1
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Kamińska J, Hecel A, Słowik J, Rombel-Bryzek A, Rowińska-Żyrek M, Witkowska D. Characterization of four peptides from milk fermented with kombucha cultures and their metal complexes-in search of new biotherapeutics. Front Mol Biosci 2024; 11:1366588. [PMID: 38638688 PMCID: PMC11024286 DOI: 10.3389/fmolb.2024.1366588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 03/18/2024] [Indexed: 04/20/2024] Open
Abstract
The most common skin diseases include eczema, psoriasis, acne, and fungal infections. There is often no effective cure for them. Increasing antimicrobial drug resistance prompts us to search for new, safe, and effective therapeutics. Among such interesting candidates are peptides derived from milk fermented with specific lactic acid bacteria or with kombucha cultures, which are a potential treasure trove of bioactive peptides. Four of them are discussed in this article. Their interactions with zinc and copper ions, which are known to improve the well-being of the skin, were characterized by potentiometry, MS, ITC, and spectroscopic methods, and their cytostatic potential was analyzed. The results suggest that they are safe for human cells and can be used alone or in complexes with copper for further testing as potential therapeutics for skin diseases.
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Affiliation(s)
- Justyna Kamińska
- Institute of Health Sciences, University of Opole, Opole, Poland
| | | | - Joanna Słowik
- Institute of Health Sciences, University of Opole, Opole, Poland
| | | | | | - Danuta Witkowska
- Institute of Health Sciences, University of Opole, Opole, Poland
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2
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Aldoukhi AH, Bilalis P, Alhattab DM, Valle-Pérez AU, Susapto HH, Pérez-Pedroza R, Backhoff-García E, Alsawaf SM, Alshehri S, Boshah H, Alrashoudi AA, Aljabr WA, Alaamery M, Alrashed M, Hasanato RM, Farzan RA, Alsubki RA, Moretti M, Abedalthagafi MS, Hauser CAE. Fusing Peptide Epitopes for Advanced Multiplex Serological Testing for SARS-CoV-2 Antibody Detection. ACS BIO & MED CHEM AU 2024; 4:37-52. [PMID: 38404747 PMCID: PMC10885102 DOI: 10.1021/acsbiomedchemau.3c00010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 07/29/2023] [Accepted: 07/31/2023] [Indexed: 02/27/2024]
Abstract
The tragic COVID-19 pandemic, which has seen a total of 655 million cases worldwide and a death toll of over 6.6 million seems finally tailing off. Even so, new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continue to arise, the severity of which cannot be predicted in advance. This is concerning for the maintenance and stability of public health, since immune evasion and increased transmissibility may arise. Therefore, it is crucial to continue monitoring antibody responses to SARS-CoV-2 in the general population. As a complement to polymerase chain reaction tests, multiplex immunoassays are elegant tools that use individual protein or peptide antigens simultaneously to provide a high level of sensitivity and specificity. To further improve these aspects of SARS-CoV-2 antibody detection, as well as accuracy, we have developed an advanced serological peptide-based multiplex assay using antigen-fused peptide epitopes derived from both the spike and the nucleocapsid proteins. The significance of the epitopes selected for antibody detection has been verified by in silico molecular docking simulations between the peptide epitopes and reported SARS-CoV-2 antibodies. Peptides can be more easily and quickly modified and synthesized than full length proteins and can, therefore, be used in a more cost-effective manner. Three different fusion-epitope peptides (FEPs) were synthesized and tested by enzyme-linked immunosorbent assay (ELISA). A total of 145 blood serum samples were used, compromising 110 COVID-19 serum samples from COVID-19 patients and 35 negative control serum samples taken from COVID-19-free individuals before the outbreak. Interestingly, our data demonstrate that the sensitivity, specificity, and accuracy of the results for the FEP antigens are higher than for single peptide epitopes or mixtures of single peptide epitopes. Our FEP concept can be applied to different multiplex immunoassays testing not only for SARS-CoV-2 but also for various other pathogens. A significantly improved peptide-based serological assay may support the development of commercial point-of-care tests, such as lateral-flow-assays.
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Affiliation(s)
- Ali H. Aldoukhi
- Laboratory
for Nanomedicine, Division of Biological and Environmental Science
and Engineering (BESE), King Abdullah University
of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Computational
Bioscience Research Center (CBRC), King
Abdullah University of Science and Technology, Thuwal 23955-69900, Saudi Arabia
| | - Panayiotis Bilalis
- Laboratory
for Nanomedicine, Division of Biological and Environmental Science
and Engineering (BESE), King Abdullah University
of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Computational
Bioscience Research Center (CBRC), King
Abdullah University of Science and Technology, Thuwal 23955-69900, Saudi Arabia
| | - Dana M. Alhattab
- Laboratory
for Nanomedicine, Division of Biological and Environmental Science
and Engineering (BESE), King Abdullah University
of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Computational
Bioscience Research Center (CBRC), King
Abdullah University of Science and Technology, Thuwal 23955-69900, Saudi Arabia
| | - Alexander U. Valle-Pérez
- Laboratory
for Nanomedicine, Division of Biological and Environmental Science
and Engineering (BESE), King Abdullah University
of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Computational
Bioscience Research Center (CBRC), King
Abdullah University of Science and Technology, Thuwal 23955-69900, Saudi Arabia
| | - Hepi H. Susapto
- Laboratory
for Nanomedicine, Division of Biological and Environmental Science
and Engineering (BESE), King Abdullah University
of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Computational
Bioscience Research Center (CBRC), King
Abdullah University of Science and Technology, Thuwal 23955-69900, Saudi Arabia
| | - Rosario Pérez-Pedroza
- Laboratory
for Nanomedicine, Division of Biological and Environmental Science
and Engineering (BESE), King Abdullah University
of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Computational
Bioscience Research Center (CBRC), King
Abdullah University of Science and Technology, Thuwal 23955-69900, Saudi Arabia
| | - Emiliano Backhoff-García
- Laboratory
for Nanomedicine, Division of Biological and Environmental Science
and Engineering (BESE), King Abdullah University
of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Sarah M. Alsawaf
- Laboratory
for Nanomedicine, Division of Biological and Environmental Science
and Engineering (BESE), King Abdullah University
of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Computational
Bioscience Research Center (CBRC), King
Abdullah University of Science and Technology, Thuwal 23955-69900, Saudi Arabia
| | - Salwa Alshehri
- Laboratory
for Nanomedicine, Division of Biological and Environmental Science
and Engineering (BESE), King Abdullah University
of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Computational
Bioscience Research Center (CBRC), King
Abdullah University of Science and Technology, Thuwal 23955-69900, Saudi Arabia
| | - Hattan Boshah
- Laboratory
