Multi-Parameter Optimization of Stapled Peptides and Other Proteins Via Directed Evolution
Creators: name: Case, Marshall
Publisher: name: My University
Year: 2023
Subjects: subject: protein engineering
subject: peptide engineering
subject: directed evolution
subject: machine learning
subject: computational biology
subject: Chemical Engineering
subject: FOS: Chemical engineering
subject: Engineering

description: There is a wealth of disease-related proteins that are ‘undruggable’ by common therapeutic modalities, owing to their difficult location within cells and lacking specific structural motifs that facilitate specific targeting. Stapled peptides, a class of therapeutic that leverages synthetic biology and protein engineering, are a promising approach to overcome these barriers, but their development is rendered difficult by complex chemical synthesis and myriad design factors. In this thesis, Stabilized Peptide Engineering by E. coli Display (SPEED), a technique that can greatly accelerate stapled peptide development, is used to explore new design criteria for stapled peptides, such as staple location, amino acid hot spots, and stapling chemistry. In this technique, methionine auxotrophic bacteria are transformed with DNA that encodes for a peptide and azide-containing non-natural amino acids are incorporated. Then, copper catalyzed click chemistry (CuAAC) is performed on the cell surface before treatment with any combination of fluorescently- or magnetically- activated proteins for subsequent property measurement or cell sorting application. To demonstrate how SPEED coupled with an expanded set of design criteria can yield therapeutic leads towards these challenging targets, high affinity and specific stapled peptides are developed towards two important targets: p53 and Bcl-2. The thesis describes the generation of highly focused protein variant libraries for multi-objective optimization. In Chapter 2, SPEED was used to confirm the hot spot analysis of p53-MDM2 with reduced affinity resulting from mutations to F19, W23, and L26. Likewise, it was used to show the importance of staple chemistry and location on binding affinity and specificity. With BIM peptides, for example, a staple location at p1 and p5 showed significant preference for Mcl-1 binding, while p7 and p14 bound Bfl-1, Bcl-xL, Bcl-w, and Bcl-2 more than Mcl-1. This analysis establishes that both staple sequence and staple location are key determinants of peptide binding affinity and specificity. Then, in Chapter 3, I engineered stapled peptides targeting Bcl-xL (a protein in the B cell lymphoma 2 protein family that promotes cancer cell survival) with high specificity. Using an enriched library design and directed evolution campaign sorting for Bcl-xL specificity, I engineered Bcl-xL binding peptides with 10 nM affinity and 100-fold specificity with novel mutations that act in accordance with apoptosis biochemistry. Finally, in Chapter 4, I describe a machine learning approach that captures hidden information from simple binary sorting experiments by using next generation sequencing datasets. The trained model is able to predict fitness in unseen sequence space to expand discovery beyond experimentally measured sequences. To validate this method, I curate five protein directed evolution campaigns via cell surface display and find that across many protein families (single chain variable fragments, fragment antigen binding, globular proteins, among others) and objectives (fluorescence, specificity, binding affinity), this method consistently predicts continuous properties and identifies high functioning variants. We then prospectively design stapled peptides to identify high functioning Bcl-2 binders using sequence optimization when experimental techniques fail to yield consistent hits. Overall, this work presents novel peptide engineering strategies for stapled peptides, next-generation sequence analysis for selecting specific binders against homologous proteins, and machine learning methods to extract data and design novel peptides and proteins beyond experimentally measures space, which should find use in many protein engineering campaigns.