Precision Analysis of Protein Modifications Using AI Prediction Technology [Reading Science]

A Step Closer to Uncovering the Causes of Intractable Diseases

Researchers have taken a significant step closer to uncovering the molecular causes of hard-to-treat diseases such as cancer by precisely detecting extremely rare protein modifications inside cells using artificial intelligence (AI) prediction technology.

On January 29, Lee Cheolju of the Korea Institute of Science and Technology (KIST) announced that a research team at the Center for Convergence of Chemical and Life Sciences has developed a technology that accurately detects rare protein modifications, which were previously difficult to distinguish through conventional analysis, by utilizing AI learning models.

Overview of AI-Based Formula Protein Discovery Technology Development Research. Graphic provided by the research team

Intractable diseases such as cancer are closely linked to subtle protein changes that occur as cells undergo stress. However, these modifications are infrequent and often resemble false signals, making them difficult to identify accurately with existing mass spectrometry techniques. As a result, there has been a growing demand for new analytical technologies capable of tracing the root causes of diseases at the molecular level.

The modification that the research team focused on is "arginylation," which regulates protein function or acts as a degradation signal. Abnormalities in this process can lead to neuronal cell damage or cancer development, but its presence in living organisms is extremely low, making it difficult to distinguish real signals from false ones. To address this, the team adopted a reverse approach by first training the AI to recognize false signals that closely resemble genuine ones.

As a result, they succeeded in eliminating approximately 90% of the false signals detected in previous analyses and identified a total of 134 actual arginylation modification sites. Notably, by applying transfer learning techniques, they demonstrated that even with small amounts of data, rare protein modifications could be precisely analyzed. Analysis of cells under stress confirmed arginylation modifications in certain proteins related to cellular energy production, offering new clues about cancer cell metabolism.

This technology integrates the discovery and primary verification of protein modifications into a single AI-based analytical system, which is expected to significantly reduce research costs and time in drug development and bio-research fields. When applied to patient blood or tissue analysis, it has great potential to rapidly and accurately detect disease-related protein changes, serving as a foundational technology for early diagnosis and precision medicine research.

Lee Cheolju of KIST stated, "This achievement boldly applies AI to areas that remained as limitations in previous research," adding, "With this world-class, AI-based proteome analysis technology developed purely through domestic research, we will contribute to the expansion of AI-driven proteome research."

This research was supported by the Ministry of Science and ICT through KIST's major projects, individual basic research projects, and the Bio Research Data Utilization Infrastructure Project. The findings were published in the international journal Nature Communications.