You are here

Scoring Function to Predict Solubility Mutagenesis

Description: 
Background: Mutagenesis 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. Results: We 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%.
Record Format: 
application/pdf
Record Date: 
2010-10-07T07:00:00Z
Publisher: 
PDXScholar
Subject: 
Mutagenicity testing
Mutagenesis -- Forecasting
Mutagenesis -- Mathematical models
Chemistry
Molecular Biology
Type: 
text
Raw Url: 
https://pdxscholar.library.pdx.edu/do/oai/?metadataPrefix=&verb=GetRecord&identifier=oai:pdxscholar.library.pdx.edu:chem_fac-1008
Source: 
Chemistry Faculty Publications and Presentations
Repository Record Id: 
oai:pdxscholar.library.pdx.edu:chem_fac-1008
SetSpec: 
publication:clas
publication:communities
publication:chem_fac
publication:chem
Record Title: 
Scoring Function to Predict Solubility Mutagenesis
Identifier: 
https://pdxscholar.library.pdx.edu/chem_fac/9
https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1008&context=chem_fac
Author: 
Tian, Ye
Deutsch, Christopher
Krishnamoorthy, Bala
Database: 
Resource OE Format: 
randomness