Computationally assisted screening and design of cell-interactive peptides by a cell-based assay using peptide arrays and a fuzzy neural network algorithm
We developed a method of effective peptide screening that combines experiments and computational analysis. The method is based on the concept that screening efficiency can be enhanced from even limited data by use of a model derived from computational analysis that serves as a guide to screening and...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2008-03-01
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| Series: | BioTechniques |
| Online Access: | https://www.future-science.com/doi/10.2144/000112693 |
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| _version_ | 1850152432734044160 |
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| author | Chiaki Kaga Mina Okochi Yasuyuki Tomita Ryuji Kato Hiroyuki Honda |
| author_facet | Chiaki Kaga Mina Okochi Yasuyuki Tomita Ryuji Kato Hiroyuki Honda |
| author_sort | Chiaki Kaga |
| collection | DOAJ |
| description | We developed a method of effective peptide screening that combines experiments and computational analysis. The method is based on the concept that screening efficiency can be enhanced from even limited data by use of a model derived from computational analysis that serves as a guide to screening and combining the model with subsequent repeated experiments. Here we focus on cell-adhesion peptides as a model application of this peptide-screening strategy. Cell-adhesion peptides were screened by use of a cell-based assay of a peptide array. Starting with the screening data obtained from a limited, random 5-mer library (643 sequences), a rule regarding structural characteristics of cell-adhesion peptides was extracted by fuzzy neural network (FNN) analysis. According to this rule, peptides with unfavored residues in certain positions that led to inefficient binding were eliminated from the random sequences. In the restricted, second random library (273 sequences), the yield of cell-adhesion peptides having an adhesion rate more than 1.5-fold to that of the basal array support was significantly high (31%) compared with the unrestricted random library (20%). In the restricted third library (50 sequences), the yield of cell-adhesion peptides increased to 84%. We conclude that a repeated cycle of experiments screening limited numbers of peptides can be assisted by the rule-extracting feature of FNN. |
| format | Article |
| id | doaj-art-d2edb2b3ee404f20a5a19cff274dc512 |
| institution | OA Journals |
| issn | 0736-6205 1940-9818 |
| language | English |
| publishDate | 2008-03-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | BioTechniques |
| spelling | doaj-art-d2edb2b3ee404f20a5a19cff274dc5122025-08-20T02:25:59ZengTaylor & Francis GroupBioTechniques0736-62051940-98182008-03-0144339340210.2144/000112693Computationally assisted screening and design of cell-interactive peptides by a cell-based assay using peptide arrays and a fuzzy neural network algorithmChiaki Kaga0Mina Okochi1Yasuyuki Tomita2Ryuji Kato3Hiroyuki Honda41Department of Biotechnology, School of Engineering, Nagoya University, Nagoya, Japan1Department of Biotechnology, School of Engineering, Nagoya University, Nagoya, Japan1Department of Biotechnology, School of Engineering, Nagoya University, Nagoya, Japan1Department of Biotechnology, School of Engineering, Nagoya University, Nagoya, Japan1Department of Biotechnology, School of Engineering, Nagoya University, Nagoya, JapanWe developed a method of effective peptide screening that combines experiments and computational analysis. The method is based on the concept that screening efficiency can be enhanced from even limited data by use of a model derived from computational analysis that serves as a guide to screening and combining the model with subsequent repeated experiments. Here we focus on cell-adhesion peptides as a model application of this peptide-screening strategy. Cell-adhesion peptides were screened by use of a cell-based assay of a peptide array. Starting with the screening data obtained from a limited, random 5-mer library (643 sequences), a rule regarding structural characteristics of cell-adhesion peptides was extracted by fuzzy neural network (FNN) analysis. According to this rule, peptides with unfavored residues in certain positions that led to inefficient binding were eliminated from the random sequences. In the restricted, second random library (273 sequences), the yield of cell-adhesion peptides having an adhesion rate more than 1.5-fold to that of the basal array support was significantly high (31%) compared with the unrestricted random library (20%). In the restricted third library (50 sequences), the yield of cell-adhesion peptides increased to 84%. We conclude that a repeated cycle of experiments screening limited numbers of peptides can be assisted by the rule-extracting feature of FNN.https://www.future-science.com/doi/10.2144/000112693 |
| spellingShingle | Chiaki Kaga Mina Okochi Yasuyuki Tomita Ryuji Kato Hiroyuki Honda Computationally assisted screening and design of cell-interactive peptides by a cell-based assay using peptide arrays and a fuzzy neural network algorithm BioTechniques |
| title | Computationally assisted screening and design of cell-interactive peptides by a cell-based assay using peptide arrays and a fuzzy neural network algorithm |
| title_full | Computationally assisted screening and design of cell-interactive peptides by a cell-based assay using peptide arrays and a fuzzy neural network algorithm |
| title_fullStr | Computationally assisted screening and design of cell-interactive peptides by a cell-based assay using peptide arrays and a fuzzy neural network algorithm |
| title_full_unstemmed | Computationally assisted screening and design of cell-interactive peptides by a cell-based assay using peptide arrays and a fuzzy neural network algorithm |
| title_short | Computationally assisted screening and design of cell-interactive peptides by a cell-based assay using peptide arrays and a fuzzy neural network algorithm |
| title_sort | computationally assisted screening and design of cell interactive peptides by a cell based assay using peptide arrays and a fuzzy neural network algorithm |
| url | https://www.future-science.com/doi/10.2144/000112693 |
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