Equivariant Protein Diffusion for Joint Sequence-Structure Design
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Solution Overview
Problem
Computational protein design faces challenges in efficiently generating protein sequences that exhibit desirable properties, such as binding affinity and stability, due to the vast number of possible sequences and conformations, and existing methods often overlook optimal solutions by segregating sequence design and structural analysis.
Innovation Solution
A protein design computation model that jointly denoises protein sequences and three-dimensional structures using a multilayer perceptron-based diffusion process, incorporating equivariant and invariant transformations, and an informative prior data distribution to generate output sequences and structures that complement target molecules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If protein sequence and structure design are performed using segregated methods (sequence design followed by structural analysis), then the design process is simpler to implement, but the likelihood of finding optimal solutions decreases
Solution Approach 1:
The patent combines sequence design and structural analysis into a unified diffusion model that jointly optimizes both aspects. The model processes sequence and structure information simultaneously through integrated neural network components (backbone network, sidechain network, and diffusion modules), enabling co-optimization of sequence and structure to achieve better binding affinity and stability compared to segregated approaches.
2Reliability
If the model processes both sequence and three-dimensional structure information jointly, then the quality of generated proteins improves, but the computational burden increases
Solution Approach 1:
The patent segments the joint design process into distinct but coordinated components: a backbone network that processes sequence information, a sidechain network that processes structural information, and diffusion modules that generate sequences and structures. This segmentation allows for efficient computation by distributing processing tasks while maintaining joint optimization, reducing the overall computational burden compared to a monolithic approach.
Solution Approach 2:
The model performs preliminary processing of sequence and structure information through the backbone and sidechain networks before the actual diffusion generation. This preliminary action pre-computes feature representations and constraints, reducing the computational complexity of the subsequent joint diffusion process while maintaining the quality of generated proteins.
3Measurement precision
If the model uses equivariant and invariant transformations to ensure physical consistency, then the accuracy of generated structures improves, but the model complexity increases
Solution Approach 1:
The patent implements equivariant and invariant transformations as universal constraints that apply to all structure generation tasks. These transformations are embedded in the diffusion model to ensure physical consistency (rotational and translational equivariance) across different protein structures. By making these transformations a fundamental property of the model architecture rather than task-specific adjustments, the patent achieves high accuracy without proportionally increasing complexity.
Data Source
AI summary
A method may include applying a protein design computation model to generate an output sequence and an output three-dimensional structure of an output protein molecule by jointly denoising an input sequence and an input three-dimensional structure of an input protein molecule. The joint denoising may include modifying the input sequence by inserting, deleting, or changing the type of one or more constituent amino acid residues while performing corresponding updates to the positions of the atoms in each amino acid residue. The protein design computation model may operate on a fixed size representation of the input protein molecule. Prior and/or subsequent to the joint denoising, the protein design computation model may modify the input three-dimensional structure to conform to bond constraints. Moreover, an informative prior data distribution may be incorporated by training the protein design computation model on training samples with noise sampled from the informative prior data distribution.