Circular Bin Chart for Epitope Binning Data Interpretation
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Solution Overview
Problem
Existing methods for interpreting epitope binning experimental data, such as heat maps and node charts, can be difficult for inexperienced users to understand and interpret, necessitating a simpler approach to extract associations from binning data.
Innovation Solution
A method and system utilizing a circular or semi-circular bin chart to visualize and associate target binding biomolecules, where each bin represents an epitope family, with interaction profiles and bin connections displayed as circle sectors and lines, allowing for the selection of a subgroup of biomolecules for further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If heat maps or node charts are used to display epitope binning data, then the data can be visualized, but inexperienced users find it difficult to understand and interpret the associations
Solution Approach 1:
The patent applies a circular layout where bins are arranged as sectors around a circle, replacing the traditional linear or grid-based heat map format. This circular arrangement allows users to visually trace interactions between bins along the circumference, making association patterns more intuitive and easier to interpret at a glance.
Solution Approach 2:
The invention transforms the two-dimensional grid structure of heat maps into a circular dimension where bins are positioned around a circle. This dimensional reorganization allows interaction data to be represented as connections between circular sectors, creating a new visual paradigm that simplifies the perception of relationships between multiple bins simultaneously.
2Measurement precision
If all antibody candidates are tested individually for affinity, then high-affinity antibodies can be identified, but the results are biased to a small number of epitopes and epitope diversity is lost
Solution Approach 1:
The patent segments the antibody library into multiple bins based on epitope families, where each bin contains antibodies targeting a specific epitope region. This segmentation allows systematic exploration of different epitope regions while maintaining organized categorization, enabling both focused affinity analysis within bins and comprehensive diversity coverage across all bins.
Solution Approach 2:
The binning process serves as a preliminary action that pre-organizes antibodies into epitope-based groups before detailed affinity analysis. This preliminary categorization enables researchers to efficiently select representative antibodies from each bin for further testing, ensuring broad epitope coverage without requiring exhaustive testing of all candidates.
3Productivity
If epitope binning is performed to maintain diversity, then more antibody candidates can be evaluated, but the complexity of analyzing and interpreting the binning data increases
Solution Approach 1:
The circular bin chart provides a simplified visual interface that reduces the perceived complexity of binning data. By arranging bins as circular sectors with clear spatial relationships, the system makes it easier for users to navigate and interpret interactions among many antibodies, thereby enabling evaluation of larger candidate pools without proportionally increasing analytical complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Facilitates easier interpretation and extraction of associations in epitope binning data, enabling inexperienced users to quickly understand and select relevant biomolecules, thereby improving the efficiency of biologic drug discovery workflows.
Implementation Method 1
Epitope binning is a competitive immunoassay used to characterize and then sort a library of for example monoclonal antibodies against a target protein. Antibodies against a similar target may be tested against all other antibodies in the library in a pairwise fashion to see which antibodies block one another's binding to an antigen.
Data Source
AI summary
Disclosed is a method of qualifying a subgroup of target binding biomolecules from a larger group of target binding biomolecules for analysis. A competitive immunoassay including a target protein is used to identify 100 interactions between different pairs of the target binding biomolecules and interaction profiles are generated 200. Each target binding biomolecule is allocated 300 to a bin representing an epitope family and identified bins are associated in a circular or semi-circular bin chart on a display with identified respective target binding biomolecule(s). Based on the association 400 between identified bins and identified respective target binding molecule(s) in the bin chart, a subgroup of target binding biomolecules is selected 500 for further analysis by selecting one or more of the target binding biomolecule(s) of one or more of the bins.