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.

本文提出了基于 MSA 的蛋白质变换器，是一种基于多序列比对的蛋白质序列生成模型，能够准确地建模表观效应和插入缺失，并且相比已有的基于专用家族模型的方法，其泛化性能更好，尤其在 MSAs 很小的情况下表现出色，能够精确推断和高效采样。