Rational Design of Temperature-Sensitive Alleles Using Computational Structure Prediction

Christopher S Poultney,David Gresham,Richard Bonneau,Michelle R Gutwein,Kevin Drew,Dennis E Shasha,Glenn L Butterfoss,Kristin C Gunsalus,Vladimir N Uversky

doi:10.1371/journal.pone.0023947

Christopher S Poultney, David Gresham + Show 7 more

Open Access

https://doi.org/10.1371/journal.pone.0023947

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Abstract

Temperature-sensitive (ts) mutations are mutations that exhibit a mutant phenotype at high or low temperatures and a wild-type phenotype at normal temperature. Temperature-sensitive mutants are valuable tools for geneticists, particularly in the study of essential genes. However, finding ts mutations typically relies on generating and screening many thousands of mutations, which is an expensive and labor-intensive process. Here we describe an in silico method that uses Rosetta and machine learning techniques to predict a highly accurate “top 5” list of ts mutations given the structure of a protein of interest. Rosetta is a protein structure prediction and design code, used here to model and score how proteins accommodate point mutations with side-chain and backbone movements. We show that integrating Rosetta relax-derived features with sequence-based features results in accurate temperature-sensitive mutation predictions.

Highlights

The study of essential genes – those genes that result in inviability of the organism or cell when nonfunctional – poses a unique challenge to the in vivo study of gene function
We evaluated all methods according to four metrics: precision, significance, correlation, and area under the receiver operating characteristic (ROC) curve (AUROC)
We have developed and tested a computational method for predicting temperature-sensitive mutations from protein structure, presenting results using the two support vector machine classifiers SVM-LIN and SVM-radial basis function (RBF)

Summary

Introduction

The study of essential genes – those genes that result in inviability of the organism or cell when nonfunctional – poses a unique challenge to the in vivo study of gene function. The ability to control the inactivation of an essential gene enables studies of the consequence of functional inactivation of essential genes and the identification of genetic interactions by means of genetic suppression studies These studies are frequently informative of the pathways and complexes in which the gene product participates. A variety of methods exist for the regulated inactivation of essential genes including the use of gene expression induction/repression systems, the workhorse of essential gene studies has long been temperature sensitive (ts) alleles. These alleles produce a functional gene product at one temperature (the permissive temperature) but are rendered non-functional at a higher – or occasionally lower – temperature (the restrictive temperature)

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