How Computers Predict Protein Shapes Faster Using Smart Templates
This patent describes a computer method to quickly predict the 3D shape of proteins by creating and refining "synthetic templates" from existing protein structures, reducing the heavy computational work usually needed.
Original patent title: “Protein structure prediction system”
This patent describes a computer method to quickly predict the 3D shape of proteins by creating and refining "synthetic templates" from existing protein structures, reducing the heavy computational work usually needed. Owned by Dnastar with 23 claims and 1 forward citation, and it is expected to expire in 2041.
Coverage
What does this patent actually cover?
The patent outlines a multi-step computer method (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1) to predict the 3D structure of a protein, which is an amino acid sequence. It starts by analyzing the protein's sequence to create a "sequence profile matrix" and identify "internal contacts" (Claim 1a-d). These features are then matched ("threaded") against a database of known protein structures, called "original templates" (Claim 1e). The method calculates "normal modes of motion" for these original templates and then "perturbs" them to create many new "synthetic templates" (Claim 1g-h). The system picks the best synthetic templates based on their energy (Claim 1i-j) and combines them with the original ones (Claim 1k). Finally, it runs "Markov Chain Monte Carlo simulations" (Claim 1m) and refines the best resulting shapes through "energy minimization" (Claim 1p) to find the most stable predicted structure (Claim 1q).
The gap
What does this patent NOT cover?
- Protein structure prediction methods that do not rely on a database of existing "original templates."
- Techniques that do not specifically "perturb" templates using "normal modes of motion" to generate new "synthetic templates."
- Prediction systems that do not employ "Markov Chain Monte Carlo simulations" for exploring different protein shapes.
- Methods that skip the final "energy minimization" step to refine the predicted protein models.
- Protein structure prediction purely based on deep learning or artificial intelligence without the specific template generation and simulation steps outlined.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The noveltynoveltyThe requirement that an invention be different from anything publicly known before its priority date.Read more → lies in creating "synthetic templates" by "perturbing" existing protein structures using their "normal modes of motion." This allows the system to efficiently explore many possible protein shapes without starting from scratch, saving a huge amount of computational time.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Dnastar Lasergene software suite
Protein modeling tools in academic research
Drug discovery platforms
Biotechnology research software
Why it matters
The bigger picture
Predicting protein structures is crucial for understanding how proteins work and for designing new drugs. Traditional methods are very slow and use a lot of computer power. This patent aims to make that process much faster and more efficient by intelligently creating new template structures, which could speed up drug discovery and biological research significantly.
Filed
May 20, 2021
Market context
Who's building on this
Companies in this space
Dnastar Inc., the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is actively developing software for molecular biology and genomics, including protein analysis tools. Other companies in computational biology and drug discovery, such as Schrödinger and Dassault Systèmes BIOVIA, also work on protein modeling and simulation.
Market impact
This type of method contributes to the broader effort to make protein structure prediction more accessible and faster. By reducing the computational burden, it enables researchers to analyze more protein candidates for drug discovery or to better understand disease mechanisms, potentially accelerating the development of new therapeutics and biotechnologies.
Claim 1 — Plain English
What this patent covers
The patent outlines a multi-step computer method (Claim 1) to predict the 3D structure of a protein, which is an amino acid sequence. It starts by analyzing the protein's sequence to create a "sequence profile matrix" and identify "internal contacts" (Claim 1a-d). These features are then matched ("threaded") against a database of known protein structures, called "original templates" (Claim 1e). The method calculates "normal modes of motion" for these original templates and then "perturbs" them to create many new "synthetic templates" (Claim 1g-h). The system picks the best synthetic templates based on their energy (Claim 1i-j) and combines them with the original ones (Claim 1k). Finally, it runs "Markov Chain Monte Carlo simulations" (Claim 1m) and refines the best resulting shapes through "energy minimization" (Claim 1p) to find the most stable predicted structure (Claim 1q).
The clever bit
The novelty lies in creating "synthetic templates" by "perturbing" existing protein structures using their "normal modes of motion." This allows the system to efficiently explore many possible protein shapes without starting from scratch, saving a huge amount of computational time.
What it does not cover
- Protein structure prediction methods that do not rely on a database of existing "original templates."
- Techniques that do not specifically "perturb" templates using "normal modes of motion" to generate new "synthetic templates."
- Prediction systems that do not employ "Markov Chain Monte Carlo simulations" for exploring different protein shapes.
- Methods that skip the final "energy minimization" step to refine the predicted protein models.
- Protein structure prediction purely based on deep learning or artificial intelligence without the specific template generation and simulation steps outlined.
Patent timeline
Application submitted to the patent office
Patent enters public domain
PatentBrief Score
Impact Score
Early stage
Citation count
6/40
Early citations
Claim breadth
15/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
0/20
Older than 20 years
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$78K – $250K
Midpoint $156K · 14.9 yr remaining · industry ×2.0
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
The original legal language
Original claims
23 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
MITCHELL, A. E., DARNELL, S. J., Larson, M. R., Blattner, F. R., & Schroeder, J. L. How Computers Predict Protein Shapes Faster Using Smart Templates (U.S. Patent No. 20,210,280,268). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/20210280268/protein-structure-prediction-system
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does How Computers Predict Protein Shapes Faster Using Smart Templates cover?
This patent describes a computer method to quickly predict the 3D shape of proteins by creating and refining "synthetic templates" from existing protein structures, reducing the heavy computational work usually needed.
Who owns patent US 20210280268?
This patent is owned by Dnastar.
When does this patent expire?
This patent is expected to expire on May 20, 2041, when the invention enters the public domain.
What is patent US 20210280268 cited by?
This patent has been cited by 1 later patents that build on its ideas.
What problem does this patent solve?
Predicting protein structures is crucial for understanding how proteins work and for designing new drugs. Traditional methods are very slow and use a lot of computer power. This patent aims to make that process much faster and more efficient by intelligently creating new template structures, which could speed up drug discovery and biological research significantly.
What does this patent NOT cover?
Protein structure prediction methods that do not rely on a database of existing "original templates."
Patent monitoring