Protein Language Models

Protein language models treat amino-acid sequences as languages, learning representations through objectives such as masked-residue prediction and autoregressive sequence generation. Researchers use these representations for tasks including remote homology, structure and function prediction, and mutation-effect scoring, evaluating them on benchmarks such as SCOPe, FLIP, and ProteinGym; fine-tuning, contrastive learning, and evolutionary or structural signals can further adapt the models. Generative approaches, including diffusion and reinforcement learning, aim to design proteins with desired properties, sometimes conditioning on structures, ligands, or natural-language instructions. A central challenge is balancing scale with efficiency and biological grounding: benchmark gains do not always transfer to new families or experiments, and predicted plausibility, interpretability, and safety require careful validation.