How to Predict Protein Shapes Better Using Lab Tests and Computers
This patent describes a method to improve predicting a protein's 3D shape by combining computer simulations with actual distance measurements from specific parts of the protein in a lab.
Original patent title: “Enhanced protein structure prediction using protein homolog discovery and constrained distograms”
This patent describes a method to improve predicting a protein's 3D shape by combining computer simulations with actual distance measurements from specific parts of the protein in a lab. Owned by Protein Evolution with 17 claims and 21 forward citations, and it is expected to expire in 2040.
Coverage
What does this patent actually cover?
The patent outlines a method for more accurately predicting the three-dimensional structure of a protein. First, it generates a "multiple sequence alignment" (MSA) of related proteins (claimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1(i)). A computer then uses this MSA to make an initial guess of the protein's shape, creating a "distogram" which is like a map of distances between amino acids (claim 1(ii)). The method then identifies specific amino acids on the protein's surface (claim 1(iii)) and experimentally measures the actual distance between them in a lab, often using a technique called FRET (claim 1(iv), claim 3). These real-world distance measurements are then fed back into the computer model to refine and improve the initial prediction, making it more accurate (claim 1(v)). For example, if the computer initially predicts two surface amino acids are 5 nanometers apart, but the FRET experiment shows they are actually 3 nanometers apart, the computer model is adjusted to reflect the 3 nanometer distance.
The gap
What does this patent NOT cover?
- Does not cover protein structure prediction methods that rely solely on computational techniques without any in vitro experimental validation.
- Does not cover in vitro distance measurements for amino acids that are not solvent-exposed.
- Does not cover methods where the experimental distance measurements are not used to constrain or refine the computational prediction algorithm.
- Does not cover protein structure prediction that does not involve generating a multiple sequence alignment (MSA) of homologous sequences.
- Does not cover methods that do not produce a distogram as an intermediate step in the structure prediction.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The core innovation is the feedback loop: taking an initial computer prediction, using it to guide specific, targeted lab experiments on solvent-exposed parts of the protein, and then using those precise experimental measurements to correct and improve the computer model. This hybrid approach leverages the strengths of both computational and experimental methods.
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
Designing new enzymes for industrial processes.
Developing antibodies for therapeutic treatments.
Engineering proteins with enhanced stability or function.
Predicting the structure of viral proteins for vaccine development.
Improving the accuracy of protein structure databases like AlphaFold's predictions.
Why it matters
The bigger picture
Understanding a protein's 3D structure is fundamental to biology and drug discovery, as a protein's shape dictates its function. This patent offers a way to make these predictions more reliable by combining the speed of computation with the accuracy of experimental data. More accurate protein structures can accelerate the design of new drugs, enzymes, and materials, impacting fields from medicine to industrial biotechnology. Protein Evolution Inc., the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, works in this space, aiming to create new proteins for various applications.
Filed
December 10, 2020
Market context
Who's building on this
Companies in this space
Protein Evolution Inc., the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is actively working on leveraging protein structure prediction and design for sustainable materials and other applications. Major pharmaceutical companies and biotech firms, such as those using AI for drug discovery (e.g., DeepMind/Isomorphic Labs, Schrödinger, Recursion Pharmaceuticals), are also heavily invested in improving protein structure prediction and design, often incorporating experimental validation. Academic research labs worldwide continue to push the boundaries of hybrid computational-experimental approaches.
Market impact
This type of hybrid approach to protein structure prediction enhances the reliability of computational models, which has a direct impact on the speed and cost of drug discovery and protein engineering. By providing more accurate structural data, it helps reduce the need for extensive and costly experimental screening, accelerating the development of new therapeutics, industrial enzymes, and biomaterials. It supports the broader shift towards AI-driven drug design and synthetic biology, potentially enabling the creation of novel proteins with tailored functions.
Claim 1 — Plain English
What this patent covers
The patent outlines a method for more accurately predicting the three-dimensional structure of a protein. First, it generates a "multiple sequence alignment" (MSA) of related proteins (claim 1(i)). A computer then uses this MSA to make an initial guess of the protein's shape, creating a "distogram" which is like a map of distances between amino acids (claim 1(ii)). The method then identifies specific amino acids on the protein's surface (claim 1(iii)) and experimentally measures the actual distance between them in a lab, often using a technique called FRET (claim 1(iv), claim 3). These real-world distance measurements are then fed back into the computer model to refine and improve the initial prediction, making it more accurate (claim 1(v)). For example, if the computer initially predicts two surface amino acids are 5 nanometers apart, but the FRET experiment shows they are actually 3 nanometers apart, the computer model is adjusted to reflect the 3 nanometer distance.
The clever bit
The core innovation is the feedback loop: taking an initial computer prediction, using it to guide specific, targeted lab experiments on solvent-exposed parts of the protein, and then using those precise experimental measurements to correct and improve the computer model. This hybrid approach leverages the strengths of both computational and experimental methods.
What it does not cover
- Does not cover protein structure prediction methods that rely solely on computational techniques without any in vitro experimental validation.
- Does not cover in vitro distance measurements for amino acids that are not solvent-exposed.
- Does not cover methods where the experimental distance measurements are not used to constrain or refine the computational prediction algorithm.
- Does not cover protein structure prediction that does not involve generating a multiple sequence alignment (MSA) of homologous sequences.
- Does not cover methods that do not produce a distogram as an intermediate step in the structure prediction.
Patent timeline
Application submitted to the patent office
Patent enters public domain
PatentBrief Score
Impact Score
Early stage
Citation count
27/40
Moderately cited
Claim breadth
11/20
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
$351K – $1.1M
Midpoint $702K · 14.4 yr remaining · industry ×3.0
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
17 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Rothberg, J. M., Reed, B., Kauderer-Abrams, E., Moghadamfalahi, M., & Zhang, Z. How to Predict Protein Shapes Better Using Lab Tests and Computers (U.S. Patent No. 20,210,174,903). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/20210174903/enhanced-protein-structure-prediction-using-protein-homolog-discovery-and-constr
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Common Questions
Frequently Asked Questions
What does How to Predict Protein Shapes Better Using Lab Tests and Computers cover?
This patent describes a method to improve predicting a protein's 3D shape by combining computer simulations with actual distance measurements from specific parts of the protein in a lab.
Who owns patent US 20210174903?
This patent is owned by Protein Evolution.
When does this patent expire?
This patent is expected to expire on December 10, 2040, when the invention enters the public domain.
What is patent US 20210174903 cited by?
This patent has been cited by 21 later patents that build on its ideas.
What problem does this patent solve?
Understanding a protein's 3D structure is fundamental to biology and drug discovery, as a protein's shape dictates its function. This patent offers a way to make these predictions more reliable by combining the speed of computation with the accuracy of experimental data. More accurate protein structures can accelerate the design of new drugs, enzymes, and materials, impacting fields from medicine to industrial biotechnology. Protein Evolution Inc., the assignee, works in this space, aiming to create new proteins for various applications.
What does this patent NOT cover?
Does not cover protein structure prediction methods that rely solely on computational techniques without any in vitro experimental validation.
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