Exploiting non-linear relationships between retention time and molecular structure of peptides originating from proteomes and comparing three multivariate approaches

Žuvela, Petar; Macur, Katarzyna; Liu, J. Jay; Bączek, Tomasz

doi:10.1016/j.jpba.2016.01.055

Cited by 16 publications

(16 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the model's chemical domain of applicability was defined to evaluate robustness and its functional prediction range. This was achieved using a Williams plot with a critical leverage ( h *) and three multiples of standard deviation of standardized residuals as warning limits.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Interpretation of ANN‐based QSAR models for prediction of antioxidant activity of flavonoids

2018

Self Cite

View full text Add to dashboard Cite

Quantitative structure-activity relationships (QSARs) built using machine learning methods, such as artificial neural networks (ANNs) are powerful in prediction of (antioxidant) activity from quantum mechanical (QM) parameters describing the molecular structure, but are usually not interpretable. This obvious difficulty is one of the most common obstacles in application of ANN-based QSAR models for design of potent antioxidants or elucidating the underlying mechanism. Interpreting the resulting models is often omitted or performed erroneously altogether. In this work, a comprehensive comparative study of six methods (PaD, PaD , weights, stepwise, perturbation and profile) for exploration and interpretation of ANN models built for prediction of Trolox-equivalent antioxidant capacity (TEAC) QM descriptors, is presented. Sum of ranking differences (SRD) was used for ranking of the six methods with respect to the contributions of the calculated QM molecular descriptors toward TEAC. The results show that the PaD, PaD and profile methods are the most stable and give rise to realistic interpretation of the observed correlations. Therefore, they are safely applicable for future interpretations without the opinion of an experienced chemist or bio-analyst. © 2018 Wiley Periodicals, Inc.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Unfortunately, these common issues are not limited only to QSARs for prediction of antioxidant activity, but also other biological activities, as well as prediction of properties such as chromatographic retention time …”

Section: Introductionmentioning

confidence: 99%

Interpretation of ANN‐based QSAR models for prediction of antioxidant activity of flavonoids

2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Meanwhile, the punishment parameter C is a regularized constant responsible for determining the trade-off between the empirical error and the model complexity25. The purpose of SVR is to train a function that predicts all data within a given ε deviation from the actual values.…”

Section: Resultsmentioning

confidence: 99%

“…Lately, B̧aczek et al . compared the prediction accuracies of three mentioned machine learning methods PLS, SVR and ANN25. It is reported that all the models exhibit high predictive power, but SVR has shown to be superior with the lowest minimizing Root Mean Square Error of Prediction (RMSEP) for both the testing and validation set.…”

mentioning

confidence: 99%

Locus-specific Retention Predictor (LsRP): A Peptide Retention Time Predictor Developed for Precision Proteomics

Liu

et al. 2017

Sci Rep

View full text Add to dashboard Cite

The precision prediction of peptide retention time (RT) plays an increasingly important role in liquid chromatography–tandem mass spectrometry (LC–MS/MS) based proteomics. Owing to the high reproducibility of liquid chromatography, RT prediction provides promising information for both identification and quantification experiment design. In this work, we present a Locus-specific Retention Predictor (LsRP) for precise prediction of peptide RT, which is based on amino acid locus information and Support Vector Regression (SVR) algorithm. Corresponding to amino acid locus, each peptide sequence was converted to a featured locus vector consisting of zeros and ones. With locus vector information from LC-MS/MS data sets, an SVR computational process was trained and evaluated. LsRP finally provided a prediction correlation coefficient of 0.95~0.99. We compared our method with two common predictors. Results showed that LsRP outperforms these methods and tracked up to 30% extra peptides in an extraction RT window of 2 min. A new strategy by combining LsRP and calibration peptide approach was then proposed, which open up new opportunities for precision proteomics.

show abstract

“…First of all, the variable selection is important in many fields, such as spectroscopy [7,8], QSPR [9,10], and other fields [11,12]. The selection of molecular descriptors largely determines the quality of the QSPR model [13][14][15]. The step of molecular descriptor screening aims to reflect more structural information so that there is no noise in the descriptors.…”

mentioning

confidence: 99%

A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network

Zhang

Liu

et al. 2018

Applied Sciences

View full text Add to dashboard Cite

Featured Application:The proposed hybrid intelligent model can be applied in engineering design, material performance prediction, numerical calculation, and the prediction of physical and chemical properties.Abstract: A quantitative structure-property relationship (QSPR) model is proposed to explore the relationship between the pKa of various compounds and their structures. Through QSPR studies, the relationship between the structure and properties can be obtained. In this study, a novel chaos-enhanced accelerated particle swarm algorithm (CAPSO) is adopted to screen molecular descriptors and optimize the weights of back propagation artificial neural network (BP ANN). Then, the QSPR model based on CAPSO and BP ANN is proposed and named the CAPSO BP ANN model. The prediction experiment showed that the CAPSO algorithm was a reliable method for screening molecular descriptors. The five molecular descriptors obtained by the CAPSO algorithm could well characterize the molecular structure of each compound in pKa prediction. The experimental results also showed that the CAPSO BP ANN model exhibited good performance in predicting the pKa values of various compounds. The absolute mean relative error, root mean square error, and square correlation coefficient are respectively 0.5364, 0.0632, and 0.9438, indicating the high prediction accuracy. The proposed hybrid intelligent model can be applied in engineering design and the prediction of physical and chemical properties.The establishment of the QSPR model mainly involves the following steps: acquisition of experimental data, construction and optimization of the molecular structure, calculation and screening of molecular descriptors, establishment and verification of the model, etc. First of all, the variable selection is important in many fields, such as spectroscopy [7,8], QSPR [9,10], and other fields [11,12]. The selection of molecular descriptors largely determines the quality of the QSPR model [13][14][15]. The step of molecular descriptor screening aims to reflect more structural information so that there is no noise in the descriptors. Many methods have been developed to screen molecular descriptors and can be mainly divided into two categories [16][17][18]. The first category includes the common methods, such as Akaike information criterion (AIC), Bayesian information criterion (BIC), and forward/backward/bi-directional stepwise multiple linear regression (MLR). The second includes the modern search algorithms, such as genetic algorithm (GA), simulated annealing algorithm (SA), ant colony algorithm (AC), particle swarm optimization (PSO), and other swarm intelligence algorithms [7,11,[19][20][21]. The common methods are the most simple and efficient, but their overall performances are low in complex nonlinear problems. The modern search algorithms based on the optimization strategy have obvious advantages and can search for optimal variables and deal with complex large data points. The model establishment is important in the QSPR study and commonly used QSPR model...

show abstract

Exploiting non-linear relationships between retention time and molecular structure of peptides originating from proteomes and comparing three multivariate approaches

Cited by 16 publications

References 31 publications

Interpretation of ANN‐based QSAR models for prediction of antioxidant activity of flavonoids

Interpretation of ANN‐based QSAR models for prediction of antioxidant activity of flavonoids

Locus-specific Retention Predictor (LsRP): A Peptide Retention Time Predictor Developed for Precision Proteomics

A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network

Contact Info

Product

Resources

About