Computational Enzyme Design via Hashing Algorithms
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Solution Overview
Problem
Current methods are inefficient and labor-intensive for designing enzymes that catalyze chemical transformations not efficiently handled by naturally occurring enzymes, as they require extensive empirical testing.
Innovation Solution
Computational techniques using hashing algorithms to identify functional reactive sites and protein backbone structures, followed by computational design of enzyme sequences and empirical testing to optimize enzymatic efficiency, including the use of Rosetta design methodology for protein structure exploration and small molecule docking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If empirical testing of potential enzyme candidates is performed, then enzymatic efficiency can be evaluated, but the process becomes time and labor intensive
Solution Approach 1:
The patent applies preliminary action by using computational methods to pre-screen and rank potential enzyme candidates before empirical testing. The system performs in silico enzyme design and virtual screening to identify promising candidates, thereby reducing the number of experiments needed and saving time while maintaining reliability through systematic evaluation of catalytic activity.
Solution Approach 2:
The patent replaces the mechanical system of extensive empirical laboratory testing with a computational system. The in silico enzyme design methodology uses computer algorithms and molecular modeling to predict enzymatic activity, substituting physical experimentation with computational analysis to reduce time and labor while evaluating enzymatic efficiency.
2Productivity
If computational tools are used for enzyme design, then design efficiency is improved, but the complexity of the computational methods increases
Solution Approach 1:
The patent applies segmentation by dividing the complex computational enzyme design process into distinct modular steps: (1) defining the catalytic mechanism and transition state, (2) searching for suitable protein scaffolds, (3) designing the active site residues, (4) optimizing the protein structure, and (5) validating the design. This segmentation makes the complex computational methodology more manageable and systematic, improving productivity through structured approach.
Solution Approach 2:
The patent uses transition state models as intermediaries to bridge the gap between desired chemical reaction and protein structure design. The transition state representation serves as a mediator that translates chemical catalytic requirements into structural constraints for enzyme design, simplifying the computational process while maintaining design efficiency.
3Reliability
If naturally occurring enzymes are used, then catalytic specificity is maintained, but the ability to catalyze non-natural reactions is limited
Solution Approach 1:
The patent applies local quality by modifying specific regions of protein scaffolds (the active site) while maintaining the overall fold and stability of the protein structure. The computational design allows precise control over the local chemical environment at the active site to achieve specific catalytic functions, enabling adaptation to non-natural reactions while preserving global structural reliability and specificity.
Solution Approach 2:
The patent demonstrates universality by using a single computational design framework to create enzymes for diverse non-natural chemical reactions. The same in silico enzyme design methodology can be applied to different reaction types by changing the transition state model and catalytic mechanism parameters, allowing one system to generate multi-functional enzymes with different specificities.
Data Source
AI summary
Disclosed herein are techniques for computationally designing enzymes. These techniques can be used to design variations of naturally occurring enzymes, as well as new enzymes having no natural counterparts. The techniques are based on first identifying functional reactive sites required to promote the desired reaction. Then, hashing algorithms are used to identify potential protein backbone structures (i.e., scaffolds) capable of supporting the required functional sites. These techniques were used to design 32 different protein sequences that exhibited aldol reaction catalytic function, 31 of which are defined in the Sequence Listing. Details of these 31 different synthetic aldolases are provided, including descriptions of how such synthetic aldolases can be differentiated from naturally occurring aldolases.