Protein Diffusion Modeling With Rotationally Invariant Structure Generation
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Solution Overview
Problem
Existing techniques for designing proteins are inefficient and burdensome, often requiring extensive computational resources and producing non-trivial errors due to the lack of rotational and translational invariance, and the difficulty in generating realistic protein structures and sequences.
Innovation Solution
A fully data-driven denoising diffusion probabilistic model is introduced, utilizing invariant point attention and a reduced protein representation based on alpha carbon backbone atoms and coordinate frames, enabling efficient generation of realistic protein structures and sequences invariant to rotations and translations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing techniques are used for protein design, then protein structures can be generated, but the process is inefficient and produces non-trivial errors
Solution Approach 1:
The patent replaces traditional physics-based energy functions and stochastic search algorithms with a data-driven deep learning model (diffusion probabilistic model). This substitution enables the system to learn protein structure-generation mappings directly from data, achieving both high accuracy and efficiency without relying on computationally intensive physics simulations
Solution Approach 2:
The patent transforms the protein representation by focusing on alpha carbon backbone atoms and their coordinate frames rather than all atomic coordinates. This parameter reduction maintains structural accuracy while significantly decreasing computational complexity and improving generation efficiency
2Adaptability or versatility
If traditional methods are used, then protein design can be performed, but rotational and translational invariance is not achieved
Solution Approach 1:
The patent introduces asymmetric coordinate frames attached to each alpha carbon atom, where the frame orientation is determined by local geometric relationships (bond vectors). This asymmetric local reference system enables the model to achieve rotational and translational invariance globally while maintaining local structural realism through the asymmetric geometric constraints
3Manufacturing precision
If comprehensive protein representation is used, then accurate structures can be generated, but computational resources increase
Solution Approach 1:
The patent extracts only the essential components for protein structure representation: alpha carbon backbone atoms and their coordinate frames. By taking out non-essential atoms and focusing on the backbone scaffold that defines protein topology, the model achieves accurate structure generation with significantly reduced computational resource requirements
Data Source
AI summary
Training a protein diffusion model includes receiving a representation of a protein as training data, the representation comprising at least three dimensions. It further includes training a protein diffusion model at least in part by performing rotational diffusion based at least in part on the representation of the protein.Generating proteins includes receiving protein conditioning information. It further includes, based at least in part on the protein conditioning information, performing conditional sampling of a protein diffusion model. The protein diffusion model is trained at least in part by performing rotational diffusion. Based at least in part on the conditional sampling of the protein diffusion model, the protein diffusion model generates one or more of a protein structure or a protein sequence.