Through an ensemble approach, the top five baseline models trained on the hybrid feature set were integrated to yield the final predictive model, NaII-Pred. Our proposed model, NaII-Pred, outperforms the baseline models and the current predictors on both datasets. We believe NaII-Pred will play a critical role in screening and identifying potential sodium ion inhibitors and will be an invaluable tool.
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Affiliation(s)
- Mir Tanveerul Hassan
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju, 54896, South Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea; Advances Electronics and Information Research Centre, Jeonbuk National University, Jeonju, 54896, South Korea.
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Shoombuatong W, Homdee N, Schaduangrat N, Chumnanpuen P. Leveraging a meta-learning approach to advance the accuracy of Na v blocking peptides prediction. Sci Rep 2024; 14:4463. [PMID: 38396246 PMCID: PMC10891130 DOI: 10.1038/s41598-024-55160-z] [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: 12/28/2023] [Accepted: 02/21/2024] [Indexed: 02/25/2024] Open
Abstract
The voltage-gated sodium (Nav) channel is a crucial molecular component responsible for initiating and propagating action potentials. While the α subunit, forming the channel pore, plays a central role in this function, the complete physiological function of Nav channels relies on crucial interactions between the α subunit and auxiliary proteins, known as protein-protein interactions (PPI). Nav blocking peptides (NaBPs) have been recognized as a promising and alternative therapeutic agent for pain and itch. Although traditional experimental methods can precisely determine the effect and activity of NaBPs, they remain time-consuming and costly. Hence, machine learning (ML)-based methods that are capable of accurately contributing in silico prediction of NaBPs are highly desirable. In this study, we develop an innovative meta-learning-based NaBP prediction method (MetaNaBP). MetaNaBP generates new feature representations by employing a wide range of sequence-based feature descriptors that cover multiple perspectives, in combination with powerful ML algorithms. Then, these feature representations were optimized to identify informative features using a two-step feature selection method. Finally, the selected informative features were applied to develop the final meta-predictor. To the best of our knowledge, MetaNaBP is the first meta-predictor for NaBP prediction. Experimental results demonstrated that MetaNaBP achieved an accuracy of 0.948 and a Matthews correlation coefficient of 0.898 over the independent test dataset, which were 5.79% and 11.76% higher than the existing method. In addition, the discriminative power of our feature representations surpassed that of conventional feature descriptors over both the training and independent test datasets. We anticipate that MetaNaBP will be exploited for the large-scale prediction and analysis of NaBPs to narrow down the potential NaBPs.
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Affiliation(s)
- Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
| | - Nutta Homdee
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, 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
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Vambhurkar G, Amulya E, Sikder A, Shah S, Famta P, Khatri DK, Singh SB, Srivastava S. Nanomedicine based potentially transformative strategies for colon targeting of peptides: State-of-the-art. Colloids Surf B Biointerfaces 2022; 219:112816. [PMID: 36108367 DOI: 10.1016/j.colsurfb.2022.112816] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/23/2022] [Accepted: 08/27/2022] [Indexed: 12/11/2022]
Abstract
Recently, peptides have attracted tremendous attention among researchers attributed to their high target specificity and efficacy compared to conventional therapeutics. The ease of self-administration and non-invasiveness confers oral as the most desirable route. However, numerous challenges associated with peptide delivery through the oral route like harsh gastrointestinal environment, enzymatic degradation, and absorption barriers hinder its clinical translation. Protease activity is more pronounced in the proximal segments of the gastrointestinal tract (GIT). Distal segments like the colon possess lower proteolytic activity, enhanced retention time, etc. which could facilitate easy absorption. However, traversing of the upper segments to reach the colon requires the circumvention of the pitfalls of the GIT. The advent of nanomedicine strategies could help in overcoming the said challenges associated with oral delivery, colon-specific targeting, and improving stability and bioavailability at the active site. Furthermore, the classification of peptides and various nanomedicine strategies for oral delivery of peptides to the colon has been conveyed. Regulatory hurdles and ways to accomplish clinical translation have been addressed.
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Affiliation(s)
- Ganesh Vambhurkar
- Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, India
| | - Etikala Amulya
- Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, India
| | - Anupama Sikder
- Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, India
| | - Saurabh Shah
- Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, India
| | - Paras Famta
- Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, India
| | - Dharmendra Kumar Khatri
- Department of Biological Sciences, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, India
| | - Shashi Bala Singh
- Department of Biological Sciences, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, India
| | - Saurabh Srivastava
- Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, India.
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Herrera-Bravo J, Farías JG, Sandoval C, Herrera-Belén L, Quiñones J, Díaz R, Beltrán JF. nAChR-PEP-PRED: A Robust Tool for Predicting Peptide Inhibitors of Acetylcholine Receptors Using the Random Forest Classifier. Int J Pept Res Ther 2022. [DOI: 10.1007/s10989-022-10460-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Yan J, Zhang B, Zhou M, Kwok HF, Siu SWI. Multi-Branch-CNN: Classification of ion channel interacting peptides using multi-branch convolutional neural network. Comput Biol Med 2022; 147:105717. [PMID: 35752114 DOI: 10.1016/j.compbiomed.2022.105717] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 05/18/2022] [Accepted: 06/05/2022] [Indexed: 11/03/2022]
Abstract
Ligand peptides that have high affinity for ion channels are critical for regulating ion flux across the plasma membrane. These peptides are now being considered as potential drug candidates for many diseases, such as cardiovascular disease and cancers. In this work, we developed Multi-Branch-CNN, a CNN method with multiple input branches for identifying three types of ion channel peptide binders (sodium, potassium, and calcium) from intra- and inter-feature types. As for its real-world applications, prediction models that are able to recognize novel sequences having high or low similarities to training sequences are required. To this end, we tested our models on two test sets: a general test set including sequences spanning different similarity levels to those of the training set, and a novel-test set consisting of only sequences that bear little resemblance to sequences from the training set. Our experiments showed that the Multi-Branch-CNN method performs better than thirteen traditional ML algorithms (TML13), yielding an improvement in accuracy of 3.2%, 1.2%, and 2.3% on the test sets as well as 8.8%, 14.3%, and 14.6% on the novel-test sets for sodium, potassium, and calcium ion channels, respectively. We confirmed the effectiveness of Multi-Branch-CNN by comparing it to the standard CNN method with one input branch (Single-Branch-CNN) and an ensemble method (TML13-Stack). The data sets, script files to reproduce the experiments, and the final predictive models are freely available at https://github.com/jieluyan/Multi-Branch-CNN.
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Affiliation(s)
- Jielu Yan
- PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macao Special Administrative Region of China
| | - Bob Zhang
- PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macao Special Administrative Region of China.
| | - Mingliang Zhou
- School of Computer Science, Chongqing University, Shapingba, Chongqing, China
| | - Hang Fai Kwok
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Taipa, Macao Special Administrative Region of China.
| | - Shirley W I Siu
- Department of Computer and Information Science, University of Macau, Taipa, Macao Special Administrative Region of China; Institute of Science and Environment, University of Saint Joseph, Estr. Marginal da Ilha Verde, Macao Special Administrative Region of China.
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Herrera-Bravo J, Farías JG, Contreras FP, Herrera-Belén L, Beltrán JF. PEP-PREDNa+: A web server for prediction of highly specific peptides targeting voltage-gated Na+ channels using machine learning techniques. Comput Biol Med 2022; 145:105414. [DOI: 10.1016/j.compbiomed.2022.105414] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 03/12/2022] [Accepted: 03/14/2022] [Indexed: 12/12/2022]
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Wang W, Guan X, Khan MT, Xiong Y, Wei DQ. LMI-DForest: A deep forest model towards the prediction of lncRNA-miRNA interactions. Comput Biol Chem 2020; 89:107406. [PMID: 33120126 DOI: 10.1016/j.compbiolchem.2020.107406] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 10/12/2020] [Accepted: 10/15/2020] [Indexed: 02/07/2023]
Abstract
The interactions between miRNAs and long non-coding RNAs (lncRNAs) are subject to intensive recent studies due to its critical role in gene regulations. Computational prediction of lncRNA-miRNA interactions has become a popular alternative strategy to the experimental methods for identification of underlying interactions. It is desirable to develop the machine learning-based models for prediction of lncRNA-miRNA based on the experimentally validated interactions between lncRNAs and miRNAs. The accuracy and robustness of existing models based on machine learning techniques are subject to further improvement. Considering that the attributes of lncRNA and miRNA contribute key importance in the interaction between these two RNAs, a deep learning model, named LMI-DForest, is proposed here by combining the deep forest and autoencoder strategies. Systematic comparison on the experiment validated datasets for lncRNA-miRNA interaction datasets demonstrates that the proposed method consistently shows superior performance over the other machine learning models in the lncRNA-miRNA interaction prediction.
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Affiliation(s)
- Wei Wang
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaoqing Guan
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Muhammad Tahir Khan
- Institute of Molecular Biology and Biotechnology, The University of Lahore Pakistan, Pakistan
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; Peng Cheng Laboratory, Shenzhen, Guangdong, China.
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