Biacore Data Analysis: Software Tools and Interpretation of Results

Surface Plasmon Resonance (SPR) is a powerful analytical technique used to study molecular interactions in real-time, providing critical insights into binding kinetics, affinity, and specificity. Biacore is the abbreviation of molecular Interaction in English "Biomolecule Interaction Analysis core technique".

Data show that the Biacore series SPR biomolecular interaction instrument produced by Cytiva company has published nearly thousands of articles in the main CNS journal. Since 1990, Biacore has launched a series of devices, from the original Biacore 1000 to Biacore 3000, Biacore 4000, Biacore T series, X series, 8K series, 1 series, and so on. At the end of 2022, after continuous technological innovation and iterative updates, Cytiva launched a powerful new generation of molecular interaction system Biacore 1 series, with Biacore 1K, Biacore 1K+, Biacore 1S+ three system configurations to choose from (while discontinuing the T200). Biacore 1 series inherits the classic SPR technology, single-needle, 6-channel molecular interaction platform, sample detection throughput increases, all models share a set of Biacore Insight software, easy to use; A variety of injection modes have been added to facilitate multi-component complex assembly, competition suppression, epitope analysis, buffer screening, and other experimental content.  

SPR technology, powered by the Biacore system, has revolutionized the study of molecular interactions by combining events with real-time, marker-free analysis. However, turning raw SPR data into meaningful biological insights requires precise tools and expertise. This paper explores the Biacore data analysis software ecosystem - including Biacore Insight, evaluation software, and Cytiva's advanced tools - and provides actionable strategies for interpreting sensor maps, calculating kinetic constants, and measuring affinity.

Whether you're navigating protein-ligand binding research or optimizing analytical repeatability, we'll break down best practices for data validation, error mitigation, and leveraging case studies to refine your workflow. Dive into the intersection of mastering cutting-edge software and powerful scientific explanations.

Biacore Analysis Software

During sample injection, when the analyte (the interaction partner in solution in Biacore analysis) combines with the ligand (the interaction partner connected to the sensor surface in Biacore analysis), a positive response can be seen in the sensor map. The reaction is weakened during dissociation. After the analysis cycle, the regenerated solution passes through the sensor chip to remove the bound analyte in preparation for the next analysis cycle.

Biacore Insight

Known for its user-friendly interface and powerful features, Biacore Insight Software provides an efficient instrument control and data analysis platform for Biacore Series 1 and Biacore Series 8 systems. With a range of optional software extensions, the software provides more functionality for different application requirements, optimizes the analysis process, and reduces the time to obtain results.

Biacore Insight software provides users with a constantly evolving platform through frequent free upgrades and extensions, and it is worth choosing from the following features:

• Biacore Insight Application Process Programming Interface (API): API is a dedicated software tool that enables fully automated integration of Biacore SPR data. Apis are common tools in modern data ecosystems that allow programmatic access to data and support communication between different software solutions. By incorporating this technology into Biacore Insight software, evaluation and run data can be used to meet users' data processing needs, such as aggregating with data from other technologies, downstream custom analytics, using it to train AI models, or automating reporting.
• Innovative and flexible injection modes: The software's molecular fishing capabilities can identify and identify analytes from complex or mixed samples (such as cell extracts, supernatants, and reaction mixtures), find stable molecules from mixtures exposed to harsh conditions, analyze proteomic networks by identifying binding partners recovered under different conditions, and even capture and elute cells for subsequent sequencing.
• Biacore Intelligent Analysis: The software supports the evaluation of kinetic and steady-state affinity data. It can automatically remove the anomaly curve and select the appropriate report point, then automatically choose to use constant Rmax or fit Rmax and select the most suitable model to fit, and finally automatically classify the results according to your requirements, helping to quickly obtain the ideal candidate molecules. By retraining the model using the user's individual choices at each stage, the model will learn how to evaluate the data like an expert.
• Streamline the flow of sample information: When the software can import sample details from the clipboard or document, the Biacore Insight software will attempt to automatically match the required information with the provided information (simplify the sample import step: allow multiple commands to be imported at once) to complete the automatic sample information mapping. In addition, it can also export the information of the cloth plate as a text document, which is convenient for sample preparation: it can be directly imported into the sampling workstation.

Figure 1: Biacore Insight software result analysis diagram. (fig from Cytiva)

Evaluation Software

Evaluation software is software that helps analyze sensor graph data by calculating kinetic constants (Kd) as well as other important parameters such as association (ka) and decoupling. It also provides tools to fit models to data to ensure optimal data interpretation.

Cytiva tools

Cytiva, the developer of Biacore systems, offers a suite of specialized software tools for customized data analysis. These tools include software for modeling complex interactions, such as multisite binding or cooperative binding, and for optimizing experimental conditions for specific applications.

