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.

What it 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.

What it doesn't 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.

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.

Why it matters

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.

Real-world examples

• 1.Designing new enzymes for industrial processes.
• 2.Developing antibodies for therapeutic treatments.
• 3.Engineering proteins with enhanced stability or function.
• 4.Predicting the structure of viral proteins for vaccine development.
• 5.Improving the accuracy of protein structure databases like AlphaFold's predictions.