# Models to Predict Protein Biomaterial Performance

> **NIH NIH U01** · TUFTS UNIVERSITY MEDFORD · 2020 · $536,409

## Abstract

Summary
Developing fundamental tools and insight into biomaterial designs for predictive functional outcomes remains
critical for the field. In our current U01 grant we have made significant progress in many areas related to this
need, starting from the overarching goal of integrating experimental-modeling-processing approaches into a
synergistic approach for protein biomaterial design, as viewed from a hierarchical perspective and with a focus
on mechanical outcomes. This approach has resulted in specific insights into the role of molecular weight,
domain sizes and distributions, hydrophobic/hydrophilic partitioning and charged termini, on protein polymer
assembly and the resulting properties, all informed via this integrated modeling-experimental feedback loop.
These insights provide the foundation upon which we plan to build in this renewal application. The key features
of our proposed approach remain to integrate modeling and experimentation at multiple scales to reach
enhanced, predictive material functions out of simple protein building blocks though the exploitation of the
accessibility and control afforded by genetically-encoded protein polymer designs. Our hypothesis in this
renewal proposal is that predictions of biomaterial performance can be attained by the combined use of
suitable experimental models to cover polymer features (chemistry, molecular weight, sequence control),
processing (to modulate hierarchical structures) and modeling at different length scales of materials structural
hierarchy (from nano- to macroscopic scales). Our goal is to further develop predictive assessments of
biomaterials to save time, animals and costs, while accelerating translation of such biomaterials for repair and
regenerative systems. In the renewal we will specifically focus on the use of modeling tools to further design
and optimize protein-based materials to achieve specific functional outcomes (Aim 1), develop dynamic,
shape-changing, protein materials (Aim 2), and address biomaterial interfaces with respect to mineralization of
protein biomaterials (Aim 3). In total, these three aims build off of our progress in the current grant, but move
the tools, experimental approaches and predictive capabilities to a new set of biomaterial challenges.
Importantly, we also have established a strong, interdisciplinary team under the current grant to continue to
foster this planned new insight, outcomes and contributions to the broader needs in the field of biomaterials.

## Key facts

- **NIH application ID:** 9955252
- **Project number:** 5U01EB014976-09
- **Recipient organization:** TUFTS UNIVERSITY MEDFORD
- **Principal Investigator:** Markus J. Buehler
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $536,409
- **Award type:** 5
- **Project period:** 2012-06-10 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9955252

## Citation

> US National Institutes of Health, RePORTER application 9955252, Models to Predict Protein Biomaterial Performance (5U01EB014976-09). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/9955252. Licensed CC0.

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