US 11,657,894 B2
Media, methods, and systems for protein design and optimization
Tong Li, San Diego, CA (US); Brian Standen, San Diego, CA (US); and Dejan Caglic, San Diego, CA (US)
Assigned to BASF SE, Ludwigshafen (DE)
Appl. No. 17/772,976
Filed by BASF SE, Ludwigshafen (DE)
PCT Filed Jul. 6, 2021, PCT No. PCT/IB2021/056049
§ 371(c)(1), (2) Date Apr. 28, 2022,
PCT Pub. No. WO2022/009092, PCT Pub. Date Jan. 13, 2022.
Claims priority of provisional application 63/048,414, filed on Jul. 6, 2020.
Prior Publication US 2023/0042150 A1, Feb. 9, 2023
Int. Cl. G16B 15/20 (2019.01); G06N 10/60 (2022.01); G16B 40/20 (2019.01); G16B 15/30 (2019.01)
CPC G16B 15/20 (2019.02) [G06N 10/60 (2022.01); G16B 15/30 (2019.02); G16B 40/20 (2019.02)] 16 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
determining, by a processor, a protein sequence for optimization based on a protein property for optimization via an artificial intelligence or machine learning algorithm;
providing, to the processor, the protein sequence for optimization;
providing, to the processor, a protein structure having the protein sequence for optimization;
providing, to the processor, the protein property for optimization;
defining a scoring function based on the protein property for optimization;
providing, to the processor, at least one of:
a position in the protein sequence to be subjected to modification, or an amino acid to be substituted for the amino acid occurring at the position, inserted at the position, or deleted from the position, or
a rotamer library to be searched for a target rotamer to be applied to one or more positions in the protein sequence;
determining, by the processor, a search space based on the at least one of the position, amino acid substitution, amino acid insertion, amino acid deletion, or rotamer from the rotamer library;
searching the search space using a quantum computing algorithm based on the scoring function, the searching comprising identifying at least one of a point mutation to the protein sequence or a combination of mutations to the protein sequence;
providing an output state of the quantum computing algorithm, the output state indicative of an optimized protein sequence of an optimized protein, the optimized protein sequence being optimized according to the scoring function based on the protein property for optimization;
experimentally testing the optimized protein to generate experimental data, the experimental data measuring one or more properties of the optimized protein, the one or more properties including at least one of:
the stability of the protein in terms of thermostability, pH stability, solvent stability, stability to other excipients, and/or stability in application;
expressibility;
solubility;
efficacy;
charge distribution;
protein folding;
activity;
specificity in terms of bond, group, substrate, stereospecificity, and/or co-factor;
reversibility;
enzyme kinetics;
substrate inhibition;
product inhibition;
resistance to protease degradation;
gain-of-new function;
the affinity of the protein to the binding agent; or
the specificity of the protein binding to similar binding partners, and
providing the experimental data to the artificial intelligence or machine learning algorithm to train the artificial intelligence or machine learning algorithm to recognize a relationship between a sequence or structure of the optimized protein and the one or more properties measured by the experimental data.