Hypomutable regions of yeast TFIIB in a unigenic evolution test represent structural domains

Estimating the evidence of selection and the reliability of inference in unigenic evolution

Background

Unigenic evolution is a large-scale mutagenesis experiment used to identify residues that are potentially important for protein function. Both currently-used methods for the analysis of unigenic evolution data analyze 'windows' of contiguous sites, a strategy that increases statistical power but incorrectly assumes that functionally-critical sites are contiguous. In addition, both methods require the questionable assumption of asymptotically-large sample size due to the presumption of approximate normality.

Results

We develop a novel approach, termed the Evidence of Selection (EoS), removing the assumption that functionally important sites are adjacent in sequence and and explicitly modelling the effects of limited sample-size. Precise statistical derivations show that the EoS score can be easily interpreted as an expected log-odds-ratio between two competing hypotheses, namely, the hypothetical presence or absence of functional selection for a given site. Using the EoS score, we then develop selection criteria by which functionally-important yet non-adjacent sites can be identified. An approximate power analysis is also developed to estimate the reliability of inference given the data. We validate and demonstrate the the practical utility of our method by analysis of the homing endonuclease I-Bmol, comparing our predictions with the results of existing methods.

Conclusions

Our method is able to assess both the evidence of selection at individual amino acid sites and estimate the reliability of those inferences. Experimental validation with I-Bmol proves its utility to identify functionally-important residues of poorly characterized proteins, demonstrating increased sensitivity over previous methods without loss of specificity. With the ability to guide the selection of precise experimental mutagenesis conditions, our method helps make unigenic analysis a more broadly applicable technique with which to probe protein function.

Availability

Software to compute, plot, and summarize EoS data is available as an open-source package called 'unigenic' for the 'R' programming language at http://www.fernandes.org/txp/article/13/an-analytical-framework-for-unigenic-evolution.

A unified genetic, computational and experimental framework identifies functionally relevant residues of the homing endonuclease I-BmoI

Insight into protein structure and function is best obtained through a synthesis of experimental, structural and bioinformatic data. Here, we outline a framework that we call MUSE (mutual information, unigenic evolution and structure-guided elucidation), which facilitated the identification of previously unknown residues that are relevant for function of the GIY-YIG homing endonuclease I-BmoI. Our approach synthesizes three types of data: mutual information analyses that identify co-evolving residues within the GIY-YIG catalytic domain; a unigenic evolution strategy that identifies hyper- and hypo-mutable residues of I-BmoI; and interpretation of the unigenic and co-evolution data using a homology model. In particular, we identify novel positions within the GIY-YIG domain as functionally important. Proof-of-principle experiments implicate the non-conserved I71 as functionally relevant, with an I71N mutant accumulating a nicked cleavage intermediate. Moreover, many additional positions within the catalytic, linker and C-terminal domains of I-BmoI were implicated as important for function. Our results represent a platform on which to pursue future studies of I-BmoI and other GIY-YIG-containing proteins, and demonstrate that MUSE can successfully identify novel functionally critical residues that would be ignored in a traditional structure-function analysis within an extensively studied small domain of approximately 90 amino acids.

Development of an unbiased statistical method for the analysis of unigenic evolution

Background

Unigenic evolution is a powerful genetic strategy involving random mutagenesis of a single gene product to delineate functionally important domains of a protein. This method involves selection of variants of the protein which retain function, followed by statistical analysis comparing expected and observed mutation frequencies of each residue. Resultant mutability indices for each residue are averaged across a specified window of codons to identify hypomutable regions of the protein. As originally described, the effect of changes to the length of this averaging window was not fully eludicated. In addition, it was unclear when sufficient functional variants had been examined to conclude that residues conserved in all variants have important functional roles.

Results

We demonstrate that the length of averaging window dramatically affects identification of individual hypomutable regions and delineation of region boundaries. Accordingly, we devised a region-independent chi-square analysis that eliminates loss of information incurred during window averaging and removes the arbitrary assignment of window length. We also present a method to estimate the probability that conserved residues have not been mutated simply by chance. In addition, we describe an improved estimation of the expected mutation frequency.

Conclusion

Overall, these methods significantly extend the analysis of unigenic evolution data over existing methods to allow comprehensive, unbiased identification of domains and possibly even individual residues that are essential for protein function.