Computational approaches to therapeutic peptide discovery

The development of peptides with therapeutic activities can be based on naturally occurring peptides or alternatively on de novo design. The discovery of natural peptides is often a matter of serendipity. In part, this is because natural peptides are typically proteolytically cleaved out from precursor proteins, a feature that averts the direct benefits of the genomic revolution. The first part of this review describes attempts to create a more systematic identification of natural peptides relying on a two step process. In the initial step, an in silico peptidome is predicted through the use of machine learning. Then, various computational biology tools are tailored to focus on peptides predicted to have the desired biological activity; for example, activating a GPCR or modulating the cellular arm of the immune system. The second part of the review is devoted to de novo peptide design and focuses on arguably the simplest scenario in which the designed peptide corresponds to a contiguous protein subsequence. Amongst these peptides, those corresponding to helical segments are prominent, mainly due to their relative ability to fold independently. Inspired by the clinical success of viral entry inhibitors, which are peptides corresponding to helical segments in viral envelope proteins, a computational tool for the identification of intramolecular helix-helix interactions was developed. Using this approach, peptides having anti-cancer, anti-angiogenic, and anti-inflammatory activities have been recently rationally designed and biologically characterized.