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Record W4401448092 · doi:10.1021/acschembio.4c00259

Maximizing the Accuracy of Equilibrium Dissociation Constants for Affinity Complexes: From Theory to Practical Recommendations

2024· review· en· W4401448092 on OpenAlexafffund
Tong Ye Wang, Jean‐Luc Rukundo, Zhiyuan Mao, Sergey N. Krylov

Bibliographic record

VenueACS Chemical Biology · 2024
Typereview
Languageen
FieldChemistry
Topicthermodynamics and calorimetric analyses
Canadian institutionsYork University
FundersYork University
KeywordsDissociation constantEquilibrium constantDissociation (chemistry)ChemistryThermodynamicsDissociation rateComputational chemistryPhysical chemistryPhysicsBiochemistryReceptor

Abstract

fetched live from OpenAlex

The equilibrium dissociation constant ( K d ) is a major characteristic of affinity complexes and one of the most frequently determined physicochemical parameters. Despite its significance, the values of K d obtained for the same complex under similar conditions often exhibit considerable discrepancies and sometimes vary by orders of magnitude. These inconsistencies highlight the susceptibility of K d determination to large systematic errors, even when random errors are small. It is imperative to both minimize and quantitatively assess the systematic errors inherent in K d determination. Traditionally, K d values are determined through nonlinear regression of binding isotherms. This analysis utilizes three variables: concentrations of two reactants and a fraction R of unbound limiting reactant. The systematic errors in K d arise directly from systematic errors in these variables. Therefore, to maximize the accuracy of K d, this study thoroughly analyzes the sources of systematic errors within the three variables, including ( i ) non-additive signals to calculate R, ( ii ) mis-calibrated experimental instruments, ( iii ) inaccurate calibration parameters, ( iv ) insufficient incubation time, ( v ) unsaturated binding isotherm, ( vi ) impurities in the reactants, and ( vii ) solute adsorption onto surfaces. Through this analysis, we illustrate how each source contributes to inaccuracies in the determination of K d and propose strategies to minimize these contributions. Additionally, we introduce a method for quantitatively assessing the confidence intervals of systematic errors in concentrations, a crucial step toward quantitatively evaluating the accuracy of K d . While presenting original findings, this paper also reiterates the fundamentals of K d determination, hence guiding researchers across all proficiency levels. By shedding light on the sources of systematic errors and offering strategies for their mitigation, our work will help researchers enhance the accuracy of K d determination, thereby making binding studies more reliable and the conclusions drawn from such studies more robust.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score0.815

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.117
GPT teacher head0.427
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations18
Published2024
Admission routes2
Has abstractyes

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