Evolutionary Probability and Stacked Regressions Enable Data-Driven Protein Engineering with Minimized Experimental Effort

Journal of Chemical Information and Modeling, vol. 64, pp. 6350–6360

Abstract

Protein engineering through directed evolution and (semi)rational approaches is routinely applied to optimize protein properties for a broad range of applications in industry and academia. The multitude of possible variants, combined with limited screening throughput, hampers efficient protein engineering. Data-driven strategies have emerged as a powerful tool to model the protein fitness landscape that can be explored in silico, significantly accelerating protein engineering campaigns. However, such methods require a certain amount of data, which often cannot be provided, to generate a reliable model of the fitness landscape. Here, we introduce MERGE, a method that combines direct coupling analysis (DCA) and machine learning (ML). MERGE enables data-driven protein engineering when only limited data are available for training, typically ranging from 50 to 500 labeled sequences. Our method demonstrates remarkable performance in predicting a protein’s fitness value and rank based on its sequence across diverse proteins and properties. Notably, MERGE outperforms state-of-the-art methods when only small data sets are available for modeling, requiring fewer computational resources, and proving particularly promising for protein engineers who have access to limited amounts of data.

Authors 4

1. Alexander-Maurice Illig Aachen Institute of Biotechnology

   RWTH Aachen University

   Affiliation as printed

   Institute of Biotechnology, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany

2. Niklas E. Siedhoff Aachen Institute of Biotechnology

   RWTH Aachen University

   Affiliation as printed

   Institute of Biotechnology, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany

3. Mehdi D. Davari corresponding

   Leibniz Institute of Plant Biochemistry

   Affiliation as printed

   Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Weinberg 3, 06120 Halle, Germany

4. Ulrich Schwaneberg corresponding Aachen Institute of Biotechnology

   RWTH Aachen University

   Affiliation as printed

   Institute of Biotechnology, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany