Quantum Annealing for Protein Design Search Space Optimization
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Solution Overview
Problem
Conventional computing systems are unable to efficiently search the vast space of protein design possibilities due to limitations in sampling multiple states simultaneously, leading to reliance on subjective methods and incomplete searches, which hampers the optimization of protein characteristics such as thermostability and solubility.
Innovation Solution
The use of quantum computing, specifically quantum annealing and quantum-inspired algorithms, allows for the exploration of larger search spaces including more positions in a protein sequence and rotamer combinations, enabling the identification of optimal protein sequences with improved properties by leveraging quantum superposition and entanglement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional computing systems are used to search protein design possibilities, then the search process is limited to small subsets of positions and rotamers, but the computational resources and time required increase exponentially as the search space expands
Solution Approach 1:
The patent combines multiple quantum computing techniques (quantum annealing, quantum-inspired algorithms, and quantum simulation) into a unified computational framework. This merging enables the system to simultaneously explore multiple regions of the protein design search space, achieving both extensive coverage and computational efficiency that neither technique could achieve alone.
Solution Approach 2:
The system dynamically adapts its search strategy by adjusting the balance between quantum annealing and quantum-inspired algorithms based on problem characteristics and available computational resources. This dynamic approach allows efficient exploration of varying search space sizes without requiring exponential resource increases.
2Productivity
If heuristic methods like simulated annealing are used to search protein design space, then the search can be performed quickly on conventional computers, but the methods cannot guarantee finding the global optimum and become intractable for large design tasks
Solution Approach 1:
The patent introduces quantum computing as an intermediary computational paradigm between classical heuristic methods and exhaustive search. Quantum annealing provides a middle ground that maintains computational efficiency while offering improved convergence properties and the ability to escape local minima, thereby achieving both speed and reliability.
Solution Approach 2:
The system changes the fundamental computational parameters by transitioning from classical binary state computation to quantum superposition states. This parameter change enables simultaneous evaluation of multiple design candidates and provides probabilistic guarantees of finding global optima that are unavailable to classical heuristic methods.
3Manufacturing precision
If the number of positions and rotamers included in the search increases, then the quality of protein optimization improves, but the computational complexity exceeds the capabilities of conventional computers
Solution Approach 1:
The patent replaces the mechanical sequential processing of conventional computers with quantum mechanical parallel processing. Quantum superposition allows the system to represent and manipulate exponentially more states simultaneously, substituting the mechanical limitations of classical computation with quantum mechanical capabilities.
Solution Approach 2:
The system transitions from the three-dimensional search space accessible to conventional computers to a higher-dimensional quantum state space. By utilizing quantum bits that can exist in superposition of multiple states, the system effectively adds dimensional capacity, enabling exploration of vastly larger search spaces without proportional increases in physical computational resources.
Data Source
AI summary
Exemplary embodiments relate to a protein engineering pipeline configured to optimize or improve proteins for specified functions. The problem space of such a task can grow quickly based on the sequence of the protein being optimized and the functions for which the protein is being designed. The solutions described herein allow the problem space to be efficiently searched by applying a combination of a protein design pipeline and an evaluation procedure performed on a quantum computer. As a result, single or multiple amino acid substitutions at a site of interest may be predicted in order to generate optimized protein variants.