Kavli Affiliate: Yi Zhou
| First 5 Authors: Wenqi Fan, Yi Zhou, Shijie Wang, Yuyao Yan, Hui Liu
| Summary:
Considering the significance of proteins, computational protein science has
always been a critical scientific field, dedicated to revealing knowledge and
developing applications within the protein sequence-structure-function
paradigm. In the last few decades, Artificial Intelligence (AI) has made
significant impacts in computational protein science, leading to notable
successes in specific protein modeling tasks. However, those previous AI models
still meet limitations, such as the difficulty in comprehending the semantics
of protein sequences, and the inability to generalize across a wide range of
protein modeling tasks. Recently, LLMs have emerged as a milestone in AI due to
their unprecedented language processing & generalization capability. They can
promote comprehensive progress in fields rather than solving individual tasks.
As a result, researchers have actively introduced LLM techniques in
computational protein science, developing protein Language Models (pLMs) that
skillfully grasp the foundational knowledge of proteins and can be effectively
generalized to solve a diversity of sequence-structure-function reasoning
problems. While witnessing prosperous developments, it’s necessary to present a
systematic overview of computational protein science empowered by LLM
techniques. First, we summarize existing pLMs into categories based on their
mastered protein knowledge, i.e., underlying sequence patterns, explicit
structural and functional information, and external scientific languages.
Second, we introduce the utilization and adaptation of pLMs, highlighting their
remarkable achievements in promoting protein structure prediction, protein
function prediction, and protein design studies. Then, we describe the
practical application of pLMs in antibody design, enzyme design, and drug
discovery. Finally, we specifically discuss the promising future directions in
this fast-growing field.
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