for Nanomedicine, Division of Biological and Environmental Science
and Engineering (BESE), King Abdullah University
of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Computational
Bioscience Research Center (CBRC), King
Abdullah University of Science and Technology, Thuwal 23955-69900, Saudi Arabia
| | - Abdulelah A. Alrashoudi
- Laboratory
for Nanomedicine, Division of Biological and Environmental Science
and Engineering (BESE), King Abdullah University
of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Computational
Bioscience Research Center (CBRC), King
Abdullah University of Science and Technology, Thuwal 23955-69900, Saudi Arabia
| | - Waleed A. Aljabr
- Research
Centre, King Fahad Medical City, Riyadh 12231, Saudi Arabia
| | - Manal Alaamery
- Developmental
Medicine Department, King Abdullah International Medical Research
Center, King Abdulaziz Medical City, Ministry of National Guard-Health
Affairs, King Saud Bin Abdulaziz University
for Health Sciences, Riyadh 11426, Saudi Arabia
- KACST-BWH
Centre of Excellence for Biomedicine, Joint Centers of Excellence
Program, King Abdulaziz City for Science
and Technology (KACST), Riyadh 12371, Saudi Arabia
- Saudi
Human Genome Project (SHGP), Satellite Lab at King Abdulaziz Medical
City (KAMC), Ministry of National Guard Health Affairs (MNG-HA), King Abdulaziz City for Science and Technology (KACST), Riyadh 11426, Saudi Arabia
| | - May Alrashed
- Department
of Clinical Laboratory Science, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia
- Chair
of Medical and Molecular Genetics Research, King Saud University, Riyadh 11433, Saudi Arabia
| | - Rana M. Hasanato
- Department
of Pathology and Laboratory Medicine, King
Saud University, Riyadh 11433, Saudi Arabia
| | - Raed A. Farzan
- Department
of Clinical Laboratory Science, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia
- Chair
of Medical and Molecular Genetics Research, King Saud University, Riyadh 11433, Saudi Arabia
| | - Roua A. Alsubki
- Department
of Clinical Laboratory Science, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia
- Chair
of Medical and Molecular Genetics Research, King Saud University, Riyadh 11433, Saudi Arabia
| | - Manola Moretti
- Laboratory
for Nanomedicine, Division of Biological and Environmental Science
and Engineering (BESE), King Abdullah University
of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Computational
Bioscience Research Center (CBRC), King
Abdullah University of Science and Technology, Thuwal 23955-69900, Saudi Arabia
| | - Malak S. Abedalthagafi
- Pathology and Laboratory Medicine, Emory
School of Medicine, Atlanta, Georgia 30329, United States
| | - Charlotte A. E. Hauser
- Laboratory
for Nanomedicine, Division of Biological and Environmental Science
and Engineering (BESE), King Abdullah University
of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
- Computational
Bioscience Research Center (CBRC), King
Abdullah University of Science and Technology, Thuwal 23955-69900, Saudi Arabia
- Red Sea
Research Center, Division of Biological and Environmental
Science and Engineering (BESE), King Abdullah
University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
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3
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Guerrero-Vázquez K, Del Rio G, Brizuela CA. Cell-penetrating peptides predictors: A comparative analysis of methods and datasets. Mol Inform 2023; 42:e202300104. [PMID: 37672879 DOI: 10.1002/minf.202300104] [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: 05/06/2023] [Revised: 07/24/2023] [Accepted: 08/18/2023] [Indexed: 09/08/2023]
Abstract
Cell-Penetrating Peptides (CPP) are emerging as an alternative to small-molecule drugs to expand the range of biomolecules that can be targeted for therapeutic purposes. Due to the importance of identifying and designing new CPP, a great variety of predictors have been developed to achieve these goals. To establish a ranking for these predictors, a couple of recent studies compared their performances on specific datasets, yet their conclusions cannot determine if the ranking obtained is due to the model, the set of descriptors or the datasets used to test the predictors. We present a systematic study of the influence of the peptide sequence's similarity of the datasets on the predictors' performance. The analysis reveals that the datasets used for training have a stronger influence on the predictors performance than the model or descriptors employed. We show that datasets with low sequence similarity between the positive and negative examples can be easily separated, and the tested classifiers showed good performance on them. On the other hand, a dataset with high sequence similarity between CPP and non-CPP will be a hard dataset, and it should be the one to be used for assessing the performance of new predictors.
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Affiliation(s)
- Karen Guerrero-Vázquez
- Department of Computer Science, CICESE Research Center, Ensenada, 22860, Mexico
- Current address: School of Mathematics & Statistical Sciences, University of Galway, Galway, H91 TK33, Ireland
| | - Gabriel Del Rio
- Department of Biochemistry and Structural Biology, Instituto de Fisiologia Celular, UNAM, Mexico City, 04510, Mexico
| | - Carlos A Brizuela
- Department of Computer Science, CICESE Research Center, Ensenada, 22860, Mexico
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4
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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: 0] [Impact Index Per Article: 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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5
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Silva PSE, Guindo AS, Oliveira PHC, de Moraes LFRN, Boleti APDA, Ferreira MA, de Oliveira CFR, Macedo MLR, Rossato L, Simionatto S, Migliolo L. Evaluation of the Synthetic Multifunctional Peptide Hp-MAP3 Derivative of Temporin-PTa. Toxins (Basel) 2023; 15:42. [PMID: 36668862 PMCID: PMC9866994 DOI: 10.3390/toxins15010042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 01/06/2023] Open
Abstract
In recent years, antimicrobial peptides isolated from amphibian toxins have gained attention as new multifunctional drugs interacting with different molecular targets. We aimed to rationally design a new peptide from temporin-PTa. Hp-MAP3 (NH2-LLKKVLALLKKVL-COOH), net charge (+4), hydrophobicity (0.69), the content of hydrophobic residues (69%), and hydrophobic moment (0.73). For the construction of the analog peptide, the physicochemical characteristics were reorganized into hydrophilic and hydrophobic residues with the addition of lysines and leucines. The minimum inhibitory concentration was 2.7 to 43 μM against the growth of Gram-negative and positive bacteria, and the potential for biofilm eradication was 173.2 μM. Within 20 min, the peptide Hp-MAP3 (10.8 μM) prompted 100% of the damage to E. coli cells. At 43.3 μM, eliminated 100% of S. aureus within 5 min. The effects against yeast species of the Candida genus ranged from 5.4 to 86.6 μM. Hp-MAP3 presents cytotoxic activity against tumor HeLa at a concentration of 21.6 μM with an IC50 of 10.4 µM. Furthermore, the peptide showed hemolytic activity against murine erythrocytes. Structural studies carried out by circular dichroism showed that Hp-MAP3, while in the presence of 50% trifluoroethanol or SDS, an α-helix secondary structure. Finally, Amphipathic Hp-MAP3 building an important model for the design of new multifunctional molecules.
