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

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REvoLd: Ultra-Large Library Screening with an Evolutionary Algorithm in Rosetta.

Paul Eisenhuth, Fabian Liessmann, Rocco Moretti

|May 7, 2024

Summary

This summary is machine-generated.

An evolutionary algorithm, RosettaEvolutionaryLigand (REvoLd), efficiently screens vast chemical libraries for drug discovery. This computational method significantly improves hit rates by exploring protein-ligand docking with flexibility.

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Area of Science:

Computational chemistry
Drug discovery
Bioinformatics

Background:

Ultra-large make-on-demand compound libraries offer billions of compounds for in-silico drug discovery.
Exhaustively screening these libraries with receptor flexibility is computationally expensive and time-consuming.

Purpose of the Study:

To develop an efficient computational method for screening large compound libraries.
To address the challenge of computational cost in in-silico drug discovery for flexible protein-ligand interactions.

Main Methods:

Proposed an evolutionary algorithm, RosettaEvolutionaryLigand (REvoLd), to search combinatorial chemical space.
Exploited the substrate and reaction-based construction of make-on-demand libraries.
Integrated REvoLd with RosettaLigand for protein-ligand docking considering full ligand and receptor flexibility.

Main Results:

REvoLd efficiently explores vast combinatorial library search spaces.
Benchmarking on five drug targets demonstrated significant improvements in hit rates.
Hit rate improvements ranged from 869 to 1,622 times compared to random selections.

Conclusions:

REvoLd offers an efficient solution for in-silico drug discovery using large compound libraries.
The algorithm effectively handles protein-ligand docking with full flexibility.
REvoLd is available as an application within the Rosetta software suite.