E1: Retrieval-Augmented Protein Encoder Models

Large language models trained on natural proteins learn powerful representations of protein sequences that are useful for downstream understanding and prediction tasks. Because they are only exposed to individual protein sequences during pretraining without any additional contextual information, conventional protein language models suffer from parameter inefficiencies in learning, baked-in phylogenetic biases, and functional performance issues at larger scales. To address these challenges, we have built Profluent-E1, a family of retrieval-augmented protein language models that explicitly condition on homologous sequences. By integrating retrieved evolutionary context through block-causal multi-sequence attention, E1 captures both general and family-specific constraints without fine-tuning. We train E1 models on four trillion tokens from the Profluent Protein Atlas and achieve state-of-the-art performance across zero-shot fitness and unsupervised contact-map prediction benchmarks - surpassing alternative sequence-only models. Performance scales with model size from 150M to 600M parameters, and E1 can be used flexibly in single-sequence or retrieval-augmented inference mode for fitness prediction, variant ranking, and embeddings for structural tasks. To encourage open science and further development in retrieval-augmented protein language models, we release three models for free research and commercial use at https://github.com/Profluent-AI/E1.