SUMMARY

The results presented here demonstrate the possibility of a novel mechanism of early protocellular evolution. This mechanism does not require the presence of a genome, nor does it rely on any form of sequence complementarity or the exact replication of protein sequences. In fact, the sloppy replication of protein sequences is an advantage in the earliest phase of evolution because it allows for the rapid exploration of the space of proteins and the discovery of new functions. It is the preservation of these functions and their interrelationships which must be maintained during this early stage of evolution, not the identity of the actors performing those functions. Further, evolution progresses through improvements of the whole community rather than the most fit individuals.

The proposed model makes truly minimal assumptions -- the existence of polymers capable of performing constructive and destructive processes and some preference for the destruction of non-functional polymers. This preference, well-motivated by the known biochemistry of protein enzymes, drives the evolution of protocells.

Although specific interactions between peptides are not included here, they can be readily incorporated into the proposed concept of evolution. In fact, there is no conflict between this concept and the work of Ghadiri and Chmielewski. Since non-genomic evolution is necessarily limited by its inability to transfer information sufficiently precisely, specificity of peptide interactions would improve the fidelity of information transfer, hence increasing evolutionary potential of the system. Ultimately, however, a truly advanced protocell would have to find a better method of transferring information to its offsprings.

The model can be naturally extended to include the possibility of producing peptides capable of performing new protocellular functions and to describe growth and division of protocells. Perhaps more importantly, recent advancements that allow the in vitro evolution of catalytic peptides [6] provide firm ground for improving the model and testing its predictions experimentally.