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.
Highlights
The study and application of peptides is nowadays an active field for different research areas, including drug discovery [1]
PepFun is a compilation of bioinformatics and chemoinformatics functionalities that are easy to implement and personalize for studying peptides at different levels: sequence, structure and their interactions
The project can be installed in any Conda virtual environment with the required dependencies, i.e., the third-party tools to run the bioinformatics and cheminformatics analysis such as Biopython and RDKit
Summary
The study and application of peptides is nowadays an active field for different research areas, including drug discovery [1]. The use of peptides has some limitations that include poor chemical and physical stability, short circulating plasma half-life, and solubility issues [3] This motivates the analysis of peptides in silico using tools able to predict physico-chemical properties, as well as model and simulate their interactions with other molecules [6]. Evolutionary algorithms, which have been customized to align proteins based on generating gaps to detect potential homologues, do not align well massive peptide-sequence sets if the peptides are considered as ligands. To avoid this issue, one alternative is using position-byposition alignments weighted based on available position-specific scoring matrices [11]
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