Automated Protein Engineering With AI-Guided Sequence Testing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional protein engineering is laborious and inefficient, relying on random sampling or guesswork, with a lengthy process involving DNA assembly, transformation, culturing, sequencing, and functional assays.
Innovation Solution
An AI-driven robotic system that automates protein engineering using reinforcement learning and upper confidence bound algorithms to optimize genotype-phenotype pairing, automating nucleic acid assembly, expression, and property detection, with minimal human input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional protein engineering methods are used, then proteins can be engineered with new or enhanced properties, but the process is laborious, time-consuming, and requires multiple manual steps including DNA assembly, transformation, culturing, sequencing, and purification
Solution Approach 1:
The system employs reinforcement learning algorithms that automatically design new protein sequences based on desired properties, eliminating the need for human researchers to manually design each protein variant. The AI agent continuously learns from experimental results and autonomously generates new hypotheses for testing, making the system self-directed and significantly increasing throughput while reducing time investment
Solution Approach 2:
The patent replaces manual mechanical operations with automated robotic liquid handling systems. The robotic system performs DNA assembly, transformation, culturing, and purification steps automatically, eliminating repetitive manual labor. This substitution of mechanical automation for human操作 dramatically increases productivity and reduces the time required for each experimental cycle
2Reliability
If random sampling or guesswork is used for protein design, then protein variants can be generated, but the probability of success is low and the process is inefficient
Solution Approach 1:
The system implements a closed-loop feedback mechanism where experimental results from protein testing are continuously fed back into the reinforcement learning algorithm. The AI agent uses this feedback to update its policy and improve future protein design decisions. This iterative learning process significantly increases the reliability of generating successful protein variants compared to random sampling, while maintaining high productivity through automated experimentation
Solution Approach 2:
The reinforcement learning algorithm performs preliminary computational design and prediction of protein properties before actual experimental synthesis. The AI agent simulates and evaluates potential protein sequences in silico, selecting only the most promising candidates for physical experimentation. This preliminary computational filtering dramatically improves success probability by avoiding futile experimental attempts, thereby increasing overall engineering efficiency
3Reliability
If human researchers perform protein engineering tasks, then experiments can be conducted with flexibility, but human error occurs and operation time is limited to working hours
Solution Approach 1:
The patent replaces human operators with automated robotic systems that perform all experimental operations including liquid handling, plate manipulation, and data recording. This substitution eliminates human error entirely, as the robotic system executes predetermined protocols with perfect consistency and accuracy. The automation also enables continuous operation beyond human working hours, with the system capable of running experiments 24/7 without fatigue or breaks
Solution Approach 2:
The system incorporates self-monitoring and self-correction capabilities through integrated sensors and control algorithms. The automated system automatically detects experimental conditions, adjusts parameters as needed, and validates results without human intervention. This self-service operation ensures consistent high accuracy while enabling uninterrupted continuous operation, as the system manages itself without requiring human oversight or maintenance during experimental runs
Data Source
AI summary
Systems and methods for protein engineering. The systems include a sequence testing subsystem and a machine learning subsystem. The sequence testing subsystem is configured to express proteins and test the expressed proteins for a given property. The machine learning subsystem is configured to model the activities of a set of possible proteins in light of the properties of the tested proteins and provide one or more untested proteins in the set to the sequence testing subsystem for subsequent testing. The system can be run in an iterative fashion and be fully automated. Methods of using the systems are provided.