Machine Learning Protein Structure Prediction with Coarse-Grained Models
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Solution Overview
Problem
Current methods for protein design face a challenging tradeoff between generating functional protein sequences and structures and the computational burden of searching vast solution spaces, making brute force searches computationally intractable.
Innovation Solution
A machine learning-based approach using a coarse-grained node representation and backbone torsion frames to modify molecular structures, applying a computation model that strategically reduces the solution space by constraining atom positions and performing denoising to generate desirable three-dimensional protein structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If brute force search is used to explore the vast solution space of protein sequences and structures, then comprehensive coverage of possible designs is achieved, but computational burden becomes intractable
Solution Approach 1:
The patent segments the protein design problem into distinct components: sequence design, structure prediction, and property optimization. By dividing the vast solution space into manageable segments that can be addressed separately and iteratively, the computational burden is reduced while maintaining comprehensive coverage through systematic exploration of each segment.
Solution Approach 2:
The patent employs preliminary action by pre-training machine learning models on extensive protein data before actual design tasks. This preliminary training phase allows the models to learn patterns and constraints, enabling more efficient and accurate protein design in subsequent applications without requiring exhaustive search of the entire solution space.
2Manufacturing precision
If detailed three-dimensional structure prediction is performed for accurate protein design, then structural precision is improved, but computational complexity increases
Solution Approach 1:
The patent replaces traditional physics-based mechanical modeling approaches with machine learning-based predictions. By substituting complex physical simulations with trained neural network models, the system achieves high structural precision while significantly reducing computational complexity, as the ML models have already learned physical constraints during training.
Solution Approach 2:
The patent changes the parameters and representation methods used in structure prediction, employing advanced ML models that process protein sequences and predict three-dimensional structures through learned patterns rather than exhaustive physical simulations. This parameter change enables accurate structure prediction with reduced computational requirements.
Data Source
AI summary
A method may include receiving a molecular structure file specifying an initial three-dimensional structure of a molecule. A representation of the molecule may be determined based on the molecular structure file. For example, the representation of the molecule may include a plurality of coarse-grained nodes, each corresponding to a structural body of two or more atoms (e.g., heavy atoms) forming an amino acid residue in the molecule. Alternatively, the representation of the molecule may include, for each residue in the molecule, a plurality of frames specifying a geometric state of the backbone of the residue and one or more torsion angles in the sidechain of the residue. A design computation model may be applied to determine a three-dimensional structure of the molecule by at least modifying the representation of the molecule. The three-dimensional structure may be associated with a desirable property and/or be configured for a downstream task.