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Directed Evolution Method in Saccharomyces cerevisiae: Mutant Library Creation and Screening

Directed Evolution Method in Saccharomyces cerevisiae: Mutant Library Creation and Screening

Published on: April 1, 2016

Ultra-large library screening with an evolutionary algorithm in Rosetta (REvoLd).

Paul Eisenhuth^1,2, Fabian Liessmann³, Rocco Moretti⁴

¹Institute for Drug Discovery, Leipzig University, Leipzig, Germany. eisenhuth@cs.uni-leipzig.de.

Communications Chemistry

|November 7, 2025

Summary

This summary is machine-generated.

Ultra-large compound libraries offer new drug discovery potential. An evolutionary algorithm, RosettaEvolutionaryLigand (REvoLd), efficiently searches these vast chemical spaces, significantly improving hit rates for drug targets.

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Area of Science:

Computational chemistry
Drug discovery
Bioinformatics

Background:

Ultra-large make-on-demand (LOD) compound libraries contain billions of compounds.
Screening these libraries computationally is time-consuming due to receptor flexibility.
In-silico drug discovery requires efficient exploration of vast chemical spaces.

Purpose of the Study:

To develop an efficient algorithm for exploring ultra-large make-on-demand chemical libraries.
To address the computational cost of screening large compound libraries with receptor flexibility.
To optimize ligand-protein docking in vast combinatorial chemical spaces.

Main Methods:

Developed RosettaEvolutionaryLigand (REvoLd), an evolutionary algorithm.
Utilized features of make-on-demand libraries (substrates and reactions).
Integrated REvoLd with RosettaLigand for protein-ligand docking with full flexibility.

Main Results:

REvoLd efficiently searches combinatorial chemical space without full enumeration.
Benchmarking on five drug targets showed hit rate improvements of 869- to 1622-fold over random selection.
Demonstrated strong and stable enrichment in drug discovery screening.

Conclusions:

REvoLd offers the most efficient algorithm to date for exploring ultra-large chemical spaces.
The evolutionary approach optimizes exploration for in-silico drug discovery.
REvoLd is available as an application within the Rosetta software suite.