Probing the origin of estrogen receptor alpha inhibition<i>via</i>large-scale QSAR study

Suvannang, Naravut; Preeyanon, Likit; Malik, Aijaz Ahmad; Schaduangrat, Nalini; Shoombuatong, Watshara; Worachartcheewan, Apilak; Tantimongcolwat, Tanawut; Nantasenamat, Chanin

doi:10.1039/c7ra10979b

Cited by 31 publications

(26 citation statements)

References 68 publications

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“…In this study, we employed two successful machine learning algorithms namely random forest (RF) and support vector machine (SVM). Previously, these approaches have been successfully used in the prediction of various functions and properties of peptides and proteins [33,36,38,46,47,48,49,50,51] as well as other biological or chemical entities [35,45,52,53,54,55]. Herein, the basic concepts and associated parameter optimizations for the two classifiers are briefly described hereafter.…”

Section: Methodsmentioning

confidence: 99%

ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides

et al. 2019

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Anticancer peptides (ACPs) have emerged as a new class of therapeutic agent for cancer treatment due to their lower toxicity as well as greater efficacy, selectivity and specificity when compared to conventional small molecule drugs. However, the experimental identification of ACPs still remains a time-consuming and expensive endeavor. Therefore, it is desirable to develop and improve upon existing computational models for predicting and characterizing ACPs. In this study, we present a bioinformatics tool called the ACPred, which is an interpretable tool for the prediction and characterization of the anticancer activities of peptides. ACPred was developed by utilizing powerful machine learning models (support vector machine and random forest) and various classes of peptide features. It was observed by a jackknife cross-validation test that ACPred can achieve an overall accuracy of 95.61% in identifying ACPs. In addition, analysis revealed the following distinguishing characteristics that ACPs possess: (i) hydrophobic residue enhances the cationic properties of α-helical ACPs resulting in better cell penetration; (ii) the amphipathic nature of the α-helical structure plays a crucial role in its mechanism of cytotoxicity; and (iii) the formation of disulfide bridges on β-sheets is vital for structural maintenance which correlates with its ability to kill cancer cells. Finally, for the convenience of experimental scientists, the ACPred web server was established and made freely available online.

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Section: Methodsmentioning

confidence: 99%

ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides

et al. 2019

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“…The search space of ntree and mtry are {100,200,…,500} and {1,2,…,10} with the steps of 100 and 1, respectively. Previously, RF model has been successfully used in the prediction of various functions and properties of peptides and proteins [55,56,57,87,88] as well as other biological or chemical entities [89,90,91,92,93].…”

Section: Methodsmentioning

confidence: 99%

TargetAntiAngio: A Sequence-Based Tool for the Prediction and Analysis of Anti-Angiogenic Peptides

Laengsri

Nantasenamat

Schaduangrat

et al. 2019

IJMS

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Cancer remains one of the major causes of death worldwide. Angiogenesis is crucial for the pathogenesis of various human diseases, especially solid tumors. The discovery of anti-angiogenic peptides is a promising therapeutic route for cancer treatment. Thus, reliably identifying anti-angiogenic peptides is extremely important for understanding their biophysical and biochemical properties that serve as the basis for the discovery of new anti-cancer drugs. This study aims to develop an efficient and interpretable computational model called TargetAntiAngio for predicting and characterizing anti-angiogenic peptides. TargetAntiAngio was developed using the random forest classifier in conjunction with various classes of peptide features. It was observed via an independent validation test that TargetAntiAngio can identify anti-angiogenic peptides with an average accuracy of 77.50% on an objective benchmark dataset. Comparisons demonstrated that TargetAntiAngio is superior to other existing methods. In addition, results revealed the following important characteristics of anti-angiogenic peptides: (i) disulfide bond forming Cys residues play an important role for inhibiting blood vessel proliferation; (ii) Cys located at the C-terminal domain can decrease endothelial formatting activity and suppress tumor growth; and (iii) Cyclic disulfide-rich peptides contribute to the inhibition of angiogenesis and cell migration, selectivity and stability. Finally, for the convenience of experimental scientists, the TargetAntiAngio web server was established and made freely available online.

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“…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%

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

Charoenkwan

Kanthawong

Schaduangrat

et al. 2020

Cells

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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.

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Probing the origin of estrogen receptor alpha inhibitionvialarge-scale QSAR study

Abstract: This study compiles a large, non-redundant set of compounds tested for ERα inhibitory activity and applies QSAR modeling for unveiling the privileged substructures governing the activity.

Cited by 31 publications

References 68 publications

ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides

ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides

TargetAntiAngio: A Sequence-Based Tool for the Prediction and Analysis of Anti-Angiogenic Peptides

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

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