Back

Serpin-Driven Green Camouflage and NIR Fluorescence in Frogs

Ravichandran, V.; Vu, T.; Harvey, G.; Menozzi, L.; Delia, J.; White, W.; Lin, P.; Galatis, E.; Johnsen, S.; Yao, J.; Taboada, C.

2026-02-12 biochemistry
10.64898/2026.02.11.704363 bioRxiv
Show abstract

Animals have evolved multiple strategies to generate optical traits and coloration. While most amphibians rely on a three-dimensional arrangement of chromatophores in the skin, hundreds of arboreal frog species achieve leaf-like green color through a different mechanism involving reduced pigmentation, subcutaneous biological mirrors, and high concentrations of the blood-derived pigment biliverdin. Although biliverdin is rapidly excreted in most vertebrates, hylid and centrolenid frogs can retain it through the binding to a serpin-family protein (BBS). Here we show that BBSs bind biliverdin with high affinity (Kd < 10 nM), comparable to hormone-receptor interactions. This interaction alters biliverdins spectral signature in ways that resemble those of green-leaf pigments. BBSs from different species exhibit distinct biophysical properties, accounting for interspecific differences in color saturation and hue. Unlike most serpins in vivo, BBS of the glassfrog Teratohyla pulverata is naturally cleaved, yielding a highly thermostable, thermodynamically favored protein, without loss of affinity. Using custom-designed hyperspectral photoacoustic tomography (PAT), we demonstrate that BBS is widely distributed throughout the body, contributing to whole-body color and camouflage. Furthermore, we show that BBSs emit near-infrared (NIR) fluorescence (>700 nm) rendering these frogs fluorescent in a spectral region where biological tissues are largely transparent. Together, BBSs shed light on serpin evolution, protein thermostability, amphibian color diversity, and the development of NIR molecular probes.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.