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Abstract Transcription factors (TF) are proteins that bind DNA in a sequence-specific manner to regulate gene transcription. Despite their unique intrinsic sequence preferences,in vivogenomic occupancy profiles of TFs differ across cellular contexts. Hence, deciphering the sequence determinants of TF binding, both intrinsic and context-specific, is essential to understand gene regulation and the impact of regulatory, non-coding genetic variation. Biophysical models trained onin vitroTF binding assays can estimate intrinsic affinity landscapes and predict occupancy based on TF concentration and affinity. However, these models cannot adequately explain context-specific,in vivobinding profiles. Conversely, deep learning models, trained onin vivoTF binding assays, effectively predict and explain genomic occupancy profiles as a function of complex regulatory sequence syntax, albeit without a clear biophysical interpretation. To reconcile these complementary models ofin vitroandin vivoTF binding, we developed Affinity Distillation (AD), a method that extracts thermodynamic affinitiesde-novofrom deep learning models of TF chromatin immunoprecipitation (ChIP) experiments by marginalizing away the influence of genomic sequence context. Applied to neural networks modeling diverse classes of yeast and mammalian TFs, AD predicts energetic impacts of sequence variation within and surrounding motifs on TF binding as measured by diversein vitroassays with superior dynamic range and accuracy compared to motif-based methods. Furthermore, AD can accurately discern affinities of TF paralogs. Our results highlight thermodynamic affinity as a key determinant ofin vivobinding, suggest that deep learning models ofin vivobinding implicitly learn high-resolution affinity landscapes, and show that these affinities can be successfully distilled using AD. This new biophysical interpretation of deep learning models enables high-throughputin silicoexperiments to explore the influence of sequence context and variation on both intrinsic affinity andin vivooccupancy.
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Rational Design and Identification of Harmine‐Inspired, N ‐Heterocyclic DYRK1A Inhibitors Employing a Functional Genomic In Vivo Drosophila Model System**
Abstract Deregulation of dual‐specificity tyrosine phosphorylation‐regulated kinase 1A (DYRK1A) plays a significant role in developmental brain defects, early‐onset neurodegeneration, neuronal cell loss, dementia, and several types of cancer. Herein, we report the discovery of three new classes ofN‐heterocyclic DYRK1A inhibitors based on the potent, yet toxic kinase inhibitors, harmine and harmol. An initial in vitro evaluation of the small molecule library assembled revealed that the core heterocyclic motifs benzofuranones, oxindoles, and pyrrolones, showed statistically significant DYRK1A inhibition. Further, the utilization of a low cost, high‐throughput functional genomic in vivo model system to identify small molecule inhibitors that normalizeDYRK1Aoverexpression phenotypes is described. This in vivo assay substantiated the in vitro results, and the resulting correspondence validates generated classes as architectural motifs that serve as potential DYRK1A inhibitors. Further expansion and analysis of these core compound structures will allow discovery of safe, more effective chemical inhibitors of DYRK1A to ameliorate phenotypes caused by DYRK1A overexpression.
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- Award ID(s):
- 1956170
- PAR ID:
- 10362969
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- ChemMedChem
- Volume:
- 17
- Issue:
- 4
- ISSN:
- 1860-7179
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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