Postdoctoral Fellowship: PRFB: Adaptive costs of multifunctionality in rhodopsins

This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2025. The fellowship supports research and training of the fellow that will contribute to biology in innovative ways. This research seeks to understand the “cost of complexity,” how multiple functions of a gene restrict the gene’s ability to adapt to changing environmental conditions.

This project uses rhodopsins from cyanobacteria (bacteria that harness energy from sunlight) as models of multifunctional genes. Rhodopsin proteins absorb specific colors of light and use the associated energy to pump protons out of the cell as a part of their energy harvesting mechanism. This research hypothesizes that rhodopsins that are forced to maintain their proton-pumping function will be less capable to adapt to absorbing alternative colors of light than rhodopsins that are allowed to lose proton-pumping functionality.

Better understanding of this evolutionary principle will bolster scientists’ ability to predict how species will adapt to changing environments. Furthermore, the color-adapted rhodopsins produced in this project will enhance cyanobacteria-based biofuel production by capturing a broader range of light. The fellow will participate in community science outreach to help the public understand the impact of scientific research on their lives and mentor undergraduate researchers to help train the next generation of scientists.

To simulate evolution on a laboratory timescale, mutants of Gloeobacter rhodopsin (GR) will be recombinantly expressed in E. coli. While GR naturally absorbs green light, GR mutants will be challenged to export a fluorescent dye (an assay for proton-pumping activity) while exposed to green, orange, or blue light. The bacteria with the least fluorescence—indicating efficient proton-pumping—will be selected using fluorescence-activated cell sorting (FACS).

These high-performing variants will then undergo further mutation and selection through directed evolution to produce green-, orange-, and blue-adapted rhodopsins with high proton-pumping ability. Once these adapted rhodopsins are obtained, the proton-pumping requirement will be relaxed. The selection step of the subsequent directed evolution cycles will be modified to select for the longest or shortest absorbance wavelength, as appropriate.

Populations from every cycle of each directed evolution stage will be sequenced to understand the progression through genotypic space. This correlated genotype-phenotype data will also be used to train a machine learning model to predict functional characteristics of rhodopsins using only previously uncharacterized amino acid sequences. Ultimately, this could enable engineering of cyanobacteria strains capable of harvesting energy efficiently from a wider spectrum of sunlight.

The fellow will receive training in phylogenetics, evolution, machine learning, and zoology, while building on their skills in synthetic biology and protein engineering. The fellow will engage in community science outreach and mentor undergraduate researchers. The research will also contribute to the scientific community by expanding the Visual Physiology Database (VPOD) and the Opsin Phenotyping Tool for Inferring Color Sensitivity (OPTICS).

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.