Deep Learning Biomolecule Optimization via Latent Space Gradient Ascent
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Solution Overview
Problem
The vast space of possible protein sequences and the complexity of predicting the effects of small changes in sequences on protein properties due to higher-order interactions between amino acids, making it difficult to generate sequences with desired properties efficiently.
Innovation Solution
A data-driven deep generative approach that transforms biomolecular data into a latent space representation, using a transformer module with an attention-based pooling mechanism and an autoencoder-type bottleneck for gradient ascent optimization to improve protein sequences directly in the latent space based on fitness functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep learning models are trained to predict fitness from sequences, then the ability to navigate sequence space is improved, but the computational cost and data requirements increase significantly
Solution Approach 1:
The patent introduces a latent space representation as an intermediary between sequence space and fitness landscape. Instead of directly mapping sequences to fitness, the model transforms sequences into a compressed latent representation that captures essential structural and functional information, enabling more efficient navigation of sequence space with reduced computational overhead
Solution Approach 2:
The patent extracts and removes redundant information from the sequence representation through the latent space transformation. By compressing the sequence into a lower-dimensional latent representation that retains only the most informative features, the system reduces the computational burden while maintaining predictive accuracy
2Ease of manufacture
If directed evolution is used to search sequence space, then the method is simple to implement, but it is extremely slow and gets stuck in local maxima
Solution Approach 1:
The patent replaces the mechanical hill-climbing process of directed evolution with a gradient-based optimization approach in latent space. Instead of randomly mutating sequences and selecting the fittest, the system uses gradient ascent to systematically navigate the fitness landscape, achieving rapid convergence to high-fitness regions without getting trapped in local maxima
Solution Approach 2:
The patent transforms the search problem from sequence space (high-dimensional discrete space) to latent space (low-dimensional continuous space). This dimensional transformation enables the use of efficient gradient-based optimization methods that are impossible to apply directly to sequence space, dramatically increasing search speed
3Measurement precision
If structure-based design is used, then the accuracy of fitness prediction is improved, but the cost of solving for structure of each variant becomes prohibitive
Solution Approach 1:
The patent creates a computational model (the deep learning fitness predictor) that copies and approximates the complex relationship between sequence and fitness without requiring actual structure solving. This surrogate model can rapidly predict fitness for millions of variants without the computational burden of structural analysis for each variant
4Productivity
If the latent space is transformed back to sequence space, then new improved sequences are generated, but the transformation accuracy and fidelity matter significantly
Solution Approach 1:
The patent implements a feedback mechanism where the latent space representation is continuously refined through gradient-based optimization, and the transformed sequences are evaluated against the fitness function. This feedback loop ensures that the transformation process maintains high fidelity by correcting any deviations from the original sequence properties
Data Source
AI summary
A system for identifying biomolecules with a desired property comprises a computer-readable medium with instructions stored thereon, which when executed by a processor perform steps comprising collecting a quantity of biomolecular data, transforming the biomolecular data from a sequence space to a latent space representation of the data, compressing the latent space representation using a pooling mechanism, compressing the coarse representation of the biomolecular data using an informational bottleneck, calculating a fitness factor of each data element in the low-dimensional representation of the biomolecular data, choosing a point from within the low-dimensional representation of the biomolecular data, calculating a set of gradients of the fitness factor, selecting an adjacent point having the highest gradient and setting it as the first point, then repeating the gradient calculating step until the fitness factor reaches a convergence point. A method for identifying biomolecules with a desired property is also disclosed.