Project Description

The role of the cell membrane is to maintain the difference in equilibrium between the outside and inside of the cell. Cells have evolved transporter proteins to move molecules from the outside to the inside and from inside to the outside. As our ability to use synthetic biology to produce molecules of interest using cellular mechanisms grows, the demand for transporters that can import substrates, export products, or toxic intermediates also grows. The current method screens transporter libraries to identify an efflux pump with the desired properties. Transporters that efflux molecules from inside the cell to the outside have been studied and are of various types. We propose to expand the knowledge of active transporters (efflux pumps, specifically ABC-type transporters), use the knowledge to generate machine learning-based approaches to understand atomistic determinants of specificity, use these approaches to design transporters for desired molecules, and test the design using biochemical and biophysical tools. The expected outcome will be a collection of transporters classified by the molecules they can transport and a validated algorithm capable of designing engineered efflux pumps for molecules from synthetic biology applications.

Start Date

Flexible- Around or after January 2025 

Post Doc Qualifications

Required qualification & qualities:
- Interest in experimental synthetic biology and computational data science algorithms
- PhD in Computer Science, Chemical Engineering, Biological Engineering, Biochemistry, Biology, Chemistry, and related fields

Desired qualities & qualifications:
- Ability to curate large datasets for data science applications
- Ability to develop machine learning models
- Expertise in molecular biology tools used for protein expression in E.coli and other workhorse organisms in synthetic biology
- Assay development to assess function of transporters towards novel substrates

Co-Advisors

Karthik Sankaranarayanan (ksankara@purdue.edu; Agricultural and Biological Engineering, https://www.ksankargroup.com/)
Ramaswamy Subramanian (subram68@purdue.edu; Biological Sciences, Biomedical Engineering; https://www.bio.purdue.edu/lab/rams/) 

Bibliography

(1) Sankaranarayanan, K.; Heid, E.; W. Coley, C.; Verma, D.; H. Green, W.; F. Jensen, K. Similarity Based Enzymatic Retrosynthesis. Chemical Science 2022, 13 (20), 6039–6053. https://doi.org/10.1039/D2SC01588A.
(2) Morgat, A.; Lombardot, T.; Coudert, E.; Axelsen, K.; Neto, T. B.; Gehant, S.; Bansal, P.; Bolleman, J.; Gasteiger, E.; de Castro, E.; Baratin, D.; Pozzato, M.; Xenarios, I.; Poux, S.; Redaschi, N.; Bridge, A.; UniProt Consortium. Enzyme Annotation in UniProtKB Using Rhea. Bioinformatics 2020, 36 (6), 1896–1901. https://doi.org/10.1093/bioinformatics/btz817.
(3) KimSoomi, BajajTeena, ChabonCole, TablanteEric, KulchinskayaTatyana, Seok M, et al. Meta-Analysis of the Expansion in the Field of Structural Biology of ABC Transporters. BioDesign Research. 2022. doi:10.34133/2022/9806979
(4) Teichmann L, Chen C, Hoffmann T, Smits SHJ, Schmitt L, Bremer E. From substrate specificity to promiscuity: hybrid ABC transporters for osmoprotectants. Molecular Microbiology. 2017;104: 761–780. doi:10.1111/mmi.13660
(5) Dhanabalan K, Cheng Y, Thach T, Subramanian R. Many locks to one key: N -acetylneuraminic acid binding to proteins. IUCrJ. 2024;11. doi:10.1107/S2052252524005360