About

C. Denise Okafor is an Assistant Professor of Biochemistry and Molecular Biology, and of Chemistry at Penn State's Eberly College of Science. She received her B.S. in biomedical chemistry from Oral Roberts University, followed by an M.S. in chemistry and a Ph.D. in biochemistry from Georgia Institute of Technology. Her dissertation research focused on the metallobiochemistry of RNA, investigating RNA folding and function as mediated by divalent cations magnesium and iron. Her postdoctoral research at Emory University, in the Ortlund lab, concentrated on nuclear receptors, a family of ligand-regulated transcription factors, using molecular dynamics simulations to explore ligand activation mechanisms. She was also an NIH-IRACDA postdoctoral fellow, during which she taught at Morehouse and Spelman colleges in Atlanta. Her research investigates the structural mechanisms of signaling and regulation in protein complexes, employing molecular dynamics simulations alongside biochemical and structural techniques. Her work aims to elucidate how conformational dynamics of proteins are altered in different functional states, with a particular focus on nuclear receptors due to their complex allosteric regulatory mechanisms and critical roles in metabolism, development, and reproduction. Her goal is to understand endogenous protein regulation to identify strategies for selective modulation of protein function.

Research topics

• Chemistry
• Computational biology
• Cell biology
• Biochemistry
• Data Mining
• Computer Science
• Genetics
• Biology
• Biophysics
• Algorithm
• Statistical physics
• Physics
• Computational chemistry
• Mathematics

Selected publications

• Impact of Replicas and Simulation Length on <i>In Silico</i> Behaviors of a Protein Domain

  ChemPhysChem · 2024 · 1 citations

  Senior authorCorresponding
  • Computer Science
  • Data Mining
  • Computer Science

  Molecular dynamics (MD) simulations are immensely valuable for studying protein structure, function and dynamics. Their ability to capture atomic-level behavior of molecules and describe their evolution over time makes it a powerful synergistic tool for biochemistry, structural biology and other life sciences. To advance research and knowledge on reasonable timescales, researchers must optimize the amount of useful information extracted from simulation data while often frugally managing computational resources. Often, this involves balancing the length of MD trajectories with the number of replicas of a given system, with the aim of maximizing sampling of the conformational landscape. However, identifying this balance is not always intuitive, and the lack of standards among researchers can produce large variability in results and predictions from MD measurements. Here, we investigate the variability in MD results when simulation length and replica numbers are varied. Using a 231-amino acid domain, we compare measurements from independent trajectories to a benchmark trajectory of 3, 1000-ns replicates. We perform these simulations on 27 protein-ligand complexes, allowing us to compare ligand-specific rankings of complexes across independent replicas. Our results reveal that some MD measurements are accurately ranked by single trajectories, while others are not. We uncover similar variability in the effects of trajectory lengths on measurements. Our findings suggest that a one-size-fits-all approach to MD simulations is not necessarily the best approach, and depending on the intended measurements and research question, it may be advantageous sometimes to prioritize longer trajectories over multiple replicas. This work provides important considerations for researchers while designing simulation studies.

  DOI

• Structural and Biophysical Mechanisms Driving Differential Hormone Response in Steroid Receptors

