1
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Ahmed Z, Shahzadi K, Temesgen SA, Ahmad B, Chen X, Ning L, Zulfiqar H, Lin H, Jin YT. A protein pre-trained model-based approach for the identification of the liquid-liquid phase separation (LLPS) proteins. Int J Biol Macromol 2024; 277:134146. [PMID: 39067723 DOI: 10.1016/j.ijbiomac.2024.134146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 07/06/2024] [Accepted: 07/23/2024] [Indexed: 07/30/2024]
Abstract
Liquid-liquid phase separation (LLPS) regulates many biological processes including RNA metabolism, chromatin rearrangement, and signal transduction. Aberrant LLPS potentially leads to serious diseases. Therefore, the identification of the LLPS proteins is crucial. Traditionally, biochemistry-based methods for identifying LLPS proteins are costly, time-consuming, and laborious. In contrast, artificial intelligence-based approaches are fast and cost-effective and can be a better alternative to biochemistry-based methods. Previous research methods employed word2vec in conjunction with machine learning or deep learning algorithms. Although word2vec captures word semantics and relationships, it might not be effective in capturing features relevant to protein classification, like physicochemical properties, evolutionary relationships, or structural features. Additionally, other studies often focused on a limited set of features for model training, including planar π contact frequency, pi-pi, and β-pairing propensities. To overcome such shortcomings, this study first constructed a reliable dataset containing 1206 protein sequences, including 603 LLPS and 603 non-LLPS protein sequences. Then a computational model was proposed to efficiently identify the LLPS proteins by perceiving semantic information of protein sequences directly; using an ESM2-36 pre-trained model based on transformer architecture in conjunction with a convolutional neural network. The model could achieve an accuracy of 85.68% and 89.67%, respectively on training data and test data, surpassing the accuracy of previous studies. The performance demonstrates the potential of our computational methods as efficient alternatives for identifying LLPS proteins.
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Affiliation(s)
- Zahoor Ahmed
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China.
| | - Kiran Shahzadi
- Department of Biotechnology, Women University of Azad Jammu and Kashmir, Bagh, Azad Kashmir, Pakistan.
| | - Sebu Aboma Temesgen
- School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China.
| | - Basharat Ahmad
- School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China.
| | - Xiang Chen
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China.
| | - Lin Ning
- School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China; School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China.
| | - Hasan Zulfiqar
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China.
| | - Hao Lin
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China.
| | - Yan-Ting Jin
- School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China.
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2
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Jin YT, Tan Y, Gan ZH, Hao YD, Wang TY, Lin H, Tang B. Identification of DNase I hypersensitive sites in the human genome by multiple sequence descriptors. Methods 2024; 229:125-132. [PMID: 38964595 DOI: 10.1016/j.ymeth.2024.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 06/01/2024] [Accepted: 06/27/2024] [Indexed: 07/06/2024] Open
Abstract
DNase I hypersensitive sites (DHSs) are chromatin regions highly sensitive to DNase I enzymes. Studying DHSs is crucial for understanding complex transcriptional regulation mechanisms and localizing cis-regulatory elements (CREs). Numerous studies have indicated that disease-related loci are often enriched in DHSs regions, underscoring the importance of identifying DHSs. Although wet experiments exist for DHSs identification, they are often labor-intensive. Therefore, there is a strong need to develop computational methods for this purpose. In this study, we used experimental data to construct a benchmark dataset. Seven feature extraction methods were employed to capture information about human DHSs. The F-score was applied to filter the features. By comparing the prediction performance of various classification algorithms through five-fold cross-validation, random forest was proposed to perform the final model construction. The model could produce an overall prediction accuracy of 0.859 with an AUC value of 0.837. We hope that this model can assist scholars conducting DNase research in identifying these sites.
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Affiliation(s)
- Yan-Ting Jin
- School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China.
| | - Yang Tan
- School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Zhong-Hua Gan
- Department of Pathology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Yu-Duo Hao
- School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China.
| | - Tian-Yu Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Hao Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, 611731 Chengdu, China.
| | - Bo Tang
- Department of Pathology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, Sichuan, China.
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3
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Skog A, Paracini N, Gerelli Y, Skepö M. Translocation of Antimicrobial Peptides across Model Membranes: The Role of Peptide Chain Length. Mol Pharm 2024; 21:4082-4097. [PMID: 38993084 PMCID: PMC11304388 DOI: 10.1021/acs.molpharmaceut.4c00450] [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: 04/25/2024] [Revised: 06/28/2024] [Accepted: 06/28/2024] [Indexed: 07/13/2024]
Abstract
Cushioned lipid bilayers are structures consisting of a lipid bilayer supported on a solid substrate with an intervening layer of soft material. They offer possibilities for studying the behavior and interactions of biological membranes more accurately under physiological conditions. In this work, we continue our studies of cushion formation induced by histatin 5 (24Hst5), focusing on the effect of the length of the peptide chain. 24Hst5 is a short, positively charged, intrinsically disordered saliva peptide, and here, both a shorter (14Hst5) and a longer (48Hst5) peptide variant were evaluated. Experimental surface active techniques were combined with coarse-grained Monte Carlo simulations to obtain information about these peptides. Results show that at 10 mM NaCl, both the shorter and the longer peptide variants behave like 24Hst5 and a cushion below the bilayer is formed. At 150 mM NaCl, however, no interaction is observed for 24Hst5. On the contrary, a cushion is formed both in the case of 14Hst5 and 48Hst5, and in the latter, an additional thick, diffuse, and highly hydrated layer of peptide and lipid molecules is formed, on top of the bilayer. Similar trends were observed from the simulations, which allowed us to hypothesize that positively charged patches of the amino acids lysine and arginine in all three peptides are essential for them to interact with and translocate over the bilayer. We therefore hypothesize that electrostatic interactions are important for the interaction between the solid-supported lipid bilayers and the peptide depending on the linear charge density through the primary sequence and the positively charged patches in the sequence. The understanding of how, why, and when the cushion is formed opens up the possibility for this system to be used in the research and development of new drugs and pharmaceuticals.
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Affiliation(s)
- Amanda
E. Skog
- Division
of Computational Chemistry, Department of Chemistry, Lund University, P.O. Box 124, SE-221 00, Lund, Sweden
| | - Nicolò Paracini
- Institut
Laue-Langevin, 71 Avenue des Martyrs, 38000 Grenoble, France
| | - Yuri Gerelli
- Institute
for Complex Systems - National Research Council (ISC−CNR), Piazzale Aldo Moro 2, 00185 Roma, Italy
- Department
of Physics, Sapienza University of Rome, Piazzale Aldo Moro 2, 00185 Roma, Italy
| | - Marie Skepö
- Division
of Computational Chemistry, Department of Chemistry, Lund University, P.O. Box 124, SE-221 00, Lund, Sweden
- NanoLund, Lund
University, Box
118, 22100 Lund, Sweden
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4
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Zhang F, Li J, Wen Z, Fang C. FusPB-ESM2: Fusion model of ProtBERT and ESM-2 for cell-penetrating peptide prediction. Comput Biol Chem 2024; 111:108098. [PMID: 38820799 DOI: 10.1016/j.compbiolchem.2024.108098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 04/18/2024] [Accepted: 05/16/2024] [Indexed: 06/02/2024]
Abstract
Cell-penetrating peptides have attracted much attention for their ability to break through cell membrane barriers, which can improve drug bioavailability, reduce side effects, and promote the development of gene therapy. Traditional wet-lab prediction methods are time-consuming and costly, and computational methods provide a short-time and low-cost alternative. Still, the accuracy and reliability need to be further improved. To solve this problem, this study proposes a feature fusion-based prediction model, where the protein pre-trained language models ProtBERT and ESM-2 are used as feature extractors, and the extracted features from both are fused to obtain a more comprehensive and effective feature representation, which is then predicted by linear mapping. Validated by many experiments on public datasets, the method has an AUC value as high as 0.983 and shows high accuracy and reliability in cell-penetrating peptide prediction.
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Affiliation(s)
- Fan Zhang
- Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Jinfeng Li
- Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Zhenguo Wen
- Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Chun Fang
- Beijing Institute of Petrochemical Technology, Beijing 102617, China.
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5
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Kuang K, Chen X, Wang M, Han W, Qiu X, Jin T, Xu R, Yuan B, Qian M, Li C, Xiang R, Li F, Zhang S, Yang Z, Du J, Li D, Zhang C, Wang Q, Jia T. Design and Discovery of New Collagen V-Derived FGF2-Blocking Natural Peptides Inhibiting Lung Squamous Cell Carcinoma In Vitro and In Vivo. J Med Chem 2024. [PMID: 39045829 DOI: 10.1021/acs.jmedchem.4c00654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/25/2024]
Abstract
Aberrant FGF2/FGFR signaling is implicated in lung squamous cell carcinoma (LSCC), posing treatment challenges due to the lack of targeted therapeutic options. Designing drugs that block FGF2 signaling presents a promising strategy different from traditional kinase inhibitors. We previously reported a ColVα1-derived fragment, HEPV (127AA), that inhibits FGF2-induced angiogenesis. However, its large size may limit therapeutic application. This study combines rational peptide design, molecular dynamics simulations, knowledge-based prediction, and GUV and FRET assays to identify smaller peptides with FGF2-blocking properties. We synthesized two novel peptides, HBS-P1 (45AA) and HBS-P2 (66AA), that retained the heparin-binding site. Both peptides demonstrated anti-LSCC and antiangiogenesis properties in cell viability and microvessel network induction assays. In two LSCC subcutaneous models, HBS-P1, with its affinity for FGF2 and enhanced penetration ability, demonstrated substantial therapeutic potential without apparent toxicities. Our study provides the first evidence supporting the development of collagen V-derived natural peptides as FGF2-blocking agents for LSCC treatment.
