Protein Structure Prediction Using Recurrent Geometric Networks
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Solution Overview
Problem
Current computational methods for protein folding are inefficient, particularly in predicting novel folds and structural consequences of sequence changes, as they rely on complex pipelines, high computational costs, and limited scalability, and are unable to handle new sequences or minor mutations effectively.
Innovation Solution
An end-to-end differentiable model using recurrent geometric networks (RGNs) that encodes protein sequences, parameterizes local structure with torsional angles, couples local and global geometry, and uses a differentiable loss function to predict 3D structures directly from amino acid sequences, optimizing global geometry while respecting covalent chemistry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If co-evolution methods are used to predict contact maps, then accuracy in predicting contact maps is improved, but the ability to predict structures for new sequences and minor mutations is lost
Solution Approach 1:
The patent segments the protein structure prediction task into multiple independent stages: (1) predicting residue contacts using co-evolution, (2) predicting secondary structure elements, (3) assembling fragments, and (4) optimizing the 3D structure. This segmentation allows each stage to be optimized independently, enabling the system to handle both well-characterized proteins and novel sequences effectively.
Solution Approach 2:
The patent performs preliminary actions by pre-computing co-evolutionary information and statistical potentials from large databases of known protein structures. These pre-computed resources are then applied to new sequences, allowing rapid prediction without re-running extensive computational analyses for each query protein.
2Measurement precision
If template-based methods are used, then accuracy for homologous proteins is improved, but computational cost and complexity increase
Solution Approach 1:
The patent merges multiple existing methods into a unified framework: it combines co-evolutionary contact map prediction with fragment assembly methods and secondary structure prediction, integrating them into a single pipeline that automatically selects and executes appropriate methods based on the input sequence characteristics.
Solution Approach 2:
The patent implements feedback mechanisms where the predicted contact map and secondary structure information are used to guide fragment selection and assembly, with the process iteratively refining the structure prediction. This feedback loop allows the system to adapt to different protein types and optimize accuracy dynamically.
3Measurement precision
If physics-based molecular dynamics simulations are used, then structural accuracy is improved, but computational cost increases
Solution Approach 1:
The patent uses computationally inexpensive statistical potentials and pre-computed fragment libraries as substitutes for expensive physics-based molecular dynamics simulations. These simplified models capture essential structural information at a fraction of the computational cost, making them suitable for high-throughput prediction of novel protein folds.
Data Source
AI summary
The present disclosure provides for systems and methods for generating and displaying a three dimensional map of a protein sequence. An exemplary method can provide for using deep learning models to predict protein folding and model protein folding using three dimensional representations. The method more effectively exploits the potential of deep learning approaches. The method approach overall involves three stages—computation, geometry, and assessment.