Combinatorial protein engineering of proteolytically resistant mesotrypsin inhibitors as candidates for cancer therapy

Cancer is a leading cause of morbidity and mortality worldwide. The results presented in the present study pave the way to develop new therapies targeting mesotrypsin, an enzyme that contributes to progression and metastasis of lung, prostate, breast and pancreatic cancers.

Rational design and protein engineering of growth factors for regenerative medicine and tissue engineering

Growth factors provide key instructive cues for tissue formation and repair. However, many natural growth factors are limited in their usefulness for tissue engineering and regenerative applications by their poor retention at desired sites of action, short half-lives in vivo, pleiotropic actions and other features. In the present article, we review approaches to rational design of synthetic growth factors based on mechanisms of receptor activation. Such synthetic molecules can function as simplified ligands with potentially tunable specificity and action. Rational and combinatorial protein engineering techniques allow introduction of additional features into these synthetic growth molecules, as well as natural growth factors, which significantly enhance their therapeutic utility.

Evaluation of Staphylococcal Cell Surface Display and Flow Cytometry for Postselectional Characterization of Affinity Proteins in Combinatorial Protein Engineering Applications

ABSTRACT For efficient generation of high-affinity protein-based binding molecules, fast and reliable downstream characterization platforms are needed. In this work, we have explored the use of staphylococcal cell surface display together with flow cytometry for affinity characterization of candidate affibody molecules directly on the cell surface. A model system comprising three closely related affibody molecules with different affinities for immunoglobulin G and an albumin binding domain with affinity for human serum albumin was used to investigate advantages and differences compared to biosensor technology in a side-by-side manner. Equilibrium dissociation constant (KD ) determinations as well as dissociation rate analysis were performed using both methods, and the results show that the on-cell determinations give both KD and dissociation rate values in a very fast and reproducible manner and that the relative affinities are very similar to the biosensor results. Interestingly, the results also show that there are differences between the absolute affinities determined with the two different technologies, and possible explanations for this are discussed. This work demonstrates the advantages of cell surface display for directed evolution of affinity proteins in terms of fast postselectional, on-cell characterization of candidate clones without the need for subcloning and subsequent protein expression and purification but also demonstrates that it is important to be aware that absolute affinities determined using different methods often vary substantially and that such comparisons therefore could be difficult.

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, ßgalactosidase (supermutant, or greatly improved enzyme) and •-glucuronidase (dud, or enzyme with ancestral level of activity), themodel accurately predicted the number of supermutants detected in experimentswithin a factor of 2. Additional simulations usingmore complex activity distributions showthe versatility of themodel. Pooling ismost suited to cases such as the directed evolution of newfunction in a protein, where the background level of activity is minimized, making it easier to detect small increases in activity level. Pooling ismost successful when a sensitive assay is employed. Using the modelwill increase the throughput of screening procedures for directed-evolution experiments and thus lead to speedier engineering of proteins.