Teaching AI to speak protein

Abstract

Large Language Models for proteins, namely protein Language Models ( pLMs ), have begun to provide an important alternative to capturing the information encoded in a protein sequence in computers. Arguably, pLMs have advanced importantly to understanding aspects of the language of life as written in proteins, and through this understanding, they are becoming an increasingly powerful means of advancing protein prediction, e.g., in the prediction of molecular function as expressed by identifying binding residues or variant effects. While benefitting from the same technology, protein structure prediction remains one of the few applications for which only using pLM embeddings from single sequences appears not to improve over or match the state-of-the-art. Fine-tuning foundation pLMs enhances efficiency and accuracy of solutions, in particular in cases with few experimental annotations. pLMs facilitate the integration of computational and experimental biology, of AI and wet-lab, in particular toward a new era of protein design. • Protein Language Models (pLMs) tap into large unlabeled data to transform protein science. • pLMs boost protein structure and function prediction performance and advance protein design. • Fine-tuned pLMs excel in protein tasks with sparse data, bridging AI and experimental biology efficiently. • Multimodal pLMs integrate sequence, structure, and function for precise protein design and diverse applications. • Challenges include predicting higher-order mutations, dynamics, and multi-protein interactions.

Topics & Concepts