Abstract: A method may produce an antibody through machine learning. Such a method of producing an antibody may be optimized for a plurality of characteristics including at least two of an expression level, binding activity, stability, and solubility; and the like. Such a method may include: (1) providing a library comprising mutants prepared by modifying at least some of residues not being an amino acid of the highest appearance frequency in a group of antibody sequences of a target antibody into the amino acid of the highest appearance frequency; (2) determining respective characteristic values indicating the plurality of characteristics of some mutants in the library, and scoring the characteristic values as one value per mutant by normalizing and integrating the characteristic values; (3) conducting machine learning using the score values and ranking the mutants; and (4) selecting an antibody optimized for the plurality of characteristics based on ranking results.

Abstract: The present invention relates to a method of producing a protein for which two or more characteristics are optimized simultaneously. More specifically, the present invention relates to a method of producing a protein for which two or more characteristics are optimized, the method comprising: 1) providing a library comprising mutants from random mutation of a target protein; 2) determining respective characteristic values that indicate the two or more characteristics of some of the mutants in the library, and scoring the two or more characteristic values as one value per mutant by normalizing and integrating the characteristic values; 3) conducting machine learning by using the score values and ranking the library; and 4) selecting a protein for which two or more characteristics are optimized, based on the ranking results, wherein the two or more characteristic values are numerical values based on different measurement data related to respective different characteristics.