Influence of Protein Self-Association on Complex Coacervation with Polysaccharide: A Monte Carlo Study

Coarse-grained Monte Carlo simulations have been applied to study complex coacervation of pectin with bovine serum albumin (BSA) and two isomers of beta-lactoglobulin (BLGA and BLGB). The influence from the specific distribution of charge and hydrophobic patches in protein surfaces on the self-association of proteins and their complex coacervation were investigated. A simple and direct method to quantify the contribution of hydrophobic interaction on protein complex formation was introduced. Highly accordant pH dependence of charges in proteins and phase boundaries for the complex coacervation was observed. Comparing to BSA, beta-lactoglobulin had a higher probability and a broader pH window to form complex coacervate. The major cause is the higher self-association proneness of beta-lactoglobulin, as evidenced by the more negative second virial coefficients. The double-point mutations of G64D/V118A from BLGB to BLGA caused the latter one to have a stronger self-association proneness. It was revealed that the larger negative charge patch in BLGA synergistically enhanced the attraction of the strongest binding site, a positive charge patch, when pH was close to or above the isoelectric point of the protein. These findings suggest that the coarse grained simulation is competent to explore the delicate influences from different proteins in protein–polysaccharide complex coacervates

PROTS-RF: A Robust Model for Predicting Mutation-Induced Protein Stability Changes

<div><p>The ability to improve protein thermostability via protein engineering is of great scientific interest and also has significant practical value. In this report we present PROTS-RF, a robust model based on the Random Forest algorithm capable of predicting thermostability changes induced by not only single-, but also double- or multiple-point mutations. The model is built using 41 features including evolutionary information, secondary structure, solvent accessibility and a set of fragment-based features. It achieves accuracies of 0.799,0.782, 0.787, and areas under receiver operating characteristic (ROC) curves of 0.873, 0.868 and 0.862 for single-, double- and multiple- point mutation datasets, respectively. Contrary to previous suggestions, our results clearly demonstrate that a robust predictive model trained for predicting single point mutation induced thermostability changes can be capable of predicting double and multiple point mutations. It also shows high levels of robustness in the tests using hypothetical reverse mutations. We demonstrate that testing datasets created based on physical principles can be highly useful for testing the robustness of predictive models.</p> </div

Linear regression and classification of the 180 double point mutations.

<p>Linear regression and classification of the 180 double point mutations.</p

The importance of each feature contributed to the regression predictive models in cross validation.

<p>The error bars denote the variation in five-fold cross validation.</p

Comparison of prediction performance in cross-validation test.

a<p>Prediction values were provided by Potapov et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047247#pone.0047247-Potapov1" target="_blank">[32]</a>.</p><p>AUC: area under ROC curve; ACC: accuracy; R: Pearson Correlation Coefficient.</p

Linear regression and classification of the 141 multiple point mutations.

<p>Linear regression and classification of the 141 multiple point mutations.</p

The performance of <i>ΔΔG</i> prediction by PROTS-RF for mutations and hypothetical reversed mutations in the D141 dataset.

<p>AUC: area under ROC curve; ACC: accuracy; R: Pearson Correlation Coefficient.</p

The features and their distributions in the training dataset.

<p>The p-values are calculated using the Kolmogorov-Smirnov test (K-S test). Boxplots of these features are available in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047247#pone.0047247.s001" target="_blank">Figure S1</a>.</p>*<p>Structure-based features. SM: stabilizing mutations; DM: destabilizing mutations.</p

Summary of the accuracy of hydrogen atoms placement by different methods as compared to high resolution X-ray and neutron diffraction structures.

<p>Summary of the accuracy of hydrogen atoms placement by different methods as compared to high resolution X-ray and neutron diffraction structures.</p

Comparison of the average number of atom clashes and its standard deviation (in parentheses) of the predicted hydrogen atoms in the models built by different methods.

<p>Comparison of the average number of atom clashes and its standard deviation (in parentheses) of the predicted hydrogen atoms in the models built by different methods.</p