Effective Design of Multifunctional Peptides by Combining Compatible Functions

Abstract

Multifunctionality is a common trait of many natural proteins and peptides, yet the rules to generate such multifunctionality remain unclear. We propose that the rules defining some protein/peptide functions are compatible. To explore this hypothesis, we trained a computational method to predict cell-penetrating peptides at the sequence level and learned that antimicrobial peptides and DNA-binding proteins are compatible with the rules of our predictor. Based on this finding, we expected that designing peptides for CPP activity may render AMP and DNA-binding activities. To test this prediction, we designed peptides that embedded two independent functional domains (nuclear localization and yeast pheromone activity), linked by optimizing their composition to fit the rules characterizing cell-penetrating peptides. These peptides presented effective cell penetration, DNA-binding, pheromone and antimicrobial activities, thus confirming the effectiveness of our computational approach to design multifunctional peptides with potential therapeutic uses. Our computational implementation is available at http://bis.ifc.unam.mx/en/software/dcf.

What Can Pleiotropic Proteins in Innate Immunity Teach Us about Bioconjugation and Molecular Design?

“…A recent paper utilized a computational method to combine cell-penetrating, DNA-binding, pheromone, and antimicrobial activities into one domain. 183…”

PSBP-SVM: A Machine Learning-Based Computational Identifier for Predicting Polystyrene Binding Peptides

“…The 188-bit (Wei et al, 2018) and Izlti (Diener et al, 2016) feature extraction algorithms are combined with the SVM classifier to generate the 188D_SVM and Iztli_SVM, respectively. The comparison of the PSBP-SVM with the 188D_SVM and Iztli_SVM is illustrated in Figure 2A.…”

A new biology of cell penetrating peptides

“…It is perhaps no surprise that machine learning, a sub category of artificial intelligence, is now being utilised to expand the possibilities for CPP prediction based upon parameters including AA composition and other physicochemical properties. As an example, Diener and co‐workers [ 30 ] developed a computational prediction method based upon the properties of individual AA s but also including calculations of biochemical parameters such as mean charge, hydrophobicity, isoelectric point, water‐octanol partition and α‐helical content. Such an approach enabled the characterisation of multifunctional CPPs, some of which also possess DNA‐binding and antimicrobial activities.…”

“…Such an approach enabled the characterisation of multifunctional CPPs, some of which also possess DNA‐binding and antimicrobial activities. [ 30 ] A similar approach can be publically accessed on the webserver C2Pred, [ 31 ] an in silico platform which employs machine learning to predict CPPs with a potential accuracy of 83.6%.…”