Discovery of low molecular weight protein-binding fragments from bead-displayed libraries: application to monomeric proteins

Profiling Protein‐Peptide Interactions by Yeast Surface Display

A protocol for discovering protein‐binding peptides using a very large, target‐agnostic yeast surface display library containing approximately 6.1 × 109 unique clones and providing broad coverage of short peptide sequence space is described.

Biophysical and Cellular Assays for the Evaluation of Peptidomimetic Inhibitors Based on Degron Sequences.

A High-Throughput Assay to Identify Specific Nascent Chain Inhibitors

“Undruggable” proteins without surface-accessible binding sites pose significant challenges to target-based drug discovery. Innovative approaches are needed to tackle these proteins. One promising strategy is targeting them in their nascent chain form at the ribosome, where they have a different conformation than in the folded form. A systematic approach to screen for such compounds has however been lacking. Here, we present a high-throughput assay to identify small molecules that specifically inhibit a protein of interest in its nascent-chain form. The assay employs a human in vitro transcription/translation system and monitors expression of the protein in real time via fluorescence detection. Specific inhibitors for the nascent chain of interest can then be identified by comparison with a counter screen. The assay was optimized to maximal sensitivity and reaction costs of ∼$0.01 per well, enabling large-scale screens. We validated performance with the reference compound PF846 on the nascent chain of the protein PCSK9. Feasibility for high-throughput screening was demonstrated using a library of 1,760 compounds against the oncogenic KRAS variant A146T and the protein ApoC3, with mean Z′ scores of 0.69 and 0.88, respectively. Sixteen global translation inhibitors were identified in each campaign, while no compounds met the criteria for POI-selective inhibition. The assay thus provides a robust platform for larger-scale screening campaigns.

Discovery of Non-Inhibitory Macrocyclic Ligands for Protein Tyrosine Phosphatase 1B Using a Function-Based, Iterative Screening Strategy

Chemically induced proximity is a powerful modality for manipulating protein function. Most of the effort in this field has focused on targeted protein degradation but recruitment of other types of post-translational modification enzymes to a target protein is also of interest. To construct such reagents, one would ideally like to have ligands that engage the enzyme without inhibiting its activity. In this study, we describe a screening platform for the discovery of non-inhibitory macrocyclic ligands for a protein tyrosine phosphatase, using PTP1B as an exemplary model target. This workflow involves sequential screens of small libraries of bead-displayed macrocycles in which only one position of the macrocycle is varied in each round of screening while the others are held as invariant placeholders. The beads co-display a high KM substrate for the phosphatase, allowing ligand-dependent recruitment of the enzyme to the bead surface to be coupled to dephosphorylation of the co-displayed substrate. This is detected by staining with a labeled anti-phosphotyrosine antibody. Finally, we demonstrate that the same general approach can be applied to proteins lacking enzymatic activity by screening against biotin ligase-target protein fusions and employing a proximity labeling-like assay to register screening hits.

Efficient Discovery of Potent AKR1B10 Inhibitors Using Co-folding Model Boltz-2

Although affinity prediction of protein–ligand binding remains an important challenge, cofolding models are expected to make virtual screening more effective for drug discovery and development. To verify the effectiveness of selecting a tractable number of candidates from a compound library using cofolding models, we strove to identify a novel inhibitory active compound using Boltz-2, a representative cofolding model, for aldo-keto reductase 1B10 (AKR1B10), which is highly expressed in various cancers. Consequently, of the 40 candidate compounds obtained after narrowing-down 867 candidates from a chemically diverse library based on prediction results by Boltz-2 and candidate selection with sufficient diversity, 70% (28 of 40 tested) compounds at IC50 < 10 μM were found to have inhibitory activity and to provide identification of multiple submicromolar inhibitors exhibiting novel scaffolds. Our results demonstrate that our sparse selection approach using Boltz-2 is helpful for enhancing AI-driven drug discovery. Moreover, the findings highlight its potential applicability for translating AI-generated predictions into experimentally actionable hits.

Massively Parallel, Single-Molecule Assessment of Synthetic Fidelity and Drug-Like Properties in a DNA-Encoded Library.

DNA-encoded libraries (DELs) are powerful drug discovery tools, enabling rapid hit generation against immobilized protein targets. However, translating these hits is often hampered by synthetic inefficiency and, for libraries of "beyond-Rule-of-5" compounds such as macrocyclic peptides, by enrichment of poorly cell-permeable members. Here we introduce LC-seq, a sequencing-based chromatographic strategy that simultaneously assesses synthetic fidelity and permeability-relevant lipophilicity for individual library members. Applying LC-seq to a proof-of-concept 120,000-member peptide library, we mapped reaction efficiency across all synthetic cycles and measured each member's lipophilicity from sequencing-count-derived retention times. We identified building-block-specific structure-reactivity trends, and the on-DNA lipophilicities of resynthesized members correlated strongly with their off-DNA lipophilicities and passive permeability in artificial membranes. This simple approach enables direct, per-member assessment of compound quality and lipophilicity, with projected scalability to libraries of millions.