# Combinatorial matrix-mimetic recombinant proteins as engineered nerve guidance conduits

> **NIH NIH R21** · STANFORD UNIVERSITY · 2020 · $436,238

## Abstract

ABSTRACT
Over 500,000 Americans suffer from peripheral nerve injury (PNI), and despite surgical interventions, most suffer
permanent loss of motor function and sensation. Current clinical options for long nerve gap PNI include naturally-
derived grafts, which provide native matrix cues to regenerate neurons but suffer from very limited supply and
batch-to-batch variability, or synthetic nerve guidance conduits (NGCs), which are easy to manufacture but often
fail due to lack of regenerative cues. The main challenge with using any NGC for treatment of PNI is the immense
trade-off between providing the complex matrix cues necessary for optimal nerve regeneration while providing a
conduit that is readily available, reproducible, and easily fabricated. To overcome this challenge, we propose an
entirely new type of biomaterial: a computationally optimized, protein-engineered recombinant NGC (rNGC). This
rNGC combines the reliability of synthetic NGCs with the presentation of multiple regenerative matrix cues of
natural NGCs. Because current understanding of cell-matrix interactions is insufficient to enable to direct design
of a fully functional rNGC, we hypothesize that the use of machine learning, computational optimization
methods will allow identification of an rNGC that promotes nerve regeneration similar to the current gold
standard autograft. We utilize a family of protein-engineered, elastin-like proteins (ELPs) that are reproducible,
with predictable, consistent material properties, and fully chemically defined for streamlined FDA approval. Due
to ELPs’ modular design, they have biomechanical (i.e. matrix stiffness) and biochemical (i.e. cell-adhesive
ligand) properties that are independently tunable over a broad range. While numerous studies detail the effects
of individual biomechanical or biochemical matrix cues on neurite outgrowth using single-variable approaches,
their combinatorial effects have been largely unexplored as insufficient knowledge exists to make accurate
predictions of their interactions a priori. This fundamentally prohibits the direct design of combinatorial matrix
cues. We hypothesize that optimized presentation of biomechanical and biochemical cues will create a
microenvironment that better mimics the native ECM milieu, resulting in synergistic ligand cross-talk to improve
nerve regeneration. In Aim 1, we use computational optimization methods to identify the combination of ligand
identities, ligand concentrations, and matrix stiffness that best enhances neurite outgrowth. We will develop and
characterize a library of ELP variants with distinct cell-adhesive ligands derived from native ECM, and assess
their ability to support neurite outgrowth from rat dorsal root ganglia (DRG). In Aim 2, we will validate our in vitro
optimization results in a preclinical, rat sciatic nerve injury model. A core-shell, ELP-based rNGC with an inner
core matrix of the optimized ELP formulation from Aim 1 will be fabricated and evaluated fo...

## Key facts

- **NIH application ID:** 9872885
- **Project number:** 1R21NS114549-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Paul George
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $436,238
- **Award type:** 1
- **Project period:** 2020-01-01 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9872885

## Citation

> US National Institutes of Health, RePORTER application 9872885, Combinatorial matrix-mimetic recombinant proteins as engineered nerve guidance conduits (1R21NS114549-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9872885. Licensed CC0.

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