Computational Tools Identify Protein Aggregation Hotspots
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Solution Overview
Problem
Current methods for stabilizing therapeutic proteins, particularly antibodies, are inadequate in preventing aggregation, which leads to reduced efficacy and potential immunological responses due to their thermodynamic instability in aqueous solutions, necessitating a deeper understanding of aggregation mechanisms and identification of aggregation-prone regions.
Innovation Solution
The development of computational tools and methods based on computer simulations to identify aggregation-prone regions in proteins, allowing for targeted substitutions to enhance stability and reduce aggregation propensity, using the Spatial-Aggregation-Propensity (SAP) calculation to pinpoint hydrophobic patches on protein surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If therapeutic proteins are stored in aqueous solutions at high concentrations, then the drug can be effective for disease treatment, but the proteins become thermodynamically unstable and aggregate over time
Solution Approach 1:
The patent applies preliminary action by using computational methods to predict aggregation-prone regions in the protein sequence before the aggregation actually occurs. This allows for proactive engineering of the protein sequence to prevent aggregation, rather than reacting to instability after it manifests. The SAP calculation identifies hot spots that would otherwise lead to aggregation during storage.
Solution Approach 2:
The patent applies local quality by focusing modifications on specific aggregation-prone regions rather than attempting to stabilize the entire protein globally. The SAP calculation identifies localized hydrophobic patches and aggregation-prone segments, allowing targeted amino acid substitutions only in those specific regions. This localized approach maintains the overall protein function while preventing aggregation at critical sites.
2Stability of the object's composition
If aggregation-prone regions are identified and modified to reduce aggregation, then protein stability is improved, but the complexity of the development process increases due to computational analysis requirements
Solution Approach 1:
The patent replaces complex experimental trial-and-error methods with computational physics-based calculations. Instead of relying on labor-intensive experimental screening to identify aggregation-prone regions, the SAP calculation uses molecular dynamics simulations and hydrophobicity analysis to predict aggregation hot spots in silico. This substitution of computational methods for experimental mechanics reduces overall process complexity despite the advanced nature of the calculations.
Solution Approach 2:
The patent applies self-service by enabling the protein sequence itself to reveal its aggregation-prone regions through the SAP calculation. The method uses the protein's own structural and sequence information to identify its own vulnerability points, eliminating the need for external experimental probing. The computational method essentially allows the protein to 'self-diagnose' its aggregation risks.
3Manufacturing precision
If computational methods are used to identify aggregation-prone regions, then the precision of targeting specific hot spots is improved, but the difficulty of detecting and measuring aggregation propensity increases
Solution Approach 1:
The patent introduces an intermediary computational metric called the Spatial-Aggregation-Propensity (SAP) score that bridges the gap between complex molecular dynamics simulations and practical aggregation prediction. The SAP calculation serves as an intermediary that translates detailed atomic-level simulation data into a simplified, interpretable score that directly indicates aggregation propensity. This intermediary metric makes the complex computational process more manageable and easier to apply to drug development workflows.
Data Source
AI summary
The present invention provides methods and computational tools based, at least in part, on computer simulations that identify macromolecule binding regions and aggregation prone regions of a protein. Substitutions may then be made in these aggregation prone regions to engineer proteins with enhanced stability and/or a reduced propensity for aggregation. Similarly, substitutions may then be made in these macromolecule binding regions to engineer proteins with altered binding affinity for the macromolecule.