S100A9 interacts with a dynamic region on CD14 to activate Toll-like receptor 4.

S100A9 is a Damage Associated Molecular Pattern (DAMP) that activates inflammatory pathways via Toll-like receptor 4 (TLR4). This activity plays important homeostatic roles in tissue repair, but can also contribute to inflammatory diseases. The mechanism of activation is unknown. Here, we follow up on a previous observation that the protein CD14 is an important co-receptor that enables S100A9 to activate TLR4. Using cell-based functional assays and a combination of mutations and pharmocological perturbations, we found that CD14 must be membrane bound to potentiate TLR4 activation by S100A9. Additionally, S100A9 is sensitive to inhibitors of pathways downstream of TLR4 internalization. Together, this suggests that S100A9 induces activity via CD14-dependent internalization of TLR4. We then used mutagenesis, structural modeling, and in vitro binding experiments to establish that S100A9 binds to CD14's N-terminus in a region that overlaps with, but is not identical to, the region where CD14 binds its canonical ligand, lipopolysaccharide (LPS). In molecular dynamics simulations, this region of the protein is dynamic, allowing it to reorganize to recognize both S100A9 (a soluble protein) and LPS (a small hydrophobic molecule). Our work is the first attempt at a molecular characterization of the S100A9/CD14 interaction, bringing us one step closer to unraveling the full mechanism by which S100A9 activates TLR4/MD-2.

Ancestral Reconstruction and the Evolution of Protein Energy Landscapes.

A protein's sequence determines its conformational energy landscape. This, in turn, determines the protein's function. Understanding the evolution of new protein functions therefore requires understanding how mutations alter the protein energy landscape. Ancestral sequence reconstruction (ASR) has proven a valuable tool for tackling this problem. In ASR, one phylogenetically infers the sequences of ancient proteins, allowing characterization of their properties. When coupled to biophysical, biochemical, and functional characterization, ASR can reveal how historical mutations altered the energy landscape of ancient proteins, allowing the evolution of enzyme activity, altered conformations, binding specificity, oligomerization, and many other protein features. In this article, we review how ASR studies have been used to dissect the evolution of energy landscapes. We also discuss ASR studies that reveal how energy landscapes have shaped protein evolution. Finally, we propose that thinking about evolution from the perspective of an energy landscape can improve how we approach and interpret ASR studies. Expected final online publication date for the Annual Review of Biophysics, Volume 53 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.