Research

The research in our group sits at the intersection of biophysics, chemical biology, and immunology, guided by a mission: to make molecular recognition understandable and engineerable. We view protein specificity not as a static one-to-one interaction, but as a dynamic, network-level property shaped by the full landscape of potential partners and by a protein’s chemical state. To reveal the rules behind this complexity, we combine experimental mapping with computational modeling to learn how interaction networks emerge, evolve, and change. In parallel, we develop synthetic and chemical tools to precisely control protein modification states so we can determine what drives function and tune recognition with precision. By integrating principles with control, we aim to reprogram biological responses, including network-aware molecular switches and engineered immune proteins, with the long-term goal of enabling more selective and safer protein therapeutics.

We study how proteins recognize the right partners in complex molecular environments. Protein interactions are shaped over time by coevolution, as binding partners adapt to each other within crowded interaction networks. True specificity cannot be defined from a single interaction; it emerges at the network level, from how a molecule behaves across many potential partners, where cross-reactivity may be beneficial, tolerated, or limiting depending on context. Understanding these network rules is essential for explaining natural signaling and immune recognition, and for engineering interactions in a predictive and programmable way.

  • Experimental mapping of recognition networks
  • Computational modeling to learn predictive rules from interaction data
  • Inferring epistasis and evolutionary paths from coevolution data
  1. Yang A*, Jiang H*, Jude KM*, Akpinaroglu D, Allenspach S*, Li AJ, Bowden J, Perez CP, Liu L, Huang P-S, Kortemme T, Listgarten J#, Garcia KC#. (2026). Structural ontogeny of protein-protein interactions. Science, 391 (6786)
  2. Yang A, Jude KM, Lai B, Minot M, Kocyla AM, Glassman CR, Nishimiya D, Kim YS, Reddy ST, Khan AA, Garcia KC. (2023). Deploying protein-protein interactions through synthetic coevolution and machine learning, Science, 381 (6656)
  3. Jiang H*, Jude KM*, Wu K*, Fallas J, Ueda G, Brunette TJ, Hicks D, Pyles H, Yang A, Carter L, Lamb M, Li X, Levine PM, Steward LJ, Garcia KC#, Baker D#. (2024) De novo design of buttressed loops for sculpting protein functions, Nature Chemical Biology, 1-7
  4. Cao L*, Coventry B*, Goreshnik I, Huang B, Park JS, Jude KM, Marković I, Kadam RU, Verschueren KHG, Verstraete K, Walsh STR, Bennett N, Phal A, Yang A, Kozodoy L, DeWitt M, Picton L, Miller L, Strauch EM, Halabiya S, Hammerson B, Yang W, Benard S, Stewart L, Wilson IA, Ruohola-Baker H, Schlessinger J, Lee S, Savvides SN, Garcia KC, Baker D. (2022). Design of protein-binding proteins from the target structure alone. Nature. 605, 551–560.

Protein function is shaped not only by sequence, but by chemical state. Post-translational modifications expand proteome diversity into distinct proteoforms that tune interactions, localization, stability, and signaling, often through regulatory cues not captured by genomic or transcriptomic readouts. We develop tools to precisely control these modifications, allowing us to directly observe how chemical state rewires protein function while enabling broader engineering approaches that incorporate both natural and non-natural protein modifications.

