New algorithms and an in silico benchmark for computational enzyme design

TLDR

Creating enzymes that catalyze arbitrary reactions remains a grand challenge in computational protein design. The authors aim to develop two hashing‑based algorithms for enzyme design and an in silico benchmark to evaluate design methods. The algorithms use hashing to search large scaffold libraries for optimal catalytic site placement, and the benchmark recapitulates native enzyme active sites for rapid testing. The benchmark shows the new methods correctly identify native sites in six of ten reactions and rank them within the top five designs, demonstrating their applicability to new enzyme design.

Abstract

The creation of novel enzymes capable of catalyzing any desired chemical reaction is a grand challenge for computational protein design. Here we describe two new algorithms for enzyme design that employ hashing techniques to allow searching through large numbers of protein scaffolds for optimal catalytic site placement. We also describe an in silico benchmark, based on the recapitulation of the active sites of native enzymes, that allows rapid evaluation and testing of enzyme design methodologies. In the benchmark test, which consists of designing sites for each of 10 different chemical reactions in backbone scaffolds derived from 10 enzymes catalyzing the reactions, the new methods succeed in identifying the native site in the native scaffold and ranking it within the top five designs for six of the 10 reactions. The new methods can be directly applied to the design of new enzymes, and the benchmark provides a powerful in silico test for guiding improvements in computational enzyme design.