3 research outputs found
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
Psymberin, a marine-derived natural product, induces cancer cell growth arrest and protein translation inhibition
Colorectal cancer (CRC) is the third most prevalent form of cancer in the United States and results in over 50,000 deaths per year. Treatments for metastatic CRC are limited, and therefore there is an unmet clinical need for more effective therapies. In our prior work, we coupled high-throughput chemical screens with patient-derived models of cancer to identify new potential therapeutic targets for CRC. However, this pipeline is limited by (1) the use of cell lines that do not appropriately recapitulate the tumor microenvironment, and (2) the use of patient-derived xenografts (PDXs), which are time-consuming and costly for validation of drug efficacy. To overcome these limitations, we have turned to patient-derived organoids. Organoids are increasingly being accepted as a “standard” preclinical model that recapitulates tumor microenvironment cross-talk in a rapid, cost-effective platform. In the present work, we employed a library of natural products, intermediates, and drug-like compounds for which full synthesis has been demonstrated. Using this compound library, we performed a high-throughput screen on multiple low-passage cancer cell lines to identify potential treatments. The top candidate, psymberin, was further validated, with a focus on CRC cell lines and organoids. Mechanistic and genomics analyses pinpointed protein translation inhibition as a mechanism of action of psymberin. These findings suggest the potential of psymberin as a novel therapy for the treatment of CRC