Adediran, B. | Search bioRxiv

bioRxiv2026

Targeting collagen biosynthesis by small molecules inhibiting the function of the peptide-substrate-binding domain of collagen prolyl 4-hydroxylases

Collagen prolyl 4-hydroxylase (C-P4H) is an essential enzyme in collagen synthesis and known to be a potential target for drugs that prevent excess collagen formation. Currently known C-P4H inhibitors target the catalytic site of C-P4H, being analogues of 2-oxoglutarate (2OG). However, in mammalian cells there are many other 2OG-dependent dioxygenases with a highly similar catalytic domain, which limits the selective specificity of drugs targeting C-P4H activity. The peptide-substrate-binding (PSB) domain is unique for the C-P4H family and known to be important for the catalytic efficiency of C-P4H. Therefore, interfering with peptide binding to the PSB domain might allow more specific inhibition of the hydroxylation activity of C-P4Hs. We developed a robust FRET-based high-throughput screening assay (Z > 0.73) based on PSB-peptide interactions. This assay was used to screen a peptidomimetic library of 15614 compounds with the PSB domains of C-P4H isoforms I and II. A hit compound (OUL-PSBi-001) was identified with IC50s of 40 {micro}M and 62 {micro}M for PSB-I and PSB-II, respectively. We also showed that this compound indeed inhibits the catalytic activity of the full-length CP4H-I and -II. This FRET assay provides a new strategy for finding selective inhibitors for the treatment of fibrotic diseases and cancer.

molecular biology↗

bioRxiv2025

AXIS: A Lab-in-the-Loop Machine Learning approach for generalized detection of macromolecular crystals.

Macromolecular crystallography provides mechanistic understanding of biological processes and can be applied in drug design. Nowadays, the use of robotic systems for crystal growth and diffraction analysis is widespread and high throughput protein-to-structure pipelines for ligand and fragment screening are revolutionizing the field. However, the identification of crystals is still largely carried out through manual inspection, sometimes involving tens of thousands of images, which represents a bottleneck in an otherwise highly automated process. Here we describe AXIS, an AI-based Crystal Identification System combining the DINOv2 computer vision model, state-of-the-art transfer learning and MARCO, the largest crystallization dataset available to date, for automated crystal detection. AXIS can operate both with visible and UV light images and integrates a Lab-In-The-Loop approach combining ML and expert inputs for continuous learning and specialization. AXIS enables automated annotation of large crystallization image datasets with performance and accuracy comparable to that of human experts and the Lab-In-The-Loop approach introduced here enables efficient adaptation to local conditions facilitating widespread application, which has been a major limitation to date. AXIS can help correct human errors in image annotation and removes critical bottlenecks, particularly in the context of extensive crystallization screens or high throughput applications like fragment and ligand screening unlocking the potential for higher levels of automation that are key both in fundamental and translational research. Appendix A. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=183 SRC="FIGDIR/small/685844v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@c47ab4org.highwire.dtl.DTLVardef@153ce2dorg.highwire.dtl.DTLVardef@1050086org.highwire.dtl.DTLVardef@165965b_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