The language of proteins: NLP, machine learning & protein sequences

Area of Science:

• Computational Biology
• Bioinformatics
• Artificial Intelligence

Background:

• Natural language processing (NLP) has seen significant advancements due to deep and machine learning breakthroughs.
• Proteins, represented as amino acid strings, share characteristics with textual data, making them suitable for NLP analysis.

Purpose of the Study:

• To review the application, potential, and limitations of NLP algorithms in protein studies.
• To explore the conceptual parallels between language and protein sequences.
• To identify protein-related tasks addressable by machine learning.

Main Methods:

• Encoding protein information into text formats for NLP analysis.
• Applying classic NLP techniques like bag-of-words and k-mers.
• Utilizing modern NLP methods including word embeddings, deep learning, and neural language models.
• Focusing on recent innovations like masked language modeling, self-supervised learning, and attention-based models.

Main Results:

• Demonstrated success and promise of NLP in protein research.
• Identified various protein-related tasks amenable to machine learning approaches.
• Highlighted conceptual similarities and differences between linguistic and protein sequences.

Conclusions:

• NLP offers powerful tools for advancing protein science.
• The intersection of NLP and protein research presents exciting opportunities and challenges.
• Continued development in NLP techniques will likely yield further insights into protein structure and function.