DG-VPD Process for Protein Structure Determination
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Solution Overview
Problem
Existing diffusion-based models are not suited for biologically-relevant protein and drug structure determination due to their inability to capture the dynamics, precision, and fine structure of proteins and protein interactions, leading to high failure rates in drug development.
Innovation Solution
The use of Discrepancy Guided and Gated Volumetric Probability Diffusion (DG-VPD) process, which involves a discrepancy measure to guide and gate the destructuring process, ensuring controlled and trackable diffusion, preserving the fine structure and biological relevance of proteins, followed by neural network training on generated training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard diffusion models are used for protein structure determination, then the general framework can be applied, but the model cannot capture the dynamics, precision, and fine structure of proteins and protein interactions
Solution Approach 1:
The patent changes the fundamental parameters of the diffusion process by replacing standard Gaussian noise with a custom noise schedule that evolves protein representations through controlled structural transitions. This allows the model to capture dynamic conformational changes and fine structural details essential for accurate protein structure determination and drug interaction prediction.
Solution Approach 2:
The patent introduces a dynamic noise schedule that adapts the diffusion process to the temporal and structural dynamics of protein conformational changes. The noise schedule is designed to reflect the biological timescales and structural transitions of proteins, enabling the model to capture dynamic behaviors that static models cannot represent.
2Ease of manufacture
If Gaussian noising is used in diffusion models, then the diffusion process is simple and well-defined, but the process cannot track biological significance of conformational changes
Solution Approach 1:
The patent modifies the noise distribution parameters from standard Gaussian to a custom-defined noise schedule that preserves biological information. The noise schedule is constructed to maintain relationships between protein conformations and their biological functions, preventing information loss during the diffusion process while remaining computationally tractable.
3Ease of operation
If random sampling is used in diffusion models, then the generation process is straightforward, but the model cannot capture the precision of protein-ligand interactions
Solution Approach 1:
The patent changes the sampling parameters by replacing uniform random sampling with a structured sampling approach that incorporates knowledge of protein-ligand interaction precision. The sampling process is guided by the custom noise schedule and protein representation dynamics, enabling generation of precise interaction structures while maintaining operational feasibility through automated inference.
Data Source
AI summary
Methods and apparatus for determining protein and ligand structure, for identifying ligand docking sites, and for obtaining both peptide and non-peptide drug ligand candidates for target proteins are presented. Methods include receiving a plurality of protein-ligand complex structures at a processor, converting to volumetric probability representation, and generating a training dataset by sequentially transforming the voxel-wise probability distributions. A discrepancy measure between consecutive transformations is bounded; that discrepancy measure between each state and the final diffused state progressively decreases; and localization probability of each residue summed over the diffusion volume is constant. A neural network is trained to learn protein and ligand residue localization, given a diffused representation. The methods serve to generate a protein structure given its sequence; or to generate a candidate ligand structure for a given target protein, given only ligand residue composition; or to determine promising candidate peptide and non-peptide drug ligands for synthesis.