# Leveraging Next-Generation Directed Evolution Platforms and Chemical Control of Proteostasis to Deliver Robust Biotechnologies and Illuminate Roles of Chaperone Networks in Protein Evolution

> **NIH NIH R35** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2024 · $22,427

## Abstract

Directed evolution mimics and accelerates natural evolution in the laboratory in order to create useful
new biomolecules and to study evolutionary processes. Although methodologies for directed evolution are well-
established in test tubes and in simple organisms like E. coli and yeast, there is still a major challenge. Specifi-
cally, novel biomolecules derived from directed evolution campaigns in these platforms often fail to function
when transferred to more complex cellular environments, such as that of human cells. To address this critical
issue, our laboratory recently pioneered a directed evolution platform that can be used to repeatedly generate
massive libraries of mutant biomolecules while continuously selecting and enriching the most functional vari-
ants directly in the human cell environment. From a chemical biology perspective, we are also deeply engaged
in studying functions of the proteostasis network – a vital and unique aspect of the human cellular environment
that ensures proteins are correctly folded, processed and trafficked. We have developed an array of chemical
genetic tools to modulate proteostasis, and we are now primed to integrate these tools with our directed evolu-
tion platform to both evolve previously inaccessible biomolecule functions and gain a deeper understanding of
how cells solve protein folding problems.
 Altogether, this NIGMS MIRA application seeks to combine two of my laboratory's primary interests: (1)
Developing and applying next-generation, human cell-based directed evolution platforms to generate biomole-
cules optimized for function in complex cells and (2) Integrating evolution with chemical modulation of proteo-
stasis to gain new insights into fundamental principles of proteostasis network function. Here, we propose to
integrate these research areas to deliver an array of biomolecules that reliably and robustly perform valuable
new functions in the complex human cellular milieu. Examples include G-protein coupled receptors controlled
by synthetic regulators for neuroscience applications, systems for incorporation of unnatural amino acids in
proteins, and inhibitors of important signaling pathways related to disease. All of these targets have proven ex-
ceedingly difficult to reliably evolve in lower organisms or test tubes. Beyond these practical advances, we will
also integrate human cell-based directed evolution with proteostasis modulation to gain insights into how the
network solves protein folding problems. For example, we will use our capacity to modulate proteostasis to test
the hypothesis that chaperones can be used to “turbo-charge” directed evolution campaigns by providing ac-
cess to otherwise biophysically unacceptable regions of the mutational landscape. Further, we will pursue an
understanding of the roles of chaperones in human protein evolution, a process that is particularly important in
the setting of tumorigenesis and in the development of drug resistance in oncogenes. Altogeth...

## Key facts

- **NIH application ID:** 11064239
- **Project number:** 3R35GM136354-05S1
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Matthew Donald Shoulders
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $22,427
- **Award type:** 3
- **Project period:** 2020-06-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11064239

## Citation

> US National Institutes of Health, RePORTER application 11064239, Leveraging Next-Generation Directed Evolution Platforms and Chemical Control of Proteostasis to Deliver Robust Biotechnologies and Illuminate Roles of Chaperone Networks in Protein Evolution (3R35GM136354-05S1). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/11064239. Licensed CC0.

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