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Affiliation(s)
- Keli Kuang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Xiang Chen
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Maolin Wang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Weijing Han
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Xue Qiu
- Key Laboratory of Marine Drug, Ministry of Education, School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China
- Laboratory forMarine Drugs and Bioproducts, Qingdao National Laboratory for Marine Scienceand Technology, Ocean University of China, Qingdao 266237, China
| | - Taoli Jin
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Rong Xu
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Bing Yuan
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Meiqi Qian
- Key Laboratory of Marine Drug, Ministry of Education, School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China
- Laboratory forMarine Drugs and Bioproducts, Qingdao National Laboratory for Marine Scienceand Technology, Ocean University of China, Qingdao 266237, China
| | - Chunyan Li
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Run Xiang
- Department of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu 610041, China
| | - Fei Li
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Shuwen Zhang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Zi Yang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Junrong Du
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Dapeng Li
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Chun Zhang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Qiantao Wang
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Tao Jia
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
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6
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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] [MESH Headings] [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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7
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Su RL, Cao XW, Zhao J, Wang FJ. A high hydrophobic moment arginine-rich peptide screened by a machine learning algorithm enhanced ADC antitumor activity. J Pept Sci 2024:e3628. [PMID: 38950972 DOI: 10.1002/psc.3628] [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: 03/10/2024] [Revised: 04/15/2024] [Accepted: 06/05/2024] [Indexed: 07/03/2024]
Abstract
Cell-penetrating peptides (CPPs) with better biomolecule delivery properties will expand their clinical applications. Using the MLCPP2.0 machine algorithm, we screened multiple candidate sequences with potential cellular uptake ability from the nuclear localization signal/nuclear export signal database and verified them through cell-penetrating fluorescent tracing experiments. A peptide (NCR) derived from the Rev protein of the caprine arthritis-encephalitis virus exhibited efficient cell-penetrating activity, delivering over four times more EGFP than the classical CPP TAT, allowing it to accumulate in lysosomes. Structural and property analysis revealed that a high hydrophobic moment and an appropriate hydrophobic region contribute to the high delivery activity of NCR. Trastuzumab emtansine (T-DM1), a HER2-targeted antibody-drug conjugate, could improve its anti-tumor activity by enhancing targeted delivery efficiency and increasing lysosomal drug delivery. This study designed a new NCR vector to non-covalently bind T-DM1 by fusing domain Z, which can specifically bind to the Fc region of immunoglobulin G and effectively deliver T-DM1 to lysosomes. MTT results showed that the domain Z-NCR vector significantly enhanced the cytotoxicity of T-DM1 against HER2-positive tumor cells while maintaining drug specificity. Our results make a useful attempt to explore the potential application of CPP as a lysosome-targeted delivery tool.
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Affiliation(s)
- Ruo-Long Su
- Department of Applied Biology, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Xue-Wei Cao
- Department of Applied Biology, East China University of Science and Technology, Shanghai, People's Republic of China
- ECUST-FONOW Joint Research Center for Innovative Medicines, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Jian Zhao
- Department of Applied Biology, East China University of Science and Technology, Shanghai, People's Republic of China
- ECUST-FONOW Joint Research Center for Innovative Medicines, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Fu-Jun Wang
- ECUST-FONOW Joint Research Center for Innovative Medicines, East China University of Science and Technology, Shanghai, People's Republic of China
- New Drug R&D Center, Zhejiang Fonow Medicine Co., Ltd., Zhejiang, People's Republic of China
- Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, People's Republic of China
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8
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Patel P, Benzle K, Pei D, Wang GL. Cell-penetrating peptides for sustainable agriculture. TRENDS IN PLANT SCIENCE 2024:S1360-1385(24)00144-4. [PMID: 38902122 DOI: 10.1016/j.tplants.2024.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/24/2024] [Accepted: 05/30/2024] [Indexed: 06/22/2024]
Abstract
Cell-penetrating peptides (CPPs) are short (typically 5-30 amino acids), cationic, amphipathic, or hydrophobic peptides that facilitate the cellular uptake of diverse cargo molecules by eukaryotic cells via direct translocation or endocytosis across the plasma membrane. CPPs can deliver a variety of bioactive cargos, including proteins, peptides, nucleic acids, and small molecules into the cell. Once inside, the delivered cargo may function in the cytosol, nucleus, or other subcellular compartments. Numerous CPPs have been used for studies and drug delivery in mammalian systems. Although CPPs have many potential uses in plant research and agriculture, the application of CPPs in plants remains limited. Here we review the structures and mechanisms of CPPs and highlight their potential applications for sustainable agriculture.
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Affiliation(s)
- Preeti Patel
- Department of Plant Pathology, The Ohio State University, Columbus, OH 43210, USA
| | - Kyle Benzle
- Department of Plant Pathology, The Ohio State University, Columbus, OH 43210, USA
| | - Dehua Pei
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH 43210, USA
| | - Guo-Liang Wang
- Department of Plant Pathology, The Ohio State University, Columbus, OH 43210, USA.
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9
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Gu ZF, Hao YD, Wang TY, Cai PL, Zhang Y, Deng KJ, Lin H, Lv H. Prediction of blood-brain barrier penetrating peptides based on data augmentation with Augur. BMC Biol 2024; 22:86. [PMID: 38637801 PMCID: PMC11027412 DOI: 10.1186/s12915-024-01883-4] [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: 01/03/2024] [Accepted: 04/05/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND The blood-brain barrier serves as a critical interface between the bloodstream and brain tissue, mainly composed of pericytes, neurons, endothelial cells, and tightly connected basal membranes. It plays a pivotal role in safeguarding brain from harmful substances, thus protecting the integrity of the nervous system and preserving overall brain homeostasis. However, this remarkable selective transmission also poses a formidable challenge in the realm of central nervous system diseases treatment, hindering the delivery of large-molecule drugs into the brain. In response to this challenge, many researchers have devoted themselves to developing drug delivery systems capable of breaching the blood-brain barrier. Among these, blood-brain barrier penetrating peptides have emerged as promising candidates. These peptides had the advantages of high biosafety, ease of synthesis, and exceptional penetration efficiency, making them an effective drug delivery solution. While previous studies have developed a few prediction models for blood-brain barrier penetrating peptides, their performance has often been hampered by issue of limited positive data. RESULTS In this study, we present Augur, a novel prediction model using borderline-SMOTE-based data augmentation and machine learning. we extract highly interpretable physicochemical properties of blood-brain barrier penetrating peptides while solving the issues of small sample size and imbalance of positive and negative samples. Experimental results demonstrate the superior prediction performance of Augur with an AUC value of 0.932 on the training set and 0.931 on the independent test set. CONCLUSIONS This newly developed Augur model demonstrates superior performance in predicting blood-brain barrier penetrating peptides, offering valuable insights for drug development targeting neurological disorders. This breakthrough may enhance the efficiency of peptide-based drug discovery and pave the way for innovative treatment strategies for central nervous system diseases.
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Affiliation(s)
- Zhi-Feng Gu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Yu-Duo Hao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Tian-Yu Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Pei-Ling Cai
- School of Basic Medical Sciences, Chengdu University, Chengdu, 610106, PR China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, PR China
| | - Ke-Jun Deng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Hao Lin
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
| | - Hao Lv
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
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10
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Eweje F, Walsh ML, Ahmad K, Ibrahim V, Alrefai A, Chen J, Chaikof EL. Protein-based nanoparticles for therapeutic nucleic acid delivery. Biomaterials 2024; 305:122464. [PMID: 38181574 PMCID: PMC10872380 DOI: 10.1016/j.biomaterials.2023.122464] [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: 09/23/2023] [Revised: 12/25/2023] [Accepted: 12/31/2023] [Indexed: 01/07/2024]
Abstract
To realize the full potential of emerging nucleic acid therapies, there is a need for effective delivery agents to transport cargo to cells of interest. Protein materials exhibit several unique properties, including biodegradability, biocompatibility, ease of functionalization via recombinant and chemical modifications, among other features, which establish a promising basis for therapeutic nucleic acid delivery systems. In this review, we highlight progress made in the use of non-viral protein-based nanoparticles for nucleic acid delivery in vitro and in vivo, while elaborating on key physicochemical properties that have enabled the use of these materials for nanoparticle formulation and drug delivery. To conclude, we comment on the prospects and unresolved challenges associated with the translation of protein-based nucleic acid delivery systems for therapeutic applications.
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Affiliation(s)
- Feyisayo Eweje
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA; Harvard and MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA; Harvard/MIT MD-PhD Program, Boston, MA, USA, 02115; Wyss Institute of Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA
| | - Michelle L Walsh
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA; Harvard and MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA; Harvard/MIT MD-PhD Program, Boston, MA, USA, 02115
| | - Kiran Ahmad
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - Vanessa Ibrahim
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - Assma Alrefai
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - Jiaxuan Chen
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA; Wyss Institute of Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA.
| | - Elliot L Chaikof
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA; Wyss Institute of Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA.
