Biology subjects

Fullenkamp, C. R.

Publications and source records attributed to Fullenkamp, C. R..

3 recordsLinked to original sources

bioRxiv2024

Individual G-quadruplex targeting with ligand-functionalized CRISPR-Cas9 uncovers transcriptional-dependent functional responses

The development of selective ligands to target DNA G-quadruplexes (G4s) and i-motifs (iMs) has revealed their relevance in transcriptional regulation. However, most of these ligands are unable to target individual G4s or iMs in the genome, severely limiting their scope. Herein, we describe a new Approach to Target Exact Nucleic Acid alternative structures (ATENA) that relies on the chemical conjugation of established G4 and iM ligands to a catalytically inactive Cas9 protein (dCas9), enabling their individual targeting in living cells. ATENA demonstrated that the selective targeting of the G4 present in the oncogene c-MYC leads to the suppression of transcripts regulated exclusively by one of its promoters (P1). Conversely, targeting the c-MYC iMs on the opposite strand leads to the selective increase of P1-driven transcripts. ATENA revealed that G4-mediated transcriptional responses are highly ligand-specific, with different ligands eliciting markedly different effects at the same G4-site. We further demonstrated that the basal expression levels of the gene targeted can be used to predict the transcriptional impact associated with G4-stabilization. Our study provides an innovative platform to investigate G4- and iM-biology with high precision and unveils the therapeutic relevance of individual DNA structures with unprecedented selectivity.

cell biology↗

bioRxiv2024

Machine learning-augmented molecular dynamics simulations (MD) reveal insights into the disconnect between affinity and activation of ZTPriboswitch ligands

The challenge of targeting RNA with small molecules necessitates a better understanding of RNA-ligand interaction mechanisms. However, the dynamic nature of nucleic acids, their ligand-induced stabilization, and how conformational changes influence gene expression pose significant difficulties for experimental investigation. This work employs a combination of computational and experimental methods to address these challenges. By integrating structure-informed design, crystallography, and machine learning-augmented all-atom molecular dynamics simulations (MD) we synthesized, biophysically and biochemically characterized, and studied the dissociation of a library of small molecule activators of the ZTP riboswitch, a ligand-binding RNA motif that regulates bacterial gene expression. We uncovered key interaction mechanisms, revealing valuable insights into the role of ligand binding kinetics on riboswitch activation. Further, we established that ligand on-rates determine activation potency as opposed to binding affinity and elucidated RNA structural differences, which provide mechanistic insights into the interplay of RNA structure on riboswitch activation.

biochemistry↗

bioRxiv2022

Machine Learning Informs RNA-Binding Chemical Space

Small molecule targeting of RNA has emerged as a new frontier in medicinal chemistry, but compared to the protein targeting literature our understanding of chemical matter that binds to RNA is limited. In this study, we report Repository Of BInders to Nucleic acids (ROBIN), a new library of nucleic acid binders identified by small molecule microarray (SMM) screening. The complete results of 36 individual nucleic acid SMM screens against a library of 24,572 small molecules are reported (including a total of 1,627,072 interactions assayed). A set of 2,003 RNA-binding small molecules is identified, representing the largest fully public, experimentally derived library of its kind to date. Machine learning is used to develop highly predictive and interpretable models to characterize RNA-binding molecules. This work demonstrates that machine learning algorithms applied to experimentally derived sets of RNA binders are a powerful method to inform RNA-targeted chemical space.

biochemistry↗