Protein Language Models: Applications and Perspectives

Large language models (LLMs) originally developed for
human text
have been adapted to proteomics as protein language models (pLMs).
These models treat amino acid sequences like sentences, and they learn
patterns from millions of sequences. pLMs are used for several key
tasks, including the prediction of protein structures, annotating
protein functions, designing novel protein sequences with specific
characteristics, and mapping the interactions between proteins and
other molecules. Compared with traditional approaches, pLMs deliver
insights more quickly but demand large computing resources and careful
data management. Developers are focused on decreasing prediction inaccuracies
and biases by exploring more efficient training techniques and smaller
models to decrease the resources required. As sequence databases continue
to grow, pLMs will improve to uncover links between proteins and disease
pathways, speeding drug development and basic research while offering
new proteome-scale insights that support experimental design and validation.