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 (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.

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.

Design of a Tumor Binding GMCSF as Intratumoral Immunotherapy of Solid Tumors.

Next-generation cancer immunotherapies may utilize immunostimulants to selectively activate the host immune system against tumor cells. Checkpoint inhibitors (CPIs) like anti-PD1/PDL-1 that inhibit immunosuppression have shown unprecedented success but are only effective in the 20-30% of patients that possess an already "hot" (immunogenic) tumor. In this regard, intratumoral (IT) injection of immunostimulants is a promising approach since they can work synergistically with CPIs to overcome the resistance to immunotherapies by inducing immune stimulation in the tumor. One such immunostimulant is granulocyte macrophage-colony-stimulating factor (GMCSF) that functions by recruiting and activating antigen-presenting cells (dendritic cells) in the tumor, thereby initiating anti-tumor immune responses. However, key problems with GMCSF are lack of efficacy and the risk of systemic toxicity caused by the leakage of GMCSF from the tumor tissue. We have designed tumor-retentive versions of GMCSF that are safe yet potent immunostimulants for the local treatment of solid tumors. The engineered GMCSFs (eGMCSF) were synthesized by recombinantly fusing tumor-ECM (extracellular matrix) binding peptides to GMCSF. The eGMCSFs exhibited enhanced tumor binding and potent immunological activity in vitro and in vivo. Upon IT administration, the tumor-retentive eGMCSFs persisted in the tumor, thereby alleviating systemic toxicity, and elicited localized immune activation to effectively turn an unresponsive immunologically "cold" tumor "hot".