Simultaneous enhancement of multiple functional properties using evolution-informed protein design.

A major challenge in protein design is to augment existing functional proteins with multiple property enhancements. Altering several properties likely necessitates numerous primary sequence changes, and novel methods are needed to accurately predict combinations of mutations that maintain or enhance function. Models of sequence co-variation (e.

Protein Structure from Experimental Evolution.

Natural evolution encodes rich information about the structure and function of biomolecules in the genetic record. Previously, statistical analysis of co-variation patterns in natural protein families has enabled the accurate computation of 3D structures. Here, we explored generating similar information by experimental evolution, starting from a single gene and performing multiple cycles of in vitro mutagenesis and functional selection in Escherichia coli.

Inferring protein 3D structure from deep mutation scans.

We describe an experimental method of three-dimensional (3D) structure determination that exploits the increasing ease of high-throughput mutational scans. Inspired by the success of using natural, evolutionary sequence covariation to compute protein and RNA folds, we explored whether 'laboratory', synthetic sequence variation might also yield 3D structures. We analyzed five large-scale mutational scans and discovered that the pairs of residues with the largest positive epistasis in the experiments are sufficient to determine the 3D fold.

Necl-4/Cadm4 recruits Par-3 to the Schwann cell adaxonal membrane.

Interactions between axons and Schwann cells are essential for the acquisition of Schwann cell radial and longitudinal polarity and myelin sheath assembly. In the internode, the largest of these longitudinal domains, axon-Schwann cell interactions are mediated by the Nectin-like (Necl) cell adhesion proteins, also known as SynCAMs or Cadms. In particular, Necl-1/Cadm3 expressed on the axon surface binds to Necl-4/Cadm4 expressed along the adaxonal membrane of myelinating Schwann cells.

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing.

Site-directed mutagenesis has long been used as a method to interrogate protein structure, function and evolution. Recent advances in massively-parallel sequencing technology have opened up the possibility of assessing the functional or fitness effects of large numbers of mutations simultaneously. Here, we present a protocol for experimentally determining the effects of all possible single amino acid mutations in a protein of interest utilizing high-throughput sequencing technology, using the 263 amino acid antibiotic resistance enzyme TEM-1 β-lactamase as an example.

Evolvability as a function of purifying selection in TEM-1 β-lactamase.

Evolvability—the capacity to generate beneficial heritable variation—is a central property of biological systems. However, its origins and modulation by environmental factors have not been examined systematically. Here, we analyze the fitness effects of all single mutations in TEM-1 β-lactamase (4,997 variants) under selection for the wild-type function (ampicillin resistance) and for a new function (cefotaxime resistance).

Fidelity of tRNA 5'-maturation: a possible basis for the functional dependence of archaeal and eukaryal RNase P on multiple protein cofactors.

RNase P, which catalyzes tRNA 5'-maturation, typically comprises a catalytic RNase P RNA (RPR) and a varying number of RNase P proteins (RPPs): 1 in bacteria, at least 4 in archaea and 9 in eukarya. The four archaeal RPPs have eukaryotic homologs and function as heterodimers (POP5•RPP30 and RPP21•RPP29). By studying the archaeal Methanocaldococcus jannaschii RPR's cis cleavage of precursor tRNA(Gln) (pre-tRNA(Gln)), which lacks certain consensus structures/sequences needed for substrate recognition, we demonstrate that RPP21•RPP29 and POP5•RPP30 can rescue the RPR's mis-cleavage tendency independently by 4-fold and together by 25-fold, suggesting that they operate by distinct mechanisms.

Quantifying protein-protein interactions in high throughput using protein domain microarrays.

Protein microarrays provide an efficient way to identify and quantify protein-protein interactions in high throughput. One drawback of this technique is that proteins show a broad range of physicochemical properties and are often difficult to produce recombinantly. To circumvent these problems, we have focused on families of protein interaction domains.

Predicting PDZ domain-peptide interactions from primary sequences.

PDZ domains constitute one of the largest families of interaction domains and function by binding the C termini of their target proteins. Using Bayesian estimation, we constructed a three-dimensional extension of a position-specific scoring matrix that predicts to which peptides a PDZ domain will bind, given the primary sequences of the PDZ domain and the peptides. The model, which was trained using interaction data from 82 PDZ domains and 93 peptides encoded in the mouse genome, successfully predicts interactions involving other mouse PDZ domains, as well as PDZ domains from Drosophila melanogaster and, to a lesser extent, PDZ domains from Caenorhabditis elegans.

PDZ domain binding selectivity is optimized across the mouse proteome.

PDZ domains have long been thought to cluster into discrete functional classes defined by their peptide-binding preferences. We used protein microarrays and quantitative fluorescence polarization to characterize the binding selectivity of 157 mouse PDZ domains with respect to 217 genome-encoded peptides. We then trained a multidomain selectivity model to predict PDZ domain-peptide interactions across the mouse proteome with an accuracy that exceeds many large-scale, experimental investigations of protein-protein interactions.

Uncovering quantitative protein interaction networks for mouse PDZ domains using protein microarrays.

One of the principal challenges in systems biology is to uncover the networks of protein-protein interactions that underlie most biological processes. To date, experimental efforts directed at this problem have largely produced only qualitative networks that are replete with false positives and false negatives. Here, we describe a domain-centered approach--compatible with genome-wide investigations--that enables us to measure the equilibrium dissociation constant (K(D)) of recombinant PDZ domains for fluorescently labeled peptides that represent physiologically relevant binding partners.