Extending proteochemometric modeling for unraveling the sorption behavior of compound–soil interaction

Nantasenamat

Prachayasittikul

et al. 2019

IJMS

Self Cite

In spite of the large-scale production and widespread distribution of vaccines and antiviral drugs, viruses remain a prominent human disease. Recently, the discovery of antiviral peptides (AVPs) has become an influential antiviral agent due to their extraordinary advantages. With the avalanche of newly-found peptide sequences in the post-genomic era, there is a great demand to develop a sequence-based predictor for timely identifying AVPs as this information is very useful for both basic research and drug development. In this study, we propose a novel sequence-based meta-predictor with an effective feature representation, called Meta-iAVP, for the accurate prediction of AVPs from given peptide sequences. Herein, the effective feature representation was extracted from a set of prediction scores derived from various machine learning algorithms and types of features. To the best of our knowledge, the model proposed herein represents the first meta-based approach for the prediction of AVPs. An overall accuracy and Matthews correlation coefficient of 95.20% and 0.90, respectively, was achieved from the independent test set on an objective benchmark dataset. Comparative analysis suggested that Meta-iAVP was superior to that of existing methods and therefore represents a useful tool for AVP prediction. Finally, in an effort to facilitate high-throughput prediction of AVPs, the model was deployed as the Meta-iAVP web server and is made freely available online at http://codes.bio/meta-iavp/ where users can submit query peptide sequences for determining the likelihood of whether or not these peptides are AVPs.

Section: Methodsmentioning

confidence: 99%

Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation

Nantasenamat

Prachayasittikul

et al. 2019

IJMS

Self Cite

“…As noticed in Table 7, the top seven important PCPs are QIAN880137, AURR980102, ROBB760113, PRAM820101, GRAR740101, PALJ810111, and Figure 3, the superior performance of our proposed model iQSP over 10-fold CV and independent validation test might mainly be due to the following reasons: (i) Performing with multiple random sampling procedure to protect against the risk of having good predictive result by chance [39][40][41][42][43]49,50]; (ii) using an efficient feature selection method (GA-SAR) to identify m informative features from 531 PCPs. Using eighteen informative PCPs could provide faster and more cost-effective models, while model developers could gain an insight into the underlying prediction processes [58,[62][63][64]; (iii) selecting a powerful method for QSP prediction. Although, iQSP displayed a superior performance over the existing methods assessed by the rigorous cross-validation methods, there is still room for further improvements, including increasing the size of QSPs by gathering peptide sequences from various data sources, utilizing an interpretable learning algorithm, such as scoring card method [44,53], improving the interpretation of important features responsible for the biological activity [50,64] and exploring different ML algorithms, such as extreme gradient boosting [65] or deep learning [66].…”

Section: Feature Contribution Analysismentioning

confidence: 99%

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

Charoenkwan

Nantasenamat

et al. 2019

IJMS

Self Cite

Understanding of quorum-sensing peptides (QSPs) in their functional mechanism plays an essential role in finding new opportunities to combat bacterial infections by designing drugs. With the avalanche of the newly available peptide sequences in the post-genomic age, it is highly desirable to develop a computational model for efficient, rapid and high-throughput QSP identification purely based on the peptide sequence information alone. Although, few methods have been developed for predicting QSPs, their prediction accuracy and interpretability still requires further improvements. Thus, in this work, we proposed an accurate sequence-based predictor (called iQSP) and a set of interpretable rules (called IR-QSP) for predicting and analyzing QSPs. In iQSP, we utilized a powerful support vector machine (SVM) cooperating with 18 informative features from physicochemical properties (PCPs). Rigorous independent validation test showed that iQSP achieved maximum accuracy and MCC of 93.00% and 0.86, respectively. Furthermore, a set of interpretable rules IR-QSP was extracted by using random forest model and the 18 informative PCPs. Finally, for the convenience of experimental scientists, the iQSP web server was established and made freely available online. It is anticipated that iQSP will become a useful tool or at least as a complementary existing method for predicting and analyzing QSPs.

“…In order to establish a robust and interpretable sequence-based tool for modeling the investigated PVPs, we followed the six prime keys 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]: (i) establishing a reliable dataset that contains experimentally validated sequences for training and validating the model; (ii) representing protein sequences with interpretable features; (iii) developing interpretable learning algorithms so as to allow the interpretation of important features responsible for the biological activity; (iv) assessing the prediction model using standard cross-validation tests; (v) constructing a user-friendly web-server for obtaining the prediction without the need to understand complex mathematical and statistical details; and (vi) analyzing and characterizing the important features derived from the developed model to provide a better understanding of the biophysical and biochemical properties of proteins. Figure 2 shows the workflow of PVPred-SCM, which works in predicting and analyzing PVPs.…”

Section: Methodsmentioning

confidence: 99%

“…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.…”

Section: Prediction Performancementioning

confidence: 99%

“…Until now, various convenient tools have been developed for representing a protein sequence using a fixed-length feature vector, such as BioSeq-Analysis2.0 [57], iFeature [58], protr/ProtrWeb [59], etc. Many studies have reported that DPC is one of the most interpretable and effective features for predicting and analyzing various types of protein and peptide functions [12,[17][18][19][20][21][22][23][24][25][26][28][29][30][31][32][33]39,40,[60][61][62][63][64][65][66]. Basically, DPC are the proportion of dipeptide in a peptide sequence P that are expressed as a fixed length of 400.…”

Section: Feature Representationmentioning

confidence: 99%

See 1 more Smart Citation

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

Charoenkwan

Kanthawong

et al. 2020

Cells

Self Cite

Although, existing methods have been successful in predicting phage (or bacteriophage) virion proteins (PVPs) using various types of protein features and complex classifiers, such as support vector machine and naïve Bayes, these two methods do not allow interpretability. However, the characterization and analysis of PVPs might be of great significance to understanding the molecular mechanisms of bacteriophage genetics and the development of antibacterial drugs. Hence, we herein proposed a novel method (PVPred-SCM) based on the scoring card method (SCM) in conjunction with dipeptide composition to identify and characterize PVPs. In PVPred-SCM, the propensity scores of 400 dipeptides were calculated using the statistical discrimination approach. Rigorous independent validation test showed that PVPred-SCM utilizing only dipeptide composition yielded an accuracy of 77.56%, indicating that PVPred-SCM performed well relative to the state-of-the-art method utilizing a number of protein features. Furthermore, the propensity scores of dipeptides were used to provide insights into the biochemical and biophysical properties of PVPs. Upon comparison, it was found that PVPred-SCM was superior to the existing methods considering its simplicity, interpretability, and implementation. Finally, in an effort to facilitate high-throughput prediction of PVPs, we provided a user-friendly web-server for identifying the likelihood of whether or not these sequences are PVPs. It is anticipated that PVPred-SCM will become a useful tool or at least a complementary existing method for predicting and analyzing PVPs.