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Affiliation(s)
- Patrícia Souza e Silva
- S-Inova Biotech, Postgraduate Program in Biotechnology, Universidade Católica Dom Bosco, Campo Grande 79117-900, Mato Grosso do Sul, Brazil
| | - Alexya Sandim Guindo
- S-Inova Biotech, Postgraduate Program in Biotechnology, Universidade Católica Dom Bosco, Campo Grande 79117-900, Mato Grosso do Sul, Brazil
| | - Pedro Henrique Cardoso Oliveira
- S-Inova Biotech, Postgraduate Program in Biotechnology, Universidade Católica Dom Bosco, Campo Grande 79117-900, Mato Grosso do Sul, Brazil
| | | | - Ana Paula de Araújo Boleti
- S-Inova Biotech, Postgraduate Program in Biotechnology, Universidade Católica Dom Bosco, Campo Grande 79117-900, Mato Grosso do Sul, Brazil
| | - Marcos Antonio Ferreira
- S-Inova Biotech, Postgraduate Program in Biotechnology, Universidade Católica Dom Bosco, Campo Grande 79117-900, Mato Grosso do Sul, Brazil
| | - Caio Fernando Ramalho de Oliveira
- Laboratório de Purificação de Proteínas e suas Funções Biológicas, Unidade de Tecnologia de Alimentos e da Saúde Pública, Universidade Federal de Mato Grosso do Sul, Campo Grande 79070-900, Mato Grosso do Sul, Brazil
| | - Maria Ligia Rodrigues Macedo
- Laboratório de Purificação de Proteínas e suas Funções Biológicas, Unidade de Tecnologia de Alimentos e da Saúde Pública, Universidade Federal de Mato Grosso do Sul, Campo Grande 79070-900, Mato Grosso do Sul, Brazil
| | - Luana Rossato
- Laboratório de Pesquisa em Ciências da Saúde, Universidade Federal da Grande Dourados UFGD, Dourados 79825-070, Mato Grosso do Sul, Brazil
| | - Simone Simionatto
- Laboratório de Pesquisa em Ciências da Saúde, Universidade Federal da Grande Dourados UFGD, Dourados 79825-070, Mato Grosso do Sul, Brazil
| | - Ludovico Migliolo
- S-Inova Biotech, Postgraduate Program in Biotechnology, Universidade Católica Dom Bosco, Campo Grande 79117-900, Mato Grosso do Sul, Brazil
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6
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Efficacy of natural antimicrobial peptides versus peptidomimetic analogues: a systematic review. Future Med Chem 2022; 14:1899-1921. [PMID: 36421051 DOI: 10.4155/fmc-2022-0160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Aims: This systematic review was carried out to determine whether synthetic peptidomimetics exhibit significant advantages over antimicrobial peptides in terms of in vitro potency. Structural features - molecular weight, charge and length - were examined for correlations with activity. Methods: Original research articles reporting minimum inhibitory concentration values against Escherichia coli, indexed until 31 December 2020, were searched in PubMed/ScienceDirect/Google Scholar and evaluated using mixed-effects models. Results: In vitro antimicrobial activity of peptidomimetics resembled that of antimicrobial peptides. Net charge significantly affected minimum inhibitory concentration values (p < 0.001) with a trend of 4.6% decrease for increments in charge by +1. Conclusion: AMPs and antibacterial peptidomimetics exhibit similar potencies, providing an opportunity to exploit the advantageous stability and bioavailability typically associated with peptidomimetics.
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7
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Juretić D. Designed Multifunctional Peptides for Intracellular Targets. Antibiotics (Basel) 2022; 11:antibiotics11091196. [PMID: 36139975 PMCID: PMC9495127 DOI: 10.3390/antibiotics11091196] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 08/25/2022] [Accepted: 08/29/2022] [Indexed: 11/25/2022] Open
Abstract
Nature’s way for bioactive peptides is to provide them with several related functions and the ability to cooperate in performing their job. Natural cell-penetrating peptides (CPP), such as penetratins, inspired the design of multifunctional constructs with CPP ability. This review focuses on known and novel peptides that can easily reach intracellular targets with little or no toxicity to mammalian cells. All peptide candidates were evaluated and ranked according to the predictions of low toxicity to mammalian cells and broad-spectrum activity. The final set of the 20 best peptide candidates contains the peptides optimized for cell-penetrating, antimicrobial, anticancer, antiviral, antifungal, and anti-inflammatory activity. Their predicted features are intrinsic disorder and the ability to acquire an amphipathic structure upon contact with membranes or nucleic acids. In conclusion, the review argues for exploring wide-spectrum multifunctionality for novel nontoxic hybrids with cell-penetrating peptides.
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Affiliation(s)
- Davor Juretić
- Mediterranean Institute for Life Sciences, 21000 Split, Croatia;
- Faculty of Science, University of Split, 21000 Split, Croatia;
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8
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Antimicrobial peptides with cell-penetrating activity as prophylactic and treatment drugs. Biosci Rep 2022; 42:231731. [PMID: 36052730 PMCID: PMC9508529 DOI: 10.1042/bsr20221789] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 08/31/2022] [Accepted: 09/01/2022] [Indexed: 01/18/2023] Open
Abstract
Health is fundamental for the development of individuals and evolution of species. In that sense, for human societies is relevant to understand how the human body has developed molecular strategies to maintain health. In the present review, we summarize diverse evidence that support the role of peptides in this endeavor. Of particular interest to the present review are antimicrobial peptides (AMP) and cell-penetrating peptides (CPP). Different experimental evidence indicates that AMP/CPP are able to regulate autophagy, which in turn regulates the immune system response. AMP also assists in the establishment of the microbiota, which in turn is critical for different behavioral and health aspects of humans. Thus, AMP and CPP are multifunctional peptides that regulate two aspects of our bodies that are fundamental to our health: autophagy and microbiota. While it is now clear the multifunctional nature of these peptides, we are still in the early stages of the development of computational strategies aimed to assist experimentalists in identifying selective multifunctional AMP/CPP to control nonhealthy conditions. For instance, both AMP and CPP are computationally characterized as amphipatic and cationic, yet none of these features are relevant to differentiate these peptides from non-AMP or non-CPP. The present review aims to highlight current knowledge that may facilitate the development of AMP’s design tools for preventing or treating illness.
