Abstract The proposed work in this MIRA application focuses on my long-term goal to create programmable toolkits for proteome editing. Inspired by research into developing broad-targeting CRISPR enzymes for programmable genome editing, my laboratory designs analogous peptide-guided enzymes for post-translationally modifying target proteins from sequence alone, enabling targeting of both structurally-stable and disordered proteins. To this point, we leverage generative artificial intelligence approaches to design these peptides, including protein language model (pLM)-based interface predictors and diffusion models for de novo peptide binder generation. Integrated with these efforts, we experimentally engineer peptide-E3 ubiquitin ligase fusions (termed ubiquibodies or uAbs) that can degrade diverse pathogenic proteins. Building upon this work, we will combine our current technologies and expand our research into three new areas to enable broad-scale proteome editing applications via generative protein language modeling. First, we will build upon our current experimental efforts to enable peptide-guided deubiquitination, thus enabling both protein degradation with our uAbs and now protein stabilization of target proteins. To accomplish this goal, we will explore fusion of generated peptide binders to deubiquitinase domains, as well as to designed recruiter domains of endogenous deubiquitinases. The results of this research will establish easy-to-design “on” and “off” switches for controlling protein states in cells. Second, we will develop a new line of investigation, specifically to enable mutant-specific targeting of protein variants. Here, we will leverage the variant prediction capabilities of state-of-the-art pLMs, alongside Gaussian Process-based predictors, to prioritize binders that discriminate between wild-type proteins and highly-similar mutant variants that confer pathogenic properties. These binders can then be integrated into our degradation and stabilization architectures for experimental validation. We envision that this new line of investigation will enable selective degradation of mutant, disease-causing proteins, such as mutant Huntingtin protein or mutant KRAS. Third, we will establish a specific focus on selectively targeting dysregulated, post-translationally-modified proteins, which have numerous implications in various diseases, such as aging and cancer. To enable discrimination between wild-type proteins and their modified counterparts, we will train new PTM-specific protein language models via both masked language modeling and autoregressive training tasks. By integrating SwissProt annotations with unique per amino acid-based tokenization strategies, the results of this research will enable the design of degraders that are selective to modified proteins, such as phosphorylated oncogenic drivers, leaving wild-type isoforms intact. Together, the work we propose in this MIRA application promises to yield robust, scalable, and...