  • Defining how chemical modifications reshape protein function and interaction behavior.
  • Strategies to install, remove, or mimic protein modifications with precision.
  1. Yang A, Ha S, Ahn J, Kim R, Kim S, Lee Y, Kim J, Söll D#, Lee HY#, Park HS#. (2016). A chemical biology route to site-specific authentic protein modifications, Science, 354(6312), 623-626.
  2. Han S*, Yang A*, Lee S, Lee HW, Park CB#, Park HS#. (2017). Expanding the genetic code of Mus musculus, Nature Communications, Volume 8, Article number 14568.
  3. Yang A, Cho K, Park HS. (2017). Chemical Biology Approaches for Studying Posttranslational Modifications. RNA Biology, September, 1-14.
  4. Ha Y*, Yang A*, Lee S, Kim K, Liew H, Suh YH#, Park HS#, Churchill DG#. (2014) Facile “Stop Codon” Method reveals elevated Neuronal Toxicity by discrete S87p α-Synuclein oligomers, Biochem. Biophys. Res. Commun. 443(3), 1085–1091. 
  5. Ha Y*, Yang A*, Lee S, Kim K, Liew H, Lee SH, Lee JE, Lee HI, Suh YH#, Park HS#, Churchill DG#. (2014) Dopamine and Cu+/2+ can induce oligomerization of α-synuclein in the absence of oxygen: Two types of oligomerization mechanisms for α-synuclein and related cell toxicity studies, J. Neurosci. Res. 92(3), 359–368
  6. Seo GJ, Yang A, Tan B, Kim S, Liang Q, Choi Y, Yuan W, Feng P, Park HS, Jung JU. (2015). Akt Kinase-Mediated Checkpoint of cGAS DNA Sensing Pathway, Cell Reports, 13(2), 440–449. 
  7. Lee S, Oh S, Yang A, Kim J, Söll D, Lee D#, Park HS#. (2013) A Facile Strategy for Selective Phosphoserine Incorporation in Histones, Angew. Chem. Int. Ed. 52(22), 5771-5775 
  8. Kim J*, Seo MH*, Lee S, Cho K, Yang A, Woo K, Kim HS#, Park HS#. (2013) Simple and efficient strategy for site-specific dual labeling of proteins for single-molecule fluorescence resonance energy transfer analysis. Analytical Chemistry, 85, 1468–1474

Cells sense and communicate through protein interactions that shape signaling dynamics and cellular decisions. We connect molecular interaction rules to functional outcomes by linking specificity and affinity, geometry, kinetics, and multivalency to control of signaling and behavior. Our goal is to move beyond optimizing binding and instead engineer interactions and modification states that produce predictable, tunable responses in receptor and immune systems.

  • Translating biophysical and interaction properties into predictable cellular functions.
  • Designing programmable, network-aware molecular switches and protein drugs to develop safer, more precise next-generation therapeutics.
  1. Schlichthaerle T*, Yang A*, Detraux D*, Johnson DE*, Peach CJ, Edman NI, Sniezek C, Williams CA, Arora S, Katiyar N, Chen I, Etemadi A, Favor A, Lee D, Kubo C, Coventry B, Huang B, Gerben S, Ennist N, Milles L, Sankaran B, Kang A, Nguyen H, Bera AK, Negahdari B, Hamazaki N, Schweppe DK, Stewart L, Ruohola-Baker H, Mathieu J, Pattwell SS, Garcia KC, Baker D. Designed NGF mimetics with reduced nociceptive signatures in neurons. bioRxiv [Preprint], 2025.04.14.648806
  2. Yang A*, Jiang H*, Jude KM*, Akpinaroglu D, Allenspach S*, Li AJ, Bowden J, Perez CP, Liu L, Huang P-S, Kortemme T, Listgarten J#, Garcia KC#. (2026). Structural ontogeny of protein-protein interactions. Science, 391 (6786)
  3. Yang A, Jude KM, Lai B, Minot M, Kocyla AM, Glassman CR, Nishimiya D, Kim YS, Reddy ST, Khan AA, Garcia KC. (2023). Deploying protein-protein interactions through synthetic coevolution and machine learning, Science, 381 (6656)
  4. Rodriguez GE*, Zhao Y*, Nishiga Y, Peprah F, Shen J, Abhiraman GC, Ogishi M, Zhang C, Saco J, Waghray D, Serasanambati M, Torres L, Simone BW, Su L, Wilson SC, Yang A, Sun Q, Picton L, Saxton RA, Bhandarkar V, Lee MJ, Andrews E, Jiang H, Obenaus M, Yen M, Atajanova T, Blish CA, Spranger S, Wherry EJ, Kirane A, Ribas A, Raulet DH, Kalbasi A, Dougan SK, Dougan M, Sage J, Garcia KC. Rewiring STAT signaling from the cell surface with Trikine immunotherapeutics. Science (in press)
  5. Cao L*, Coventry B*, Goreshnik I, Huang B, Park JS, Jude KM, Marković I, Kadam RU, Verschueren KHG, Verstraete K, Walsh STR, Bennett N, Phal A, Yang A, Kozodoy L, DeWitt M, Picton L, Miller L, Strauch EM, Halabiya S, Hammerson B, Yang W, Benard S, Stewart L, Wilson IA, Ruohola-Baker H, Schlessinger J, Lee S, Savvides SN, Garcia KC, Baker D. (2022). Design of protein-binding proteins from the target structure alone. Nature. 605, 551–560.