RESUMEN
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.
Asunto(s)
Diseño de Fármacos , Péptidos/química , Ingeniería de Proteínas/métodos , Algoritmos , Secuencia de Aminoácidos , Animales , Péptidos Catiónicos Antimicrobianos/química , Péptidos Catiónicos Antimicrobianos/genética , Péptidos Catiónicos Antimicrobianos/fisiología , Péptidos de Penetración Celular/química , Péptidos de Penetración Celular/genética , Péptidos de Penetración Celular/fisiología , Células Cultivadas , Biología Computacional , Proteínas de Unión al ADN/química , Proteínas de Unión al ADN/genética , Proteínas de Unión al ADN/fisiología , Escherichia coli/efectos de los fármacos , Escherichia coli/crecimiento & desarrollo , Aprendizaje Automático , Ratones , Modelos Estadísticos , Datos de Secuencia Molecular , Señales de Localización Nuclear , Péptidos/genética , Péptidos/fisiología , Unión Proteica , Ingeniería de Proteínas/estadística & datos numéricos , Estructura Secundaria de ProteínaRESUMEN
Cell penetrating peptides (CPPs) are molecules capable of passing through biological membranes. This capacity has been used to deliver impermeable molecules into cells, such as drugs and DNA probes, among others. However, the internalization of these peptides lacks specificity: CPPs internalize indistinctly on different cell types. Two major approaches have been described to address this problem: (i) targeting, in which a receptor-recognizing sequence is added to a CPP, and (ii) activation, where a non-active form of the CPP is activated once it interacts with cell target components. These strategies result in multifunctional peptides (i.e., penetrate and target recognition) that increase the CPP's length, the cost of synthesis and the likelihood to be degraded or become antigenic. In this work we describe the use of machine-learning methods to design short selective CPP; the reduction in size is accomplished by embedding two or more activities within a single CPP domain, hence we referred to these as moonlighting CPPs. We provide experimental evidence that these designed moonlighting peptides penetrate selectively in targeted cells and discuss areas of opportunity to improve in the design of these peptides.