PPubMed9 Jun 2026

Editorial: Advances in protein structure biology – use of AI and beyond Pal et al. 10.3389/fmolb.2026.1903318

Arumay Pal, Emmanouela Filippidi, Sucharita Dey

Abstract

Artificial intelligence (AI) has driven major technological breakthroughs in structural biology over the past several years, with the advent of AlphaFold and RoseTTAFold in 2020 and the subsequent advance to AlphaFold 3 in 2024 (; ; ). Together with structure prediction models such as ESMFold () and ensemble-sampling frameworks like BioEmu (), these approaches now form a foundational computational infrastructure underpinning applications ranging from protein engineering to drug discovery ([F1]). Perhaps the most transformative impact of AI/ML in structural biology is being realized in protein

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AI is rapidly transforming protein structure biology, enabling breakthroughs in prediction, design, disease mutation analysis, and therapeutic discovery.

What’s new

i.AI-driven advances in protein structure prediction (AlphaFold, RoseTTAFold, AlphaFold 3, ESMFold).
ii.Deep learning generative models (Rfdiffusion, BindCraft, ProteinMPNN, IgFold) enabling de novo protein design and engineering.
iii.AI-assisted analysis of disease-associated mutations (DNA2 helicase, TP53 variants) linking structural changes to dysfunction.
iv.Integration of AI with therapeutic discovery, exemplified by AI‑guided identification of bioactive compounds from Adansonia digitata.
v.Open-source tools (OpenFold) democratizing access to state‑of‑the‑art protein AI models.

How they did it

The editorial surveys six contributions—three original research articles, two reviews, and one methods paper—highlighting how AI models are applied across protein structure prediction, interaction network mapping, mutation analysis, and drug discovery. It synthesizes findings from these works to illustrate emerging AI-driven workflows in structural biology.

What they found

Rfdiffusion and BindCraft successfully designed high‑affinity protein binders and novel architectures.
ProteinMPNN and IgFold expanded functional protein engineering directly from sequence or structural data.
AI‑guided workflow linked specific electronic signatures to TP53 variant dysfunction.

Caveats

✕Integrating AI predictions with experimental interactome data remains challenging.
✕Scalable interpretation of disease‑associated mutations requires further methodological development.
✕Ensuring data privacy while retraining proprietary AI models is an ongoing concern.
✕Experimental validation of AI‑designed proteins is needed to confirm predicted functions.

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