osFP: a web server for predicting the oligomeric states of fluorescent proteins

Abstract

BackgroundCurrently, monomeric fluorescent proteins (FP) are ideal markers for protein tagging. The prediction of oligomeric states is helpful for enhancing live biomedical imaging. Computational prediction of FP oligomeric states can accelerate the effort of protein engineering efforts of creating monomeric FPs. To the best of our knowledge, this study represents the first computational model for predicting and analyzing FP oligomerization directly from the amino acid sequence.ResultsAfter data curation, an exhaustive data set consisting of 397 non-redundant FP oligomeric states was compiled from the literature. Results from benchmarking of the protein descriptors revealed that the model built with amino acid composition descriptors was the top performing model with accuracy, sensitivity and specificity in excess of 80% and MCC greater than 0.6 for all three data subsets (e.g. training, tenfold cross-validation and external sets). The model provided insights on the important residues governing the oligomerization of FP. To maximize the benefit of the generated predictive model, it was implemented as a web server under the R programming environment.ConclusionosFP affords a user-friendly interface that can be used to predict the oligomeric state of FP using the protein sequence. The advantage of osFP is that it is platform-independent meaning that it can be accessed via a web browser on any operating system and device. osFP is freely accessible at http://codes.bio/osfp/ while the source code and data set is provided on GitHub at https://github.com/chaninn/osFP/.Graphical Abstract.

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

“…Rigorous 10-fold CV and independent validation test with ten independent rounds of these classifiers based on the optimal feature subset are reported in Table 6 and Figure 5. The more details of the parameter optimization of these three classifiers were described in the works [37,38,[55][56][57][58][59][60][61]. Based the independent validation test, we noticed that the Ac, MCC and auROC values of iQSP were higher than those of other classifiers by >2%, >4%, and >2%, respectively, suggesting that iQSP holds very high potential to provide an accurate and reliable result in unseen peptides when compared to the existing methods and the conventional classifiers developed in this study.…”

“…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.…”

“…SVM method is an effective ML algorithm for supervised pattern and has been widely used in various biological problems [36][37][38][39]41,43,45,52,[71][72][73][74][75][76][77][78]. This method is based on the Vapnik-Chervonenkis theory of statistical learning [79][80][81].…”

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

“…Although, experiment #9 was not in the three-top ranked experiments over 10-fold CV, it provides a promising result in terms of ACC, MCC, and auROC with 92.52%, 0.846, and 0.948, respectively, which was not significantly different from the result of experiment #3 (95.11%, 0.894, and 0.966). Moreover, due to the fact that the independent test was the most rigorous cross-validation method to demonstrate the robustness and reliability of the model in real-world applications [17][18][19][20]28,29,31,33,[39][40][41], it could be noted that experiment #9 provided an important contribution to PVP prediction. For convenience, the best PVP predictor based on the SCM method in conjunction with the propensity scores of dipeptides from experiment #9 would be referred to as PVPred-SCM.…”

Protein classification using modifiedn-gramandskip-grammodels

“…The m-NGSG feature extraction method was also compared with other generalized extraction methods. Here, the Quantitative Structure-Property Relationship (QSAR) based feature generation method (Simeon et al, 2016) was implemented on the Subchlo60 data set from Subcholo model, and compared with the m-NGSG feature generation model. A logistic regression classifier was used with a regularization parameter of C=1, and evaluated using Jackknife, 5-fold, and 10-fold cross validation.…”