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J. Skolnick

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#artificial intelligence Preprint Oct 2026

A Shortcut to Structure in AlphaFold 3

AlphaFold 3 predicts protein structures with remarkable accuracy, yet how structural information emerges within the model remains poorly understood. Here, through causal interventions on internal representations and direct probing of every Pairformer block, we trace the formation of global protein geometry and identify...

J. Feldman, J. Skolnick · 0 citations
Open access Aug 2026

Metabolites form a globally connected chemical network across protein families

Metabolites are generally viewed as substrates, products, cofactors, or regulators of individual proteins, whereas metabolites recurring across many protein families are often regarded as promiscuous binders. Here, we analyzed 989,058 BioLiP2 protein–ligand binding sites and assigned 929,546 sites to ECOD v295 homologo...

J. Skolnick, B. Srinivasan · 0 citations
Open access Sep 2026

The human metabolite–protein interactome reveals a global layer of cellular coordination

A global metabolite-mediated architecture with the capacity to coordinate proteins across otherwise distinct cellular systems is revealed and experimentally established metabolite-dependent complexes validate the local mechanism independently of LIGMAP.

J. Skolnick, B. Srinivasan · 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. · 1 citation

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