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9
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Arif M, Kabir M, Ahmed S, Khan A, Ge F, Khelifi A, Yu DJ. DeepCPPred: A Deep Learning Framework for the Discrimination of Cell-Penetrating Peptides and Their Uptake Efficiencies. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2749-2759. [PMID: 34347603 DOI: 10.1109/tcbb.2021.3102133] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Cell-penetrating peptides (CPPs) are special peptides capable of carrying a variety of bioactive molecules, such as genetic materials, short interfering RNAs and nanoparticles, into cells. Recently, research on CPP has gained substantial interest from researchers, and the biological mechanisms of CPPS have been assessed in the context of safe drug delivery agents and therapeutic applications. Correct identification and synthesis of CPPs using traditional biochemical methods is an extremely slow, expensive and laborious task particularly due to the large volume of unannotated peptide sequences accumulating in the World Bank repository. Hence, a powerful bioinformatics predictor that rapidly identifies CPPs with a high recognition rate is urgently needed. To date, numerous computational methods have been developed for CPP prediction. However, the available machine-learning (ML) tools are unable to distinguish both the CPPs and their uptake efficiencies. This study aimed to develop a two-layer deep learning framework named DeepCPPred to identify both CPPs in the first phase and peptide uptake efficiency in the second phase. The DeepCPPred predictor first uses four types of descriptors that cover evolutionary, energy estimation, reduced sequence and amino-acid contact information. Then, the extracted features are optimized through the elastic net algorithm and fed into a cascade deep forest algorithm to build the final CPP model. The proposed method achieved 99.45 percent overall accuracy with the CPP924 benchmark dataset in the first layer and 95.43 percent accuracy in the second layer with the CPPSite3 dataset using a 5-fold cross-validation test. Thus, our proposed bioinformatics tool surpassed all the existing state-of-the-art sequence-based CPP approaches.
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10
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Rational Discovery of Antimicrobial Peptides by Means of Artificial Intelligence. MEMBRANES 2022; 12:membranes12070708. [PMID: 35877911 PMCID: PMC9320227 DOI: 10.3390/membranes12070708] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/05/2022] [Accepted: 07/06/2022] [Indexed: 11/16/2022]
Abstract
Antibiotic resistance is a worldwide public health problem due to the costs and mortality rates it generates. However, the large pharmaceutical industries have stopped searching for new antibiotics because of their low profitability, given the rapid replacement rates imposed by the increasingly observed resistance acquired by microorganisms. Alternatively, antimicrobial peptides (AMPs) have emerged as potent molecules with a much lower rate of resistance generation. The discovery of these peptides is carried out through extensive in vitro screenings of either rational or non-rational libraries. These processes are tedious and expensive and generate only a few AMP candidates, most of which fail to show the required activity and physicochemical properties for practical applications. This work proposes implementing an artificial intelligence algorithm to reduce the required experimentation and increase the efficiency of high-activity AMP discovery. Our deep learning (DL) model, called AMPs-Net, outperforms the state-of-the-art method by 8.8% in average precision. Furthermore, it is highly accurate to predict the antibacterial and antiviral capacity of a large number of AMPs. Our search led to identifying two unreported antimicrobial motifs and two novel antimicrobial peptides related to them. Moreover, by coupling DL with molecular dynamics (MD) simulations, we were able to find a multifunctional peptide with promising therapeutic effects. Our work validates our previously proposed pipeline for a more efficient rational discovery of novel AMPs.
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11
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Prediction of Cell-Penetrating Peptides Using a Novel HSIC-Based Multiview TSK Fuzzy System. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Cell-penetrating peptides (CPPs) are short peptides that can carry cargo into cells. CPPs are widely utilized due to their powerful loading capacity and transduction efficiency. Identifying CPPs is the basis for studying their functions and mechanisms; however, experimental methods to identify CPPs are expensive and time-consuming. Recently, CPP predictors based on machine learning methods have become a research hotspot. Although considerable progress has been made, some challenges remain unresolved. First, most predictors employ a variety of feature descriptors to transform an original sequence into multiview data; however, extant methods ignore the relationships between different views, limiting further performance improvement. Second, most machine learning models are actually black boxes and cannot offer insightful advice. In this paper, a novel Hilbert–Schmidt independence criterion (HSIC)-based multiview TSK fuzzy system is proposed. Compared with other machine learning methods, TSK fuzzy systems have better interpretability, and the introduction of multiview mechanisms provides comprehensive insight into the intrinsic laws of the data. HSIC is utilized here to measure the independence and enhance the complementarity between different views. Notably, the proposed method attained prediction accuracy results of 92.2% and 96.2% for the training and independent test sets, respectively. The empirical results show that our promising approach features greater recognition performance than the state-of-the-art method.
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12
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Liu P, Ding Y, Rong Y, Chen D. Prediction of cell penetrating peptides and their uptake efficiency using random forest‐based feature selections. AIChE J 2022. [DOI: 10.1002/aic.17781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Peng Liu
- Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China
- Institute of Yangtze Delta Region (Quzhou) University of Electronic Science and Technology of China Quzhou China
| | - Yijie Ding
- Institute of Yangtze Delta Region (Quzhou) University of Electronic Science and Technology of China Quzhou China
| | - Ying Rong
- Beidahuang Industry Group General Hospital Harbin China
| | - Dong Chen
- College of Electrical and Information Engineering, Quzhou University Quzhou China
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13
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Meng C, Ju Y, Shi H. TMPpred: A support vector machine-based thermophilic protein identifier. Anal Biochem 2022; 645:114625. [PMID: 35218736 DOI: 10.1016/j.ab.2022.114625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 02/18/2022] [Accepted: 02/21/2022] [Indexed: 11/13/2022]
Abstract
MOTIVATION The thermostability of proteins will cause them to break the temperature binding and play more functions. Using machine learning, we explored the mechanism of and reasons for protein thermostability characteristics. RESULTS Different from other methods that only pursue the performance of models, we aim to find important features so as to provide a powerful reference for in vitro experiments. We transformed this problem into a binary classification problem, that is, the distinction between thermophilic proteins and nonthermophilic proteins. Using support vector machine-based model construction and analysis, we inferred that Gly, Ala, Ser and Thr may be the most important components at the residue level that determine the thermal stability of proteins. It is also noteworthy that our proposed model obtains an Sn of 0.892, an Sp of 0.857, an ACC of 0.87566 and an AUC of 0.874. To facilitate other researchers, we wrapped our model and deployed it as a web server, which is accessible at http://112.124.26.17:7000/TMPpred/index.html.
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Affiliation(s)
- Chaolu Meng
- College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China; Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry, Hohhot, China
| | - Ying Ju
- School of Informatics, Xiamen University, Xiamen, China.
| | - Hua Shi
- School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China.
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Wan H, Zhang J, Ding Y, Wang H, Tian G. Immunoglobulin Classification Based on FC* and GC* Features. Front Genet 2022; 12:827161. [PMID: 35140745 PMCID: PMC8819591 DOI: 10.3389/fgene.2021.827161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 12/22/2021] [Indexed: 11/13/2022] Open
Abstract
Immunoglobulins have a pivotal role in disease regulation. Therefore, it is vital to accurately identify immunoglobulins to develop new drugs and research related diseases. Compared with utilizing high-dimension features to identify immunoglobulins, this research aimed to examine a method to classify immunoglobulins and non-immunoglobulins using two features, FC* and GC*. Classification of 228 samples (109 immunoglobulin samples and 119 non-immunoglobulin samples) revealed that the overall accuracy was 80.7% in 10-fold cross-validation using the J48 classifier implemented in Weka software. The FC* feature identified in this study was found in the immunoglobulin subtype domain, which demonstrated that this extracted feature could represent functional and structural properties of immunoglobulins for forecasting.
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Affiliation(s)
- Hao Wan
- Institute of Advanced Cross-field Science, College of Life Science, Qingdao University, Qingdao, China
| | - Jina Zhang
- Geneis (Beijing) Co., Ltd., Beijing, China
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Hetian Wang
- Beidahuang Industry Group General Hospital, Harbin, China
- *Correspondence: Hetian Wang, ; Geng Tian,
| | - Geng Tian
- Geneis (Beijing) Co., Ltd., Beijing, China
- *Correspondence: Hetian Wang, ; Geng Tian,
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Su R, Hu J, Zou Q, Manavalan B, Wei L. Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools. Brief Bioinform 2021; 21:408-420. [PMID: 30649170 DOI: 10.1093/bib/bby124] [Citation(s) in RCA: 103] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 11/30/2018] [Accepted: 11/30/2018] [Indexed: 12/16/2022] Open
Abstract
Cell-penetrating peptides (CPPs) facilitate the delivery of therapeutically relevant molecules, including DNA, proteins and oligonucleotides, into cells both in vitro and in vivo. This unique ability explores the possibility of CPPs as therapeutic delivery and its potential applications in clinical therapy. Over the last few decades, a number of machine learning (ML)-based prediction tools have been developed, and some of them are freely available as web portals. However, the predictions produced by various tools are difficult to quantify and compare. In particular, there is no systematic comparison of the web-based prediction tools in performance, especially in practical applications. In this work, we provide a comprehensive review on the biological importance of CPPs, CPP database and existing ML-based methods for CPP prediction. To evaluate current prediction tools, we conducted a comparative study and analyzed a total of 12 models from 6 publicly available CPP prediction tools on 2 benchmark validation sets of CPPs and non-CPPs. Our benchmarking results demonstrated that a model from the KELM-CPPpred, namely KELM-hybrid-AAC, showed a significant improvement in overall performance, when compared to the other 11 prediction models. Moreover, through a length-dependency analysis, we find that existing prediction tools tend to more accurately predict CPPs and non-CPPs with the length of 20-25 residues long than peptides in other length ranges.
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Affiliation(s)
- Ran Su
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Jie Hu
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Leyi Wei
- College of Intelligence and Computing, Tianjin University, Tianjin, China
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16
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Morán-Torres R, Castillo González DA, Durán-Pastén ML, Aguilar-Maldonado B, Castro-Obregón S, Del Rio G. Selective Moonlighting Cell-Penetrating Peptides. Pharmaceutics 2021; 13:1119. [PMID: 34452080 PMCID: PMC8400200 DOI: 10.3390/pharmaceutics13081119] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 07/06/2021] [Accepted: 07/19/2021] [Indexed: 11/16/2022] Open
Abstract
Cell penetrating peptides (CPPs) are molecules capable of passing through biological membranes. This capacity has been used to deliver impermeable molecules into cells, such as drugs and DNA probes, among others. However, the internalization of these peptides lacks specificity: CPPs internalize indistinctly on different cell types. Two major approaches have been described to address this problem: (i) targeting, in which a receptor-recognizing sequence is added to a CPP, and (ii) activation, where a non-active form of the CPP is activated once it interacts with cell target components. These strategies result in multifunctional peptides (i.e., penetrate and target recognition) that increase the CPP's length, the cost of synthesis and the likelihood to be degraded or become antigenic. In this work we describe the use of machine-learning methods to design short selective CPP; the reduction in size is accomplished by embedding two or more activities within a single CPP domain, hence we referred to these as moonlighting CPPs. We provide experimental evidence that these designed moonlighting peptides penetrate selectively in targeted cells and discuss areas of opportunity to improve in the design of these peptides.
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Affiliation(s)
- Rafael Morán-Torres
- Department of Biochemistry and Structural Biology, Institute of Cellular Physiology, National Autonomous University of Mexico, UNAM, Mexico City 04510, Mexico; (R.M.-T.); (D.A.C.G.)
| | - David A. Castillo González
- Department of Biochemistry and Structural Biology, Institute of Cellular Physiology, National Autonomous University of Mexico, UNAM, Mexico City 04510, Mexico; (R.M.-T.); (D.A.C.G.)
| | - Maria Luisa Durán-Pastén
- Laboratorio Nacional de Canalopatias, National Autonomous University of Mexico, UNAM, Mexico City 04510, Mexico;
| | - Beatriz Aguilar-Maldonado
- Department of Neurodevelopment and Physiology, Institute of Cellular Physiology, National Autonomous University of Mexico, Mexico City 04510, Mexico; (B.A.-M.); (S.C.-O.)
| | - Susana Castro-Obregón
- Department of Neurodevelopment and Physiology, Institute of Cellular Physiology, National Autonomous University of Mexico, Mexico City 04510, Mexico; (B.A.-M.); (S.C.-O.)
| | - Gabriel Del Rio
- Department of Biochemistry and Structural Biology, Institute of Cellular Physiology, National Autonomous University of Mexico, UNAM, Mexico City 04510, Mexico; (R.M.-T.); (D.A.C.G.)
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17
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Spänig S, Mohsen S, Hattab G, Hauschild AC, Heider D. A large-scale comparative study on peptide encodings for biomedical classification. NAR Genom Bioinform 2021; 3:lqab039. [PMID: 34046590 PMCID: PMC8140742 DOI: 10.1093/nargab/lqab039] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 04/13/2021] [Accepted: 04/26/2021] [Indexed: 01/19/2023] Open
Abstract
Owing to the great variety of distinct peptide encodings, working on a biomedical classification task at hand is challenging. Researchers have to determine encodings capable to represent underlying patterns as numerical input for the subsequent machine learning. A general guideline is lacking in the literature, thus, we present here the first large-scale comprehensive study to investigate the performance of a wide range of encodings on multiple datasets from different biomedical domains. For the sake of completeness, we added additional sequence- and structure-based encodings. In particular, we collected 50 biomedical datasets and defined a fixed parameter space for 48 encoding groups, leading to a total of 397 700 encoded datasets. Our results demonstrate that none of the encodings are superior for all biomedical domains. Nevertheless, some encodings often outperform others, thus reducing the initial encoding selection substantially. Our work offers researchers to objectively compare novel encodings to the state of the art. Our findings pave the way for a more sophisticated encoding optimization, for example, as part of automated machine learning pipelines. The work presented here is implemented as a large-scale, end-to-end workflow designed for easy reproducibility and extensibility. All standardized datasets and results are available for download to comply with FAIR standards.
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Affiliation(s)
- Sebastian Spänig
- Data Science in Biomedicine, Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, D-35032 Marburg, Germany
| | - Siba Mohsen
- Data Science in Biomedicine, Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, D-35032 Marburg, Germany
| | - Georges Hattab
- Data Science in Biomedicine, Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, D-35032 Marburg, Germany
| | - Anne-Christin Hauschild
- Data Science in Biomedicine, Department of Mathematics and Computer Science, University of Marburg, Hans-Meerwein-Str. 6, D-35032 Marburg, Germany
| | - Dominik Heider
- To whom correspondence should be addressed. Tel: +49 6421 2821579;
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18
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Porosk L, Gaidutšik I, Langel Ü. Approaches for the discovery of new cell-penetrating peptides. Expert Opin Drug Discov 2020; 16:553-565. [PMID: 33874824 DOI: 10.1080/17460441.2021.1851187] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
Introduction: The capability of cell-penetrating peptides (CPP), also known as protein transduction domains (PTD), to enter into cells possibly with an attached cargo, makes their application as delivery vectors or as direct therapeutics compelling. They are generally biocompatible, nontoxic, and easy to synthesize and modify. Three decades after the discovery of the first CPPs, ~2,000 CPP sequences have been identified, and many more predicted. Nevertheless, the field has a strong commitment to authenticate new, more efficient, and specific CPPs.Areas covered: Although a scattering of CPPs have been found by chance, various systematic approaches have been developed and refined over the years to directly aid the identification and depiction of new peptide-based delivery vectors or therapeutics. Here, the authors give an overview of CPPs, and review various approaches of discovering new ones. An emphasis is placed on in silico methods, as these have advanced rapidly in recent years.Expert opinion: Although there are many known CPPs, there is a need to find more efficient and specific CPPs. Several approaches are used to identify such sequences. The success of these approaches depends on the advancement of others and the successful prediction of CPP sequences relies on experimental data.
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Affiliation(s)
- Ly Porosk
- Institute of Technology, University of Tartu, Tartu, Estonia
| | - Ilja Gaidutšik
- Institute of Technology, University of Tartu, Tartu, Estonia
| | - Ülo Langel
- Department Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
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19
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Meng C, Wu J, Guo F, Dong B, Xu L. CWLy-pred: A novel cell wall lytic enzyme identifier based on an improved MRMD feature selection method. Genomics 2020; 112:4715-4721. [DOI: 10.1016/j.ygeno.2020.08.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 08/04/2020] [Accepted: 08/13/2020] [Indexed: 10/25/2022]
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20
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Meng C, Guo F, Zou Q. CWLy-SVM: A support vector machine-based tool for identifying cell wall lytic enzymes. Comput Biol Chem 2020; 87:107304. [PMID: 32580129 DOI: 10.1016/j.compbiolchem.2020.107304] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 06/07/2020] [Accepted: 06/08/2020] [Indexed: 12/21/2022]
Abstract
Cell wall lytic enzymes, as an important biotechnical tool in drug development, agriculture and the food industry, have attracted more research attention. In this research, the accurate identification of cell wall lytic enzymes is one of the key and fundamental tasks. In this study, in order to eliminate the inefficiency of in vitro experiments, a support vector machine-based cell wall lytic enzyme identification model was constructed using bioinformatics. This machine learning process includes feature extraction, feature selection, model training and optimization. According to the jackknife cross validation test, this model obtained a sensitivity of 0.853, a specificity of 0.977, an MCC of 0.845 and an AUC of 0.915. These benchmark results demonstrate that the proposed model outperforms the state-of-the-art method and that it has powerful cell wall lytic enzyme identification ability. Furthermore, we comprehensively analyzed the selected optimal features and used the proposed model to construct a user friendly web server called the CWLy-SVM to identify cell wall lytic enzymes, which is available at http://server.malab.cn/CWLy-SVM/index.jsp.
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Affiliation(s)
- Chaolu Meng
- College of Intelligence and Computing, Tianjin University, Tianjin, China; College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Fei Guo
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.
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21
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Meng C, Hu Y, Zhang Y, Guo F. PSBP-SVM: A Machine Learning-Based Computational Identifier for Predicting Polystyrene Binding Peptides. Front Bioeng Biotechnol 2020; 8:245. [PMID: 32296690 PMCID: PMC7137786 DOI: 10.3389/fbioe.2020.00245] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 03/09/2020] [Indexed: 12/11/2022] Open
Abstract
Polystyrene binding peptides (PSBPs) play a key role in the immobilization process. The correct identification of PSBPs is the first step of all related works. In this paper, we proposed a novel support vector machine-based bioinformatic identification model. This model contains four machine learning steps, including feature extraction, feature selection, model training and optimization. In a five-fold cross validation test, this model achieves 90.38, 84.62, 87.50, and 0.90% SN, SP, ACC, and AUC, respectively. The performance of this model outperforms the state-of-the-art identifier in terms of the SN and ACC with a smaller feature set. Furthermore, we constructed a web server that includes the proposed model, which is freely accessible at http://server.malab.cn/PSBP-SVM/index.jsp.
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Affiliation(s)
- Chaolu Meng
- College of Intelligence and Computing, Tianjin University, Tianjin, China.,College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Yang Hu
- School of Life Sciences and Technology, Harbin Institute of Technology, Harbin, China
| | - Ying Zhang
- Department of Pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Fei Guo
- College of Intelligence and Computing, Tianjin University, Tianjin, China
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22
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Cai J, Li X, Du H, Jiang C, Xu S, Cao Y. Immunomodulatory significance of natural peptides in mammalians: Promising agents for medical application. Immunobiology 2020; 225:151936. [PMID: 32209241 DOI: 10.1016/j.imbio.2020.151936] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 02/18/2020] [Accepted: 03/14/2020] [Indexed: 12/14/2022]
Abstract
Modulation of immune responses by immunoregulatory agents, such as the natural or synthetic immunomodulatory peptides, has been suggested as a potential strategy to modulate immune system against infection and other immune-related diseases. These compositionally simple peptides have attracted much attention for many drug developers, due to their high activity, low toxicity and clear target specificity. Host defence peptides and milk-derived peptides are two kinds of natural immunomodulatory peptides which have been widely studied in mammalians. They could participate at the interface of innate and adaptive immunity by regulating immune effector cells. This review summarizes the recent advances in host defence peptides and milk-derived peptides as well as their general characteristics, immunomodulatory functions and possible applications.
