Aviv Spinner

The paper introduces GROQ-seq, a high-throughput platform for linking protein function to sequencing data, and presents pilot datasets for transcription factors, polymerases, and proteases.

This study demonstrates that GROQ-seq provides reproducible, scalable measurements of protein function across different facilities.

The paper benchmarks sequence- and structure-based protein models using a unified framework, showing structure-based models outperform others in generating functional variants.

The paper uses GROQ-seq to profile function across a diverse protease library, showing robust activity across sequence divergence.

The paper introduces a protein engineering competition to benchmark computational models for protein design and characterization, with results from a pilot tournament involving seven teams.

LOL-EVE predicts promoter variant effects using a transformer model trained on mammalian promoter sequences, showing improved performance in indel prediction and haplotype analysis.

This paper introduces a method to rapidly evolve genes in the lab, revealing new insights into enzyme adaptation that machine learning models cannot predict.