Decoding Life’s Language: The Pros and Cons of Scaling Up Protein Language Models

The scientific world has long been fascinated by the correlation between the syntax-semantics of natural languages and the sequence function of proteins. Comparable to letters forming words that take on specific meanings, amino acids combine to create proteins that perform particular functions. More recently, Natural Language Processing (NLP), which is the artificial intelligence arm that deciphers human language, has focused increasingly on understanding life’s language, namely proteins. This has resulted in a transformative shift in our understanding, but has also raised questions on the feasibility of scaling up Protein Language Models (PLMs), delving into a territory where size and accessibility might come at odds.

With languages, whether biological or computational, there is an inevitable paradox. Language intricacies in respective domains do not lend themselves to identical outcomes. For instance, while a certain sentence structure might be acceptable in English but incorrect in French, a protein sequence that works in one organism could be dysfunctional in another. Similarly, scaling language models in the NLP realm does not directly translate to protein language models. The focus on enlarging models, unfortunately, comes at the expense of accessibility and computational power, presenting the first of many hurdles in this ambitious endeavor.

To better understand the state-of-art in protein language models, let’s examine ProtTrans’s ProtT5-XL-U50, which operates at the upper echelon of PLM performance. Considered a trailblazer in the field, it has evolved from its embryonic stages of 106 parameters to an impressive size of 109 parameters. This colossal leap not only highlights the potential capability of these models but also underlines the hefty computational requirements they carry.

The RITA family of language models provides an excellent example showcasing how scale impacts model performance. This family hosts four different models, each distinguished by size and performance, running the gamut from 85 million parameters to an enormous 1.2 billion parameters. Each successive iteration of the model reveals a significant increase in competence, reinforcing the narrative that size does matter.

This increasing trend of model scalability does not end here. ProGen2 and ESM-2 have also contributed significantly in this field, offering models ranging from a hefty 650 million to a staggering 15 billion parameters. While these numbers are mind-boggling, they serve as a testimony of the relentless pursuit of scaling in the world of protein language models.

Despite these achievements, a “larger is better” mentality might not be the best route forward. Focusing predominantly on size neglects crucial factors such as computational costs and the viability of task-agnostic models. These factors might limit smaller research institutes from contributing to this field, thereby potentially stifling the innovative spirit that is paramount for breakthroughs in scientific research.

The debate between model size and data quality is an ongoing one. Undoubtedly, size significantly impacts goal achievement. However, the quality of the pre-training dataset is just as important. In other words, feeding the model with accurate and diverse data is as crucial as scale, once again questioning the “bigger is always better” mindset.

Understanding protein language models’ scalability is not an end in itself. The primary objective of increasing model size is to incorporate knowledge-guided optimization and navigate the conditional landscapes of model scaling. If successfully implemented, this could shape our understanding of proteins, solve complex biological puzzles, and open the door to uncharted territories. Therefore, it’s not just about deciphering life’s language – it’s about translating it into groundbreaking applications for the betterment of humanity.