  The FASEB Journal · 2022

  Senior authorCorresponding
  • Chemistry
  • Biology
  • Cell biology

  Steroid receptors (SRs) belong to the superfamily of ligand‐activated transcription factors that regulate a myriad of biological processes upon binding of cholesterol‐derived hormones. A poorly understood aspect of this transcriptional regulation is how diverse steroid hormones modulate the ligand binding domain (LBD) of steroid receptors to achieve selective transcriptional outcomes. Here we investigate hormone specificity in the ancestral steroid receptor 2 (ancSR2) which is transcriptionally activated by 3‐ketosteroid hormones and unresponsive to 3‐hydroxylated, A‐ring aromatized steroids (i.e., estrogens). Previous work uncovered evolutionarily conserved ligand sensing‐residues in the binding pocket of LBD which also enables hormone discrimination. Specifically, the L42 (Helix3)‐M75 (Helix5) interaction was predicted to enable discrimination between 3‐ketosteroids and estrogens. To further probe the role of these ligand sensors in mediating differential hormone responses, we mutated M75 to modulate the Helix3‐Helix5 interaction and determined how transcriptional function, protein structure and dynamics are impacted. Specifically, we have combined site‐directed mutagenesis with biophysical experiments, cell‐based assays, and molecular dynamics (MD) simulations. While our secondary structure measurements reveal minor impacts on structure, MD simulations reveal that M75 mutations modulate the status of the H3‐H5 interaction, subsequently affecting ancSR2 conformations in a hormone‐dependent manner. Additionally, hydrogen deuterium exchange‐mass spectrometry (HDX‐MS) measurements reveal conformational effects at regions distant from the mutation site that may modulate ancSR2 inter‐residue interaction networks for differential hormone response. The differential hormone response is supported in luciferase reporter assays as well as ligand binding assays. These studies will provide biophysical and structural insight into how steroid receptors achieve hormone‐specific transcriptional responses.

  PublisherDOI

• Bile acids and the gut microbiota: metabolic interactions and impacts on disease

  Nature Reviews Microbiology · 2022 · 1016 citations

  • Biology
  • Biochemistry
  • Microbiology
  PublisherDOI

• Ligand Specificity in Progesterone Receptor

  The FASEB Journal · 2022

  Senior authorCorresponding
  • Chemistry
  • Biochemistry
  • Cell biology

  Progesterone receptor, nuclear receptor subfamily 3, group C, member 3 (PR, NR3C3) is a member of the steroid receptor subfamily of nuclear receptors (NR3), i.e. ligand‐regulated transcription factors. PR regulates multiple biological processes in response to binding of steroid‐derived hormones. PR is promiscuously activated by various steroid hormones, including estrogens, androgens, and corticosteroids. Currently, it is not understood how steroid hormones differentially modulate the ligand binding domain (LBD) of PR to achieve various transcriptional outcomes. Here, we use a computational approach to investigate how dynamics of PR are altered by ligand binding to achieve distinct transcriptional properties. Previous studies on the PR LBD revealed that residues in helices 3, 5 and 12 are functionally conserved in nuclear receptors and play important roles in stabilizing agonistic conformations. To probe the roles of these residues in discriminating between steroid hormones, we performed long MD simulations, using a library of 34 steroidal ligands with EC50 values ranging from inactive to 1E‐02 nM. To investigate ligand‐specific allosteric signaling, we investigated communication pathways between the ligand binding pocket and key regulatory surfaces on the PR LBD surface to determine the specificity with which ligands modulate allosteric coupling between two sites. Our weighted dynamic network analysis revealed that the active and inactive steroids modulated key regions of PR distinctly. Additional analysis of communication paths allowed us to further categorize agonists into three groups, consistent with their potency of activation. We used in silico mutagenesis to investigate roles for specific residues in achieving ligand‐selective PR signaling. Combined, the results of the study provide structural and dynamic insight into how PR achieves ligand specificity. Further, these studies can illuminate strategies for the design of novel PR ligands for therapeutical uses.

  DOI

Frequent coauthors

• Riley K Eisert-Sasse

  Pennsylvania State University

  2 shared

• Tracy Yu

  Pennsylvania State University

  2 shared

• Sabab Hasan Khan

  Pennsylvania State University

  2 shared

• Kesaban Sankar Roy Choudhuri

  Pennsylvania State University

  1 shared

• Nathan T. Jui

  Emory University

  1 shared

• Autumn R. Flynn

  Emory University

  1 shared

• Sean M. Braet

  Pennsylvania State University

  1 shared

• Elizabeth Elacqua

  Pennsylvania State University

  1 shared

Labs

• C. Denise Okafor LaboratoryPI

Awards & honors

• Dorothy Foehr Huck and J. Lloyd Huck Early Career Chair in B…
• Cottrell Scholars Award (2024)
• Marion Milligan Mason Award (2023)
• NIH Director's New Innovator Award (2022)
• NSF CAREER Award (2022)

Similar researchers at Pennsylvania State University

• Long-Qing Chenh-150
• Qin Wangh-123
• Richard Alleyh-120
• William H. Bruneh-99
• Ayusman Senh-96