NLM DIR Seminar Schedule
UPCOMING SEMINARS
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July 15, 2025 Noam Rotenberg
Cell phenotypes in the biomedical literature: a systematic analysis and the NLM CellLink text mining corpus
RECENT SEMINARS
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July 3, 2025 Matthew Diller
Using Ontologies to Make Knowledge Computable -
July 1, 2025 Yoshitaka Inoue
Graph-Aware Interpretable Drug Response Prediction and LLM-Driven Multi-Agent Drug-Target Interaction Prediction -
June 10, 2025 Aleksandra Foerster
Interactions at pre-bonding distances and bond formation for open p-shell atoms: a step toward biomolecular interaction modeling using electrostatics -
June 3, 2025 MG Hirsch
Interactions among subclones and immunity controls melanoma progression -
May 29, 2025 Harutyun Sahakyan
In silico evolution of globular protein folds from random sequences
Scheduled Seminars on Feb. 8, 2024
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
Although most proteins adopt a single energetically favorable fold, some proteins have been evolutionarily selected to reversibly interconvert between distinct folds that regulate biological processes or perform different functions. One such fold-switching protein is Escherichia coli RfaH, a member of the only known family of universally conserved transcription factors. RfaH is composed of an N-terminal NGN domain and a C-terminal KOW domain expected to fold into a beta-roll structure. Strikingly, RfaH’s KOW domain adopts an alpha-helical fold bound to the NGN domain in its apo form, but upon binding its target DNA sequence and RNA polymerase, RfaH KOW dissociates from the NGN domain and reversibly switches to the expected beta-roll topology. Previous biophysical measurements indicate that RfaH’s KOW domain is marginally stable and interconverts with a sparsely populated unfolded state with alpha-helical propensity. Although these factors may poise RfaH’s KOW domain to switch folds, the transition between the two distinctly folded states has not been observed. One possible explanation is RfaH’s poor solubility, particularly of its NGN domain, which aggregates at concentrations above 2 uM and hampers biophysical characterization. To circumvent this problem, we used the deep learning tool ProteinMPNN, to design an RfaH-like protein sequence with a soluble NGN domain. We then fine-tuned this sequence to switch folds. In this work, we use circular dichroism and nuclear magnetic resonance to characterize the unique folds assumed by the designed KOW domain. This structural characterization paves the way to biophysically characterize the fold-switching behavior of an RfaH-like protein.