Protein Engineering via FMO Grid Probes and Energy Matching
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Solution Overview
Problem
Current protein engineering methods, such as random mutagenesis and directed evolution, are inefficient and resource-intensive, often resulting in suboptimal protein variants due to reliance on chance mutations and labor-intensive experimental iterations, lacking a nuanced understanding of protein structures and functions.
Innovation Solution
A 3D grid system using molecular probes and the Fragment Molecular Orbital (FMO) method to calculate pair interaction energies, identifying unstable regions in proteins for strategic amino acid substitutions, combined with neural networks for iterative optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If random mutagenesis is used to generate protein diversity, then a diverse pool of protein variants can be obtained, but predicting which mutations will result in beneficial changes is challenging
Solution Approach 1:
The patent replaces random mechanical mutagenesis with a computational physics-based system using FMO method to calculate pair interaction energies. This substitution transforms the random search process into a deterministic energy-based prediction system, where mutations are evaluated based on their impact on protein stability and function through quantum mechanical calculations rather than random chance.
Solution Approach 2:
The patent introduces molecular probes as intermediaries to mediate between the protein structure and the evaluation criteria. These probes interact with the protein at specific grid points, and their interaction energies serve as indicators for predicting beneficial mutations. The probes act as mediators that translate structural features into quantifiable energy values for selection.
2Reliability
If directed evolution is used to select proteins with desired traits, then potent mutants can be obtained, but the process requires multiple iterative rounds and a vast number of experiments making it resource-intensive
Solution Approach 1:
The patent performs preliminary computational screening of potential mutations using FMO energy calculations before actual protein construction and experimentation. By pre-evaluating the stability and functional impact of mutations through pair interaction energy calculations, the system identifies high-priority candidates for experimental validation, eliminating the need for multiple iterative rounds of random screening and reducing resource consumption.
Solution Approach 2:
The patent creates a computational model (copy) of the protein system using FMO methodology to simulate and evaluate mutation effects in silico. This computational copy allows virtual testing of numerous mutations without physical experimentation, enabling efficient selection of promising candidates before wet-lab validation and reducing the number of experimental iterations required.
3Ease of manufacture
If rational redesign based on sequence homology is used to suggest amino acid substitutions, then specific mutations can be proposed, but the method might overlook the intricate structural characteristics of proteins
Solution Approach 1:
The patent applies local quality analysis by evaluating mutations at specific grid points around the protein structure using molecular probes. Instead of uniform sequence-based homology comparison, the FMO method calculates pair interaction energies locally at each probe-protein interaction site, capturing the unique structural and energetic characteristics of each region. This localized energy-based evaluation ensures that both sequence and structural context are considered for each proposed mutation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables precise and efficient engineering of proteins with enhanced stability and functionality, predicting the effects of mutations with high confidence and accelerating the protein engineering process.
Implementation Method 1
The pair interaction energy between probes and protein amino acids is calculated using the fragment molecular orbital (FMO) method, which provides detailed insights into protein stability.
Implementation Method 2
molecular probes representing amino acid side chains and solvent molecules interact with the target protein
Data Source
AI summary
Presented herein is a computer-implemented method for protein engineering that constructs a three-dimensional gridspace around a protein. Different probes, simulating the interactions of amino acids are iteratively placed within the grid. Pair Interaction Energy is calculated using FMO technique for probe-protein interactions. An algorithmic process calculates the sum of PIEs, extending from each grid point to its neighbors, until a higher cumulative PIE is obtained. Grid points are then grouped into patches and an alignment process matches a derived query probe pattern from each patch against existing patterns of an internal database. Mutations are made based on the highest PIE probe-amino acid pairs from the matched pattern, leading to a modified protein. This process can be iterated for generating optimal variants and can be localized to any part of the protein for identification of patches. The invention covers a computer system and non-transitory computer-readable medium to implement the disclosed method.