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  PepFun: Open Source Protocols for Peptide-Related Computational Analysis

Ochoa, R., & Cossio, P. (2021). PepFun: Open Source Protocols for Peptide-Related Computational Analysis. Molecules, 26(6): 1664. doi:10.3390/molecules26061664.

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 Creators:
Ochoa, Rodrigo1, Author
Cossio, Pilar1, 2, Author           
Affiliations:
1Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellin, Colombia, ou_persistent22              
2Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society, ou_2068292              

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Free keywords: bioinformatics, cheminformatics, peptide, python
 Abstract: Peptide research has increased during the last years due to their applications as biomarkers, therapeutic alternatives or as antigenic sub-units in vaccines. The implementation of computational resources have facilitated the identification of novel sequences, the prediction of properties, and the modelling of structures. However, there is still a lack of open source protocols that enable their straightforward analysis. Here, we present PepFun, a compilation of bioinformatics and cheminformatics functionalities that are easy to implement and customize for studying peptides at different levels: sequence, structure and their interactions with proteins. PepFun enables calculating multiple characteristics for massive sets of peptide sequences, and obtaining different structural observables derived from protein-peptide complexes. In addition, random or guided library design of peptide sequences can be customized for screening campaigns. The package has been created under the python language based on built-in functions and methods available in the open source projects BioPython and RDKit. We present two tutorials where we tested peptide binders of the MHC class II and the Granzyme B protease.

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Language(s): eng - English
 Dates: 2021-02-152021-03-152021-03-16
 Publication Status: Published online
 Pages: 12
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.3390/molecules26061664
BibTex Citekey: ochoa_pepfun_2021
 Degree: -

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Title: Molecules
Source Genre: Journal
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Publ. Info: Basel : MDPI
Pages: - Volume / Issue: 26 (6) Sequence Number: 1664 Start / End Page: - Identifier: ISSN: 1420-3049
CoNE: https://pure.mpg.de/cone/journals/resource/954925623244