SCMCRYS: Predicting Protein Crystallization Using an Ensemble Scoring Card Method with Estimating Propensity Scores of P-Collocated Amino Acid Pairs

Abstract

Existing methods for predicting protein crystallization obtain high accuracy using various types of complemented features and complex ensemble classifiers, such as support vector machine (SVM) and Random Forest classifiers. It is desirable to develop a simple and easily interpretable prediction method with informative sequence features to provide insights into protein crystallization. This study proposes an ensemble method, SCMCRYS, to predict protein crystallization, for which each classifier is built by using a scoring card method (SCM) with estimating propensity scores of p-collocated amino acid (AA) pairs (p = 0 for a dipeptide). The SCM classifier determines the crystallization of a sequence according to a weighted-sum score. The weights are the composition of the p-collocated AA pairs, and the propensity scores of these AA pairs are estimated using a statistic with optimization approach. SCMCRYS predicts the crystallization using a simple voting method from a number of SCM classifiers. The experimental results show that the single SCM classifier utilizing dipeptide composition with accuracy of 73.90% is comparable to the best previously-developed SVM-based classifier, SVM_POLY (74.6%), and our proposed SVM-based classifier utilizing the same dipeptide composition (77.55%). The SCMCRYS method with accuracy of 76.1% is comparable to the state-of-the-art ensemble methods PPCpred (76.8%) and RFCRYS (80.0%), which used the SVM and Random Forest classifiers, respectively. This study also investigates mutagenesis analysis based on SCM and the result reveals the hypothesis that the mutagenesis of surface residues Ala and Cys has large and small probabilities of enhancing protein crystallizability considering the estimated scores of crystallizability and solubility, melting point, molecular weight and conformational entropy of amino acids in a generalized condition. The propensity scores of amino acids and dipeptides for estimating the protein crystallizability can aid biologists in designing mutation of surface residues to enhance protein crystallizability. The source code of SCMCRYS is available at http://iclab.life.nctu.edu.tw/SCMCRYS/.

iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides Using Informative Physicochemical Properties

“…The analysis of feature importance can provide valuable information for predicting its function and activity. Previously, AAC has been used for analyzing the inherent characteristics and patterns of many therapeutic peptides [36][37][38][39][40][41] and protein functions [42][43][44]. In this study, the mean decrease of Gini index (MDGI) was utilized to rank the importance of each AAC feature.…”

SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides

“…The original SCM algorithm was first proposed by Huang et al [10] and was consequently applied to discriminate and analyze proteins with various functions [8–10, 13, 14] based on their sequence information. To train the classifier, two FASTA files are expected as the input: one for the positive training data and one for the negative training data.…”

“…IGA computes a fitness function, where the area under the ROC curve (AUC) [15], and the Pearson’s correlation coefficient (R-value) between the initial and the optimized propensity scores of 20 amino acids are linearly combined. The weights for the AUC and R value were set based on previous studies [8–10]. (See Eq.…”

“…We believe that our tool can significantly increase the rate of amassing knowledge about BY-kinases. SCMBYK uses a newly developed scoring card method (SCM) [8–10] to compute propensities of amino acids and dipeptides in order to discriminate BY-kinases from non-BY-kinases. A dataset consisting of 558 BY-kinases and 544 non-BY-kinases was established to design the SCMBYK predictor.…”

Computational crystallization

“…In total 428 protein properties were considered, including pI, average hydrophobicity and the frequency of neighboring pairs of amino acids. The accuracy for this method is between 75% and 81% and MCC ranges from 0.50 to 0.63.SCMCRYS is an ensemble method that creates “scoring cards” based on the identity of amino acid pairs separated by 0–9 residues as set of features [57]. The amino acid pairs are associated with a score derived from the frequencies of the pair in crystallizable and non-crystallizable proteins in the training set.…”