Rapid in silico directed evolution by a protein language model with EVOLVEpro

Directed protein evolution is central to biomedical applications but faces challenges such as experimental complexity, inefficient multiproperty optimization, and local maxima traps. Although in silico methods that use protein language models (PLMs) can provide modeled fitness landscape guidance, they struggle to generalize across diverse protein families and map to protein activity. We present EVOLVEpro, a few-shot active learning framework that combines PLMs and regression models to rapidly improve protein activity. EVOLVEpro surpasses current methods, yielding up to 100-fold improvements in desired properties. We demonstrate its effectiveness across six proteins in RNA production, genome editing, and antibody binding applications. These results highlight the advantages of few-shot active learning with minimal experimental data over zero-shot predictions. EVOLVEpro opens new possibilities for artificial intelligence-guided protein engineering in biology and medicine.

Authors

Institutions

Publication Details

Journal
Science
Published
2024-11-21
DOI
https://doi.org/10.1126/science.adr6006
Citations
130
Primary Topic
CRISPR and Genetic Engineering
Type
article
Field-Weighted Citation Impact
30.40
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Rapid in silico directed evolution by a protein language model with EVOLVEpro

Lukas Villiger, Zhaoqing Yan, Omar O. Abudayyeh, Hiroshi Nishimasu et al.
130 citations
Science
CRISPR and Genetic Engineering
30.40
article

Rapid in silico directed evolution by a protein language model with EVOLVEpro

Lukas Villiger, Zhaoqing Yan, Omar O. Abudayyeh, Hiroshi Nishimasu, Matteo Di Bernardo, Masahiro Hiraizumi, Jonathan S. Gootenberg, B.J. Kim, Alişan Kayabölen, Kaiyi Jiang, Samantha R. Sgrizzi, Josephine K. Carscadden
article en
130 citations

Abstract

Directed protein evolution is central to biomedical applications but faces challenges such as experimental complexity, inefficient multiproperty optimization, and local maxima traps. Although in silico methods that use protein language models (PLMs) can provide modeled fitness landscape guidance, they struggle to generalize across diverse protein families and map to protein activity. We present EVOLVEpro, a few-shot active learning framework that combines PLMs and regression models to rapidly improve protein activity. EVOLVEpro surpasses current methods, yielding up to 100-fold improvements in desired properties. We demonstrate its effectiveness across six proteins in RNA production, genome editing, and antibody binding applications. These results highlight the advantages of few-shot active learning with minimal experimental data over zero-shot predictions. EVOLVEpro opens new possibilities for artificial intelligence-guided protein engineering in biology and medicine.

ScienceVol. 387(6732)
Brigham and Women's Hospital (US), Beth Israel Deaconess Medical Center (US), Kantonsspital St. Gallen (CH), Inamori Foundation (JP), Whitehead Institute for Biomedical Research (US), Mass General Brigham (US), Massachusetts Institute of Technology (US), The University of Tokyo (JP)
Openalex Percentile: Top 0%
CRISPR and Genetic Engineering
30.40
DOISourceOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.