Protein Diffusion Sampling Under Design Constraints
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Solution Overview
Problem
Existing computational techniques struggle to efficiently explore the vast protein space due to combinatorially large computations and often pigeonhole into a small subset of known protein sequences, making it difficult to model the relationship between amino acid sequences, protein structure, and function.
Innovation Solution
An analytics system employs a diffusion model guided by design conditions to conditionally sample the protein space, utilizing a modular energy function and low-temperature sampling with hybrid Langevin dynamics to generate novel protein designs that satisfy specific constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing computational techniques are used to sample protein space, then protein designs can be generated, but the computations become combinatorially large and the methods are ill-equipped to traverse the vast protein space
Solution Approach 1:
The patent transforms the discrete, combinatorial protein design problem into a continuous optimization problem by representing protein structures as points in a continuous space and using gradient-based diffusion models. This parameter transformation enables efficient traversal of protein space by replacing combinatorial sampling with continuous gradient descent, dramatically reducing computational complexity while maintaining design capability
Solution Approach 2:
The patent replaces traditional iterative sampling algorithms (mechanical search methods) with a diffusion model based on learned probability flows. Instead of mechanically iterating through possible protein configurations, the system uses a trained neural network to directly guide the sampling process through learned gradients, substituting mechanical computation with intelligent, data-driven prediction
2Adaptability or versatility
If existing methods attempt to discover de novo protein sequences, then some protein designs are generated, but the models pigeonhole into a small subset of the protein space around prior known protein sequences
Solution Approach 1:
The patent performs preliminary training of the diffusion model on a diverse set of known protein structures before deployment. This preliminary action embeds broad protein space knowledge into the model's parameters, enabling it to generalize beyond the training data and explore novel regions of protein space while maintaining the ability to satisfy specific design conditions when needed
Solution Approach 2:
The patent employs dynamic temperature scheduling during the diffusion process, allowing the system to transition between exploration (higher temperature, broader protein space coverage) and exploitation (lower temperature, focused search around promising regions). This dynamic adjustment enables the model to both discover novel protein sequences and reliably satisfy specific design conditions
Data Source
AI summary
A system is disclosed for de novo protein generation. The system receives a set of design condition(s) that specify target characteristics of a synthetic protein. The system defines a modular energy function as a composition of a diffusion energy component and one or more conditioner energy components. The system applies a diffusion model to determine a denoised protein backbone. In applying the diffusion model, in each sampling step: the system transforms one prior sampled state of the synthetic protein from unconstrained space into constrained space based on the one or more design conditions, denoises the prior sampled state in the constrained space, and samples a subsequent sampled stated by applying a gradient of the modular energy function to the denoised prior sampled state in the constrained space. The final sampled state is a denoised protein backbone for the synthetic protein that satisfies the set of design condition(s).