BackgroundThe evolution of complex sub-cellular structures such as the synapse requires the assembly of multiple proteins, each conferring added functionality to the integrated structure. Tracking the early evolution of synapses has not been possible without genomic information from the earliest branching animals. As the closest extant relatives to the Eumetazoa, Porifera (sponges) represent a pivotal group for understanding the evolution of nervous systems, because sponges lack neurons with clearly recognizable synapses, in contrast to eumetazoan animals.Methodology/Principal FindingsWe show that the genome of the demosponge Amphimedon queenslandica possesses a nearly complete set of post-synaptic protein homologs whose conserved interaction motifs suggest assembly into a complex structure. In the critical synaptic scaffold gene, dlg, residues that make hydrogen bonds and van der Waals interactions with the PDZ ligand are 100% conserved between sponge and human, as is the motif organization of the scaffolds. Expression in Amphimedon of multiple post-synaptic gene homologs in larval flask cells further supports the existence of an assembled structure. Among the few post-synaptic genes absent from Amphimedon, but present in Eumetazoa, are receptor genes including the entire ionotropic glutamate receptor family.Conclusions/SignificanceHighly conserved protein interaction motifs and co-expression in sponges of multiple proteins whose homologs interact in eumetazoan synapses indicate that a complex protein scaffold was present at the origin of animals, perhaps predating nervous systems. A relatively small number of crucial innovations to this pre-existing structure may represent the founding changes that led to a post-synaptic element.

We report a method for in vitro selection of catalytically active enzymes from large libraries of variants displayed on the surface of the yeast S. cerevisiae. Two libraries, each containing approximately 2 x 10(6) variants of horseradish peroxidase (HRP), were constructed; one involved error-prone PCR that sampled mutations throughout the coding sequence, whereas the other involved complete combinatorial enumeration of five positions near the active site to non-cysteine residues. The enzyme variants displayed on the yeast surface were allowed to modify it with a fluorescently labeled substrate. A combination of positive and negative selection applied to the active-site-directed library resulted in variants with up to an 8-fold altered enantioselectivity, including its reversal, toward L/D-tyrosinol. In contrast, the library constructed by using error-prone PCR yielded no HRP variants with a significantly improved enantioselectivity.

Collective experience in structure-based lead progression has found electrostatic interactions to be more difficult to optimize than shape-based ones. A major reason for this is that the net electrostatic contribution observed includes a significant nonintuitive desolvation component in addition to the more intuitive intermolecular interaction component. To investigate whether knowledge of the ligand optimal charge distribution can facilitate more intuitive design of electrostatic interactions, we took a series of small-molecule influenza neuraminidase inhibitors with known protein cocrystal structures and calculated the difference between the optimal and actual charge distributions. This difference from the electrostatic optimum correlates with the calculated electrostatic contribution to binding (r 2 ) 0.94) despite small changes in binding modes caused by chemical substitutions, suggesting that the optimal charge distribution is a useful design goal. Furthermore, detailed suggestions for chemical modification generated by this approach are in many cases consistent with observed improvements in binding affinity, and the method appears to be useful despite discrete chemical constraints. Taken together, these results suggest that charge optimization is useful in facilitating generation of compound ideas in lead optimization. Our results also provide insight into design of neuraminidase inhibitors.

Evolutionary protein engineering has been dramatically successful, producing a wide variety of new proteins with altered stability, binding affinity, and enzymatic activity. However, the success of such procedures is often unreliable, and the impact of the choice of protein, engineering goal, and evolutionary procedure is not well understood. We have created a framework for understanding aspects of the protein engineering process by computationally mapping regions of feasible sequence space for three small proteins using structure-based design protocols. We then tested the ability of different evolutionary search strategies to explore these sequence spaces. The results point to a non-intuitive relationship between the error-prone PCR mutation rate and the number of rounds of replication. The evolutionary relationships among feasible sequences reveal hub-like sequences that serve as particularly fruitful starting sequences for evolutionary search. Moreover, genetic recombination procedures were examined, and tradeoffs relating sequence diversity and search efficiency were identified. This framework allows us to consider the impact of protein structure on the allowed sequence space and therefore on the challenges that each protein presents to error-prone PCR and genetic recombination procedures.