Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain.

The continual evolution of SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine-learning-guided protein engineering technology, which is used to investigate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as Omicron, thus guiding the development of therapeutic antibody treatments and vaccines for COVID-19.

Driving CARs with alternative navigation tools - the potential of engineered binding scaffolds.

T cells that are genetically engineered to express chimeric antigen receptors (CAR T cells) have shown impressive clinical efficacy against B-cell malignancies. In contrast to these highly potent CD19-targeting CAR T cells, many of those directed against other tumor entities and antigens currently suffer from several limitations. For example, it has been demonstrated that many scFvs used as antigen-binding domains in CARs show some degree of oligomerization, which leads to tonic signaling, T cell exhaustion, and poor performance in vivo. Therefore, in many cases alternatives to scFvs would be beneficial. Fortunately, due to the development of powerful protein engineering technologies, also non-immunoglobulin-based scaffolds can be engineered to specifically recognize antigens, thus eliminating the historical dependence on antibody-based binding domains. Here, we discuss the advantages and disadvantages of such engineered binding scaffolds, in particular with respect to their application in CARs. We review recent studies, collectively showing that there is no functional or biochemical aspect that necessitates the use of scFvs in CARs. Instead, antigen recognition can also be mediated efficiently by engineered binding scaffolds, as well as natural ligands or receptors fused to the CAR backbone. Finally, we critically discuss the risk of immunogenicity and show that the extent of nonhuman amino acid stretches in engineered scaffolds-even in those based on nonhuman proteins-is more similar to humanized scFvs than might be anticipated. Together, we expect that engineered binding scaffolds and natural ligands and receptors will be increasingly used for the design of CAR T cells.

Rapid Screen for Tyrosine Kinase Inhibitor Resistance Mutations and Substrate Specificity.

We present a rapid and high-throughput yeast and flow cytometry based method for predicting kinase inhibitor resistance mutations and determining kinase peptide substrate specificity. Despite the widespread success of targeted kinase inhibitors as cancer therapeutics, resistance mutations arising within the kinase domain of an oncogenic target present a major impediment to sustained treatment efficacy. Our method, which is based on the previously reported YESS system, recapitulated all validated BCR-ABL1 mutations leading to clinical resistance to the second-generation inhibitor dasatinib, in addition to identifying numerous other mutations which have been previously observed in patients, but not yet validated as drivers of resistance. Further, we were able to demonstrate that the newer inhibitor ponatinib is effective against the majority of known single resistance mutations, but ineffective at inhibiting many compound mutants. These results are consistent with preliminary clinical and in vitro reports, indicating that mutations providing resistance to ponatinib are significantly less common; therefore, predicting ponatinib will be less susceptible to clinical resistance relative to dasatinib. Using the same yeast-based method, but with random substrate libraries, we were able to identify consensus peptide substrate preferences for the SRC and LYN kinases. ABL1 lacked an obvious consensus sequence, so a machine learning algorithm utilizing amino acid covariances was developed which accurately predicts ABL1 kinase peptide substrates.

Yeast Endoplasmic Reticulum Sequestration Screening for the Engineering of Proteases from Libraries Expressed in Yeast.

There is significant interest in engineering proteases with desired proteolytic properties. We describe a high-throughput fluorescence-activated cell sorting (FACS) assay for detecting altered proteolytic activity of protease in yeast, at the single cell level. This assay relies on coupling yeast endoplasmic reticulum (ER) retention, yeast surface display, and FACS analysis. The method described here allows facile screening of large libraries, and of either protease or substrate variants, including the screening of protease libraries against substrate libraries. We demonstrate the application of this technique in the screening of libraries of Tobacco Etch Virus protease (TEV-P) for altered proteolytic activities. In addition, the generality of this method is also validated by other proteases such as human granzyme K and the hepatitis C virus protease, and the human Abelson tyrosine kinase.

Recombinant antibodies to histone post-translational modifications.

Variability in the quality of antibodies to histone post-translational modifications (PTMs) is a widely recognized hindrance in epigenetics research. Here, we produced recombinant antibodies to the trimethylated lysine residues of histone H3 with high specificity and affinity and no lot-to-lot variation. These recombinant antibodies performed well in common epigenetics applications, and enabled us to identify positive and negative correlations among histone PTMs.

Engineering of TEV protease variants by yeast ER sequestration screening (YESS) of combinatorial libraries.

Myriad new applications of proteases would be enabled by an ability to fine-tune substrate specificity and activity. Herein we present a general strategy for engineering protease selectivity and activity by capitalizing on sequestration of the protease to be engineered within the yeast endoplasmic reticulum (ER). A substrate fusion protein composed of yeast adhesion receptor subunit Aga2, selection and counterselection substrate sequences, multiple intervening epitope tag sequences, and a C-terminal ER retention sequence is coexpressed with a protease library. Cleavage of the substrate fusion protein by the protease eliminates the ER retention sequence, facilitating transport to the yeast surface. Yeast cells that display Aga2 fusions in which only the selection substrate is cleaved are isolated by multicolor FACS with fluorescently labeled antiepitope tag antibodies. Using this system, the Tobacco Etch Virus protease (TEV-P), which strongly prefers Gln at P1 of its canonical ENLYFQ↓S substrate, was engineered to recognize selectively Glu or His at P1. Kinetic analysis indicated an overall 5,000-fold and 1,100-fold change in selectivity, respectively, for the Glu- and His-specific TEV variants, both of which retained high catalytic turnover. Human granzyme K and the hepatitis C virus protease were also shown to be amenable to this unique approach. Further, by adjusting the signaling strategy to identify phosphorylated as opposed to cleaved sequences, this unique system was shown to be compatible with the human Abelson tyrosine kinase.