Skip to content
Log in
Review

AI-Driven Protein Research: From Prediction to Design.

Guodong Min Huan Peng
2026 · Methods in molecular biology · Vol 3030, pp. 213-226 · 0 citations
Medicine

TL;DR

This mini review traces the evolution of AI-driven methods in protein research, from early residue-contact prediction using coevolutionary information to transformative breakthroughs, the rise of protein language models (PLMs), and the emerging era of generative design and functional modeling.

DOI PubMed Semantic Scholar
View source

Similar papers

Jul 2026

Deep Learning for Proteins Notebook Series Teaches AI for Biomolecular Structure Prediction and Design

Deep Learning for Proteins: a series of 10 interactive notebook modules that introduce fundamental machine-learning concepts, guide users through training machine-learning models for protein-related tasks, and ultimately present cutting-edge protein structure prediction and design pipelines are developed.

Michael Chungyoun, G. Au, Britnie Carpentier et al. · 0 citations
Jul 2026

Generalizable Protein Folding Pathway Exploration with DA2-GRASP: Extending Beyond Miniproteins.

Elucidating protein dynamics is crucial for deciphering fundamental biological processes, from enzyme catalysis to cellular signaling, as its dysregulation directly causes protein misfolding diseases such as Alzheimer's and Parkinson's. While artificial intelligence has revolutionized static protein structure prediction, capturing the high-dimensional dynamics of protein folding remains a formidable challenge that limits our ability to fully understand these vital biological phenomena. Here we present DA2-GRASP, a computational framework that overcomes this barrier by integrating deep learning with advanced sampling techniques to map protein folding pathways with unprecedented efficiency and accuracy. DA2-GRASP learns low-dimensional latent representations of protein conformations via a variational autoencoder and combines multidirectional generative sampling guided by local potential energy gradients to efficiently steer conformational transitions along energetically favorable paths, enabling accurate and efficient reconstruction of folding pathways. Our method achieves sublinear computational scaling with sequence length, contrasting the quadratical scaling of molecular dynamics-based conventional approaches, enabling tractable simulations. It maintains high precision in quantifying mutation-induced perturbations to folding thermodynamics, crucial for understanding disease mutations. It also enables atomistic characterization of the folding process of medium-sized proteins such as ubiquitin and small ubiquitin-like modifier (SUMO, ∼80 residues) on standard workstations, a task typically requiring specialized supercomputing platforms such as Anton. Analysis of these proteins provides new mechanistic insights into how structurally similar folds with low sequence identity navigate divergent folding pathways. DA2-GRASP thus establishes a versatile and powerful framework for exploring protein-folding dynamics and their functional consequences.

Yanbing Wen, Hao Dong · 0 citations
Review Jul 2026

The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.

Addressing and predicting ligand-binding sites in protein structures, as well as the prediction of reliable structures of proteins interacting with other proteins, will be pivotal for fully details of structural mechanisms and dynamics.

Pradeep Bk, Shi-Jie Chen, R. Dima et al. · 0 citations
Open access Aug 2026

Interpreting Protein Language Models: high attention sites predict functional regions

The utility of HA sites for suggesting candidate binding sites and the biological interpretability of PLM representations is explored, demonstrating the biological interpretability of PLM representations and offers a valuable method to prioritize functionally relevant protein residues for targeted biomedical research.

Sophia J. Pribus, Russ B. Altman, Gowri Nayar · 0 citations