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Affiliation(s)
- Jinyang Cai
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, Jiangsu, China
| | - Xin Li
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, 210023, Jiangsu, China
| | - Hongming Du
- Department of Oral and Maxillofacial Surgery, Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Chengfei Jiang
- Department of Pathology, Nanjing Medical University, Nanjing, 211166, Jiangsu, China
| | - Siliang Xu
- State Key Laboratory of Reproductive Medicine, Clinical Center of Reproductive Medicine, First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Yan Cao
- Nanjing Maternity and Child Health Care Institute, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, 210004, Jiangsu, China.
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Arif M, Ahmad S, Ali F, Fang G, Li M, Yu DJ. TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree. J Comput Aided Mol Des 2020; 34:841-856. [PMID: 32180124 DOI: 10.1007/s10822-020-00307-z] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 03/09/2020] [Indexed: 02/08/2023]
Abstract
Cell-penetrating peptides (CPPs) are short length permeable proteins have emerged as drugs delivery tool of therapeutic agents including genetic materials and macromolecules into cells. Recently, CPP has become a hotspot avenue for life science research and paved a new way of disease treatment without harmful impact on cell viability due to nontoxic characteristic. Therefore, the correct identification of CPPs will provide hints for medical applications. Considering the shortcomings of traditional experimental CPPs identification, it is urgently needed to design intelligent predictor for accurate identification of CPPs for the large scale uncharacterized sequences. We develop a novel computational method, called TargetCPP, to discriminate CPPs from Non-CPPs with improved accuracy. In TargetCPP, first the peptide sequences are formulated with four distinct encoding methods i.e., composite protein sequence representation, composition transition and distribution, split amino acid composition, and information theory features. These dominant feature vectors were fused and applied intelligent minimum redundancy and maximum relevancy feature selection method to choose an optimal subset of features. Finally, the predictive model is learned through different classification algorithms on the optimized features. Among these classifiers, gradient boost decision tree algorithm achieved excellent performance throughout the experiments. Notably, the TargetCPP tool attained high prediction Accuracy of 93.54% and 88.28% using jackknife and independent test, respectively. Empirical outcomes prove the superiority and potency of proposed bioinformatics method over state-of-the-art methods. It is highly anticipated that the outcomes of this study will provide a strong background for large scale prediction of CPPs and instructive guidance in clinical therapy and medical applications.
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Affiliation(s)
- Muhammad Arif
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Saeed Ahmad
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Farman Ali
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Ge Fang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Min Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
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Affiliation(s)
- John Howl
- Research Institute in Healthcare Science, University of Wolverhampton Wolverhampton UK
| | - Sarah Jones
- Research Institute in Healthcare Science, University of Wolverhampton Wolverhampton UK
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25
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Meng C, Zhang J, Ye X, Guo F, Zou Q. Review and comparative analysis of machine learning-based phage virion protein identification methods. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2020; 1868:140406. [PMID: 32135196 DOI: 10.1016/j.bbapap.2020.140406] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Revised: 02/14/2020] [Accepted: 02/27/2020] [Indexed: 02/01/2023]
Abstract
Phage virion protein (PVP) identification plays key role in elucidating relationships between phages and hosts. Moreover, PVP identification can facilitate the design of related biochemical entities. Recently, several machine learning approaches have emerged for this purpose and have shown their potential capacities. In this study, the proposed PVP identifiers are systemically reviewed, and the related algorithms and tools are comprehensively analyzed. We summarized the common framework of these PVP identifiers and constructed our own novel identifiers based upon the framework. Furthermore, we focus on a performance comparison of all PVP identifiers by using a training dataset and an independent dataset. Highlighting the pros and cons of these identifiers demonstrates that g-gap DPC (dipeptide composition) features are capable of representing characteristics of PVPs. Moreover, SVM (support vector machine) is proven to be the more effective classifier to distinguish PVPs and non-PVPs.
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Affiliation(s)
- Chaolu Meng
- College of Intelligence and Computing, Tianjin University, Tianjin, China; College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China
| | - Jun Zhang
- Rehabilitation Department, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Xiucai Ye
- Department of Computer Science, University of Tsukuba, Tsukuba, Science City, Japan
| | - Fei Guo
- College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.
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26
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Fu X, Cai L, Zeng X, Zou Q. StackCPPred: a stacking and pairwise energy content-based prediction of cell-penetrating peptides and their uptake efficiency. Bioinformatics 2020; 36:3028-3034. [DOI: 10.1093/bioinformatics/btaa131] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Revised: 02/19/2020] [Accepted: 02/25/2020] [Indexed: 12/19/2022] Open
Abstract
Abstract
Motivation
Cell-penetrating peptides (CPPs) are a vehicle for transporting into living cells pharmacologically active molecules, such as short interfering RNAs, nanoparticles, plasmid DNAs and small peptides, thus offering great potential as future therapeutics. Existing experimental techniques for identifying CPPs are time-consuming and expensive. Thus, the prediction of CPPs from peptide sequences by using computational methods can be useful to annotate and guide the experimental process quickly. Many machine learning-based methods have recently emerged for identifying CPPs. Although considerable progress has been made, existing methods still have low feature representation capabilities, thereby limiting further performance improvements.
Results
We propose a method called StackCPPred, which proposes three feature methods on the basis of the pairwise energy content of the residue as follows: RECM-composition, PseRECM and RECM–DWT. These features are used to train stacking-based machine learning methods to effectively predict CPPs. On the basis of the CPP924 and CPPsite3 datasets with jackknife validation, StackDPPred achieved 94.5% and 78.3% accuracy, which was 2.9% and 5.8% higher than the state-of-the-art CPP predictors, respectively. StackCPPred can be a powerful tool for predicting CPPs and their uptake efficiency, facilitating hypothesis-driven experimental design and accelerating their applications in clinical therapy.
Availability and implementation
Source code and data can be downloaded from https://github.com/Excelsior511/StackCPPred.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiangzheng Fu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Lijun Cai
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
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Wei HH, Yang W, Tang H, Lin H. The Development of Machine Learning Methods in Cell-Penetrating Peptides Identification: A Brief Review. Curr Drug Metab 2019; 20:217-223. [DOI: 10.2174/1389200219666181010114750] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 05/21/2018] [Accepted: 08/02/2018] [Indexed: 11/22/2022]
Abstract
Background:Cell-penetrating Peptides (CPPs) are important short peptides that facilitate cellular intake or uptake of various molecules. CPPs can transport drug molecules through the plasma membrane and send these molecules to different cellular organelles. Thus, CPP identification and related mechanisms have been extensively explored. In order to reveal the penetration mechanisms of a large number of CPPs, it is necessary to develop convenient and fast methods for CPPs identification.Methods:Biochemical experiments can provide precise details for accurately identifying CPP, but these methods are expensive and laborious. To overcome these disadvantages, several computational methods have been developed to identify CPPs. We have performed review on the development of machine learning methods in CPP identification. This review provides an insight into CPP identification.Results:We summarized the machine learning-based CPP identification methods and compared the construction strategies of 11 different computational methods. Furthermore, we pointed out the limitations and difficulties in predicting CPPs.Conclusion:In this review, the last studies on CPP identification using machine learning method were reported. We also discussed the future development direction of CPP recognition with computational methods.
