TransMEP: Transfer learning on large protein language models to predict mutation effects of proteins from a small known dataset

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract Machine learning-guided optimization has become a driving force for recent improvements in protein engineering. In addition, new protein language models are learning the grammar of evolutionarily occurring sequences at large scales. This work combines both approaches to make predictions about mutational effects that support protein engineering. To this end, an easy-to-use software tool called TransMEP is developed using transfer learning by feature extraction with Gaussian process regression. A large collection of datasets is used to evaluate its quality, which scales with the size of the training set, and to show its improvements over previous fine-tuning approaches. Wet-lab studies are simulated to evaluate the use of mutation effect prediction models for protein engineering. This showed that TransMEP finds the best performing mutants with a limited study budget by considering the trade-off between exploration and exploitation. Graphical TOC Entry

Authors 2

1. Tilman Hoffbauer corresponding Aachen

   RWTH Aachen University · Forschungszentrum Jülich

   Affiliation as printed

   Institute of Biological Information Processing: Structural Biochemistry (IBI-7)

   RWTH Aachen University

   Institute of Biological Information Processing: Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52428 Jülich, Germany

   RWTH Aachen University, 52062 Aachen, Germany

2. Birgit Strodel corresponding

   Forschungszentrum Jülich · Heinrich Heine University Düsseldorf

   Affiliation as printed

   Institute of Biological Information Processing: Structural Biochemistry (IBI-7)

   Institute of Theoretical and Computational Chemistry, Heinrich Heine University Düsseldorf

   Institute of Biological Information Processing: Structural Biochemistry (IBI-7), Forschungszentrum Jülich, 52428 Jülich, Germany

   Institute of Theoretical and Computational Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany