htrSPRanalysis: An open source R package for expedited analysis of high-throughput binding kinetics data

Janice M McCarthy, Kan Li, Georgia D Tomaras and S Moses Dennison

PLOS Computational Biology, 2026, vol. 22, issue 7, 1-14

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, htrSPR analysis 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.Author summary: Surface plasmon resonance (SPR) is widely used to measure the strength and speed of molecular interactions, such as how antibodies recognize their targets. SPR does not require labeling and can provide real-time kinetics information. Recent advances in high-throughput SPR (HT-SPR) instruments allow hundreds of interactions to be measured simultaneously, which is especially valuable for the screening of large panels of biomolecules. Despite these advances, data analysis remains a major challenge. Most existing software is proprietary, designed for small-scale studies, or requires a time-consuming manual effort for analyzing each molecular interaction, limiting the pace and scalability of research. We developed htrSPRanalysis, an open-source R package designed specifically to analyze high-throughput binding kinetics data. The package leverages multi-core computing to analyze data for many molecular interactions in parallel, dramatically reducing processing time. It also includes automated routines for steps to determine the optimal data analysis strategy, including bulk shift correction, optimal concentration range selection, and dissociation window determination, ensuring more consistent and reproducible analyses. By reducing manual effort and providing scalable analysis tools, htrSPRanalysis helps researchers efficiently interpret HT-SPR data, accelerating the discovery and development of therapeutic antibodies.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1014581

DOI: 10.1371/journal.pcbi.1014581

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