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11
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Zhang HQ, Liu SH, Li R, Yu JW, Ye DX, Yuan SS, Lin H, Huang CB, Tang H. MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier. ACS OMEGA 2024; 9:8439-8447. [PMID: 38405489 PMCID: PMC10882704 DOI: 10.1021/acsomega.3c09587] [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: 12/01/2023] [Revised: 01/16/2024] [Accepted: 01/22/2024] [Indexed: 02/27/2024]
Abstract
In biological organisms, metal ion-binding proteins participate in numerous metabolic activities and are closely associated with various diseases. To accurately predict whether a protein binds to metal ions and the type of metal ion-binding protein, this study proposed a classifier named MIBPred. The classifier incorporated advanced Word2Vec technology from the field of natural language processing to extract semantic features of the protein sequence language and combined them with position-specific score matrix (PSSM) features. Furthermore, an ensemble learning model was employed for the metal ion-binding protein classification task. In the model, we independently trained XGBoost, LightGBM, and CatBoost algorithms and integrated the output results through an SVM voting mechanism. This innovative combination has led to a significant breakthrough in the predictive performance of our model. As a result, we achieved accuracies of 95.13% and 85.19%, respectively, in predicting metal ion-binding proteins and their types. Our research not only confirms the effectiveness of Word2Vec technology in extracting semantic information from protein sequences but also highlights the outstanding performance of the MIBPred classifier in the problem of metal ion-binding protein types. This study provides a reliable tool and method for the in-depth exploration of the structure and function of metal ion-binding proteins.
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Affiliation(s)
- Hong-Qi Zhang
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Shang-Hua Liu
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Rui Li
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Jun-Wen Yu
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Dong-Xin Ye
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Shi-Shi Yuan
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Hao Lin
- School
of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of
China, Chengdu 610054, China
| | - Cheng-Bing Huang
- School
of Computer Science and Technology, Aba Teachers University, Aba 623002, China
| | - Hua Tang
- School
of Basic Medical Sciences, Southwest Medical
University, Luzhou 646000, China
- Central
Nervous System Drug Key Laboratory of Sichuan Province, Luzhou 646000, China
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12
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Preto AJ, Caniceiro AB, Duarte F, Fernandes H, Ferreira L, Mourão J, Moreira IS. POSEIDON: Peptidic Objects SEquence-based Interaction with cellular DOmaiNs: a new database and predictor. J Cheminform 2024; 16:18. [PMID: 38365724 PMCID: PMC10874016 DOI: 10.1186/s13321-024-00810-7] [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: 10/17/2023] [Accepted: 02/07/2024] [Indexed: 02/18/2024] Open
Abstract
Cell-penetrating peptides (CPPs) are short chains of amino acids that have shown remarkable potential to cross the cell membrane and deliver coupled therapeutic cargoes into cells. Designing and testing different CPPs to target specific cells or tissues is crucial to ensure high delivery efficiency and reduced toxicity. However, in vivo/in vitro testing of various CPPs can be both time-consuming and costly, which has led to interest in computational methodologies, such as Machine Learning (ML) approaches, as faster and cheaper methods for CPP design and uptake prediction. However, most ML models developed to date focus on classification rather than regression techniques, because of the lack of informative quantitative uptake values. To address these challenges, we developed POSEIDON, an open-access and up-to-date curated database that provides experimental quantitative uptake values for over 2,300 entries and physicochemical properties of 1,315 peptides. POSEIDON also offers physicochemical properties, such as cell line, cargo, and sequence, among others. By leveraging this database along with cell line genomic features, we processed a dataset of over 1,200 entries to develop an ML regression CPP uptake predictor. Our results demonstrated that POSEIDON accurately predicted peptide cell line uptake, achieving a Pearson correlation of 0.87, Spearman correlation of 0.88, and r2 score of 0.76, on an independent test set. With its comprehensive and novel dataset, along with its potent predictive capabilities, the POSEIDON database and its associated ML predictor signify a significant leap forward in CPP research and development. The POSEIDON database and ML Predictor are available for free and with a user-friendly interface at https://moreiralab.com/resources/poseidon/ , making them valuable resources for advancing research on CPP-related topics. Scientific Contribution Statement: Our research addresses the critical need for more efficient and cost-effective methodologies in Cell-Penetrating Peptide (CPP) research. We introduced POSEIDON, a comprehensive and freely accessible database that delivers quantitative uptake values for over 2,300 entries, along with detailed physicochemical profiles for 1,315 peptides. Recognizing the limitations of current Machine Learning (ML) models for CPP design, our work leveraged the rich dataset provided by POSEIDON to develop a highly accurate ML regression model for predicting CPP uptake.
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Affiliation(s)
- António J Preto
- Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504, Coimbra, Portugal
- PhD Programme in Experimental Biology and Biomedicine, Institute for Interdisciplinary Research (IIIUC), University of Coimbra, Casa Costa Alemão, 3030-789, Coimbra, Portugal
| | - Ana B Caniceiro
- Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504, Coimbra, Portugal
- Department of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456, Coimbra, Portugal
| | - Francisco Duarte
- Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504, Coimbra, Portugal
| | - Hugo Fernandes
- CNC - Center for Neuroscience and Cell Biology, CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
- FMUC - Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- MIA - Multidisciplinary Institute of Ageing, University of Coimbra, Coimbra, Portugal
| | - Lino Ferreira
- CNC - Center for Neuroscience and Cell Biology, CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
- FMUC - Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Joana Mourão
- CNC - Center for Neuroscience and Cell Biology, CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - Irina S Moreira
- Department of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456, Coimbra, Portugal.
- CNC - Center for Neuroscience and Cell Biology, CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal.
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13
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Shi K, Xiong Y, Wang Y, Deng Y, Wang W, Jing B, Gao X. PractiCPP: a deep learning approach tailored for extremely imbalanced datasets in cell-penetrating peptide prediction. Bioinformatics 2024; 40:btae058. [PMID: 38305405 PMCID: PMC11212486 DOI: 10.1093/bioinformatics/btae058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/25/2024] [Accepted: 01/30/2024] [Indexed: 02/03/2024] Open
Abstract
MOTIVATION Effective drug delivery systems are paramount in enhancing pharmaceutical outcomes, particularly through the use of cell-penetrating peptides (CPPs). These peptides are gaining prominence due to their ability to penetrate eukaryotic cells efficiently without inflicting significant damage to the cellular membrane, thereby ensuring optimal drug delivery. However, the identification and characterization of CPPs remain a challenge due to the laborious and time-consuming nature of conventional methods, despite advances in proteomics. Current computational models, however, are predominantly tailored for balanced datasets, an approach that falls short in real-world applications characterized by a scarcity of known positive CPP instances. RESULTS To navigate this shortfall, we introduce PractiCPP, a novel deep-learning framework tailored for CPP prediction in highly imbalanced data scenarios. Uniquely designed with the integration of hard negative sampling and a sophisticated feature extraction and prediction module, PractiCPP facilitates an intricate understanding and learning from imbalanced data. Our extensive computational validations highlight PractiCPP's exceptional ability to outperform existing state-of-the-art methods, demonstrating remarkable accuracy, even in datasets with an extreme positive-to-negative ratio of 1:1000. Furthermore, through methodical embedding visualizations, we have established that models trained on balanced datasets are not conducive to practical, large-scale CPP identification, as they do not accurately reflect real-world complexities. In summary, PractiCPP potentially offers new perspectives in CPP prediction methodologies. Its design and validation, informed by real-world dataset constraints, suggest its utility as a valuable tool in supporting the acceleration of drug delivery advancements. AVAILABILITY AND IMPLEMENTATION The source code of PractiCPP is available on Figshare at https://doi.org/10.6084/m9.figshare.25053878.v1.
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Affiliation(s)
- Kexin Shi
- Syneron Technology, Guangzhou 510000, China
- Individualized Interdisciplinary Program (Data Science and Analytics), The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | | | - Yu Wang
- Syneron Technology, Guangzhou 510000, China
| | - Yifan Deng
- Syneron Technology, Guangzhou 510000, China
| | - Wenjia Wang
- Data Science and Analytics Thrust, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou, 511400, Guangdong, China
| | - Bingyi Jing
- Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen 518000, China
| | - Xin Gao
- Syneron Technology, Guangzhou 510000, China
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
- Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
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14
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Shin HJ, Lee BK, Kang HA. Transdermal Properties of Cell-Penetrating Peptides: Applications and Skin Penetration Mechanisms. ACS APPLIED BIO MATERIALS 2024; 7:1-16. [PMID: 38079575 DOI: 10.1021/acsabm.3c00659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Cell-penetrating peptides (CPPs) consist of 5-30 amino acids with intracellular transduction abilities and diverse physicochemical properties, origins, and sequences. Although recent developments in bioinformatics have facilitated the prediction of CPP candidates with the potential for transduction into cells, the mechanisms by which CPPs penetrate cells and various tissues have not yet been elucidated at the molecular interaction level. Recently, the skin-penetrating ability of CPPs has gained wide attention and emerged as a simple and effective strategy for the delivery of macromolecules into the skin. Studies on the skin structure have suggested that the penetration potential of CPPs is based on the molecular interactions and characteristics of the lipid lamellar structure between corneocytes in the stratum corneum. This review provides a brief overview of the general properties, transduction mechanisms, applications, and safety issues of CPPs, focusing on CPPs with transdermal properties, that are currently being used to develop therapeutics and cosmetics.
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Affiliation(s)
- Hee Je Shin
- ProCell R&D Center, ProCell Therapeutics, Inc., #1009 Ace-Twin Tower II, 273, Digital-ro, Guro-gu, Seoul 08381, Republic of Korea
- Department of Life Science, College of Natural Science, Chung-Ang University, 84, Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of Korea
| | - Byung Kyu Lee
- ProCell R&D Center, ProCell Therapeutics, Inc., #1009 Ace-Twin Tower II, 273, Digital-ro, Guro-gu, Seoul 08381, Republic of Korea
| | - Hyun Ah Kang
- Department of Life Science, College of Natural Science, Chung-Ang University, 84, Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of Korea
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15
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Pham NT, Phan LT, Seo J, Kim Y, Song M, Lee S, Jeon YJ, Manavalan B. Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach. Brief Bioinform 2023; 25:bbad433. [PMID: 38058187 PMCID: PMC10753650 DOI: 10.1093/bib/bbad433] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 10/30/2023] [Accepted: 11/05/2023] [Indexed: 12/08/2023] Open
Abstract
The worldwide appearance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has generated significant concern and posed a considerable challenge to global health. Phosphorylation is a common post-translational modification that affects many vital cellular functions and is closely associated with SARS-CoV-2 infection. Precise identification of phosphorylation sites could provide more in-depth insight into the processes underlying SARS-CoV-2 infection and help alleviate the continuing COVID-19 crisis. Currently, available computational tools for predicting these sites lack accuracy and effectiveness. In this study, we designed an innovative meta-learning model, Meta-Learning for Serine/Threonine Phosphorylation (MeL-STPhos), to precisely identify protein phosphorylation sites. We initially performed a comprehensive assessment of 29 unique sequence-derived features, establishing prediction models for each using 14 renowned machine learning methods, ranging from traditional classifiers to advanced deep learning algorithms. We then selected the most effective model for each feature by integrating the predicted values. Rigorous feature selection strategies were employed to identify the optimal base models and classifier(s) for each cell-specific dataset. To the best of our knowledge, this is the first study to report two cell-specific models and a generic model for phosphorylation site prediction by utilizing an extensive range of sequence-derived features and machine learning algorithms. Extensive cross-validation and independent testing revealed that MeL-STPhos surpasses existing state-of-the-art tools for phosphorylation site prediction. We also developed a publicly accessible platform at https://balalab-skku.org/MeL-STPhos. We believe that MeL-STPhos will serve as a valuable tool for accelerating the discovery of serine/threonine phosphorylation sites and elucidating their role in post-translational regulation.
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Affiliation(s)
- Nhat Truong Pham
- Department of Integrative Biotechnology and of Biopharmaceutical Convergence, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| | - Le Thi Phan
- Department of Integrative Biotechnology and of Biopharmaceutical Convergence, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| | - Jimin Seo
- Department of Integrative Biotechnology and of Biopharmaceutical Convergence, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| | - Yeonwoo Kim
- Department of Integrative Biotechnology and of Biopharmaceutical Convergence, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| | - Minkyung Song
- Department of Integrative Biotechnology and of Biopharmaceutical Convergence, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| | - Sukchan Lee
- Department of Integrative Biotechnology and of Biopharmaceutical Convergence, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| | - Young-Jun Jeon
- Department of Integrative Biotechnology and of Biopharmaceutical Convergence, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
| | - Balachandran Manavalan
- Department of Integrative Biotechnology and of Biopharmaceutical Convergence, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea
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16
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Zou X, Ren L, Cai P, Zhang Y, Ding H, Deng K, Yu X, Lin H, Huang C. Accurately identifying hemagglutinin using sequence information and machine learning methods. Front Med (Lausanne) 2023; 10:1281880. [PMID: 38020152 PMCID: PMC10644030 DOI: 10.3389/fmed.2023.1281880] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction Hemagglutinin (HA) is responsible for facilitating viral entry and infection by promoting the fusion between the host membrane and the virus. Given its significance in the process of influenza virus infestation, HA has garnered attention as a target for influenza drug and vaccine development. Thus, accurately identifying HA is crucial for the development of targeted vaccine drugs. However, the identification of HA using in-silico methods is still lacking. This study aims to design a computational model to identify HA. Methods In this study, a benchmark dataset comprising 106 HA and 106 non-HA sequences were obtained from UniProt. Various sequence-based features were used to formulate samples. By perform feature optimization and inputting them four kinds of machine learning methods, we constructed an integrated classifier model using the stacking algorithm. Results and discussion The model achieved an accuracy of 95.85% and with an area under the receiver operating characteristic (ROC) curve of 0.9863 in the 5-fold cross-validation. In the independent test, the model exhibited an accuracy of 93.18% and with an area under the ROC curve of 0.9793. The code can be found from https://github.com/Zouxidan/HA_predict.git. The proposed model has excellent prediction performance. The model will provide convenience for biochemical scholars for the study of HA.
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Affiliation(s)
- Xidan Zou
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Peiling Cai
- School of Basic Medical Sciences, Chengdu University, Chengdu, China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Hui Ding
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Kejun Deng
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaolong Yu
- School of Materials Science and Engineering, Hainan University, Haikou, China
| | - Hao Lin
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chengbing Huang
- School of Computer Science and Technology, Aba Teachers University, Aba, China
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17
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Yan Y, Shi Z, Wei H. ROSes-FINDER: a multi-task deep learning framework for accurate prediction of microorganism reactive oxygen species scavenging enzymes. Front Microbiol 2023; 14:1245805. [PMID: 37744924 PMCID: PMC10513406 DOI: 10.3389/fmicb.2023.1245805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 08/21/2023] [Indexed: 09/26/2023] Open
Abstract
Reactive oxygen species (ROS) are highly reactive molecules that play important roles in microbial biological processes. However, excessive accumulation of ROS can lead to oxidative stress and cellular damage. Microorganism have evolved a diverse suite of enzymes to mitigate the harmful effects of ROS. Accurate prediction of ROS scavenging enzymes classes (ROSes) is crucial for understanding the mechanisms of oxidative stress and developing strategies to combat related diseases. Nevertheless, the existing approaches for categorizing ROS-related proteins exhibit certain drawbacks with regards to their precision and inclusiveness. To address this, we propose a new multi-task deep learning framework called ROSes-FINDER. This framework integrates three component methods using a voting-based approach to predict multiple ROSes properties simultaneously. It can identify whether a given protein sequence is a ROSes and determine its type. The three component methods used in the framework are ROSes-CNN, which extracts raw sequence encoding features, ROSes-NN, which predicts protein functions based on sequence information, and ROSes-XGBoost, which performs functional classification using ensemble machine learning. Comprehensive experiments demonstrate the superior performance and robustness of our method. ROSes-FINDER is freely available at https://github.com/alienn233/ROSes-Finder for predicting ROSes classes.
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Affiliation(s)
- Yueyang Yan
- College of Veterinary Medicine, Jilin University, Changchun, China
| | - Zhanpeng Shi
- College of Veterinary Medicine, Jilin University, Changchun, China
| | - Haijian Wei
- Department of Organ Transplantation, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai City, China
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18
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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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19
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Momanyi BM, Zulfiqar H, Grace-Mercure BK, Ahmed Z, Ding H, Gao H, Liu F. CFNCM: Collaborative filtering neighborhood-based model for predicting miRNA-disease associations. Comput Biol Med 2023; 163:107165. [PMID: 37315383 DOI: 10.1016/j.compbiomed.2023.107165] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/31/2023] [Accepted: 06/08/2023] [Indexed: 06/16/2023]
Abstract
MicroRNAs have a significant role in the emergence of various human disorders. Consequently, it is essential to understand the existing interactions between miRNAs and diseases, as this will help scientists better study and comprehend the diseases' biological mechanisms. Findings can be employed as biomarkers or drug targets to advance the detection, diagnosis, and treatment of complex human disorders by foretelling possible disease-related miRNAs. This study proposed a computational model for predicting potential miRNA-disease associations called the Collaborative Filtering Neighborhood-based Classification Model (CFNCM), in light of the shortcomings of conventional and biological experiments, which are expensive and time-consuming. The model generated integrated miRNA and disease similarity matrices using the validated associations and miRNA and disease similarity information and used them as the input features for CFNCM. To produce class labels, we first determined the association scores for brand-new pairs using user-based collaborative filtering. With zero as the threshold, the associations with scores >0 were labelled 1, indicating a potential positive association, otherwise, it is marked as 0. Then, we developed classification models using various machine-learning algorithms. By comparison, we discovered that the support vector machine (SVM) produced the best AUC of 0.96 with 10-fold cross-validation through the GridSearchCV technique for identifying optimal parameter values. In addition, the models were evaluated and verified by analyzing the top 50 breast and lung neoplasms-related miRNAs, of which 46 and 47 associations were verified in two authoritative databases, dbDEMC and miR2Disease.
