The genes of all organisms have been shaped by selective pressures. The relationship between gene sequence and fitness has tremendous implications for understanding both evolutionary processes and functional constraints on the encoded proteins. Here, we have exploited deep sequencing technology to experimentally determine the fitness of all possible individual point mutants under controlled conditions for a nine-amino acid region of Hsp90. Over the past five decades, limited glimpses into the relationship between gene sequence and function have sparked a long debate regarding the distribution, relative proportion, and evolutionary significance of deleterious, neutral, and advantageous mutations. Our systematic experimental measurement of fitness effects of Hsp90 mutants in yeast, evaluated in the light of existing population genetic theory, are remarkably consistent with a nearly neutral model of molecular evolution.

Machines of protein destruction-including energy-dependent proteases and disassembly chaperones of the AAA(+) ATPase family-function in all kingdoms of life to sculpt the cellular proteome, ensuring that unnecessary and dangerous proteins are eliminated and biological responses to environmental change are rapidly and properly regulated. Exciting progress has been made in understanding how AAA(+) machines recognize specific proteins as targets and then carry out ATP-dependent dismantling of the tertiary and/or quaternary structure of these molecules during the processes of protein degradation and the disassembly of macromolecular complexes.

We report the development and initial experimental validation of a computational design procedure aimed at generating enzymelike protein catalysts called ''protozymes.'' Our design approach utilizes a ''compute and build'' strategy that is based on the physical͞chemical principles governing protein stability and catalytic mechanism. By using the catalytically inert 108-residue Escherichia coli thioredoxin as a scaffold, the histidine-mediated nucleophilic hydrolysis of p-nitrophenyl acetate as a model reaction, and the ORBIT protein design software to compute sequences, an active site scan identified two promising catalytic positions and surrounding active-site mutations required for substrate binding. Experimentally, both candidate protozymes demonstrated catalytic activity significantly above background. One of the proteins, PZD2, displayed ''burst'' phase kinetics at high substrate concentrations, consistent with the formation of a stable enzyme intermediate. The kinetic parameters of PZD2 are comparable to early catalytic Abs. But, unlike catalytic Ab design, our design procedure is independent of fold, suggesting a possible mechanism for examining the relationships between protein fold and the evolvability of protein function.A prominent goal of protein design is the generation of proteins with novel functions, including the catalytic rate enhancement of chemical reactions. The ability to design an enzyme to perform a given chemical reaction has considerable practical application for industry and medicine, particularly for the synthesis of pharmaceuticals (1). Significant progress has been made at enhancing the catalytic properties of existing enzymes through directed evolution (2). In contrast, the design of proteins with novel catalytic properties has met with relatively limited success (3-5). We present a general computational approach for the design of enzyme-like proteins with novel catalytic activities.The use of transition-state analogs as haptens to elicit catalytic Abs has been the most successful technique to date for generating novel protein catalysts (6). Natural enzymes combine transition-state stabilization with precisely oriented catalytic side chains. Although a reactive hapten has been used in the generation of an Ab with a powerful nucleophile at the active site (7), current catalytic Ab technology does not efficiently select for both catalytic side chains and tight noncovalent affinity in the same molecule. The relationship between the general backbone fold of an enzyme and its catalytic properties is not well understood. This observation is particularly relevant to catalytic Abs that are currently constrained to the Ab fold and which have yet to show catalytic activity on par with natural enzymes.Rational design efforts have recently succeeded at altering the catalytic reactions of two different enzymes (8, 9). Cyclophilin, a cis-trans isomerase of X-Pro peptide bonds, was engineered into an endopeptidase by grafting a triad of catalytic residues commonly found in serine proteases at the...

The challenge of distinguishing genetic drift from selection remains a central focus of population genetics. Time-sampled data may provide a powerful tool for distinguishing these processes, and we here propose approximate Bayesian, maximum likelihood, and analytical methods for the inference of demography and selection from time course data. Utilizing these novel statistical and computational tools, we evaluate whole-genome datasets of an influenza A H1N1 strain in the presence and absence of oseltamivir (an inhibitor of neuraminidase) collected at thirteen time points. Results reveal a striking consistency amongst the three estimation procedures developed, showing strongly increased selection pressure in the presence of drug treatment. Importantly, these approaches re-identify the known oseltamivir resistance site, successfully validating the approaches used. Enticingly, a number of previously unknown variants have also been identified as being positively selected. Results are interpreted in the light of Fisher's Geometric Model, allowing for a quantification of the increased distance to optimum exerted by the presence of drug, and theoretical predictions regarding the distribution of beneficial fitness effects of contending mutations are empirically tested. Further, given the fit to expectations of the Geometric Model, results suggest the ability to predict certain aspects of viral evolution in response to changing host environments and novel selective pressures.

