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Daisuke Kihara

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Open access Aug 2026

Computational Approaches for Protein–DNA/RNA Complex Modeling for Cryo‐EM Maps

How to use two methods developed by the group, ComplexModeler and CryoZeta, with a focus on modeling protein–nucleic acid complexes through the EMSuite web server through two modeling examples is described.

Anika Jain, Ke-Fan Cao, Daisuke Kihara · 0 citations
#protein folding Open access Aug 2026

PathFold: Predicting the Entire Protein Folding Pathway from Protein Sequence Alone

Recent advances in protein structure prediction, exemplified by AlphaFold, have largely addressed the determination of static structures, one aspect of the protein folding problem. However, predicting folding pathways, by which proteins reach their native states, remains a significant challenge. Here, we present PathFo...

Zi-Cong Zhang, Nabil Ibtehaz, Yuki Kagaya et al. · 0 citations
#protein folding Open access Aug 2026

Prot-LAMBDA: Explicit Distance Learning Enhances Structural Reasoning in Protein Language Models

Prot-LAMBDA is introduced, a PLM that explicitly incorporates spatial relationships by coupling residue embeddings with inter-residue contacts and LambdaFold, a lightweight distance-guided structure prediction framework that achieves performance comparable to ESMFold on proteins strictly non-redundant to the training d...

Nabil Ibtehaz, Zi-Cong Zhang, Yuki Kagaya et al. · 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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