PRO-GO: Reference Guided Protein Sequence Generation using Gene Ontology Terms
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De novo protein sequence generation models produce valid protein candidates on demand, but are currently either unable to specify what kinds of proteins to generate, or are limited to several broad classes.We present a novel method for controllable de novo protein sequence generation that leverages reference samples to guide generation specified through Gene Ontology (GO) terms. We design an evaluation pipeline based on a Top-TMScore target metric, that prioritises closest-shape matching.We evaluate the effectiveness of our reference-guided controllability approach for protein design across various models, demonstrating high accuracy results when compared with the target benchmarks.
