Predictive QSAR modeling of aldose reductase inhibitors using Monte Carlo feature selection

Nantasenamat, Chanin; Monnor, Teerawat; Worachartcheewan, Apilak; Mandi, Prasit; Isarankura‐Na‐Ayudhya, Chartchalerm; Prachayasittikul, Virapong

doi:10.1016/j.ejmech.2014.02.043

Cited by 26 publications

(5 citation statements)

References 36 publications

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“…24 The advancement in solution SAXS techniques and the related theories and models make it a robust approach to investigate the low-resolution structure of many biomacromolecules and their complexes. 25 It can reliably unveil structure information such as the folding domain through a Kratky plot, 26 the globular size of protein or protein complex using a Guinier plot, 27 the correlation length among particles, the mass or surface fractal dimension, 28 and the shape of complex particles using the pair distance distribution function (PDDF). 29 In this work, we applied solution SAXS to study the formation and aggregation of protein−tea polyphenol complexes, as well as the conformation change of proteins in the presence of catechin and EGCG.…”

Section: ■ Introductionmentioning

confidence: 99%

Small-Angle X-ray Scattering Study of Protein Complexes with Tea Polyphenols

Shi

Tang

Xiao

et al. 2017

J. Agric. Food Chem.

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Exploration of the structure of protein complexes, especially the change in conformation and aggregation behavior of proteins upon ligand binding, is crucial to clarify their bioactivities at the molecular level. We applied solution small-angle X-ray scattering (SAXS) to study the complex structure of bovine serum albumin (BSA) and trypsin binding with tea polyphenols, that is, catechin and epigallocatechin gallate (EGCG). We found that tea polyphenols can steadily promote the aggregation of proteins and protein complexes through their bridging effect. The numbers of proteins in the complexes and in the aggregates of complexes are extracted from SAXS intensity profiles, and their dependences as a function of the molar ratio of polyphenol to protein are discussed. EGCG has stronger capability than catechin to promote complex formation and further aggregation, and the aggregates of complexes have a denser core with a relatively smooth surface. The aggregates induced by catechin are loosely packed with a rough surface. BSA shows higher stability than trypsin in the formation of complex with a well-folded conformation. The synergistic unfolding of trypsin results in larger aggregates in the mixtures with more tea polyphenols. The binding affinity and number of tea polyphenols bound to each protein are further determined using fluorescence spectroscopy. The structure of protein complexes explored in this work is referable in the preparation of protein complex-based particles and the understanding of polyphenol-induced formation and further aggregation of protein complexes.

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Section: ■ Introductionmentioning

confidence: 99%

Small-Angle X-ray Scattering Study of Protein Complexes with Tea Polyphenols

Shi

Tang

Xiao

et al. 2017

J. Agric. Food Chem.

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“…Considering this limitation, a Monte Carlo-based descriptor selection approach may be able to bypass this issue if one were to consider setting a target descriptor distribution as the one that results in a high model scoring metric, and this type of approach has been previously reported for both classification and regression problems. − Furthermore, by including different nonlinear analogues of various descriptors within the search space through descriptor engineering, it may be possible to reduce the problem of a linear combination of terms by selecting the correct analogues. In this work, a Monte Carlo method-based descriptor search algorithm is proposed to navigate complex spaces with large numbers of engineered descriptors without prior knowledge of their relation to the predicted variable, with the objective of continuously improving the prediction’s score by implementing random descriptor additions and removals.…”

Section: Introductionmentioning

confidence: 99%

MonteCat: A Basin-Hopping-Inspired Catalyst Descriptor Search Algorithm for Machine Learning Models

Garcia-Escobar,

Taniike,

Takahashi

2024

J. Chem. Inf. Model.

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Proposing relevant catalyst descriptors that can relate the information on a catalyst's composition to its actual performance is an ongoing area in catalyst informatics, as it is a necessary step to improve our understanding on the target reactions. Herein, a small descriptor-engineered data set containing 3289 descriptor variables and the performance of 200 catalysts for the oxidative coupling of methane (OCM) is analyzed, and a descriptor search algorithm based on the workflow of the Basin-hopping optimization methodology is proposed to select the descriptors that better fit a predictive model. The algorithm, which can be considered wrapper in nature, consists of the successive generation of randombased modifications to the descriptor subset used in a regression model and adopting them depending on their effect on the model's score. The results are presented after being tested on linear and Support Vector Regression models with average cross-validation r 2 scores of 0.8268 and 0.6875, respectively.

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“…Quantitative structure‐activity relationship (QSAR) studies are of great importance in computational chemistry. The principle of QSAR is to model several biological activities of a collection of chemical compounds of their structural properties . Typically, the 2 fundamental objectives for evaluating the quality of a QSAR model are the high prediction accuracy and the discovery of relevant molecular descriptors …”

Section: Introductionmentioning

confidence: 99%

High‐dimensional QSAR classification model for anti‐hepatitis C virus activity of thiourea derivatives based on the sparse logistic regression model with a bridge penalty

Algamal

Lee

Al-Fakih

et al. 2017

Journal of Chemometrics

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This study addresses the problem of the high-dimensionality of quantitative structure-activity relationship (QSAR) classification modeling. A new selection of descriptors that truly affect biological activity and a QSAR classification model estimation method are proposed by combining the sparse logistic regression model with a bridge penalty for classifying the anti-hepatitis C virus activity of thiourea derivatives. Compared to other commonly used sparse methods, the proposed method shows superior results in terms of classification accuracy and model interpretation.

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Predictive QSAR modeling of aldose reductase inhibitors using Monte Carlo feature selection

Cited by 26 publications

References 36 publications

Small-Angle X-ray Scattering Study of Protein Complexes with Tea Polyphenols

Small-Angle X-ray Scattering Study of Protein Complexes with Tea Polyphenols

MonteCat: A Basin-Hopping-Inspired Catalyst Descriptor Search Algorithm for Machine Learning Models

High‐dimensional QSAR classification model for anti‐hepatitis C virus activity of thiourea derivatives based on the sparse logistic regression model with a bridge penalty

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