The RNA-binding protein Msi2 regulates autophagy during myogenic differentiation.

Skeletal muscle development is a highly ordered process orchestrated transcriptionally by the myogenic regulatory factors. However, the downstream molecular mechanisms of myogenic regulatory factor functions in myogenesis are not fully understood. Here, we identified the RNA-binding protein Musashi2 (Msi2) as a myogenin target gene and a post-transcriptional regulator of myoblast differentiation. Msi2 knockdown in murine myoblasts blocked differentiation without affecting the expression of MyoD or myogenin. Msi2 overexpression was also sufficient to promote myoblast differentiation and myocyte fusion. Msi2 loss attenuated autophagosome formation via down-regulation of the autophagic protein MAPL1LC3/ATG8 (LC3) at the early phase of myoblast differentiation. Moreover, forced activation of autophagy effectively suppressed the differentiation defects incurred by Msi2 loss. Consistent with its functions in myoblasts in vitro, mice deficient for Msi2 exhibited smaller limb skeletal muscles, poorer exercise performance, and muscle fiber-type switching in vivo. Collectively, our study demonstrates that Msi2 is a novel regulator of mammalian myogenesis and establishes a new functional link between muscular development and autophagy regulation.

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.