Scoring function to predict solubility mutagenesis.

Ye Tian,Christopher Deutsch,Bala Krishnamoorthy

doi:10.1186/1748-7188-5-33

Abstract

BackgroundMutagenesis is commonly used to engineer proteins with desirable properties not present in the wild type (WT) protein, such as increased or decreased stability, reactivity, or solubility. Experimentalists often have to choose a small subset of mutations from a large number of candidates to obtain the desired change, and computational techniques are invaluable to make the choices. While several such methods have been proposed to predict stability and reactivity mutagenesis, solubility has not received much attention.ResultsWe use concepts from computational geometry to define a three body scoring function that predicts the change in protein solubility due to mutations. The scoring function captures both sequence and structure information. By exploring the literature, we have assembled a substantial database of 137 single- and multiple-point solubility mutations. Our database is the largest such collection with structural information known so far. We optimize the scoring function using linear programming (LP) methods to derive its weights based on training. Starting with default values of 1, we find weights in the range [0,2] so that predictions of increase or decrease in solubility are optimized. We compare the LP method to the standard machine learning techniques of support vector machines (SVM) and the Lasso. Using statistics for leave-one-out (LOO), 10-fold, and 3-fold cross validations (CV) for training and prediction, we demonstrate that the LP method performs the best overall. For the LOOCV, the LP method has an overall accuracy of 81%.AvailabilityExecutables of programs, tables of weights, and datasets of mutants are available from the following web page: http://www.wsu.edu/~kbala/OptSolMut.html.

Highlights

Correlations between sequence and structure influence to a large extent how proteins fold, and how they function
Four body contacts defined using the concept of Delaunay tessellation (DT) [17] of protein structures have been employed for computational mutagenesis of protein stability [3,18,19] and enzyme activity [5]
With protein solubility in mind, we introduce the degree of buriedness for three body contacts under the framework of DT, which estimates the extent of surface exposure or buriedness of contacts without measuring the actual surface areas

Summary

Introduction

Correlations between sequence and structure influence to a large extent how proteins fold, and how they function Working under this premise, most computational methods used for predicting various aspects of structure and function employ scoring functions, which quantify the propensities of groups of amino acids to form specific structural or functional units. Experimentalists often have to choose a small subset of mutations from a large number of candidates to obtain the desired change, and computational techniques are invaluable to make the choices. While several such methods have been proposed to predict stability and reactivity mutagenesis, solubility has not received much attention

Methods

Results

Conclusion