Computationally mapping sequence space to understand evolutionary protein engineering.

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.

Selection of horseradish peroxidase variants with enhanced enantioselectivity by yeast surface display.

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.

A post-synaptic scaffold at the origin of the animal kingdom.

Background: The 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.

Optimal charges in lead progression: a structure-based neuraminidase case study.

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.