# Empirically testing the accuracy and bias of ancestral protein resurrection methods

> **NIH NIH R01** · BRANDEIS UNIVERSITY · 2022 · $345,313

## Abstract

Project Summary/Abstract
Ancestral sequence reconstruction (ASR) methods have now become widely used to experimentally analyze the
properties of ancient biomolecules and to elucidate the mechanisms of molecular evolution. By recapitulating
the structural, mechanistic, and functional changes of proteins during their evolution, ASR has been able to
address many fundamental and challenging evolutionary questions where more traditional methods have failed.
ASR methodology has also been highly successful in addressing biophysical problems in modern proteins, and
it has recently drawn attention for its practical applications in protein bioengineering and design.
Despite these advances, the accuracy, precision, and bias of resurrected ancestral sequences is currently
unknown. Are the most probable ancestral sequences systematically biased to have anomalous biophysical
properties? How well do the biochemical properties of resurrected proteins recapitulate the properties of the
true ancestral biomolecules? Which evolutionary models provide the most accurate ancestral reconstructions?
To give one well-known example of a potential bias, ancestrally resurrected proteins are often much more
thermostable than their modern descendants, and it is currently controversial whether the observed high
thermostability is a bona fide property of ancient proteins or rather is a methodological artifact. These
questions are extremely difficult to answer definitively because the real ancestral proteins are generally lost to
history, but we aim to provide experimental answers. This proposal will develop experimental methods for (1)
evaluating the systematic bias in ancestral resurrections, (2) assessing the accuracy of reconstructed protein
properties by comparison with the properties of the actual proteins, (3) validating competing evolutionary
models for ASR analyses, and (4) high throughput analysis of ancestral posterior distributions for evolutionary
studies and protein engineering.

## Key facts

- **NIH application ID:** 10470385
- **Project number:** 5R01GM132499-04
- **Recipient organization:** BRANDEIS UNIVERSITY
- **Principal Investigator:** Douglas Lowell Theobald
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $345,313
- **Award type:** 5
- **Project period:** 2019-09-17 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10470385

## Citation

> US National Institutes of Health, RePORTER application 10470385, Empirically testing the accuracy and bias of ancestral protein resurrection methods (5R01GM132499-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10470385. Licensed CC0.

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