Biomedical subjects

Karen M Polizzi

Publications and source records attributed to Karen M Polizzi.

5 recordsLinked to original sources

PubMed2007

Better library design: data-driven protein engineering.

Data-driven protein engineering is increasingly used as an alternative to rational design and combinatorial engineering because it uses available knowledge to limit library size, while still allowing for the identification of unpredictable substitutions that lead to large effects. Recent advances in computational modeling and bioinformatics, as well as an increasing databank of experiments on functional variants, have led to new strategies to choose particular amino acid residues to vary in order to increase the chances of obtaining a variant protein with the desired property. Strategies for limiting diversity at each position, design of small sub-libraries, and the performance of scouting experiments, have also been developed or even automated, further reducing the library size.

Computational Biology↗

PubMed2006

High-throughput screening for enhanced protein stability.

High thermostability of proteins is a prerequisite for their implementation in biocatalytic processes and in the evolution of new functions. Various protein engineering methods have been applied to the evolution of increased thermostability, including the use of combinatorial design where a diverse library of proteins is generated and screened for variants with increased stability. Current trends are toward the use of data-driven methods that reduce the library size by using available data to choose areas of the protein to target, without specifying the precise changes. For example, the half-lives of subtilisin and a Bacillus subtilis lipase were increased 1500-fold and 300-fold, respectively, using a crystal structure to guide mutagenesis choices. Sequence homology based methods have also produced libraries where 50% of the variants have improved thermostability. Moreover, advances in the high-throughput measurement of denaturation curves and the application of selection methods to thermostability evolution have enabled the screening of larger libraries. The combination of these methods will lead to the rapid improvement of protein stability for biotechnological purposes.

Computational Biology↗

PubMed2006

Structure-guided consensus approach to create a more thermostable penicillin G acylase.

The thermostabilization of penicillin G acylase (PGA) is a difficult problem due to the large size of the protein and its complex maturation process. We developed a data-driven protein design method that requires fewer homologous sequences than the traditional consensus approach and utilizes structural information to limit the number of variants created. Approximately 50% of our 21 single-point mutants were found experimentally to be more thermostable than the wild-type PGA, two had almost threefold longer half-life at 50 degrees C, with very little effect on activity. An analysis of four programs that predict the thermostability conferred by point mutations shows little agreement between the programs and with the experimental data, emphasizing that the chosen stabilizing mutations are very difficult to predict, but that our data-driven design method should prove useful.

Bacteria↗

PubMed2005

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.

Cells, Cultured↗

PubMedDate not supplied

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.

Binding Sites↗