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Affiliation(s)
- Huan-Huan Wei
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Wuritu Yang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hua Tang
- Department of Pathophysiology, Southwest Medical University, Luzhou, China
| | - Hao Lin
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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28
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Qiang X, Zhou C, Ye X, Du PF, Su R, Wei L. CPPred-FL: a sequence-based predictor for large-scale identification of cell-penetrating peptides by feature representation learning. Brief Bioinform 2018; 21:11-23. [PMID: 30239616 DOI: 10.1093/bib/bby091] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 08/13/2018] [Accepted: 08/22/2018] [Indexed: 11/14/2022] Open
Abstract
Cell-penetrating peptides (CPPs) have been shown to be a transport vehicle for delivering cargoes into live cells, offering great potential as future therapeutics. It is essential to identify CPPs for better understanding of their functional mechanisms. Machine learning-based methods have recently emerged as a main approach for computational identification of CPPs. However, one of the main challenges and difficulties is to propose an effective feature representation model that sufficiently exploits the inner difference and relevance between CPPs and non-CPPs, in order to improve the predictive performance. In this paper, we have developed CPPred-FL, a powerful bioinformatics tool for fast, accurate and large-scale identification of CPPs. In our predictor, we introduce a new feature representation learning scheme that enables one to learn feature representations from totally 45 well-trained random forest models with multiple feature descriptors from different perspectives, such as compositional information, position-specific information and physicochemical properties, etc. We integrate class and probabilistic information into our feature representations. To improve the feature representation ability, we further remove redundant and irrelevant features by feature space optimization. Benchmarking experiments showed that CPPred-FL, using 19 informative features only, is able to achieve better performance than the state-of-the-art predictors. We anticipate that CPPred-FL will be a powerful tool for large-scale identification of CPPs, facilitating the characterization of their functional mechanisms and accelerating their applications in clinical therapy.
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Affiliation(s)
- Xiaoli Qiang
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China
| | - Chen Zhou
- School of Computer Science and Technology, Tianjin University, Tianjin, China
| | - Xiucai Ye
- Department of Computer Science, University of Tsukuba, Tsukuba, Japan
| | - Pu-Feng Du
- School of Software, Tianjin University, Tianjin, China
| | - Ran Su
- School of Software, Tianjin University, Tianjin, China
| | - Leyi Wei
- School of Computer Science and Technology, Tianjin University, Tianjin, China
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29
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Lee MW, Lee EY, Wong GCL. What Can Pleiotropic Proteins in Innate Immunity Teach Us about Bioconjugation and Molecular Design? Bioconjug Chem 2018; 29:2127-2139. [PMID: 29771496 DOI: 10.1021/acs.bioconjchem.8b00176] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
A common bioengineering strategy to add function to a given molecule is by conjugation of a new moiety onto that molecule. Adding multiple functions in this way becomes increasingly challenging and leads to composite molecules with larger molecular weights. In this review, we attempt to gain a new perspective by looking at this problem in reverse, by examining nature's strategies of multiplexing different functions into the same pleiotropic molecule using emerging analysis techniques such as machine learning. We concentrate on examples from the innate immune system, which employs a finite repertoire of molecules for a broad range of tasks. An improved understanding of how diverse functions are multiplexed into a single molecule can inspire new approaches for the deterministic design of multifunctional molecules.
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30
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Wei L, Xing P, Su R, Shi G, Ma ZS, Zou Q. CPPred-RF: A Sequence-based Predictor for Identifying Cell-Penetrating Peptides and Their Uptake Efficiency. J Proteome Res 2017; 16:2044-2053. [PMID: 28436664 DOI: 10.1021/acs.jproteome.7b00019] [Citation(s) in RCA: 126] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Cell-penetrating peptides (CPPs), have been proven as important drug-delivery vehicles, demonstrating the potential as therapeutic candidates. The past decade has witnessed a rapid growth in CPP-based research. Recently, many computational efforts have been made to develop machine-learning-based methods for identifying CPPs. Although much progress has been made, existing methods still suffer low feature representation capability that limits further performance improvement. In this study, we propose a novel predictor called CPPred-RF, in which we integrate multiple sequence-based feature descriptors to sufficiently explore distinct information embedded in CPPs, employ a well-established feature selection technique to improve the feature representation, and, for the first time, construct a two-layer prediction framework based on the random forest algorithm. The jackknife results on benchmark data sets show that the proposed CPPred-RF is at least competitive with the state-of-the-art predictors. Moreover, we establish the first online Web server in terms of predicting CPPs and their uptake efficiency simultaneously. It is freely available at http://server.malab.cn/CPPred-RF .
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Affiliation(s)
- Leyi Wei
- School of Computer Science and Technology, Tianjin University , Tianjin 300072, China
| | - PengWei Xing
- School of Computer Science and Technology, Tianjin University , Tianjin 300072, China
| | - Ran Su
- School of Software, Tianjin University , Tianjin 300354, China
| | - Gaotao Shi
- School of Computer Science and Technology, Tianjin University , Tianjin 300072, China
| | - Zhanshan Sam Ma
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences , Kunming 650223, China
| | - Quan Zou
- School of Computer Science and Technology, Tianjin University , Tianjin 300072, China.,State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences , Kunming 650223, China
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31
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Kim S, Hyun S, Lee Y, Lee Y, Yu J. Nonhemolytic Cell-Penetrating Peptides: Site Specific Introduction of Glutamine and Lysine Residues into the α-Helical Peptide Causes Deletion of Its Direct Membrane Disrupting Ability but Retention of Its Cell Penetrating Ability. Biomacromolecules 2016; 17:3007-15. [DOI: 10.1021/acs.biomac.6b00874] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Seoyeon Kim
- Department of Chemistry & Education, Seoul National University, Seoul 08826, Korea
| | - Soonsil Hyun
- Department of Chemistry & Education, Seoul National University, Seoul 08826, Korea
| | - Yuri Lee
- Department of Chemistry & Education, Seoul National University, Seoul 08826, Korea
| | - Yan Lee
- Department of Chemistry, Seoul National University, Seoul 08826, Korea
| | - Jaehoon Yu
- Department of Chemistry & Education, Seoul National University, Seoul 08826, Korea
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