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How Precise Are Our Quantitative Structure-Activity Relationship Derived Predictions for New Query Chemicals?

ABSTRACT: Quantitative structure-activity relationship (QSAR) models have long been used for making predictions and data gap filling in diverse fields including medicinal chemistry, predictive toxicology, environmental fate modeling, materials science, agricultural science, nanoscience, food science, and so forth. Usually a QSAR model is developed based on chemical information of a properly designed training set and corresponding experimental response data while the model is validated using one or more test set(s) for which the experimental response data are available. However, it is interesting to estimate the reliability of predictions when the model is applied to a completely new data set (true external set) even when the new data points are within applicability domain (AD) of the developed model. In the present study, we have categorized the quality of predictions for the test set or true external set into three groups (good, moderate, and bad) based on absolute prediction errors. Then, we have used three criteria [(a) mean absolute error of leave-one-out predictions for 10 most close training compounds for each query molecule; (b) AD in terms of similarity based on the standardization approach; and (c) proximity of the predicted value of the query compound to the mean training response] in different weighting schemes for making a composite score of predictions. It was found that using the most frequently appearing weighting scheme 0.5-0-0.5, the composite score-based categorization showed concordance with absolute prediction error-based categorization for more than 80% test data points while working with 5 different datasets with 15 models for each set derived in three different splitting techniques. These observations were also confirmed with true external sets for another four endpoints suggesting applicability of the scheme to judge the reliability of predictions for new datasets. The scheme has been implemented in a tool "Prediction Reliability Indicator" available at http://dtclab.webs.com/software-tools and http://teqip.jdvu.ac.in/QSAR_Tools/DTCLab/, and the tool is presently valid for multiple linear regression models only.

SUBMITTER: Roy K

PROVIDER: S-EPMC6645132 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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How Precise Are Our Quantitative Structure-Activity Relationship Derived Predictions for New Query Chemicals?

Roy Kunal K Ambure Pravin P Kar Supratik S

ACS omega 20180919 9

Quantitative structure-activity relationship (QSAR) models have long been used for making predictions and data gap filling in diverse fields including medicinal chemistry, predictive toxicology, environmental fate modeling, materials science, agricultural science, nanoscience, food science, and so forth. Usually a QSAR model is developed based on chemical information of a properly designed training set and corresponding experimental response data while the model is validated using one or more te ...[more]

PMID: 31459245

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Project description:Humic-like substances (HLSs) isolated by alkaline oxidative hydrolysis from lignin-rich agro-industrial residues have been shown to exert biostimulant activity toward maize (Zea mays L.) germination and early growth. The definition of a quantitative structure-activity relationship (QSAR) between HLS and their bioactivity could be useful to predict their biological properties and tailor plant biostimulants for specific agronomic and industrial uses. Here, we created several projection on latent structure (PLS) regression by using published analytical data on the molecular composition of lignin-derived HLS obtained by both 13C-CPMAS-NMR spectra directly on samples and 31P-NMR spectra after derivatization of hydroxyl functions with a P-containing reagent (2-chloro-4,4,5,5-tetramethyl-1,3,2-dioxaphospholane). These spectral data were used to model the effect of HLS on the elongation of primary root, lateral seminal roots, total root apparatus, and coleoptile of maize. The 13C-CPMAS-NMR data suggested that methoxyl and aromatic moieties positively affected plant growth, while the carboxyl/esterified functions showed a negative impact on the overall seedling development. Alkyl C seems to promote Col elongation while concomitantly reducing that of the root system. Additionally, 31P-NMR-derived spectra revealed that the elongation of roots and Col were enhanced by the occurrence of aliphatic hydroxyl groups, and guaiacyl and p-Hydroxyphenyl lignin monomers. The PLS models based on raw dataset from 13C-CPMAS-NMR spectra explained more than 74% of the variance for the length of lateral seminal roots, total root system and coleoptile, while other parameters derived from 13C-CPMAS-NMR spectra, namely the Hydrophobicity and Hydrophilicity of materials were necessary to explain 83% of the variance of the primary root length. The results from 31P-NMR spectra explained the observed biological variance by 90, 96, 96, and 93% for the length of primary root, lateral seminal roots, total root system and coleoptile, respectively. This work shows that different NMR spectroscopy techniques can be used to build up PLS models which can predict the bioactivity of lignin-derived HLS toward early growth of maize plants. The established QSAR may also be exploited to enhance by chemical techniques the bioactive properties of HLS and enhance their plant stimulation capacity.

Project description:Calpains are intracellular Ca(2+)-regulated cysteine proteases that are essential for various cellular functions. Mammalian conventional calpains (calpain-1 and calpain-2) modulate the structure and function of their substrates by limited proteolysis. Thus, it is critically important to determine the site(s) in proteins at which calpains cleave. However, the calpains' substrate specificity remains unclear, because the amino acid (aa) sequences around their cleavage sites are very diverse. To clarify calpains' substrate specificities, 84 20-mer oligopeptides, corresponding to P10-P10' of reported cleavage site sequences, were proteolyzed by calpains, and the catalytic efficiencies (kcat/Km) were globally determined by LC/MS. This analysis revealed 483 cleavage site sequences, including 360 novel ones. Thekcat/Kms for 119 sites ranged from 12.5-1,710 M(-1)s(-1) Although most sites were cleaved by both calpain-1 and -2 with a similarkcat/Km, sequence comparisons revealed distinct aa preferences at P9-P7/P2/P5'. The aa compositions of the novel sites were not statistically different from those of previously reported sites as a whole, suggesting calpains have a strict implicit rule for sequence specificity, and that the limited proteolysis of intact substrates is because of substrates' higher-order structures. Cleavage position frequencies indicated that longer sequences N-terminal to the cleavage site (P-sites) were preferred for proteolysis over C-terminal (P'-sites). Quantitative structure-activity relationship (QSAR) analyses using partial least-squares regression and >1,300 aa descriptors achievedkcat/Kmprediction withr= 0.834, and binary-QSAR modeling attained an 87.5% positive prediction value for 132 reported calpain cleavage sites independent of our model construction. These results outperformed previous calpain cleavage predictors, and revealed the importance of the P2, P3', and P4' sites, and P1-P2 cooperativity. Furthermore, using our binary-QSAR model, novel cleavage sites in myoglobin were identified, verifying our predictor. This study increases our understanding of calpain substrate specificities, and opens calpains to "next-generation,"i.e.activity-related quantitative and cooperativity-dependent analyses.

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How Precise Are Our Quantitative Structure-Activity Relationship Derived Predictions for New Query Chemicals?

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How Precise Are Our Quantitative Structure-Activity Relationship Derived Predictions for New Query Chemicals?

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