The amino acid sequence of a protein governs its function. We used bulk competition and focused deep sequencing to investigate the effects of all ubiquitin point mutants on yeast growth rate. Many aspects of ubiquitin function have been carefully studied, which enabled interpretation of our growth analyses in light of a rich structural, biophysical and biochemical knowledge base. In one highly sensitive cluster on the surface of ubiquitin almost every amino acid substitution caused growth defects. In contrast, the opposite face tolerated virtually all possible substitutions. Surface locations between these two faces exhibited intermediate mutational tolerance. The sensitive face corresponds to the known interface for many binding partners. Across all surface positions, we observe a strong correlation between burial at structurally characterized interfaces and the number of amino acid substitutions compatible with robust growth. This result indicates that binding is a dominant determinant of ubiquitin function. In the solvent inaccessible core of ubiquitin all positions tolerated a limited number of substitutions, with hydrophobic amino acids especially interchangeable. Some mutations null for yeast growth were previously shown to populate folded conformations indicating that for these mutants subtle changes to conformation caused functional defects. The most sensitive region to mutation within the core was located near the C-terminus that is a focal binding site for many critical binding partners. These results indicate that core mutations may frequently cause functional defects through subtle disturbances to structure or dynamics.

ATP hydrolysis by AAA+ ClpX hexamers powers protein unfolding and translocation during ClpXP degradation. Although ClpX is a homohexamer, positive and negative allosteric interactions partition six potential nucleotide binding sites into three classes with asymmetric properties. Some sites release ATP rapidly, others release ATP slowly, and at least two sites remain nucleotide free. Recognition of the degradation tag of protein substrates requires ATP binding to one set of sites and ATP or ADP binding to a second set of sites, suggesting a mechanism that allows repeated unfolding attempts without substrate release over multiple ATPase cycles. Our results rule out concerted hydrolysis models involving ClpX(6)*ATP(6) or ClpX(6)*ADP(6) and highlight structures of hexameric AAA+ machines with three or four nucleotides as likely functional states. These studies further emphasize commonalities between distant AAA+ family members, including protein and DNA translocases, helicases, motor proteins, clamp loaders, and other ATP-dependent enzymes.

The role of adaptation in the evolutionary process has been contentious for decades. At the heart of the century-old debate between neutralists and selectionists lies the distribution of fitness effects (DFE)—that is, the selective effect of all mutations. Attempts to describe the DFE have been varied, occupying theoreticians and experimentalists alike. New high-throughput techniques stand to make important contributions to empirical efforts to characterize the DFE, but the usefulness of such approaches depends on the availability of robust statistical methods for their interpretation. We here present and discuss a Bayesian MCMC approach to estimate fitness from deep sequencing data and use it to assess the DFE for the same 560 point mutations in a coding region of Hsp90 in Saccharomyces cerevisiae across six different environmental conditions. Using these estimates, we compare the differences in the DFEs resulting from mutations covering one-, two-, and three-nucleotide steps from the wild type—showing that multiple-step mutations harbor more potential for adaptation in challenging environments, but also tend to be more deleterious in the standard environment. All observations are discussed in the light of expectations arising from Fisher’s geometric model.

The role of adaptation in molecular evolution has been contentious for decades. Here, we shed light on the adaptive potential in Saccharomyces cerevisiae by presenting systematic fitness measurements for all possible point mutations in a region of Hsp90 under four environmental conditions. Under elevated salinity, we observe numerous beneficial mutations with growth advantages up to 7% relative to the wild type. All of these beneficial mutations were observed to be associated with high costs of adaptation. We thus demonstrate that an essential protein can harbor adaptive potential upon an environmental challenge, and report a remarkable fit of the data to a version of Fisher's geometric model that focuses on the fitness trade-offs between mutations in different environments.