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Affiliation(s)
- Biffon Manyura Momanyi
- School of Computer Science and Engineering, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hasan Zulfiqar
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China; Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, 313001, China
| | - Bakanina Kissanga Grace-Mercure
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Zahoor Ahmed
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China; Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, 313001, China
| | - Hui Ding
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Hui Gao
- School of Computer Science and Engineering, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.
| | - Fen Liu
- Department of Radiation Oncology, Peking University Cancer Hospital (Inner Mongolia Campus), Affiliated Cancer Hospital of Inner Mongolia Medical University, Inner Mongolia Cancer Hospital, Hohhot, China.
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20
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Gori A, Lodigiani G, Colombarolli SG, Bergamaschi G, Vitali A. Cell Penetrating Peptides: Classification, Mechanisms, Methods of Study, and Applications. ChemMedChem 2023; 18:e202300236. [PMID: 37389978 DOI: 10.1002/cmdc.202300236] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/28/2023] [Accepted: 06/28/2023] [Indexed: 07/02/2023]
Abstract
Cell-penetrating peptides (CPPs) encompass a class of peptides that possess the remarkable ability to cross cell membranes and deliver various types of cargoes, including drugs, nucleic acids, and proteins, into cells. For this reason, CPPs are largely investigated in drug delivery applications in the context of many diseases, such as cancer, diabetes, and genetic disorders. While sharing this functionality and some common structural features, such as a high content of positively charged amino acids, CPPs represent an extremely diverse group of elements, which can differentiate under many aspects. In this review, we summarize the most common characteristics of CPPs, introduce their main distinctive features, mechanistic aspects that drive their function, and outline the most widely used techniques for their structural and functional studies. We highlight current gaps and future perspectives in this field, which have the potential to significantly impact the future field of drug delivery and therapeutics.
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Affiliation(s)
- Alessandro Gori
- SCITEC - Istituto di Scienze e Tecnologie Chimiche "Giulio Natta", National Research Council of Italy, Via Mario Bianco 9, 20131, Milano, Italy
| | - Giulia Lodigiani
- SCITEC - Istituto di Scienze e Tecnologie Chimiche "Giulio Natta", National Research Council of Italy, Via Mario Bianco 9, 20131, Milano, Italy
| | - Stella G Colombarolli
- Istituto di Scienze e Tecnologie Chimiche "Giulio Natta", National Research Council of Italy, L.go F. Vito 1, 00168, Roma, Italy
| | - Greta Bergamaschi
- SCITEC - Istituto di Scienze e Tecnologie Chimiche "Giulio Natta", National Research Council of Italy, Via Mario Bianco 9, 20131, Milano, Italy
| | - Alberto Vitali
- Istituto di Scienze e Tecnologie Chimiche "Giulio Natta", National Research Council of Italy, L.go F. Vito 1, 00168, Roma, Italy
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Saini S, Rathore A, Sharma S, Saini A. Exploratory data analysis of physicochemical parameters of natural antimicrobial and anticancer peptides: Unraveling the patterns and trends for the rational design of novel peptides. BIOIMPACTS : BI 2023; 14:26438. [PMID: 38327633 PMCID: PMC10844588 DOI: 10.34172/bi.2023.26438] [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: 05/06/2022] [Revised: 11/04/2022] [Accepted: 12/04/2022] [Indexed: 02/09/2024]
Abstract
Introduction Peptide-based research has attained new avenues in the antibiotics and cancer drug resistance era. The basis of peptide design research lies in playing with or altering physicochemical parameters. Here in this work, we have done exploratory data analysis (EDA) of physicochemical parameters of antimicrobial peptides (AMPs) and anticancer peptides (ACPs), two promising therapeutics for microbial and cancer drug resistance to deduce patterns and trends. Methods Briefly, we have captured the natural AMPs and ACPs data from the APD3 database. After cleaning the data manually and by CD-HIT web server, further data analysis has been done using Python-based packages, modlAMP and Pandas. We have extracted the descriptive statistics of 10 physicochemical parameters of AMPs and ACPs to build a comprehensive dataset containing all major parameters. The global analysis of datasets has been done using modlAMP to find the initial patterns in global data. The subsets of AMPs and ACPs were curated based on the length of the peptides and were analyzed by Pandas package to deduce the graphical profile of AMPs and ACPs. Results EDA of AMPs and ACPs shows selectivity in the length and amino acid compositions. The distribution of physicochemical parameters in defined quartile ranges was observed in the descriptive statistical and graphical analysis. The preferred length range of AMPs and ACPs was found to be 21-30 amino acids, whereas few outliers in each parameter were evident after EDA analysis. Conclusion The derived patterns from natural AMPs and ACPs can be used for the rational design of novel peptides. The statistical and graphical data distribution findings will help in combining the different parameters for potent design of novel AMPs and ACPs.
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Affiliation(s)
- Sandeep Saini
- Department of Biophysics, Panjab University, Sector 25, Chandigarh 160014, India
- Department of Bioinformatics, Goswami Ganesh Dutta Sanatan Dharma College, Sector 32-C, Chandigarh 160030, India
| | - Aayushi Rathore
- Institute of Bioinformatics and Applied Biotechnology, Biotech Park, Bengaluru 560100, India
| | - Sheetal Sharma
- Department of Biophysics, Panjab University, Sector 25, Chandigarh 160014, India
| | - Avneet Saini
- Department of Biophysics, Panjab University, Sector 25, Chandigarh 160014, India
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22
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Zhu W, Yuan SS, Li J, Huang CB, Lin H, Liao B. A First Computational Frame for Recognizing Heparin-Binding Protein. Diagnostics (Basel) 2023; 13:2465. [PMID: 37510209 PMCID: PMC10377868 DOI: 10.3390/diagnostics13142465] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 07/13/2023] [Accepted: 07/21/2023] [Indexed: 07/30/2023] Open
Abstract
Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear neutrophils and an important biomarker of infectious diseases. The correct identification of HBP is of great significance to the study of infectious diseases. This work provides the first HBP recognition framework based on machine learning to accurately identify HBP. By using four sequence descriptors, HBP and non-HBP samples were represented by discrete numbers. By inputting these features into a support vector machine (SVM) and random forest (RF) algorithm and comparing the prediction performances of these methods on training data and independent test data, it is found that the SVM-based classifier has the greatest potential to identify HBP. The model could produce an auROC of 0.981 ± 0.028 on training data using 10-fold cross-validation and an overall accuracy of 95.0% on independent test data. As the first model for HBP recognition, it will provide some help for infectious diseases and stimulate further research in related fields.
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Affiliation(s)
- Wen Zhu
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou 571158, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou 571158, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China
| | - Shi-Shi Yuan
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jian Li
- School of Basic Medical Sciences, Chengdu University, Chengdu 610106, China
| | - Cheng-Bing Huang
- School of Computer Science and Technology, ABa Teachers University, Chengdu 623002, China
| | - Hao Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bo Liao
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou 571158, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou 571158, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China
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23
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da Silva Sanches PR, Sanchez-Velazquez R, Batista MN, Carneiro BM, Bittar C, De Lorenzo G, Rahal P, Patel AH, Cilli EM. Antiviral Evaluation of New Synthetic Bioconjugates Based on GA-Hecate: A New Class of Antivirals Targeting Different Steps of Zika Virus Replication. Molecules 2023; 28:4884. [PMID: 37446546 PMCID: PMC10343505 DOI: 10.3390/molecules28134884] [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: 05/18/2023] [Revised: 06/05/2023] [Accepted: 06/15/2023] [Indexed: 07/15/2023] Open
Abstract
Re-emerging arboviruses represent a serious health problem due to their rapid vector-mediated spread, mainly in urban tropical areas. The 2013-2015 Zika virus (ZIKV) outbreak in South and Central America has been associated with cases of microcephaly in newborns and Guillain-Barret syndrome. We previously showed that the conjugate gallic acid-Hecate (GA-FALALKALKKALKKLKKALKKAL-CONH2)-is an efficient inhibitor of the hepatitis C virus. Here, we show that the Hecate peptide is degraded in human blood serum into three major metabolites. These metabolites conjugated with gallic acid were synthesized and their effect on ZIKV replication in cultured cells was evaluated. The GA-metabolite 5 (GA-FALALKALKKALKKL-COOH) was the most efficient in inhibiting two ZIKV strains of African and Asian lineage at the stage of both virus entry (virucidal and protective) and replication (post-entry). We also demonstrate that GA-metabolite 5 does not affect cell growth after 7 days of continuous treatment. Thus, this study identifies a new synthetic antiviral compound targeting different steps of ZIKV replication in vitro and with the potential for broad reactivity against other flaviviruses. Our work highlights a promising strategy for the development of new antivirals based on peptide metabolism and bioconjugation.
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Affiliation(s)
- Paulo Ricardo da Silva Sanches
- School of Pharmaceutical Science, São Paulo State University, Araraquara 14800-903, SP, Brazil
- MRC—University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G12 8QQ, UK; (R.S.-V.); (G.D.L.); (A.H.P.)
