The dynamic landscape of peptide activity prediction
Peptides are known to possess a plethora of beneficial properties and activities: antimicrobial, anticancer, anti-inflammatory or the ability to cross the blood–brain barrier are only a few examples of their functional diversity. For this reason, bioinformaticians are constantly developing and upgra...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037022005384 |
| _version_ | 1797978121659482112 |
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| author | Oriol Bárcenas Carlos Pintado-Grima Katarzyna Sidorczuk Felix Teufel Henrik Nielsen Salvador Ventura Michał Burdukiewicz |
| author_facet | Oriol Bárcenas Carlos Pintado-Grima Katarzyna Sidorczuk Felix Teufel Henrik Nielsen Salvador Ventura Michał Burdukiewicz |
| author_sort | Oriol Bárcenas |
| collection | DOAJ |
| description | Peptides are known to possess a plethora of beneficial properties and activities: antimicrobial, anticancer, anti-inflammatory or the ability to cross the blood–brain barrier are only a few examples of their functional diversity. For this reason, bioinformaticians are constantly developing and upgrading models to predict their activity in silico, generating a steadily increasing number of available tools. Although these efforts have provided fruitful outcomes in the field, the vast and diverse amount of resources for peptide prediction can turn a simple prediction into an overwhelming searching process to find the optimal tool. This minireview aims at providing a systematic and accessible analysis of the complex ecosystem of peptide activity prediction, showcasing the variability of existing models for peptide assessment, their domain specialization and popularity. Moreover, we also assess the reproducibility of such bioinformatics tools and describe tendencies observed in their development. The list of tools is available under https://biogenies.info/peptide-prediction-list/. |
| first_indexed | 2024-04-11T05:18:00Z |
| format | Article |
| id | doaj.art-059d0bdbc9b1445499128796c2ffab14 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-04-11T05:18:00Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-059d0bdbc9b1445499128796c2ffab142022-12-24T04:55:21ZengElsevierComputational and Structural Biotechnology Journal2001-03702022-01-012065266533The dynamic landscape of peptide activity predictionOriol Bárcenas0Carlos Pintado-Grima1Katarzyna Sidorczuk2Felix Teufel3Henrik Nielsen4Salvador Ventura5Michał Burdukiewicz6Autonomous University of Barcelona, Institute of Biotechnology and Biomedicine, SpainAutonomous University of Barcelona, Institute of Biotechnology and Biomedicine, SpainUniversity of Wrocław, Faculty of Biotechnology, PolandUniversity of Copenhagen, Copenhagen, Denmark; Novo Nordisk A/S, Digital Science and Innovation, DenmarkTechnical University of Denmark, DenmarkAutonomous University of Barcelona, Institute of Biotechnology and Biomedicine, Spain; Corresponding authors at: Autonomous University of Barcelona, Institute of Biotechnology and Biomedicine, Spain.Autonomous University of Barcelona, Institute of Biotechnology and Biomedicine, Spain; Medical University of Białystok, Clinical Research Centre, Poland; Corresponding authors at: Autonomous University of Barcelona, Institute of Biotechnology and Biomedicine, Spain.Peptides are known to possess a plethora of beneficial properties and activities: antimicrobial, anticancer, anti-inflammatory or the ability to cross the blood–brain barrier are only a few examples of their functional diversity. For this reason, bioinformaticians are constantly developing and upgrading models to predict their activity in silico, generating a steadily increasing number of available tools. Although these efforts have provided fruitful outcomes in the field, the vast and diverse amount of resources for peptide prediction can turn a simple prediction into an overwhelming searching process to find the optimal tool. This minireview aims at providing a systematic and accessible analysis of the complex ecosystem of peptide activity prediction, showcasing the variability of existing models for peptide assessment, their domain specialization and popularity. Moreover, we also assess the reproducibility of such bioinformatics tools and describe tendencies observed in their development. The list of tools is available under https://biogenies.info/peptide-prediction-list/.http://www.sciencedirect.com/science/article/pii/S2001037022005384PeptidesActivityPredictionFunctional peptidesMachine learningDeep learning |
| spellingShingle | Oriol Bárcenas Carlos Pintado-Grima Katarzyna Sidorczuk Felix Teufel Henrik Nielsen Salvador Ventura Michał Burdukiewicz The dynamic landscape of peptide activity prediction Computational and Structural Biotechnology Journal Peptides Activity Prediction Functional peptides Machine learning Deep learning |
| title | The dynamic landscape of peptide activity prediction |
| title_full | The dynamic landscape of peptide activity prediction |
| title_fullStr | The dynamic landscape of peptide activity prediction |
| title_full_unstemmed | The dynamic landscape of peptide activity prediction |
| title_short | The dynamic landscape of peptide activity prediction |
| title_sort | dynamic landscape of peptide activity prediction |
| topic | Peptides Activity Prediction Functional peptides Machine learning Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2001037022005384 |
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