Data augmentation enables label-specific generation of homologous protein sequences

Accurately annotating and controlling protein function from sequence data remains a major challenge, particularly within homologous families where annotated sequences are scarce and structural variation is minimal. We present a two-stage approach for semi-supervised functional annotation and conditional sequence generation in protein families using representation learning. First, we demonstrate that protein language models, pretrained on large and diverse sequence datasets and possibly finetuned via contrastive learning, provide embeddings that robustly capture fine-grained functional specificities, even with limited labeled data. Second, we use the inferred annotations to train a generative probabilistic model, an annotation-aware Restricted Boltzmann Machine, capable of producing synthetic sequences with prescribed functional labels. Across several protein families, we show that this approach achieves highly accurate annotation quality and supports the generation of functionally coherent sequences. Our findings underscore the power of combining self-supervised learning with light supervision to overcome data scarcity in protein function prediction and design.