Bootstrap methods for quantifying the uncertainty of binding constants in the hard modeling of spectrophotometric titration data

Kazmierczak, Nathanael P.; Chew, Joyce A.; Griend, Douglas A. Vander

doi:10.1016/j.aca.2022.339834

Cited by 17 publications

(10 citation statements)

References 36 publications

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“…Association constants of Receptor 3 in acetonitrile with various tetrabutylammonium or tetraethylammonium salts were determined via UV/vis titrations, using SIVVU for data analysis (Table and Section S2, Supporting Information). , We chose a list of environmentally and biologically relevant monoanionic guests to probe the binding behavior of Receptor 3 . Asymmetric errors on the binding constant terms were calculated by bootstrapping each data set column-wise 1000 times . The errors of at least three separate titrations were then combined according to model 2 of Barlow’s paper and were reported as 95% confidence intervals .…”

Section: Resultsmentioning

confidence: 99%

“…Asymmetric errors on the binding constant terms were calculated by bootstrapping each data set column-wise 1000 times. 49 The errors of at least three separate titrations were then combined according to model 2 of Barlow's paper and were reported as 95% confidence intervals. 50 The absorbance of all species, including the host, guest, and complexes thereof, was refined as part of our data analysis.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

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Electroactive Anion Receptor with High Affinity for Arsenate

2020

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Herein we present the synthesis and characterization of a macrocyclic polyamide cage that incorporates redox-active 1,4-dithiin units. UV/Vis titration experiments with eight anions in acetonitrile revealed high affinity for H 2 AsO 4 -(logβ 2 = 10.4 +0.4 -0.4 ) and HCO 3 -( ) over other common anionic guests, such as Cl -( ), HSO 4 -( ) and logβ 2 = 8.3 +0.3 -0.4 logK 1:1 = 3.20 +0.03 -0.02 logK 1:1 = 3.57 +0.02 -0.03 H 2 PO 4 -( ), by the selective formation of HG 2 complexes. The recognition of arsenate over phosphate is rare among logK 1:1 = 4.24 +0.05 -0.04both proteins and synthetic receptors, and exploiting the difference in binding stoichiometry represents an underexplored avenue towards developing receptors that can differentiate between the two anions. Additional analysis by 1 H-NMR in 1:3 CD 2 Cl 2 :MeCNd 3 found strong dependence of anion binding stoichiometry with the solvent employed. Finally, titration experiments with cyclic voltammetry provided varying and complex responses for each anion tested, though reaction between the anion and receptors was observed in most cases. These results implicate 1,4-dithiins as interesting recognition moieties in the construction of supramolecular receptors.

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Section: Resultsmentioning

confidence: 99%

Section: ■ Results and Discussionmentioning

confidence: 99%

Electroactive Anion Receptor with High Affinity for Arsenate

2020

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“…The usage of Monte Carlo simulation (MC) and Bootstrapping (BS) to estimate confidence intervals using the percentile methods as proposed previously (Thordarson, 2011;Lowe, Pfeffer & Thordarson, 2012) has already been presented in an earlier article (Hübler, 2022b). Recently, Kazmierczak, Chew & Vander Griend (2022b) have analysed BS in context of photometric titration experiments, including stock solution errors.…”

Section: Methods and Implementation Monte Carlo Simulation (Mc)mentioning

confidence: 99%

“…All parameters are unsymmetrically distributed around the best-fit values, which is in agreement with the general findings by Motulsky and Christopoulos (Motulsky & Christopoulos, 2003). A detailed analysis in case of stability constants has later been done by Thordarson (Thordarson, 2011) and Vander Griend (Kazmierczak, Chew & Vander Griend, 2022b). In the 1:1 model, the confidence limits for lg K 11 are a nearly twice the limits of lg K 11 in the 1:1/1:2 model.…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

Analysing binding stoichiometries in NMR titration experiments using Monte Carlo simulation and resampling techniques