How to Analyze SPR Data

Analyzing SPR data includes interpreting sensor diagrams - diagrams that describe binding interactions over time. For interpretation of the sensor map, you can refer to the article Surface Plasmon Resonance Sensorgram a Step-by-Step Guide, which describes some related concepts.

Sensorgrams

The primary data generated from SPR experiments are sensorgrams, which show the change in refractive index as molecules interact on the sensor surface. The sensorgram typically has a "binding curve" during the association phase (when the analyte binds to the immobilized ligand) and a "dissociation curve" when the analyte dissociates from the surface. Accurate interpretation of these curves is critical for understanding interaction kinetics.

Kinetic constants

In the surface plasmon resonance experiments of the Biacore instrument, Kinetic constants are important parameters for the analysis of molecular interactions, especially the binding rate constant and the dissociation rate constant. These constants provide information about the rate of interaction between molecules and are a central indicator of the binding and decoupling process of biomolecules. By modeling the sensorgram in the SPR data, these kinetic constants can be precisely obtained.

In experiments, the sensor diagram usually shows two parts: in the binding phase, the analyte and ligand interact, and the sensor signal rises; In the dissociation stage, the binding of the analyte and ligand gradually unraveled, and the signal decreases. By analyzing these signal changes, the Biacore software is able to fit the data and calculate the ka and kd values. Ka represents the rate at which the ligand binds to the analyte, while Kd represents the rate at which the two dissociate. With these two rate constants, researchers can gain insight into the dynamics of molecular interactions.

Affinity measurements

In surface plasmon resonance experiments with Biacore instruments, affinity measurement is a key step in assessing the strength of intermolecular interactions. Affinity is usually expressed by an equilibrium dissociation constant, but in addition to KD, other parameters such as binding rate constant and dissociation rate constant are also important factors affecting affinity evaluation. Affinity measurement is usually performed by extracting relevant data from a sensorgram at the binding and dissociation stages and accurately evaluating the data by fitting it.

The Biacore software is able to calculate the ka and kd from the sensor map signal changes. The binding rate constant ka represents the rate at which the analyte binds to the ligand, and the dissociation rate constant kd represents the dissociation rate. When the two are combined, the final KD value obtained can be used to measure the strength of affinity.

However, affinity is not limited to the calculation of KD. In some cases, by changing the experimental conditions, such as temperature, pH, or the ionic strength of the buffer, the binding kinetics can be affected, thereby changing the affinity. The Biacore instrument provides accurate, real-time data to help researchers optimize and compare affinity under different conditions. Through multiple experiments and data analysis, researchers can fully understand the stability of intermolecular interactions, which provides an important reference for drug discovery, antibody engineering, and other studies.

Tips for Accurate Data Interpretation

In the Biacore SPR experiment, the accuracy of data interpretation directly affects the reliability of experimental conclusions.

First, choosing the right model is fundamental to ensuring accurate interpretation of the data. Depending on the experimental design and the characteristics of the interaction, the researchers should choose the appropriate dynamic model. If the wrong model is used, it can lead to poor fit, which in turn affects the accuracy of parameters such as Ka, Kd, KD, etc. For complex multi-site or cooperative interactions, the choice of multi-site models or other special models will help to better describe the actual binding process.

Secondly, conducting repeated experiments is an important step to verify the reliability of data. By repeating the experiment many times and comparing the results, systematic errors or random fluctuations in the experiment can be identified. In general, at least three repetitions of the experiment can improve the reliability of the data and ensure that the results are statistically significant.

Monitoring baseline stability is also an important guarantee of data accuracy. Before each analysis, ensure that the sensor surface is free of contamination and that the baseline is stable. If the baseline is unstable, it may lead to signal errors, which in turn affect the analysis of the combined data. The stability check of the baseline should be part of every experiment to ensure the quality of the sensor map.

Case Studies

Case studies demonstrate the ability of SPR to provide detailed insights into biomolecular interactions, enhancing drug discovery and diagnostic development. For more specific Applications, you can choose to read A Comprehensive Guide to Biacore Instruments Features, Specifications, and Applications.

Protein-Ligand Binding Analysis: In a study involving a protein-ligand interaction, SPR data was analyzed to determine the affinity of a small molecule binding to a receptor. By fitting the sensorgram data to a kinetic model, the team was able to calculate the kinetic constants and determine the dissociation constant, which indicated a strong binding interaction.

Antibody-Antigen Interactions: Another case study focused on measuring the binding affinity between a monoclonal antibody and its antigen. By analyzing the sensorgram, the research team determined the optimal concentration of antibody for further therapeutic development.