Computational framework for generating synthetic signal peptides

Abstract

We have developed a computational framework for constructing synthetic signal peptides from a base set of protein sequences. A large number of structured "building blocks", represented as m-step ordered pairs of amino acids, are extracted from the base sequences. Using a straightforward procedure, the building blocks enable the construction of a diverse set of synthetic signal peptides and targeting sequences that have the potential for industrial and therapeutic purposes. We have validated the proposed framework using several state-of-the-art sequence prediction platforms such as Signal-BLAST, SignalP-5.0, MULocDeep, and DeepMito. Experimental results show the computational framework can successfully generate synthetic signal peptides and targeting sequences and transform non-signaling sequences into synthetic signal peptides.