Hübler

2022

PeerJ Analytical Chemistry

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The application of Monte Carlo simulation and resampling techniques to analyse possible binding stoichiometries in NMR titration experiments is presented. Four simulated NMR titration experiments having complex species with 1:1, 2:1 and 1:2 stoichiometries were each analysed using a 1:1, 2:1/1:1, 1:1/1:2 and a 2:1/1:1/1:2 model as implemented in SupraFit. Each best-fit model was inspected using Monte Carlo simulation (MC), Cross Validation (CV) and a new protocol termed Reduction Analysis (RA). The results of the statistical post-processes were used to calculate characteristic descriptors that are the base of the judgment for both, the models and individual stability constants. The results indicate promising approaches to correctly identify 1:1, 2:1/1:1 and 1:1/1:2 models, however with some limitations in case of the 2:1/1:1/1:2 model. All simulations and post-processing protocols were performed with the newly presented SupraFit.

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“…Provision of explanations about how model predictions are researched and providing accurate summary statistics for model accuracy metrics (e.g., AUROC, Sensitivity, Specificity, F1, Balanced Accuracy) will increase the transparency of machine learning methods and increase confidence when using their predictions [8,9,26,27]. Potential solutions to these weaknesses in machine learning that have been applied within the field of computer science are SHapely Additive exPlanations (SHAP) for model interpretability and bootstrap simulation for quantifying the statistical distribution of model accuracy metrics [28][29][30]. However, little is known about the efficacy of SHAP and Bootstrap in evaluating machine-learning methods for medical outcomes such as heart disease.…”

Section: Introductionmentioning

confidence: 99%

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

Huang

2023

PLoS ONE

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Machine learning methods are widely used within the medical field. However, the reliability and efficacy of these models is difficult to assess, making it difficult for researchers to identify which machine-learning model to apply to their dataset. We assessed whether variance calculations of model metrics (e.g., AUROC, Sensitivity, Specificity) through bootstrap simulation and SHapely Additive exPlanations (SHAP) could increase model transparency and improve model selection. Data from the England National Health Services Heart Disease Prediction Cohort was used. After comparison of model metrics for XGBoost, Random Forest, Artificial Neural Network, and Adaptive Boosting, XGBoost was used as the machine-learning model of choice in this study. Boost-strap simulation (N = 10,000) was used to empirically derive the distribution of model metrics and covariate Gain statistics. SHapely Additive exPlanations (SHAP) to provide explanations to machine-learning output and simulation to evaluate the variance of model accuracy metrics. For the XGBoost modeling method, we observed (through 10,000 completed simulations) that the AUROC ranged from 0.771 to 0.947, a difference of 0.176, the balanced accuracy ranged from 0.688 to 0.894, a 0.205 difference, the sensitivity ranged from 0.632 to 0.939, a 0.307 difference, and the specificity ranged from 0.595 to 0.944, a 0.394 difference. Among 10,000 simulations completed, we observed that the gain for Angina ranged from 0.225 to 0.456, a difference of 0.231, for Cholesterol ranged from 0.148 to 0.326, a difference of 0.178, for maximum heart rate (MaxHR) ranged from 0.081 to 0.200, a range of 0.119, and for Age ranged from 0.059 to 0.157, difference of 0.098. Use of simulations to empirically evaluate the variability of model metrics and explanatory algorithms to observe if covariates match the literature are necessary for increased transparency, reliability, and utility of machine learning methods. These variance statistics, combined with model accuracy statistics can help researchers identify the best model for a given dataset.

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Bootstrap methods for quantifying the uncertainty of binding constants in the hard modeling of spectrophotometric titration data

Cited by 17 publications

References 36 publications

Electroactive Anion Receptor with High Affinity for Arsenate

Electroactive Anion Receptor with High Affinity for Arsenate

Analysing binding stoichiometries in NMR titration experiments using Monte Carlo simulation and resampling techniques

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

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