Projects

Design and development of chemically-inducible kinase variants for spatiotemporal control of cellular signaling processes. Protein phosphorylation, mediated by protein kinases, is one of the most widespread regulatory mechanisms in eukaryotes. Inside the cell, protein kinases, phosphatases, and their respective substrates are organized into integrated phosphorylation networks that govern nearly all aspects of cellular physiology. Recently, the Hahn lab described a generalizable method for the construction of rapamycin-inducible kinases based on a universal regulatory domain (uniRap-kinases) (Karginov et al, 2010). The generation of uniRap-kinases involves the integration of a rationally-designed FKBP12-FRB fusion protein adjacent to a highly-conserved glycine loop (G-loop) within the kinase active site. Computational modeling suggests that, in the absence of rapamycin, the conformational flexibility of the FKBP12-FRB regulatory domain disrupts interactions between the G-loop and ATP, rendering the kinase inactive. Binding of rapamycin (or its non-immunosuppressive analog, iRap) stabilizes the regulatory domain, restoring kinase activity. This, coupled with the recent development of a photoactivatable analog of rapamycin (pRap) (Karginov, et al, 2014), allows unprecedented spatiotemporal control of kinase activity inside cells. Importantly, because the G-loop is universally conserved among kinase family members, this approach is likely to be transferable to many other kinases. In support of this notion, uniRap-kinase variants have been created for several key kinases (Karginov, et al, 2014). For instance, uniRap-kinases have been developed for the mitogen-activated protein kinase (MAPK) family member, p38, and focal adhesion kinase (FAK), both of which are involved in cell migration (Dagliyan et al, 2013). As a consequence, the migration patterns of cells expressing these uniRap-kinases can be controlled by light. Moreover, because uniRap-kinases can be activated by irradiation of cell permeable pRap with non-toxic 360 nm light, this system is compatible with most fluorescent protein (FP)-based detection systems commonly used as reporter genes in synthetic biology systems. During the REU project, students will apply integrated synthetic biology strategies involving computational modeling, molecular biology, and biochemical analysis to develop and test novel uniRap-kinase variants.

Mentors. Dr. Robert Newman (Biology), Dr. Gregory Goins (Biology), Dr. Kristin Rhinehardt (Computational Data Science and Engineering), and Dr. Ming Dong (Chemistry)

References.

• Dagliyan O, Shirvanyants D, Karginov AV, Ding F, Fee L, Chandrasekaran SN, Freisinger CM, Smolen GA, Huttenlocher A, Hahn KM, Dokholyan NV. Rational Design of a ligand-controlled protein conformational switch. Proc. Nat. Acad. Sci. USA (2013) 110:6800-4.

• Karginov AV, Ding F, Kota P, Dokholyan NV, Hahn KM. Engineered allosteric activation of kinases in living cells. (2010) Nat. Biotechnol. 28:743-7.

• Karginov AV, Hahn KM, Deiters A. Optochemical activation of kinase function in living cells. Meth. Mol. Biol. (2014) 1148:31-43.

Computational development and modeling of synthetic collagen composites. Composite biomaterials have many uses, from knee replacements to coatings, and require a balance of physical, chemical and mechanical parameters. One commonly used material is collagen, which is a prevalent, triple helical, extracellular matrix (ECM) protein that comprises approximately 25% of the total dry weight of mammals [1]. Collagen has notable biodegradability and biocompatibility but low mechanical strength which could be improved with the addition of materials like chitin, carbon nanotubes (CNTs), fullerenes and calcium phosphate (CaP) components. Natural collagen’s extraction from natural sources is time consuming, sometimes costly, and it is also difficult to render and could prompt undesired biological and pathogenic changes. Nanoscale collagen mimetic peptides (i.e., Synthetic Collagen), without the unwanted biological entities present in the medium, has been shown to mimic the unique properties that are present in natural collagen [2, 3]. While there have been many studies with natural collagen, there is still much to be discovered about its synthetic derivatives. Synthetic collagen has many properties that are similar to natural collagen but the variations and potential customization may be of great interest [4]. Minor changes in the collagen composition and its interactions with other materials will have potentially large impacts on its function and properties. These small-scale interactions are not easily observable using traditional wet lab techniques. However, insights can be gathered using computational means. Studies have been done that utilize molecular dynamics and biomodeling to study synthetic collagen and protein interactions [5-7]. In this research project, students will examine the mechanical and physical properties of synthetic and natural collagen composites using computational methods, such as molecular dynamics, quantum mechanics, molecular docking, and deep learning. With the computing power available, computational studies of these protein composite materials can help to identify interactions and behaviors of these synthetic collagen composites in conjunction with beneficial mechanical and physical properties.

Project Mentor. Dr. Kristen Rhinehardt (Computational Data Science and Engineering)

References.

1. Parenteau-Bareil, R., R. Gauvin, and F. Berthod, Collagen-based biomaterials for tissue engineering applications. Materials, 2010. 3(3): p. 1863-1887.

2. Miranda-Nieves, D. and E.L. Chaikof, Collagen and Elastin Biomaterials for the Fabrication of Engineered Living Tissues. ACS Biomaterials Science & Engineering, 2017. 3(5): p. 694-711.

