Prediction and analysis of protein solubility using a novel scoring card method with dipeptide composition

Abstract

Background Existing methods for predicting protein solubility on overexpression in Escherichia coli advance performance by using ensemble classifiers such as two-stage support vector machine (SVM) based classifiers and a number of feature types such as physicochemical properties, amino acid and dipeptide composition, accompanied with feature selection. It is desirable to develop a simple and easily interpretable method for predicting protein solubility, compared to existing complex SVM-based methods. Results This study proposes a novel scoring card method (SCM) by using dipeptide composition only to estimate solubility scores of sequences for predicting protein solubility. SCM calculates the propensities of 400 individual dipeptides to be soluble using statistic discrimination between soluble and insoluble proteins of a training data set. Consequently, the propensity scores of all dipeptides are further optimized using an intelligent genetic algorithm. The solubility score of a sequence is determined by the weighted sum of all propensity scores and dipeptide composition. To evaluate SCM by performance comparisons, four data sets with different sizes and variation degrees of experimental conditions were used. The results show that the simple method SCM with interpretable propensities of dipeptides has promising performance, compared with existing SVM-based ensemble methods with a number of feature types. Furthermore, the propensities of dipeptides and solubility scores of sequences can provide insights to protein solubility. For example, the analysis of dipeptide scores shows high propensity of α-helix structure and thermophilic proteins to be soluble. Conclusions The propensities of individual dipeptides to be soluble are varied for proteins under altered experimental conditions. For accurately predicting protein solubility using SCM, it is better to customize the score card of dipeptide propensities by using a training data set under the same specified experimental conditions. The proposed method SCM with solubility scores and dipeptide propensities can be easily applied to the protein function prediction problems that dipeptide composition features play an important role. Availability The used datasets, source codes of SCM, and supplementary files are available at http://iclab.life.nctu.edu.tw/SCM/.

Solubility-Weighted Index: fast and accurate prediction of protein solubility

“…Higher helix propensity has been reported to increase solubility (Idicula-Thomas and Balaji 2005; Huang et al 2012) . However, our analysis has shown that helical and turn propensities anti-correlate with solubility, whereas sheet propensity lacks correlation with solubility, suggesting that disordered regions may tend to be more soluble (Fig 3).…”

PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

“…To make a fair comparison with the existing PVP predictors [9,10,[12][13][14], the same benchmark and independent datasets that have been used in previous studies [12] were used to develop our proposed model. Due to the non-deterministic characteristic of the GA algorithm [26,32], ten SCM models in conjunction with ten different optimized dipeptide propensity scores (opti-DPS) [21][22][23][24][25][26][27]38] were performed to generate ten different prediction results. Tables 2 and 3 list the performance comparisons of ten independent runs evaluated by 10-fold CV and independent validation test, respectively.…”

“…Owing to the complex architecture of computational models and low interpretable features used in the study, it is not easy to identify and assess which features are beneficial for the biological activities of PVPs. As mentioned in a series of recent publications [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] and summarized in several comprehensive review papers [29,[34][35][36], one of the main values of bioinformatics tools should be its ability to provide insight into mechanisms of action under study. Secondly, few existing methods were not assessed using an independent dataset, indicating that these methods might provide misleading results with overestimated accuracy.…”

Structure-aware Protein Solubility Prediction From Sequence Through Graph Convolutional Network And Predicted Contact Map

“…For the machine/deep learning techniques, several sequence-based methods have been developed for protein solubility prediction including PROSO II (Smialowski, et al, 2012), CCSOL (Agostini, et al, 2012), SOLpro (Magnan, et al, 2009), and the scoring card method (SCM) (Huang, et al, 2012). The majority of these methods adopted the support vector machine(SVM) (AK, 2002) as the core discriminative model on biologically relevant handcrafted features from protein sequences to discriminate the soluble and insoluble proteins.…”