Pooling for improved screening of combinatorial libraries for directed evolution.

Following diversity generation in combinatorial protein engineering, a significant amount of effort is expended in screening the library for improved variants. Pooling, or combining multiple cells into the same assay well when screening, is a means to increase throughput and screen a larger portion of the library with less time and effort. We have developed and validated a Monte Carlo simulation model of pooling and used it to screen a library of beta-galactosidase mutants randomized in the active site to increase their activity toward fucosides. Here, we show that our model can successfully predict the number of highly improved mutants obtained via pooling and that pooling does increase the number of good mutants obtained. In unpooled conditions, we found a total of three mutants with higher activity toward p-nitrophenyl-beta-D-fucoside than that of the wild-type beta-galactosidase, whereas when pooling 10 cells per well we found a total of approximately 10 improved mutants. In addition, the number of "supermutants", those with the highest activity increase, was also higher when pooling was used. Pooling is a useful tool for increasing the efficiency of screening combinatorial protein engineering libraries.

Simulation modeling of pooling for combinatorial protein engineering.

Pooling in directed-evolution experiments will greatly increase the throughput of screening systems, but important parameters such as the number of good mutants created and the activity level increase of the good mutants will depend highly on the protein being engineered. The authors developed and validated a Monte Carlo simulation model of pooling that allows the testing of various scenarios in silico before starting experimentation. Using a simplified test system of 2 enzymes, betagalactosidase (supermutant, or greatly improved enzyme) and beta-glucuronidase (dud, or enzyme with ancestral level of activity), the model accurately predicted the number of supermutants detected in experiments within a factor of 2. Additional simulations using more complex activity distributions show the versatility of the model. Pooling is most suited to cases such as the directed evolution of new function in a protein, where the background level of activity is minimized, making it easier to detect small increases in activity level. Pooling is most successful when a sensitive assay is employed. Using the model will increase the throughput of screening procedures for directed-evolution experiments and thus lead to speedier engineering of proteins.

Evaluation of Hofmeister effects on the kinetic stability of proteins.

Dissolved salts are known to affect properties of proteins in solution including solubility and melting temperature, and the effects of dissolved salts can be ranked qualitatively by the Hofmeister series. We seek a quantitative model to predict the effects of salts in the Hofmeister series on the deactivation kinetics of enzymes. Such a model would allow for a better prediction of useful biocatalyst lifetimes or an improved estimation of protein-based pharmaceutical shelf life. Here we consider a number of salt properties that are proposed indicators of Hofmeister effects in the literature as a means for predicting salt effects on the deactivation of horse liver alcohol dehydrogenase (HL-ADH), alpha-chymotrypsin, and monomeric red fluorescent protein (mRFP). We find that surface tension increments are not accurate predictors of salt effects but find a common trend between observed deactivation constants and B-viscosity coefficients of the Jones-Dole equation, which are indicative of ion hydration. This trend suggests that deactivation constants (log k(d,obs)) vary linearly with chaotropic B-viscosity coefficients but are relatively unchanged in kosmotropic solutions. The invariance with kosmotropic B-viscosity coefficients suggests the existence of a minimum deactivation constant for proteins. Differential scanning calorimetry is used to measure protein melting temperatures and thermodynamic parameters, which are used to calculate the intrinsic irreversible deactivation constant. We find that either the protein unfolding rate or the rate of intrinsic irreversible deactivation can control the observed deactivation rates.