- Institute of Chemistry, São Paulo State University, Araraquara 14800-900, SP, Brazil
| | - Ricardo Sanchez-Velazquez
- MRC—University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G12 8QQ, UK; (R.S.-V.); (G.D.L.); (A.H.P.)
| | - Mariana Nogueira Batista
- Laboratory of Virology and Infectious Diseases, The Rockefeller University, New York, NY 10065, USA; (M.N.B.)
| | - Bruno Moreira Carneiro
- School of Health Science, Federal University of Rondonópolis, Rondonópolis 78736-900, MT, Brazil;
| | - Cintia Bittar
- School of Health Science, Federal University of Rondonópolis, Rondonópolis 78736-900, MT, Brazil;
| | - Giuditta De Lorenzo
- MRC—University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G12 8QQ, UK; (R.S.-V.); (G.D.L.); (A.H.P.)
| | - Paula Rahal
- Institute of Bioscience, Humanities and Exact Science, São Paulo State University, São José do Rio Preto 15054-000, SP, Brazil;
| | - Arvind H. Patel
- MRC—University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G12 8QQ, UK; (R.S.-V.); (G.D.L.); (A.H.P.)
| | - Eduardo Maffud Cilli
- Institute of Chemistry, São Paulo State University, Araraquara 14800-900, SP, Brazil
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24
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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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25
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Firoz A, Malik A, Ali HM, Akhter Y, Manavalan B, Kim CB. PRR-HyPred: A two-layer hybrid framework to predict pattern recognition receptors and their families by employing sequence encoded optimal features. Int J Biol Macromol 2023; 234:123622. [PMID: 36773859 DOI: 10.1016/j.ijbiomac.2023.123622] [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: 11/08/2022] [Revised: 02/03/2023] [Accepted: 02/06/2023] [Indexed: 02/12/2023]
Abstract
Pattern recognition receptors (PRRs) recognize distinct features on the surface of pathogens or damaged cells and play key roles in the innate immune system. PRRs are divided into various families, including Toll-like receptors, retinoic acid-inducible gene-I-like receptors, nucleotide oligomerization domain-like receptors, and C-type lectin receptors. As these are implicated in host health and several diseases, their accurate identification is indispensable for their functional characterization and targeted therapeutic approaches. Here, we construct PRR-HyPred, a novel two-layer hybrid framework in which the first layer predicts whether a given sequence is PRR or non-PRR using a support vector machine, and in the second, the predicted PRR sequence is assigned to a specific family using a random forest-based classifier. Based on a 10-fold cross-validation test, PRR-HyPred achieved 83.4 % accuracy in the first layer and 95 % in the second, with Matthew's correlation coefficient values of 0.639 and 0.816, respectively. This is the first study that can simultaneously predict and classify PRRs into specific families. PRR-HyPred is available as a web portal at https://procarb.org/PRRHyPred/. We hope that it could be a valuable tool for the large-scale prediction and classification of PRRs and subsequently facilitate future studies.
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Affiliation(s)
- Ahmad Firoz
- Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia; Princess Dr. Najla Bint Saud Al- Saud Center for Excellence Research in Biotechnology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Adeel Malik
- Institute of Intelligence Informatics Technology, Sangmyung University, Seoul, 03016, Republic of Korea.
| | - Hani Mohammed Ali
- Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia; Princess Dr. Najla Bint Saud Al- Saud Center for Excellence Research in Biotechnology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Yusuf Akhter
- Department of Biotechnology, Babasaheb Bhimrao Ambedkar University, Vidya Vihar, Raebareli Road, Lucknow, Uttar Pradesh, 226025, India
| | - 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.
| | - Chang-Bae Kim
- Department of Biotechnology, Sangmyung University, Seoul, 03016, Republic of Korea.
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26
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Yang YH, Ma CY, Gao D, Liu XW, Yuan SS, Ding H. i2OM: Toward a better prediction of 2'-O-methylation in human RNA. Int J Biol Macromol 2023; 239:124247. [PMID: 37003392 DOI: 10.1016/j.ijbiomac.2023.124247] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 03/06/2023] [Accepted: 03/22/2023] [Indexed: 04/03/2023]
Abstract
2'-O-methylation (2OM) is an omnipresent post-transcriptional modification in RNAs. It is important for the regulation of RNA stability, mRNA splicing and translation, as well as innate immunity. With the increase in publicly available 2OM data, several computational tools have been developed for the identification of 2OM sites in human RNA. Unfortunately, these tools suffer from the low discriminative power of redundant features, unreasonable dataset construction or overfitting. To address those issues, based on four types of 2OM (2OM-adenine (A), cytosine (C), guanine (G), and uracil (U)) data, we developed a two-step feature selection model to identify 2OM. For each type, the one-way analysis of variance (ANOVA) combined with mutual information (MI) was proposed to rank sequence features for obtaining the optimal feature subset. Subsequently, four predictors based on eXtreme Gradient Boosting (XGBoost) or support vector machine (SVM) were presented to identify the four types of 2OM sites. Finally, the proposed model could produce an overall accuracy of 84.3 % on the independent set. To provide a convenience for users, an online tool called i2OM was constructed and can be freely access at i2om.lin-group.cn. The predictor may provide a reference for the study of the 2OM.
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Affiliation(s)
- Yu-He Yang
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Cai-Yi Ma
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Dong Gao
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xiao-Wei Liu
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Shi-Shi Yuan
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Hui Ding
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
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27
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Topical Delivery of Cell-Penetrating Peptide-Modified Human Growth Hormone for Enhanced Wound Healing. Pharmaceuticals (Basel) 2023; 16:ph16030394. [PMID: 36986493 PMCID: PMC10053240 DOI: 10.3390/ph16030394] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 03/01/2023] [Accepted: 03/03/2023] [Indexed: 03/08/2023] Open
Abstract
Protein drugs have been emerging as a class of promising therapeutics. However, their topical application has been limited by their high molecular weight and poor permeability to the cell membrane. In this study, we aimed to enhance human growth hormone (hGH) permeability for topical application by conjugation of TAT peptide, a cell-penetrating peptide, to hGH via crosslinker. After TAT was conjugated to hGH, TAT-hGH was purified by affinity chromatography. TAT-hGH significantly increased cell proliferation compared with the control. Interestingly, the effect of TAT-hGH was higher than hGH at the same concentration. Furthermore, the conjugation of TAT to hGH enhanced the permeability of TAT-hGH across the cell membrane without affecting its biological activity in vitro. In vivo, the topical application of TAT-hGH into scar tissue markedly accelerated wound healing. Histological results showed that TAT-hGH dramatically promoted the re-epithelialization of wounds in the initial stage. These results demonstrate TAT-hGH as a new therapeutic potential drug for wound healing treatment. This study also provides a new method for topical protein application via enhancement of their permeability.
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28
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Porosk L, Härk HH, Bicev RN, Gaidutšik I, Nebogatova J, Armolik EJ, Arukuusk P, da Silva ER, Langel Ü. Aggregation Limiting Cell-Penetrating Peptides Derived from Protein Signal Sequences. Int J Mol Sci 2023; 24:ijms24054277. [PMID: 36901707 PMCID: PMC10002422 DOI: 10.3390/ijms24054277] [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: 01/23/2023] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 02/24/2023] Open
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disease (ND) and the leading cause of dementia. It is characterized by non-linear, genetic-driven pathophysiological dynamics with high heterogeneity in the biological alterations and the causes of the disease. One of the hallmarks of the AD is the progression of plaques of aggregated amyloid-β (Aβ) or neurofibrillary tangles of Tau. Currently there is no efficient treatment for the AD. Nevertheless, several breakthroughs in revealing the mechanisms behind progression of the AD have led to the discovery of possible therapeutic targets. Some of these include the reduction in inflammation in the brain, and, although highly debated, limiting of the aggregation of the Aβ. In this work we show that similarly to the Neural cell adhesion molecule 1 (NCAM1) signal sequence, other Aβ interacting protein sequences, especially derived from Transthyretin, can be used successfully to reduce or target the amyloid aggregation/aggregates in vitro. The modified signal peptides with cell-penetrating properties reduce the Aβ aggregation and are predicted to have anti-inflammatory properties. Furthermore, we show that by expressing the Aβ-EGFP fusion protein, we can efficiently assess the potential for reduction in aggregation, and the CPP properties of peptides in mammalian cells.
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Affiliation(s)
- Ly Porosk
- Institute of Technology, University of Tartu, Nooruse 1, 50411 Tartu, Estonia
- Correspondence:
| | - Heleri Heike Härk
- Institute of Technology, University of Tartu, Nooruse 1, 50411 Tartu, Estonia
| | - Renata Naporano Bicev
- Departamento de Biofísica, Universidade Federal de São Paulo, São Paulo 04023-062, Brazil
| | - Ilja Gaidutšik
- Institute of Technology, University of Tartu, Nooruse 1, 50411 Tartu, Estonia
| | | | - Eger-Jasper Armolik
- Institute of Technology, University of Tartu, Nooruse 1, 50411 Tartu, Estonia
| | - Piret Arukuusk
- Institute of Technology, University of Tartu, Nooruse 1, 50411 Tartu, Estonia
| | | | - Ülo Langel
- Institute of Technology, University of Tartu, Nooruse 1, 50411 Tartu, Estonia
- Department Biochemistry and Biophysics, Stockholm University, S.Arrheniusv. 16B, Room C472, 106 91 Stockholm, Sweden
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29
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Zhang YF, Wang YH, Gu ZF, Pan XR, Li J, Ding H, Zhang Y, Deng KJ. Bitter-RF: A random forest machine model for recognizing bitter peptides. Front Med (Lausanne) 2023; 10:1052923. [PMID: 36778738 PMCID: PMC9909039 DOI: 10.3389/fmed.2023.1052923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 01/05/2023] [Indexed: 01/27/2023] Open
Abstract
Introduction Bitter peptides are short peptides with potential medical applications. The huge potential behind its bitter taste remains to be tapped. To better explore the value of bitter peptides in practice, we need a more effective classification method for identifying bitter peptides. Methods In this study, we developed a Random forest (RF)-based model, called Bitter-RF, using sequence information of the bitter peptide. Bitter-RF covers more comprehensive and extensive information by integrating 10 features extracted from the bitter peptides and achieves better results than the latest generation model on independent validation set. Results The proposed model can improve the accurate classification of bitter peptides (AUROC = 0.98 on independent set test) and enrich the practical application of RF method in protein classification tasks which has not been used to build a prediction model for bitter peptides. Discussion We hope the Bitter-RF could provide more conveniences to scholars for bitter peptide research.
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Affiliation(s)
- Yu-Fei Zhang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yu-Hao Wang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhi-Feng Gu
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xian-Run Pan
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jian Li
- School of Basic Medical Sciences, Chengdu University, Chengdu, China
| | - Hui Ding
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China,*Correspondence: Hui Ding,
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China,Yang Zhang,
| | - Ke-Jun Deng
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China,Ke-Jun Deng,
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30
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Zhang X, Wei L, Ye X, Zhang K, Teng S, Li Z, Jin J, Kim MJ, Sakurai T, Cui L, Manavalan B, Wei L. SiameseCPP: a sequence-based Siamese network to predict cell-penetrating peptides by contrastive learning. Brief Bioinform 2023; 24:6958502. [PMID: 36562719 DOI: 10.1093/bib/bbac545] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/07/2022] [Accepted: 11/10/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Cell-penetrating peptides (CPPs) have received considerable attention as a means of transporting pharmacologically active molecules into living cells without damaging the cell membrane, and thus hold great promise as future therapeutics. Recently, several machine learning-based algorithms have been proposed for predicting CPPs. However, most existing predictive methods do not consider the agreement (disagreement) between similar (dissimilar) CPPs and depend heavily on expert knowledge-based handcrafted features. RESULTS In this study, we present SiameseCPP, a novel deep learning framework for automated CPPs prediction. SiameseCPP learns discriminative representations of CPPs based on a well-pretrained model and a Siamese neural network consisting of a transformer and gated recurrent units. Contrastive learning is used for the first time to build a CPP predictive model. Comprehensive experiments demonstrate that our proposed SiameseCPP is superior to existing baseline models for predicting CPPs. Moreover, SiameseCPP also achieves good performance on other functional peptide datasets, exhibiting satisfactory generalization ability.
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Affiliation(s)
- Xin Zhang
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Lesong Wei
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Xiucai Ye
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Kai Zhang
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Saisai Teng
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Zhongshen Li
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Junru Jin
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Min Jae Kim
- Department of integrative Biotechnology, College of Biotechnology & Bioengineering, Sungkyunkwan University, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do 16419, Republic of Korea
| | - Tetsuya Sakurai
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Lizhen Cui
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Balachandran Manavalan
- Department of integrative Biotechnology, College of Biotechnology & Bioengineering, Sungkyunkwan University, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do 16419, Republic of Korea
| | - Leyi Wei
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
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31
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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: 17] [Impact Index Per Article: 17.0] [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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32
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Su W, Deng S, Gu Z, Yang K, Ding H, Chen H, Zhang Z. Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition. Front Genet 2023; 14:1157021. [PMID: 36926588 PMCID: PMC10011625 DOI: 10.3389/fgene.2023.1157021] [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: 02/02/2023] [Accepted: 02/20/2023] [Indexed: 03/08/2023] Open
Abstract
Introduction: Apoptosis proteins play an important role in the process of cell apoptosis, which makes the rate of cell proliferation and death reach a relative balance. The function of apoptosis protein is closely related to its subcellular location, it is of great significance to study the subcellular locations of apoptosis proteins. Many efforts in bioinformatics research have been aimed at predicting their subcellular location. However, the subcellular localization of apoptotic proteins needs to be carefully studied. Methods: In this paper, based on amphiphilic pseudo amino acid composition and support vector machine algorithm, a new method was proposed for the prediction of apoptosis proteins\x{2019} subcellular location. Results and Discussion: The method achieved good performance on three data sets. The Jackknife test accuracy of the three data sets reached 90.5%, 93.9% and 84.0%, respectively. Compared with previous methods, the prediction accuracies of APACC_SVM were improved.
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Affiliation(s)
- Wenxia Su
- College of Science, Inner Mongolia Agriculture University, Hohhot, China
| | - Shuyi Deng
- School of Life Science and Technology, Center for Information Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhifeng Gu
- 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
| | - Hui Chen
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, 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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33
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Zulfiqar H, Ahmed Z, Kissanga Grace-Mercure B, Hassan F, Zhang ZY, Liu F. Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique. Front Microbiol 2023; 14:1170785. [PMID: 37125199 PMCID: PMC10133480 DOI: 10.3389/fmicb.2023.1170785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 03/17/2023] [Indexed: 05/02/2023] Open
Abstract
Promotors are those genomic regions on the upstream of genes, which are bound by RNA polymerase for starting gene transcription. Because it is the most critical element of gene expression, the recognition of promoters is crucial to understand the regulation of gene expression. This study aimed to develop a machine learning-based model to predict promotors in Agrobacterium tumefaciens (A. tumefaciens) strain C58. In the model, promotor sequences were encoded by three different kinds of feature descriptors, namely, accumulated nucleotide frequency, k-mer nucleotide composition, and binary encodings. The obtained features were optimized by using correlation and the mRMR-based algorithm. These optimized features were inputted into a random forest (RF) classifier to discriminate promotor sequences from non-promotor sequences in A. tumefaciens strain C58. The examination of 10-fold cross-validation showed that the proposed model could yield an overall accuracy of 0.837. This model will provide help for the study of promoters in A. tumefaciens C58 strain.
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Affiliation(s)
- Hasan Zulfiqar
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
- *Correspondence: Hasan Zulfiqar
| | - Zahoor Ahmed
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China
| | - Bakanina Kissanga Grace-Mercure
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Farwa Hassan
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhao-Yue Zhang
- School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
- Zhao-Yue Zhang
| | - Fen Liu
- Department of Radiation Oncology, Peking University Cancer Hospital (Inner Mongolia Campus), Affiliated Cancer Hospital of Inner Mongolia Medical University, Inner Mongolia Cancer Hospital, Hohhot, China
- Fen Liu
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34
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Ayala-Ruano S, Marrero-Ponce Y, Aguilera-Mendoza L, Pérez N, Agüero-Chapin G, Antunes A, Aguilar AC. Network Science and Group Fusion Similarity-Based Searching to Explore the Chemical Space of Antiparasitic Peptides. ACS OMEGA 2022; 7:46012-46036. [PMID: 36570318 PMCID: PMC9773354 DOI: 10.1021/acsomega.2c03398] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 11/21/2022] [Indexed: 05/13/2023]
Abstract
Antimicrobial peptides (AMPs) have appeared as promising compounds to treat a wide range of diseases. Their clinical potentialities reside in the wide range of mechanisms they can use for both killing microbes and modulating immune responses. However, the hugeness of the AMPs' chemical space (AMPCS), represented by more than 1065 unique sequences, has represented a big challenge for the discovery of new promising therapeutic peptides and for the identification of common structural motifs. Here, we introduce network science and a similarity searching approach to discover new promising AMPs, specifically antiparasitic peptides (APPs). We exploited the network-based representation of APPs' chemical space (APPCS) to retrieve valuable information by using three network types: chemical space (CSN), half-space proximal (HSPN), and metadata (METN). Some centrality measures were applied to identify in each network the most important and nonredundant peptides. Then, these central peptides were considered as queries (Qs) in group fusion similarity-based searches against a comprehensive collection of known AMPs, stored in the graph database StarPepDB, to propose new potential APPs. The performance of the resulting multiquery similarity-based search models (mQSSMs) was evaluated in five benchmarking data sets of APP/non-APPs. The predictions performed by the best mQSSM showed a strong-to-very-strong performance since their external Matthews correlation coefficient (MCC) values ranged from 0.834 to 0.965. Outstanding MCC values (>0.85) were attained by the mQSSM with 219 Qs from both networks CSN and HSPN with 0.5 as similarity threshold in external data sets. Then, the performance of our best mQSSM was compared with the APPs prediction servers AMPDiscover and AMPFun. The proposed model showed its relevance by outperforming state-of-the-art machine learning models to predict APPs. After applying the best mQSSM and additional filters on the non-APP space from StarPepDB, 95 AMPs were repurposed as potential APP hits. Due to the high sequence diversity of these peptides, different computational approaches were applied to identify relevant motifs for searching and designing new APPs. Lastly, we identified 11 promising APP lead candidates by using our best mQSSMs together with diversity-based network analyses, and 24 web servers for activity/toxicity and drug-like properties. These results support that network-based similarity searches can be an effective and reliable strategy to identify APPs. The proposed models and pipeline are freely available through the StarPep toolbox software at http://mobiosd-hub.com/starpep.
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Affiliation(s)
- Sebastián Ayala-Ruano
- Grupo
de Medicina Molecular y Traslacional (MeM&T), Escuela de Medicina,
Colegio de Ciencias de la Salud (COCSA), Universidad San Francisco de Quito, Av. Interoceánica Km 12 1/2 y Av. Florencia, Quito 17-1200-841, Ecuador
- Colegio
de Ciencias e Ingenierías “El Politécnico”, Universidad San Francisco de Quito (USFQ), Quito 170901, Ecuador
| | - Yovani Marrero-Ponce
- Grupo
de Medicina Molecular y Traslacional (MeM&T), Escuela de Medicina,
Colegio de Ciencias de la Salud (COCSA), Universidad San Francisco de Quito, Av. Interoceánica Km 12 1/2 y Av. Florencia, Quito 17-1200-841, Ecuador
- Computer-Aided
Molecular “Biosilico” Discovery and Bioinformatics Research
International Network (CAMD-BIR IN), Cumbayá, Quito 170901, Ecuador
- Universidad
San Francisco de Quito (USFQ), Instituto
de Simulación Computacional (ISC-USFQ), Diego de Robles y vía Interoceánica, Quito 170157, Pichincha, Ecuador
- Departamento
de Ciencias de la Computación, Centro
de Investigación Científica y de Educación Superior
de Ensenada (CICESE), Baja California 22860, Mexico
| | - Longendri Aguilera-Mendoza
- Departamento
de Ciencias de la Computación, Centro
de Investigación Científica y de Educación Superior
de Ensenada (CICESE), Baja California 22860, Mexico
| | - Noel Pérez
- Colegio
de Ciencias e Ingenierías “El Politécnico”, Universidad San Francisco de Quito (USFQ), Quito 170901, Ecuador
| | - Guillermin Agüero-Chapin
- CIIMAR/CIMAR,
Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton
de Matos s/n, 4450-208 Porto, Portugal
- Department
of Biology, Faculty of Sciences, University
of Porto, Rua do Campo
Alegre, 4169-007 Porto, Portugal
| | - Agostinho Antunes
- CIIMAR/CIMAR,
Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton
de Matos s/n, 4450-208 Porto, Portugal
- Department
of Biology, Faculty of Sciences, University
of Porto, Rua do Campo
Alegre, 4169-007 Porto, Portugal
| | - Ana Cristina Aguilar
- Grupo
de Medicina Molecular y Traslacional (MeM&T), Escuela de Medicina,
Colegio de Ciencias de la Salud (COCSA), Universidad San Francisco de Quito, Av. Interoceánica Km 12 1/2 y Av. Florencia, Quito 17-1200-841, Ecuador
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De Novo Design of AC-P19M, a Novel Anticancer Peptide with Apoptotic Effects on Lung Cancer Cells and Anti-Angiogenic Activity. Int J Mol Sci 2022; 23:ijms232415594. [PMID: 36555235 PMCID: PMC9779372 DOI: 10.3390/ijms232415594] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/06/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
Despite the current developments in cancer therapeutics, efforts to excavate new anticancer agents continue rigorously due to obstacles, such as side effects and drug resistance. Anticancer peptides (ACPs) can be utilized to treat cancer because of their effectiveness on a variety of molecular targets, along with high selectivity and specificity for cancer cells. In the present study, a novel ACP was de novo designed using in silico methods, and its functionality and molecular mechanisms of action were explored. AC-P19M was discovered through functional prediction and sequence modification based on peptide sequences currently available in the database. The peptide exhibited anticancer activity against lung cancer cells, A549 and H460, by disrupting cellular membranes and inducing apoptosis while showing low toxicity towards normal and red blood cells. In addition, the peptide inhibited the migration and invasion of lung cancer cells and reversed epithelial-mesenchymal transition. Moreover, AC-P19M showed anti-angiogenic activity through the inhibition of vascular endothelial growth factor receptor 2 signaling. Our findings suggest that AC-P19M is a novel ACP that directly or indirectly targets cancer cells, demonstrating the potential development of an anticancer agent and providing insights into the discovery of functional substances based on an in silico approach.
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36
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Identification of adaptor proteins using the ANOVA feature selection technique. Methods 2022; 208:42-47. [DOI: 10.1016/j.ymeth.2022.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 10/01/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
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Porosk L, Langel Ü. Approaches for evaluation of novel CPP-based cargo delivery systems. Front Pharmacol 2022; 13:1056467. [PMID: 36339538 PMCID: PMC9634181 DOI: 10.3389/fphar.2022.1056467] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 10/10/2022] [Indexed: 08/05/2023] Open
Abstract
Cell penetrating peptides (CPPs) can be broadly defined as relatively short synthetic, protein derived or chimeric peptides. Their most remarkable property is their ability to cross cell barriers and facilitate the translocation of cargo, such as drugs, nucleic acids, peptides, small molecules, dyes, and many others across the plasma membrane. Over the years there have been several approaches used, adapted, and developed for the evaluation of CPP efficacies as delivery systems, with the fluorophore attachment as the most widely used approach. It has become progressively evident, that the evaluation method, in order to lead to successful outcome, should concede with the specialties of the delivery. For characterization and assessment of CPP-cargo a combination of research tools of chemistry, physics, molecular biology, engineering, and other fields have been applied. In this review, we summarize the diverse, in silico, in vitro and in vivo approaches used for evaluation and characterization of CPP-based cargo delivery systems.
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Affiliation(s)
- Ly Porosk
- Laboratory of Drug Delivery, Institute of Technology, Faculty of Science and Technology, University of Tartu, Tartu, Estonia
| | - Ülo Langel
- Laboratory of Drug Delivery, Institute of Technology, Faculty of Science and Technology, University of Tartu, Tartu, Estonia
- Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
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A Statistical Analysis of the Sequence and Structure of Thermophilic and Non-Thermophilic Proteins. Int J Mol Sci 2022; 23:ijms231710116. [PMID: 36077513 PMCID: PMC9456548 DOI: 10.3390/ijms231710116] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/29/2022] [Accepted: 08/31/2022] [Indexed: 11/17/2022] Open
Abstract
Thermophilic proteins have various practical applications in theoretical research and in industry. In recent years, the demand for thermophilic proteins on an industrial scale has been increasing; therefore, the engineering of thermophilic proteins has become a hot direction in the field of protein engineering. However, the exact mechanism of thermostability of proteins is not yet known, for engineering thermophilic proteins knowing the basis of thermostability is necessary. In order to understand the basis of the thermostability in proteins, we have made a statistical analysis of the sequences, secondary structures, hydrogen bonds, salt bridges, DHA (Donor-Hydrogen-Accepter) angles, and bond lengths of ten pairs of thermophilic proteins and their non-thermophilic orthologous. Our findings suggest that polar amino acids contribute to thermostability in proteins by forming hydrogen bonds and salt bridges which provide resistance against protein denaturation. Short bond length and a wider DHA angle provide greater bond stability in thermophilic proteins. Moreover, the increased frequency of aromatic amino acids in thermophilic proteins contributes to thermal stability by forming more aromatic interactions. Additionally, the coil, helix, and loop in the secondary structure also contribute to thermostability.
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Thi Phan L, Woo Park H, Pitti T, Madhavan T, Jeon YJ, Manavalan B. MLACP 2.0: An updated machine learning tool for anticancer peptide prediction. Comput Struct Biotechnol J 2022; 20:4473-4480. [PMID: 36051870 PMCID: PMC9421197 DOI: 10.1016/j.csbj.2022.07.043] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 07/25/2022] [Accepted: 07/25/2022] [Indexed: 12/24/2022] Open
Abstract
We present a novel meta-approach, MLACP 2.0, and implement it as a user-friendly webserver for the accurate identification of ACPs. MLACP 2.0 employed 11 different encoding schemes and eight different classifiers, including convolutional neural networks, to create a stable meta-model. Benchmarking study has demonstrated that MLACP 2.0 achieves superior performance in ACP prediction compared to publicly available state-of-the-art predictors.
Anticancer peptides are emerging anticancer drug that offers fewer side effects and is more effective than chemotherapy and targeted therapy. Predicting anticancer peptides from sequence information is one of the most challenging tasks in immunoinformatics. In the past ten years, machine learning-based approaches have been proposed for identifying ACP activity from peptide sequences. These methods include our previous method MLACP (developed in 2017) which made a significant impact on anticancer research. MLACP tool has been widely used by the research community, however, its robustness must be improved significantly for its continued practical application. In this study, the first large non-redundant training and independent datasets were constructed for ACP research. Using the training dataset, the study explored a wide range of feature encodings and developed their respective models using seven different conventional classifiers. Subsequently, a subset of encoding-based models was selected for each classifier based on their performance, whose predicted scores were concatenated and trained through a convolutional neural network (CNN), whose corresponding predictor is named MLACP 2.0. The evaluation of MLACP 2.0 with a very diverse independent dataset showed excellent performance and significantly outperformed the recent ACP prediction tools. Additionally, MLACP 2.0 exhibits superior performance during cross-validation and independent assessment when compared to CNN-based embedding models and conventional single models. Consequently, we anticipate that our proposed MLACP 2.0 will facilitate the design of hypothesis-driven experiments by making it easier to discover novel ACPs. The MLACP 2.0 is freely available at https://balalab-skku.org/mlacp2.
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40
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Computational Resources for Molecular Biology 2022. J Mol Biol 2022; 434:167625. [PMID: 35569508 DOI: 10.1016/j.jmb.2022.167625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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