Biology subjects

Wilms, J.

Publications and source records attributed to Wilms, J..

2 recordsLinked to original sources

bioRxiv2026

Minimal N-methylated and stapled peptide ligands for the autophagy protein GABARAP

The LC3/GABARAP protein family is a promising target for selective inhibition of autophagy. Further, LC3/GABARAP ligands have been used as targeted degraders of soluble proteins, protein aggregates, mitochondria, lipid droplets, and RNA. However, the small molecules used for such applications have poor binding affinity and known off-target effects. LC3/GABARAP proteins are challenging targets for small-molecule drug development due to their long, shallow binding grooves. In this work, we evaluate multiple approaches to stabilizing the extended structure of the native binding motif, producing N-methylated peptides and stapled peptides with low nanomolar affinity. A crystal structure and molecular dynamics simulations support a model where the N-methylation pre-organizes the motif into an extended, strand-like structure. N-methylation allowed minimization of the binding motif to a tetrapeptide that retained sub-micromolar affinity while minimizing charge and overall molecular weight. The truncated, N-methylated tetrapeptide showed passive permeability in artificial membrane and cell-based transwell assays. These results highlight new drug-like space for LC3/GABARAP ligands with high affinity and subfamily selectivity. Table of Contents GraphicDrawn and developed by Mollie McGibbon and Joshua Kritzer O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/710535v2_ufig1.gif" ALT="Figure 1"> View larger version (68K): org.highwire.dtl.DTLVardef@1b74a1org.highwire.dtl.DTLVardef@861c09org.highwire.dtl.DTLVardef@9abc2corg.highwire.dtl.DTLVardef@b7f745_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2024

Accurate de novo design of high-affinity protein binding macrocycles using deep learning

The development of macrocyclic binders to therapeutic proteins typically relies on large-scale screening methods that are resource-intensive and provide little control over binding mode. Despite considerable progress in physics-based methods for peptide design and deep-learning methods for protein design, there are currently no robust approaches for de novo design of protein-binding macrocycles. Here, we introduce RFpeptides, a denoising diffusion-based pipeline for designing macrocyclic peptide binders against protein targets of interest. We test 20 or fewer designed macrocycles against each of four diverse proteins and obtain medium to high-affinity binders against all selected targets. Designs against MCL1 and MDM2 demonstrate KD between 1-10 M, and the best anti-GABARAP macrocycle binds with a KD of 6 nM and a sub-nanomolar IC50 in vitro. For one of the targets, RbtA, we obtain a high-affinity binder with KD < 10 nM despite starting from the target sequence alone due to the lack of an experimentally determined target structure. X-ray structures determined for macrocycle-bound MCL1, GABARAP, and RbtA complexes match very closely with the computational design models, with three out of the four structures demonstrating Ca RMSD of less than 1.5 [A] to the design models. In contrast to library screening approaches for which determining binding mode can be a major bottleneck, the binding modes of RFpeptides-generated macrocycles are known by design, which should greatly facilitate downstream optimization. RFpeptides thus provides a powerful framework for rapid and custom design of macrocyclic peptides for diagnostic and therapeutic applications.

biochemistry↗