Protein Binder Search with Targeted Iterative Model Refinement
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Solution Overview
Problem
Existing protein binder search methods are limited by the inefficiencies of deep learning models, requiring additional computational resources to improve prediction accuracy and reliability.
Innovation Solution
A multi-track biological language reasoning model is trained on protein sequences and structures, including multimer data, to predict binder proteins that can bind with a specified target protein, utilizing iterative search techniques and binding quality evaluations to refine predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional compute resources are applied to improve deep learning prediction results, then prediction accuracy is improved, but computational cost and time increase
Solution Approach 1:
The system performs preliminary actions by using a biological language model to generate initial binder protein predictions and define a targeted search space before applying more computationally intensive iterative search techniques. This preliminary modeling approach narrows down the search space, allowing subsequent resource-intensive operations to focus only on relevant candidates, thereby improving accuracy without proportionally increasing computational cost.
Solution Approach 2:
The prediction process is segmented into multiple stages: (1) initial binder protein generation using a biological language model, (2) definition of a targeted search space based on initial results, and (3) iterative search and evaluation using additional compute resources. This segmentation allows the system to distribute computational workload across stages with different resource requirements, optimizing the balance between accuracy and computational cost.
2Reliability
If iterative search techniques are used to refine binder protein predictions, then prediction reliability is improved, but search time increases
Solution Approach 1:
The iterative search technique applies local quality by focusing computational effort on specific regions of the search space identified as most promising by the biological language model. Rather than uniformly searching the entire protein space, the system concentrates iterative refinement on localized areas defined by initial predictions and binding quality evaluations, thereby improving reliability without linearly increasing search time.
Solution Approach 2:
The system implements feedback mechanisms where binding quality evaluations of candidate binder proteins feed back into the search process. This feedback allows the iterative search to prioritize regions with higher binding quality scores, efficiently converging on reliable predictions while minimizing unnecessary search time in low-quality regions.
3Measurement precision
If a targeted search space is explored beyond initial model results, then binder identification accuracy is improved, but device complexity increases
Solution Approach 1:
The biological language model serves as an intermediary that bridges the gap between simple deep learning predictions and complex iterative search techniques. It translates biological constraints into a targeted search space definition, allowing the subsequent iterative search to operate with reduced complexity by working within a pre-defined relevant subset of the search space rather than the entire protein space.
Data Source
AI summary
A specification of a binding target protein is received. A machine learning model is used to predict a plurality of candidates for a property of a selected amino acid position of a binder protein to bind to the binding target protein. For each selected property candidate of the plurality of property candidates, the selected property candidate is used as a model input to predict properties for one or more other amino acid positions into a corresponding candidate set of properties. The corresponding candidate sets are evaluated using a binding quality evaluation. Based on the evaluation, one of the plurality of property candidates is selected as a determined result property of the selected amino acid position. The determined result property is used as a model input to predict a plurality of candidates for a property of a different selected amino acid position included in the binder protein.