ArticlePLoS computational biology2026
htrSPRanalysis: An open source R package for expedited analysis of high-throughput binding kinetics data.
Article in PLoS computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Surface plasmon resonance (SPR) enables label-free detection of binding kinetics and has been widely applied to the biophysical characterization of molecular interactions such as antibody-antigen binding. With the advent of high-throughput SPR (HT-SPR) instruments, hundreds of binding interactions can be detected simultaneously, combining the details of kinetic measurements with the capability of large-panel biomolecule screening. However, binding kinetics analysis for large panels of antibody or antigen often requires a combination of fitting strategies to address different types of sensorgrams. While software packages exist for SPR binding kinetics data analysis, they are associated with a number of limitations: 1) currently most of the software packages are proprietary, prohibiting widespread use; 2) most of the software packages, including open source packages, are designed primarily for low-throughput data analysis, making analyzing a large number of kinetics data sets labor-intensive; 3) the software typically requires multiple iterative user-interface interactions when analyzing large data sets. Here, we present htrSPRanalysis, an open source R package designed primarily for high-throughput binding kinetics data analysis, currently focusing on 1:1 binding analysis. htrSPRanalysis leverages the increasingly commonplace multi-core computing architecture to efficiently analyze a large number of sensorgrams with minimal user-interface interaction. It also offers automated generation of analysis output for all sensorgrams. Furthermore, beyond manual optimization of sensorgram fitting strategies, htrSPRanalysis accelerates the analysis process by providing automated procedures to determine the optimal concentration range, choose the optimal dissociation window for fitting, and detect bulk shift. The high-throughput functionalities and automation of fitting optimization makes htrSPRanalysis especially useful for speeding up data analysis to get results for implementing further steps in therapeutic antibody discovery research.
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