3. Eldridge, B., et al., ACS Biomaterials Science & Engineering. 2016, American Chemical Society.

4. Kotch, F.W. and R.T. Raines, Self-assembly of synthetic collagen triple helices. Proceedings of the National Academy of Sciences of the United States of America, 2006. 103(9): p. 3028-3033.

5. Rhinehardt, K.L., G. Srinivas, and R.V. Mohan, Molecular Dynamics Simulation Analysis of Anti-MUC1 Aptamer and Mucin 1 Peptide Binding. The Journal of Physical Chemistry B, 2015. 119(22): p. 6571-6583.

6. Rawal, A., K.L. Rhinehardt, and R.V. Mohan, Molecular Dynamics Investigation of Self- Association of Synthetic Collagen and Spider Silk Composite System for Biomaterial Applications. MRS Advances, 2020. 5(16): p. 797- 804.

7. Rawal, A., K.L. Rhinehardt, and R.V. Mohan. Mechanical Behavior of Collagen Mimetic Peptides Under Fraying Deformation via Molecular Dynamics. in ASME International Mechanical Engineering Congress and Exposition. 2019. American Society of Mechanical Engineers.

Semiconductors, Synthetic Biology, and Future Biomanufacturing. Semiconductors enable information technology that affects almost every aspect of the modern world. Scaling, automation, and energy consumption during information storage, processing, and communication are some of the critical challenges faced by the semiconductor industry[5]. Though the performance and capacity of semiconductors continue to increase with concomitant decreases in their energy consumption and cost, the industry is reaching its physical limits. Therefore, the race has started to discover scalable, unconventional materials that complement or replace commonly used semiconductor materials such as silicon. Biological materials efficiently and inexpensively store, process, and communicate the information in living systems. Moreover, data processing and communication between biological systems and semiconductors, e.g., in wearable devices and information-of-things, encourage the merger of bio and semiconductor manufacturing processes.

Dr. Zadegan's lab employs biomolecules to inform the future development of hybrid semiconductor-bio-manufacturing systems. For example, a universal DNA-based digital memory may solve the imminent information storage crisis resulting from the physical and economic limitations of silicon-based digital storage[1-8]. As a result of DNA's theoretical superiority for spatial capacity, energy efficiency, and information durability, DNA digital storage has become a global conversation and an industrial opportunity[10]. However, most proposed DNA memory systems depend on de novo DNA synthesis processes that lack the scalability and sustainability aspects of a viable technology[6,9]. In the Zadegan lab, we are using synthetic biology strategies to create a DNA-based data storage system that will use universal template DNA molecules to store data and satisfy the scalability and environmental sustainability of DNA-based memory large-scale manufacturing. Similar to writing data into universal blank tapes, the proposed technology does not require de novo DNA synthesis since the information is overwritten into the sequence content of the template DNA domains. During their REU project, students will use coding, molecular biology, and biophysics methods to develop DNA-based memory algorithms, induce mutations to DNA templates, and characterize the products.

Mentor. Dr. Reza Zadegan (Department of Nanoengineering)

References.

1. Reinsel, D., Gantz, J. & Rydning, J. The Digitization of the World From Edge to Core. (Retrieved on May 2020 from https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf

2. Hilbert, M. & López, P. The World's Technological Capacity to Store, Communicate, and Compute information. Science 332, 60-65 (2011), doi:10.1126/science.1200970.

3. Markets and Markets. Non-Volatile Memory Market by Type (Flash (NAND, NOR), EEPROM, NVSRAM, Embedded, EPROM, 3D NAND, MRAM/STTMRAM, FRAM, RERAM/CBRAM, 3D XPOINT, NRAM), End-User Industry, and Geography - Global Forecast to 2022. (Retrieved on May 2020 from https://www.marketsandmarkets.com/Market- Reports/non-volatile-memory-market-1371262.html).

4. Zhirnov, V., Zadegan, R., Sandhu, G. S., Church, G. M. & Hughes, W. L. Nucleic Acid Memory. Nature Materials 15, 366- 370 (2016), doi:10.1038/nmat4594.

5. International Technology Roadmap for Semiconductors (Retrieved on May 2020 from https://www.semiconductors.org/clientuploads/Research_Technology/ITRS/2015/0_2015%20ITRS%202.0%20Executive %20Report%20(1).pdf).

6. Zhrinov, V. & Rasic, D. 2018 Semiconductor Synthetic Biology Roadmap. (Retrieved on May 2020 from https://www.src.org/library/publication/p095387/p095387.pdf).

7. Bathe, M., Chrisey, L. A., Herr, D. J. C., Lin, Q., Rasic, D., Woolley, A. T., Zadegan, R. & Zhirnov, V. V. Roadmap on biological pathways for electronic nanofabrication and materials. Nano Futures 3, 012001 (2019), doi:10.1088/2399- 1984/aaf7d5.

8. SemiSynBio Consortium and Roadmap Development. (Retrieved on May 2020 from https://www.src.org/program/grc/semisynbio/semisynbio-consortium-roadmap/).

9. Ceze, L., Nivala, J. & Strauss, K. Molecular digital data storage using DNA. Nature Reviews Genetics 20, 456-466 (2019), doi:10.1038/s41576-019-0125-3.