Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

TL;DR

This paper discusses how AI and advanced proteomics techniques are revolutionizing biological research by improving data analysis, biological insights, and enabling virtual cell modeling.

Contribution

It highlights key AI-driven innovations in proteomics, including data ecosystem development, protein identification, interaction analysis, and multi-omics integration for biological discovery.

Findings

Enhanced proteomics data quality and scale

AI-driven protein and interaction analysis methods

Progress towards AI-empowered virtual cells

Abstract

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis; improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and ultimately enabling AI-empowered virtual cells.