Toward Molecular Cooperation by De Novo Peptides.

Affiliation

Sibilska-Kaminski IK(1), Yin J(2).
Author information:
(1)Department of Chemical and Biological Engineering, Wisconsin Institute for Discovery , University of Wisconsin-Madison, 330 N. Orchard Street, Madison, WI, 53715, USA.
(2)Department of Chemical and Biological Engineering, Wisconsin Institute for Discovery , University of Wisconsin-Madison, 330 N. Orchard Street, Madison, WI, 53715, USA. [Email]

Abstract

Theoretical models of the chemical origins of life depend on self-replication or autocatalysis, processes that arise from molecular interactions, recruitment, and cooperation. Such models often lack details about the molecules and reactions involved, giving little guidance to those seeking to detect signs of interaction, recruitment, or cooperation in the laboratory. Here, we develop minimal mathematical models of reactions involving specific chemical entities: amino acids and their condensation reactions to form de novo peptides. Reactions between two amino acids form a dipeptide product, which enriches linearly in time; subsequent recruitment of such products to form longer peptides exhibit super-linear growth. Such recruitment can be reciprocated: a peptide contributes to and benefits from the formation of one or more other peptides; in this manner, peptides can cooperate and thereby exhibit autocatalytic or exponential growth. We have started to test these predictions by quantitative analysis of de novo peptide synthesis conducted by wet-dry cycling of a five-amino acid mixture over 21 days. Using high-performance liquid chromatography, we tracked abundance changes for >60 unique peptide species. Some species were highly transient, with the emergence of up to 17 new species and the extinction of nine species between samplings, while other species persisted across many cycles. Of the persisting species, most exhibited super-linear growth, a sign of recruitment anticipated by our models. This work shows how mathematical modeling and quantitative analysis of kinetic data can guide the search for prebiotic chemistries that have the potential to cooperate and replicate.