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Biology subjects

Feng, J. A.

Publications and source records attributed to Feng, J. A..

2 recordsLinked to original sources

Discovery of a first-in-class small molecule ligand for WDR91 using DNA-encoded chemical library selection followed by machine learning

WD40 repeat-containing protein 91 regulates endosomal phosphatidylinositol 3-phosphate levels at the critical stage of endosome maturation and plays vital roles in endosome fusion, recycling, and transport by mediating protein-protein interactions. Due to its various roles in endocytic pathways, WDR91 has recently been identified as a potential host factor responsible for viral infection. We employed DNA-Encoded Chemical Library (DEL) selection against the WDR domain of WDR91, followed by machine learning to generate a model that was then used to predict ligands from the synthetically accessible Enamine REAL database. Screening of predicted compounds enabled us to identify the hit compound 1, which binds selectively to WDR91 with a KD of 6 {+/-} 2 M by surface plasmon resonance. The co-crystal structure confirmed the binding of 1 to the WDR91 side pocket, in proximity to cysteine 487. Machine learning-assisted structure activity relationship-by-catalog validated the chemotype of 1 and led to the discovery of covalent analogs 18 and 19. Intact mass LC-MS and differential scanning fluorimetry confirmed the formation of a covalent adduct, and thermal stabilization, respectively. The discovery of 1, 18, 19, accompanying SAR, and co-crystal structures will provide valuable insights for designing more potent and selective compounds against WDR91, thus accelerating the development of novel chemical tools to evaluate the therapeutic potential of WDR91 in disease.

molecular biology↗

A deep learning approach for the discovery of tumor-targeting small organic ligands from DNA-Encoded Chemical Libraries

DNA-Encoded Chemical Libraries (DELs) emerged as efficient and cost-effective ligand discovery tools, which enable the generation of protein-ligand interaction data of unprecedented size. In this article, we present an approach that combines DEL screening and instance-level deep learning modeling to identify tumor-targeting ligands against Carbonic Anhydrase IX (CAIX), a clinically validated marker of hypoxia and clear cell Renal Cell Carcinoma. We present a new ligand identification and HIT-to-LEAD strategy driven by Machine Learning (ML) models trained on DELs, which expand the scope of DEL-derived chemical motifs. CAIX screening datasets obtained from three different DELs were used to train ML models for generating novel HITs, dissimilar to elements present in the original DELs. Out of the 152 novel potential HITs that were identified with our approach and screened in an in vitro enzymatic inhibition assay, 70% displayed submicromolar activities (IC50 < 1 M). Based on the first HIT set, the model was further used to prioritize and generate LEAD compounds with nanomolar affinity for in vivo tumor-targeting applications. Three LEAD candidates showed accumulation on the surface of CAIX-expressing tumor cells in cellular binding assays. The best compound displayed in vitro KD of 5.7 nM and selectively targeted tumors in mice bearing human Renal Cell Carcinoma lesions. Our results demonstrate the synergy between DEL and machine learning for the identification of novel HITs and for the successful translation of LEAD candidates for in vivo targeting applications. Graphical Abstracts O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/525453v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@105913corg.highwire.dtl.DTLVardef@823522org.highwire.dtl.DTLVardef@6e85e9org.highwire.dtl.DTLVardef@19a611_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