Protein Viscosity Prediction via Spatial Charge Maps
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Solution Overview
Problem
Current methods for predicting the viscosity of protein solutions, such as those containing antibodies, are cumbersome and inaccurate, relying on qualitative electrostatic potential assessments that require human inspection and are limited in accuracy.
Innovation Solution
A computer-implemented method that analyzes computer-generated protein structures to predict viscosity using spatial charge maps, eliminating the need for human inspection and providing a quantitative, high-throughput analysis by calculating numeric values indicative of structural properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If qualitative electrostatic potential assessment methods are used, then human inspection can identify viscosity issues, but the process is cumbersome, slow, and limited in accuracy
Solution Approach 1:
The patent replaces the manual visual inspection process (mechanical/human system) with an automated computational method that calculates electrostatic potential values and predicts viscosity quantitatively. The computer-implemented method automatically computes electrostatic potential maps and viscosity predictions without requiring human visual inspection, thereby eliminating the time loss associated with manual analysis while improving measurement precision through quantitative calculations.
Solution Approach 2:
The computational method enables the system to perform viscosity prediction automatically without human intervention. The computer-implemented method self-evaluates electrostatic potential properties and generates viscosity predictions independently, eliminating the need for cumbersome human inspection processes and significantly reducing analysis time while maintaining or improving accuracy.
2Measurement precision
If quantitative computational methods are used, then viscosity prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts and focuses on specific key electrostatic potential parameters (such as maximum electrostatic potential values and their locations on the protein surface) that are most relevant to viscosity prediction. By isolating these critical features from the complete electrostatic potential map, the method achieves accurate viscosity predictions while reducing computational complexity compared to analyzing the entire potential distribution in detail.
3Ease of operation
If manual visual inspection of electrostatic potential is required, then qualitative assessment can be performed, but the process is cumbersome and slow
Solution Approach 1:
The patent replaces manual visual inspection with an automated computational algorithm that processes electrostatic potential data and generates viscosity predictions. This substitution eliminates the operational burden of manual inspection while dramatically increasing screening throughput, as the computer-implemented method can rapidly evaluate multiple protein candidates without human intervention.
Solution Approach 2:
The computational method enables continuous automated processing of viscosity predictions without the interruptions inherent in manual inspection. The computer-implemented method can continuously calculate electrostatic potential parameters and generate viscosity predictions for multiple proteins in sequence, maximizing productivity while maintaining ease of operation through automated workflows.
Data Source
AI summary
Provided herein are high-throughput methods for identifying a candidate antibody based on viscosity of the candiate antibody.