Amyloid Aggregation Models for Inhibitor Discovery
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Solution Overview
Problem
Current methods for identifying compounds that inhibit amyloid protein aggregation and modeling amyloid receptors are limited by the lack of three-dimensional, non-crystallographic models, which hinder the discovery of new inhibitors and the optimization of existing compounds, particularly for amyloid diseases like Alzheimer's and Parkinson's, where existing models require synthesis and procurement of candidate compounds and lack descriptions of binding pockets for de novo design.
Innovation Solution
Development of three-dimensional, non-crystallographic models of amyloid aggregation and receptors that allow for the identification and validation of compounds that modulate amyloid protein aggregation, enabling the prediction of interactions and improvement of compound potency without the need for crystal structures, using computer modeling programs to construct pseudo-crystal structures and validate their anti-amyloid activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If in vitro or in vivo models are used for high-throughput screening, then compound discovery can proceed, but synthesis and procurement of every candidate compound is required, increasing time and resource consumption
Solution Approach 1:
The patent applies preliminary action by pre-computing three-dimensional models of amyloid aggregation and identifying binding pockets before actual compound screening begins. This allows virtual screening and de novo design of compounds to occur without requiring physical synthesis and procurement of every candidate, thereby reducing time loss while maintaining productivity
Solution Approach 2:
The patent uses copying by creating virtual three-dimensional models and pseudo-crystal structures of amyloid proteins that can be used for in silico screening. These computational copies allow researchers to evaluate compound interactions without physically synthesizing each candidate, thus maintaining high throughput while eliminating the time-consuming synthesis and procurement steps
2Productivity
If existing in vitro and in vivo models are used, then screening can be performed, but no binding pocket description is provided, preventing de novo design and potency optimization
Solution Approach 1:
The patent performs preliminary action by pre-computing detailed three-dimensional models of amyloid aggregation structures and identifying binding pockets before screening begins. This provides the necessary structural information in advance, enabling both screening and de novo design without losing critical binding pocket data
Solution Approach 2:
The patent replaces the mechanical/experimental approach of determining binding pocket structures through physical crystallography with computational methods. By using molecular dynamics simulations and pseudo-crystal structure generation, the binding pocket information is obtained computationally, preserving all necessary structural details for de novo design while avoiding the limitations of experimental methods
3Reliability
If peptidic compounds are used as modulators, then disease target modulation is achieved, but in vivo stability issues reduce their utility as drugs
Solution Approach 1:
The patent applies parameter changes by using computational models to evaluate and compare different compound classes (peptidic vs. non-peptidic) based on their predicted binding affinity and stability parameters. This allows identification of non-peptidic compounds that can achieve reliable target modulation while having improved in vivo stability characteristics
4Productivity
If non-peptidic compound classes are discovered, then inhibitors of beta-amyloid build-up are identified, but further discovery and optimization remains haphazard due to lack of crystal structures
Solution Approach 1:
The patent performs preliminary action by pre-computing three-dimensional models and binding pocket descriptions before the optimization phase. This systematic preparation enables structured, rational optimization of non-peptidic compounds rather than haphazard approaches, improving ease of manufacture through guided structural modifications
Solution Approach 2:
The patent replaces the need for physical crystal structures with computationally generated pseudo-crystal structures and three-dimensional models. This substitution provides systematic optimization capability through in silico analysis, allowing rational compound design and improvement without requiring actual crystallographic data
Data Source
AI summary
Methods for identifying compounds that are inhibitors or are likely to be inhibitors of amyloid protein aggregation, as well as three-dimensional, non-crystallographic models (i.e. “pseudo-crystal structures”) of amyloid aggregation utilized in the methods, are described. Means for creating the three-dimensional, non-crystallographic models (i.e. “pseudo-crystal structures”) of amyloid aggregation are also described.