Protein Binder Design Using Target-Specific Affinity Prediction
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Solution Overview
Problem
Conventional methods for designing proteins, such as antibodies, are time-consuming and inefficient, often requiring extensive experimental evaluation of non-functional proteins due to the vast search space of protein sequences, and existing machine-learning approaches lack target-specificity and fail to evaluate designed libraries before experimentation.
Innovation Solution
A machine learning-driven approach that utilizes a trained model to predict binding affinities between proteins and targets, enabling the identification of a subset of proteins with higher binding affinities than a threshold, and incorporates optimization algorithms to generate diverse sequences, allowing for in silico evaluation and target-specific training data to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional experimental methods are used to evaluate all candidate proteins, then comprehensive binding affinity data can be obtained, but the process becomes extremely time-consuming and resource-intensive due to the vast search space
Solution Approach 1:
The patent applies preliminary action by training a machine learning model on experimental binding affinity data before the actual protein design process. This pre-trained model can then rapidly evaluate candidate sequences in silico, filtering out low-probability candidates before experimental testing. This preliminary computational evaluation reduces the number of sequences requiring experimental assessment from potentially millions to a manageable subset, dramatically reducing time and resource consumption while maintaining comprehensive evaluation of binding affinity.
Solution Approach 2:
The patent creates a computational copy of the binding affinity measurement process through a machine learning model. Instead of physically testing every candidate sequence experimentally, the model generates predicted binding affinities that mirror experimental results. This computational copy allows rapid evaluation of vast sequence spaces without the time and resource costs of physical experimentation, while still providing comprehensive binding affinity data for decision-making.
2Reliability
If the entire protein sequence space is explored to ensure target-specificity, then all potential binders can be identified, but the computational and experimental resources required become prohibitively large
Solution Approach 1:
The patent uses preliminary action by pre-training the machine learning model on target-specific binding data before the design phase. This pre-trained model encodes target-specific binding patterns and can rapidly score candidate sequences against the specific target. This allows comprehensive exploration of sequence space with respect to target-specificity without requiring physical testing of every candidate, as the model has already learned target-specific binding characteristics from training data.
Solution Approach 2:
The patent creates a computational model that copies and generalizes target-specific binding behavior. The model learns from a training set of target-specific interactions and can then predict binding affinity for novel sequences without requiring exhaustive experimental testing. This computational copy enables reliable target-specificity assessment across the entire sequence space while using minimal physical resources, as the model performs the heavy lifting of sequence evaluation in silico.
3Productivity
If existing machine-learning approaches are used without target-specific training, then rapid predictions can be made, but the predictions lack accuracy for specific target proteins
Solution Approach 1:
The patent applies parameter changes by adapting the machine learning model to target-specific parameters through training on target-specific binding data. The model's internal parameters (weights and biases) are adjusted during training to capture the specific binding characteristics of the target protein. This allows the model to maintain rapid prediction speeds while achieving high accuracy for the specific target, as the trained parameters encode target-specific binding patterns that generalize across different candidate sequences.
Data Source
AI summary
Described herein are techniques for designing proteins for binding to a target. In some embodiments, the techniques include: obtaining an amino acid sequence for a candidate protein that binds to the target with a candidate binding affinity; determining, for proteins in a set of proteins, probabilities that binding affinities between the proteins and the target are greater than the candidate binding affinity, and identifying a subset of the set of proteins based on the determined probabilities. Determining a first probability that a first binding affinity between a first protein and the target is greater than the candidate binding affinity may include: processing a first amino acid sequence of the first protein using a trained machine learning model to obtain a first output indicative of the first binding affinity; and determining the first probability using the first output indicative of the first binding affinity between the first protein and the target.