Few Shot Protein Generation

• URL: http://arxiv.org/abs/2204.01168v1
• Date: Sun, 3 Apr 2022 22:14:02 GMT
• Title: Few Shot Protein Generation
• Authors: Soumya Ram and Tristan Bepler
• Abstract summary: We present the MSA-to-protein transformer, a generative model of protein sequences conditioned on protein families represented by multiple sequence alignments (MSAs) Unlike existing approaches to learning generative models of protein families, the MSA-to-protein transformer conditions sequence generation directly on a learned encoding of the multiple sequence alignment. Our generative approach accurately models epistasis and indels and allows for exact inference and efficient sampling unlike other approaches.
• Score: 4.7210697296108926
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present the MSA-to-protein transformer, a generative model of protein sequences conditioned on protein families represented by multiple sequence alignments (MSAs). Unlike existing approaches to learning generative models of protein families, the MSA-to-protein transformer conditions sequence generation directly on a learned encoding of the multiple sequence alignment, circumventing the need for fitting dedicated family models. By training on a large set of well-curated multiple sequence alignments in Pfam, our MSA-to-protein transformer generalizes well to protein families not observed during training and outperforms conventional family modeling approaches, especially when MSAs are small. Our generative approach accurately models epistasis and indels and allows for exact inference and efficient sampling unlike other approaches. We demonstrate the protein sequence modeling capabilities of our MSA-to-protein transformer and compare it with alternative sequence modeling approaches in comprehensive benchmark experiments.

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