Programmable Protein Stabilization with Language Model-Derived Peptide Guides.

Dysregulated protein degradation via the ubiquitin-proteasomal pathway can induce numerous disease phenotypes, including cancer, neurodegeneration, and diabetes. Stabilizing improperly ubiquitinated proteins via target-specific deubiquitination is thus a critical therapeutic goal. Building off the major advances in targeted protein degradation (TPD) using bifunctional small-molecule degraders, targeted protein stabilization (TPS) modalities have been described recently. However, these rely on a limited set of chemical linkers and warheads, which are difficult to generate de novo for new targets and do not exist for classically "undruggable" targets. To address the limited reach of small molecule-based degraders, we previously engineered ubiquibodies (uAbs) by fusing computationally-designed "guide" peptides to E3 ubiquitin ligase domains for modular, CRISPR-analogous TPD. Here, we expand the TPS target space by engineering "deubiquibodies" (duAbs) via fusion of computationally-designed guides to the catalytic domain of the potent OTUB1 deubiquitinase. In human cells, duAbs effectively stabilize exogenous and endogenous proteins in a DUB-dependent manner. To demonstrate duAb modularity, we swap in new target-binding peptides designed via our generative language models to stabilize diverse target proteins, including key tumor suppressor proteins such as p53 and WEE1, as well as heavily-disordered fusion oncoproteins, such as PAX3::FOXO1. In total, our duAb system represents a simple, programmable, CRISPR-analogous strategy for TPS.

Cannabis and Driving in Older Adults.

Importance: Epidemiological studies have found that cannabis increases the risk of a motor vehicle collision. Cannabis use is increasing in older adults, but laboratory studies of the association between cannabis and driving in people aged older than 65 years are lacking. Objective: To investigate the association between cannabis, simulated driving, and concurrent blood tetrahydrocannabinol (THC) levels in older adults. Design, Setting, and Participants: Using an ecologically valid counterbalanced design, in this cohort study, regular cannabis users operated a driving simulator before, 30 minutes after, and 180 minutes after smoking their preferred legal cannabis or after resting. This study was conducted in Toronto, Canada, between March and November 2022 with no follow-up period. Data were analyzed from December 2022 to February 2023. Exposures: Most participants chose THC-dominant cannabis with a mean (SD) content of 18.74% (6.12%) THC and 1.46% (3.37%) cannabidiol (CBD). Main outcomes and measures: The primary end point was SD of lateral position (SDLP, or weaving). Secondary outcomes were mean speed (MS), maximum speed, SD of speed, and reaction time. Driving was assessed under both single-task and dual-task (distracted) conditions. Blood THC and metabolites of THC and CBD were also measured at the time of the drives. Results: A total of 31 participants (21 male [68%]; 29 White [94%], 1 Latin American [3%], and 1 mixed race [3%]; mean [SD] age, 68.7 [3.5] years), completed all study procedures. SDLP was increased and MS was decreased at 30 but not 180 minutes after smoking cannabis compared with the control condition in both the single-task (SDLP effect size [ES], 0.30; b = 1.65; 95% CI, 0.37 to 2.93; MS ES, -0.58; b = -2.46; 95% CI, -3.56 to -1.36) and dual-task (SDLP ES, 0.27; b = 1.75; 95% CI, 0.21 to 3.28; MS ES, -0.47; b = -3.15; 95% CI, -5.05 to -1.24) conditions. Blood THC levels were significantly increased at 30 minutes but not 180 minutes. Blood THC was not correlated with SDLP or MS at 30 minutes, and SDLP was not correlated with MS. Subjective ratings remained elevated for 5 hours and participants reported that they were less willing to drive at 3 hours after smoking. Conclusions and relevance: In this cohort study, the findings suggested that older drivers should exercise caution after smoking cannabis.

De Novo Design of Peptide Binders to Conformationally Diverse Targets with Contrastive Language Modeling.

