JOHNS HOPKINS UNIVERSITYEST. 1876

America’s First Research University
Kim Reynolds

Kim Reynolds

Associate Research Professor

Contact Information

Research Interests: High-throughput assays of protein function, bacterial functional genomics and metabolism. Molecular evolution and modeling of fitness landscapes. Design of synthetic proteins, allosteric regulation, and small cellular systems.

Education: PhD, University of California, Berkeley

Program in Molecular Biophysics > Kim Reynolds

Kim Reynolds grew up in San Antonio, Texas and attended Rice University where she obtained a BA in Biochemistry. She received her Ph.D. in Biophysics from UC Berkeley in 2006. In her thesis work, Kim used physics-based computational protein design to engineer protein-protein interfaces with Dr. Tracy Handel.

As a postdoctoral fellow, Kim worked with Dr. Ruben Abagyan at The Scripps Research Institute modeling the differential conformational changes associated with GPCR agonist and antagonist binding. She then worked with Dr. Rama Ranganathan at UT Southwestern. There she studied statistical models of protein sequence, and showed that conserved sites on a protein surface act as allosteric hotspots. She established her independent research group at UT Southwestern in 2014. She joins the Jenkins Department of Biophysics in 2025.

The broad goal of the Reynolds lab is to quantify and model how variation in protein sequence, abundance, and activity interact with genetic background and environment to influence phenotype. We work on this problem primarily in bacteria, a system where we can make many perturbations (mutations, genetic knockdowns/knockouts) and quantify the impact on phenotype in high throughput. We use this experimental information to predict the effect of mutations associated with drug resistance and disease, construct fitness landscapes, and design synthetic protein systems that function appropriately in the interaction-rich context of the cell. Our work combines classic approaches from in vitro biochemistry and biophysics with high-throughput functional assays and machine learning.

  1. W. McCormick, J.C. Dinan, M.A.X. Russo, K.A Reynolds (2024). Local disorder is associated with enhanced catalysis in an engineered photoswitch. [preprint] bioRxiv doi: 10.1101/2024.11.26.625553
  2. Zhao, T.P. Wytock, K.A. Reynolds, A.E. Motter (2024). Irreversibility in bacterial regulatory networks. Science Advances (10):eado3232.
  3. N. Nguyen, C. Ingle, S. Thompson, K.A. Reynolds (2024). The genetic landscape of a metabolic interaction. Nature Communications (15):3351.
  4. M. Otto, A. Turska-Nowak, P.M. Brown, K.A. Reynolds (2024). A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment. Cell Systems (15):134-148.e7
  5. C. Dinan, J.W. McCormick, K.A. Reynolds (2023). Engineering proteins using statistical models of coevolutionary sequence information. Perspectives on Machine Learning for Protein Science and Engineering, Cold Spring Harb Perspect Biol. (Ed. Peter K. Koo, Christian Dallago, Ananthan Nambiar, and Kevin K. Yang.) (16):a041463.
  6. McCormick, M.A.X. Russo, S. Thompson, A. Blevins, K.A. Reynolds (2021). Structurally distributed surface sites tune allosteric regulation. Elife (10):e68346.
  7. A. Reynolds, E. Rosa-Molinar, R.E. Ward, H. Zhang, B.R. Urbanowicz, A.M. Settles (2021). Accelerating biological insight for understudied genes. Integr Comp Biol (icab029).
  8. D. Mathis, R.M. Otto, K.A. Reynolds (2021). A simplified strategy for titrating gene expression reveals new relationships between genotype, environment, and bacterial growth. Nucleic Acids Research (49):e6.
  9. Thompson, Y. Zhang, C. Ingle, K.A. Reynolds, and T. Kortemme (2020). Altered expression of a quality control protease in E. coli reshapes the in vivo mutational landscape of a model enzyme. Elife (9):e53476.
  10. W. McCormick, D. Pincus, O. Resnekov, and K.A. Reynolds (2019). Strategies for engineering and rewiring kinase regulation. Trends Biochem Sci (19):30234.
  11. F.Schober, A.D. Mathis, C.Ingle, J.O. Park, L. Chen, J.D. Rabinowitz, I. Junier, O. Rivoire, and K.A. Reynolds (2019). A two-enzyme adaptive unit within bacterial folate metabolism. Cell Reports (27):3359.
  12. T. Tamer, I.K. Gaszek, H. Abdizadeh, T. Batur, K. Reynolds, A.R. Atilgan, C. Atilgan, E. Toprak (2019). High-order epistasis in catalytic power of dihydrofolate reductase gives rise to a rugged fitness landscape in the presence of trimethoprim selection. Molecular Biology and Evolution (36):1533.
  13. Pincus, J. Pandey, Z.A. Feder, P. Creixell, O. Resnekov, and K.A. Reynolds (2018). Engineering allosteric regulation in protein kinases. Science Signaling (11):555.
  14. Rosensweig, K.A. Reynolds, P. Gao, Y. Shan, R. Ranganathan, J.S. Takahashi, C.B Green (2018). An evolutionary hotspot defines functional differences between CRYPTOCHROMES. Nature Communications (9):1138.
  15. Narayanan, D. Gagné, K.A. Reynolds, N. Doucet (2017). Conserved amino acid networks modulate discrete functional properties in an enzyme superfamily. Scientific Reports (7):3207.
  16. Pincus, O. Resnekov, K.A. Reynolds (2017). An evolution-based strategy for engineering allosteric regulation. Physical Biology (14): 025002.
  17. Rivoire, K.A. Reynolds, R. Ranganathan (2016). Evolution-based functional decomposition of proteins. PLoS Comp Biol (12): e1004817.
  18. A. Reynolds, W. P. Russ, M. Socolich, R. Ranganathan (2013). Evolution based design of proteins. Methods in Enzymology (523):213.
  19. A. Reynolds, R.N.McLaughlin, R. Ranganathan (2011). Hotspots for allosteric regulation on protein surfaces. Cell(147):1564.