Dataset Information

Catch strip assay for the relative assessment of two-dimensional protein association kinetics.

ABSTRACT: Accurate interpretation of recruitment rate measurements of microscale particles, such as cells and microbeads, to biofunctional surfaces is difficult because factors such as uneven ligand distributions, particle collisions, variable particle fluxes, and molecular-scale surface separation distances obfuscate the ability to link the observed particle behavior with the governing nanoscale biophysics. We report the development of a hydrodynamically conditioned micropattern catch strip assay to measure microparticle recruitment kinetics. The assay exploited patterning within microfluidic channels and the mechanostability of selectin bonds to create reaction geometries that confined a microbead flux to within 200 nm of the surface under flow conditions. Systematic control of capillary action enabled the creation of homogeneous or gradient ligand distributions. The method enabled the measurement of particle recruitment rates (keff, s-1) that were primarily determined by the interaction of the biomolecular pair being investigated. The method is therefore well suited for relative measurements of delivery vehicle and cellular recruitment potential as governed by surface-bound molecules.

SUBMITTER: Schmidt BJ

PROVIDER: S-EPMC3335339 | biostudies-literature | 2008 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Catch strip assay for the relative assessment of two-dimensional protein association kinetics.

Schmidt Brian J BJ Huang Peter P Breuer Kenneth S KS Lawrence Michael B MB

Analytical chemistry 20080125 4

Accurate interpretation of recruitment rate measurements of microscale particles, such as cells and microbeads, to biofunctional surfaces is difficult because factors such as uneven ligand distributions, particle collisions, variable particle fluxes, and molecular-scale surface separation distances obfuscate the ability to link the observed particle behavior with the governing nanoscale biophysics. We report the development of a hydrodynamically conditioned micropattern catch strip assay to meas ...[more]

PMID: 18217724

Similar Datasets

Project description:A growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (??G) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (??G < -0.5 kcal/mol) with high (83-98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods.

Dataset Information

Catch strip assay for the relative assessment of two-dimensional protein association kinetics.

Publications

Catch strip assay for the relative assessment of two-dimensional protein association kinetics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets