Inspired by the transformative success of large language models (LLMs) in natural language processing (NLP), numerous protein language models (PLMs) have recently emerged, revolutionizing the field of protein bioinformatics. PLMs have demonstrated remarkable achievements in representing proteins and designing new ones, capturing intrinsic structural and functional information trained on vast datasets of proteins, PLMs have demonstrated exceptional performance across a variety of bioinformatics tasks, including classification, function prediction, and de novo protein design. This chapter explores the evolution of PLMs, tracing their origins from NLP-based transformers and large language models (LLMs). A comprehensive summary of notable PLMs is presented, with a particular focus on encoder-only, encoder-decoder, and decoder-only architectures. Additionally, we delve into cutting-edge trends in PLM applications, such as fine-tuning methods, multimodal architectures, and the use of reduced alphabets. These innovations underscore the growing potential of PLMs to tackle complex biological problems and drive future breakthroughs in the field.