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Bayesian Estimation of the Active Concentration and Affinity Constants Using Surface Plasmon Resonance Technology.

ABSTRACT: Surface plasmon resonance (SPR) has previously been employed to measure the active concentration of analyte in addition to the kinetic rate constants in molecular binding reactions. Those approaches, however, have a few restrictions. In this work, a Bayesian approach is developed to determine both active concentration and affinity constants using SPR technology. With the appropriate prior probabilities on the parameters and a derived likelihood function, a Markov Chain Monte Carlo (MCMC) algorithm is applied to compute the posterior probability densities of both the active concentration and kinetic rate constants based on the collected SPR data. Compared with previous approaches, ours exploits information from the duration of the process in its entirety, including both association and dissociation phases, under partial mass transport conditions; do not depend on calibration data; multiple injections of analyte at varying flow rates are not necessary. Finally the method is validated by analyzing both simulated and experimental datasets. A software package implementing our approach is developed with a user-friendly interface and made freely available.

SUBMITTER: Feng F

PROVIDER: S-EPMC4476803 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Bayesian Estimation of the Active Concentration and Affinity Constants Using Surface Plasmon Resonance Technology.

Feng Feng F Kepler Thomas B TB

PloS one 20150622 6

Surface plasmon resonance (SPR) has previously been employed to measure the active concentration of analyte in addition to the kinetic rate constants in molecular binding reactions. Those approaches, however, have a few restrictions. In this work, a Bayesian approach is developed to determine both active concentration and affinity constants using SPR technology. With the appropriate prior probabilities on the parameters and a derived likelihood function, a Markov Chain Monte Carlo (MCMC) algorit ...[more]

PMID: 26098764

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Project description:Type 1 diabetes mellitus (DM) is characterized by autoimmune aggression against pancreatic beta cells resulting in absolute deficiency of insulin secretion. The first detectable sign of emerging autoimmunity during the preclinical asymptomatic period is the appearance of diabetes-related autoantibodies. In children at risk for type 1 DM, high-affinity Insulin autoantibodies reactive to proinsulin, are associated with diabetes risk. Autoantibodies are usually measured by radioligand binding assay (RBA) that provides quasi-quantitative values reflecting potency (product between concentration and affinity) of specific autoantibodies. Aiming to improve the characterization of the specific humoral immune response, we selected surface plasmon resonance (SPR) as an alternative method to measure proinsulin autoantibodies (PAA). This novel technology has allowed real time detection of antibodies interaction and kinetic analysis. Herein, we have employed SPR to characterize the PAA present in sera from 28 childhood-onset (mean age 8.31±4.20) and 23 adult-onset diabetic patients (?65 years old, BMI<30) in terms of concentration and affinity. When evaluating comparatively samples from both groups, childhood-onset diabetic patients presented lower PAA concentrations and higher affinities (median 67.12×10(-9) M and 3.50×10(7) M(-1), respectively) than the adults (median 167.4×10(-9) M and 0.84×10(7) M(-1), respectively). These results are consistent with those from the reference method RBA (Standard Deviation score median 9.49 for childhood-onset group and 5.04 for adult-onset group) where the binding can be directly related to the intrinsic affinity of the antibody, suggesting that there is a different etiopathogenic pathway between both types of clinical presentation of the disease. This technology has shown to be a useful tool for the characterization of PAAs parameters as an alternative to radioimmunoassay, with high versatility and reproducibility associated to low occupational and environmental risk. However, this technology is not eligible for routine marker screening, but this is a powerful technique for a fine description of the thermodynamic parameters of antigen-antibody interaction.

Dataset Information

Bayesian Estimation of the Active Concentration and Affinity Constants Using Surface Plasmon Resonance Technology.

Publications

Bayesian Estimation of the Active Concentration and Affinity Constants Using Surface Plasmon Resonance Technology.

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