Red fluorescent proteins (RFPs) are used extensively in biological research because their longer emission wavelengths are less phototoxic and allow deeper imaging of animal tissue. However, far-red RFPs generally display low brightness, emphasizing the need to develop brighter variants. Here, we investigate three approaches to rigidify the RFP chromophore to increase the quantum yield, and thereby brightness. We first used computational protein design on a maturation-efficient mRojo-VHSV variant previously engineered in our lab to introduce a Superdecker motif, a parallel pi-stack comprising aromatic residue side chains and the phenolate moiety of the chromophore, which we hypothesized would enhance chromophore packing and reduce non-radiative decay. The best mutants identified showed up to 1.7-fold higher quantum yield at pH 9, relative to their parent protein. We next postulated that brightness could be further increased by rigidifying the chromophore via branched aliphatic residues. Computational protein design was performed on a dim mCherry variant, mRojoA, followed by directed evolution on the brightest mutant. The combination of these methodologies yielded mSandy2, the brightest Discosoma-derived monomeric RFP with an emission maximum above 600 nm. Finally, we aimed to increase brightness by focusing on positions where residue rigidity correlated to quantum yield in mCherry-related RFPs according to NMR data that had been previously acquired in our lab. Combinatorial site-saturation mutagenesis was performed on two different surface patches of mCherry at positions 144/145/198 and 194/196/220. Our results demonstrated that surface residues may not be adequate targets for this approach. Altogether, the work herein presents unique rational design methodologies that can be used to increase brightness in RFPs.

Protein engineering can be performed by combinatorial techniques (directed evolution) and data-driven methods using machine-learning algorithms. The main characteristic of directed evolution (DE) is the application of an effective and efficient screen or selection on a diverse mutant library. As it is important to have a diverse mutant library for the success of DE, we compared the performance of DNA-shuffling and recombination PCR on fluorescent proteins using sequence information as well as statistical methods. We found that the diversity of the libraries DNA-shuffling and recombination PCR generates were dependent on type of skew primers used and sensitive to nucleotide identity levels between genes. DNA-shuffling and recombination PCR produced libraries with different crossover tendencies, suggesting that the two protocols could be used in combination to produce better libraries. Data-driven protein engineering uses sequence, structure and function data along with analyzed empirical activity information to guide library design. Boolean Learning Support Vector Machines (BLSVM) to identify interacting residues in fluorescent proteins and the gene templates were modified to preserve interactions post recombination. By site-directed mutagenesis, recombination and expression experiments, we validated that BLSVM can be used to identify interacting residues and increase the fraction of active proteins in the library. As an extension to the above experiments, DE was applied on monomeric Red Fluorescent Proteins to improve its spectral characteristics and structure-guided protein engineering was performed on penicillin G acylase (PGA), an industrially relevant catalyst, to change its substrate specificity.