Designing binders to target undruggable proteins presents a formidable challenge in drug discovery, requiring innovative approaches to overcome the lack of putative binding sites. Recently, generative models have been trained to design binding proteins via three-dimensional structures of target proteins, but as a result, struggle to design binders to disordered or conformationally unstable targets. In this work, we provide a generalizable algorithmic framework to design short, target-binding linear peptides, requiring only the amino acid sequence of the target protein. To do this, we propose a process to generate naturalistic peptide candidates through Gaussian perturbation of the peptidic latent space of the ESM-2 protein language model, and subsequently screen these novel linear sequences for target-selective interaction activity via a CLIP-based contrastive learning architecture. By integrating these generative and discriminative steps, we create a Peptide Prioritization via CLIP (PepPrCLIP) pipeline and validate highly-ranked, target-specific peptides experimentally, both as inhibitory peptides and as fusions to E3 ubiquitin ligase domains, demonstrating functionally potent binding and degradation of conformationally diverse protein targets in vitro. Overall, our design strategy provides a modular toolkit for designing short binding linear peptides to any target protein without the reliance on stable and ordered tertiary structure, enabling generation of programmable modulators to undruggable and disordered proteins such as transcription factors and fusion oncoproteins.

PIC recruitment by synthetic reader-actuators to polycomb-silenced genes blocks triple-negative breast cancer invasion.

Scientists have used small molecule inhibitors and genetic knockdown of gene-silencing polycomb repressive complexes (PRC1/2) to determine if restoring the expression of tumor suppressor genes can block proliferation and invasion of cancer cells. A major limitation of this approach is that inhibitors can not restore key transcriptional activators that are mutated in many cancers, such as p53 and members of the BRAF SWI/SNF complex. Furthermore, small molecule inhibitors can alter the activity of, rather than inhibit, the polycomb enzyme EZH2. While chromatin has been shown to play a major role in gene regulation in cancer, poor clinical results for polycomb chromatin-targeting therapies for diseases like triple-negative breast cancer (TNBC) could discourage further development of this emerging avenue for treatment. To overcome the limitations of inhibiting polycomb to study epigenetic regulation, we developed an engineered chromatin protein to manipulate transcription. The synthetic reader-actuator (SRA) is a fusion protein that directly binds a target chromatin modification and regulates gene expression. Here, we report the activity of an SRA built from polycomb chromodomain and VP64 modules that bind H3K27me3 and subunits of the Mediator complex, respectively. In SRA-expressing BT-549 cells, we identified 122 upregulated differentially expressed genes (UpDEGs, ≥ 2-fold activation, adjusted p < 0.05). On-target epigenetic regulation was determined by identifying UpDEGs at H3K27me3-enriched, closed chromatin. SRA activity induced activation of genes involved in cell death, cell cycle arrest, and the inhibition of migration and invasion. SRA-expressing BT-549 cells showed reduced spheroid size in Matrigel over time, loss of invasion, and activation of apoptosis. These results show that Mediator-recruiting regulators broadly targeted to silenced chromatin activate silenced tumor suppressor genes and stimulate anti-cancer phenotypes. Therefore further development of gene-activating epigenetic therapies might benefit TNBC patients.

Synthetic Reader-Actuators Targeted to Polycomb-Silenced Genes Block Triple-Negative Breast Cancer Proliferation and Invasion.

Scientists have used pharmacological inhibitors of polycomb proteins to restore the expression of tumor suppressor genes and stop cancer proliferation and invasion. A major limitation of this approach is that key transcriptional activators, such as TP53 and BAF SWI/SNF, are often mutated in cancer. Poor clinical results for polycomb-targeting therapies in solid cancers, including triple-negative breast cancer (TNBC), could discourage the further development of epigenetic monotherapies. Here, we performed epigenome actuation with a synthetic reader-actuator (SRA) that binds trimethylated histone H3 lysine 27 in polycomb chromatin and modulates core transcriptional activators. In SRA-expressing TNBC BT-549 cells, 122 genes become upregulated ≥2-fold, including the genes involved in cell death, cell cycle arrest, and migration inhibition. The SRA-expressing spheroids showed reduced size in Matrigel and loss of invasion. Therefore, targeting Mediator-recruiting regulators to silenced chromatin can activate tumor suppressors and stimulate anti-cancer phenotypes, and further development of robust gene regulators might benefit TNBC patients.

PepMLM: Target Sequence-Conditioned Generation of Peptide Binders via Masked Language Modeling.

