Predicting antibody affinity changes upon mutations by combining multiple predictors.

ABSTRACT: Antibodies are proteins working in our immune system with high affinity and specificity for target antigens, making them excellent tools for both biotherapeutic and bioengineering applications. The prediction of antibody affinity changes upon mutations ([Formula: see text]) is important for antibody engineering. Numerous computational methods have been proposed based on different approaches including molecular mechanics and machine learning. However, the accuracy by each individual predictor is not enough for efficient antibody development. In this study, we develop a new prediction method by combining multiple predictors based on machine learning. Our method was tested on the SiPMAB database, evaluating the Pearson's correlation coefficient between predicted and experimental [Formula: see

SUBMITTER: Kurumida Y 

PROVIDER: S-EPMC7658247 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature