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.
This framework provides a clearer understanding of how methodological shifts have shaped the capabilities, limitations, and practical roles of recent models.
Wen-Gan He, Yong-Sheng Luo, Lihong Jiang et al.· 0 citations
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.· The Biophysicist· 0 citations
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 predictio...
Yanbing Wen, Hao Dong· Journal of Chemical Theory a...· 0 citations
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.· Journal of Molecular Biology· 1 citation
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· bioRxiv· 0 citations
A pipeline reformulating kinase-substrate modeling as a Bayesian inference problem is presented and it is revealed that the interaction types and distances to the catalytic pocket significantly influence pathogenicity scores.
Jinyuan Hu, Shimian Li, Yue Xue et al.· Journal of Chemical Informat...· 0 citations
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