Empirically testing the accuracy and bias of ancestral protein resurrection methods

NIH RePORTER · NIH · R01 · $345,313 · view on reporter.nih.gov ↗

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
10019575
Project number
5R01GM132499-02
Recipient
BRANDEIS UNIVERSITY
Principal Investigator
Douglas Lowell Theobald
Activity code
R01
Funding institute
NIH
Fiscal year
2020
Award amount
$345,313
Award type
5
Project period
2019-09-17 → 2023-08-31