Target proteins that lack accessible binding pockets and conformational stability have posed increasing challenges for drug development. Induced proximity strategies, such as PROTACs and molecular glues, have thus gained attention as pharmacological alternatives, but still require small molecule docking at binding pockets for targeted protein degradation (TPD). The computational design of protein-based binders presents unique opportunities to access "undruggable" targets, but have often relied on stable 3D structures or predictions for effective binder generation. Recently, we have leveraged the expressive latent spaces of protein language models (pLMs) for the prioritization of peptide binders from sequence alone, which we have then fused to E3 ubiquitin ligase domains, creating a CRISPR-analogous TPD system for target proteins. However, our methods rely on training discriminator models for ranking heuristically or unconditionally-derived "guide" peptides for their target binding capability. In this work, we introduce PepMLM, a purely target sequence-conditioned de novo generator of linear peptide binders. By employing a novel masking strategy that uniquely positions cognate peptide sequences at the terminus of target protein sequences, PepMLM tasks the state-of-the-art ESM-2 pLM to fully reconstruct the binder region, achieving low perplexities matching or improving upon previously-validated peptide-protein sequence pairs. After successful in silico benchmarking with AlphaFold-Multimer, we experimentally verify PepMLM's efficacy via fusion of model-derived peptides to E3 ubiquitin ligase domains, demonstrating endogenous degradation of target substrates in cellular models. In total, PepMLM enables the generative design of candidate binders to any target protein, without the requirement of target structure, empowering downstream programmable proteome editing applications.

SaLT&PepPr is an interface-predicting language model for designing peptide-guided protein degraders.

Protein-protein interactions (PPIs) are critical for biological processes and predicting the sites of these interactions is useful for both computational and experimental applications. We present a Structure-agnostic Language Transformer and Peptide Prioritization (SaLT&PepPr) pipeline to predict interaction interfaces from a protein sequence alone for the subsequent generation of peptidic binding motifs. Our model fine-tunes the ESM-2 protein language model (pLM) with a per-position prediction task to identify PPI sites using data from the PDB, and prioritizes motifs which are most likely to be involved within inter-chain binding. By only using amino acid sequence as input, our model is competitive with structural homology-based methods, but exhibits reduced performance compared with deep learning models that input both structural and sequence features. Inspired by our previous results using co-crystals to engineer target-binding "guide" peptides, we curate PPI databases to identify partners for subsequent peptide derivation. Fusing guide peptides to an E3 ubiquitin ligase domain, we demonstrate degradation of endogenous ß-catenin, 4E-BP2, and TRIM8, and highlight the nanomolar binding affinity, low off-targeting propensity, and function-altering capability of our best-performing degraders in cancer cells. In total, our study suggests that prioritizing binders from natural interactions via pLMs can enable programmable protein targeting and modulation.

PAM-Flexible Genome Editing with an Engineered Chimeric Cas9.

CRISPR enzymes require a defined protospacer adjacent motif (PAM) flanking a guide RNA-programmed target site, limiting their sequence accessibility for robust genome editing applications. In this study, we recombine the PAM-interacting domain of SpRY, a broad-targeting Cas9 possessing an NRN > NYN PAM preference, with the N-terminus of Sc++, a Cas9 with simultaneously broad, efficient, and accurate NNG editing capabilities, to generate a chimeric enzyme with highly flexible PAM preference: SpRYc. We demonstrate that SpRYc leverages properties of both enzymes to specifically edit diverse NNN PAMs and disease-related loci for potential therapeutic applications. In total, the unique approaches to generate SpRYc, coupled with its robust flexibility, highlight the power of integrative protein design for Cas9 engineering and motivate downstream editing applications that require precise genomic positioning.

PAM-flexible genome editing with an engineered chimeric Cas9.

CRISPR enzymes require a defined protospacer adjacent motif (PAM) flanking a guide RNA-programmed target site, limiting their sequence accessibility for robust genome editing applications. In this study, we recombine the PAM-interacting domain of SpRY, a broad-targeting Cas9 possessing an NRN > NYN (R = A or G, Y = C or T) PAM preference, with the N-terminus of Sc + +, a Cas9 with simultaneously broad, efficient, and accurate NNG editing capabilities, to generate a chimeric enzyme with highly flexible PAM preference: SpRYc. We demonstrate that SpRYc leverages properties of both enzymes to specifically edit diverse PAMs and disease-related loci for potential therapeutic applications. In total, the approaches to generate SpRYc, coupled with its robust flexibility, highlight the power of integrative protein design for Cas9 engineering and motivate downstream editing applications that require precise genomic positioning.