We have a postdoc opening. Please check
careers.iscb.org/jobs/view/9910

Our new book chapter has just been published! "Computational approaches for protein complex modeling for intermediate resolution cryo-EM maps" We introduced our recently developed tools available on the EM server (em.kiharalab.org).
You can read it here:
www.sciencedirect.com/science/chap...

Following our PFP server update, two more related tools:

• NaviGO – explore & visualize GO term relationships
kiharalab.org/navigo

• Queryome – search protein function across genomes
kiharalab.org/queryome

Together with PFP, a full ecosystem for protein function analysis.

We’ve rebuilt our Protein Function Prediction (PFP) server from the ground up.
New features:
• Domain-PFP: self-supervised, domain-aware function prediction
• GO2Sum: converts GO terms into UniProt-style functional summaries
All methods (PFP, Phylo-PFP, ESG) now in one interface.
kiharalab.org/pfp

Our review on peptide–protein docking is out in Chemical Communications. "Peptide-protein docking: from physics-based models to generative intelligence". We discuss the shift from physics-based docking to deep learning & generative models. pubs.rsc.org/en/content/a...

You should also try cryoZETA. It's quite impressive. em.kiharalab.org/algorithm/Cr...

The March update of DAQ-Score DB! Includes Cryo-EM protein model quality evaluations for 275,728 proteins. Now, we use a language model to interpret evaluation in text!
🔗 daqdb.kiharalab.org
DAQ is easy to compute for structure validation in your paper: em.kiharalab.org/algorithm/da...

New collaboration paper with the Bou-Abdallah lab at SUNY Potsdam: "Ferritin iron uptake and oxidation are dynamically modulated by nucleotide phosphate architecture via electrostatic gating", International Journal of Biological Macromolecules. sciencedirect.com/science/arti...

Our presentations at Biophysical Society Meeting at San Francisco!
On em.kiharalab.org by Joon Hong Park, em.kiharalab.org/algorithm/DM... by Genki Terashi,
colab.research.google.com/github/kihar... by Yuki Kagaya. #bps2026

Accurate Macromolecular Complex Modeling for Cryo-EM with CryoZeta [new]
expands modern structure prediction by integrating cryo-EM density and sequence info via a diffusion network for accurate de novo macromolecular modeling.

Accurate Macromolecular Complex Modeling for Cryo-EM with CryoZeta www.biorxiv.org/content/10.64898/2026.02.13.705846v1 #cryoEM

🧬 New review out in Cell Reports Physical Science!
We survey the rapidly evolving landscape of RNA 3D structure prediction & design, from classical physics-based methods to cutting-edge deep learning, MSA-free models, and generative design.
👉 doi.org/10.1016/j.xc...

DAQ-Score: Automatic AI-based Cryo-EM Structure Model Validation! YouTube video by Kihara Bioinformatics Lab

A quick tutorial on the DAQ structure validation score for protein models derived from cryo-EM. DAQ evaluates amino acid–level accuracy in your model. Consider including a DAQ validation report in your next publication!
www.youtube.com/watch?v=YRAT...

🚀 First DAQ-Score DB update of 2026 is live!
Cryo-EM protein model quality evaluations now cover 266,577 protein chains.
🔗 daqdb.kiharalab.org
🏁Check out also our New ChimeraX plugin! It lets you check DAQ scores during modeling!
cxtoolshed.rbvi.ucsf.edu/apps/chimera...

DAQ score has now ChimeraX plugin! You can monitor DAQ interactively while modeling your proteins in an EM map on ChimeraX!

PDB 7jsn colored by DAQ scores of fit in cryoEM map.

The ChimeraX DAQplugin computes DAQ scores showing the agreement between atomic models and cryoEM maps. Available from ChimeraX menu Tools / More Tools. cxtoolshed.rbvi.ucsf.edu/apps/chimera...

New paper:🧬 Queryome: A multi-agent AI system for biomedical literature analysis.
Queryome is a deep research system with specialized LLM agents that orchestrate to a wide range of queries based on Pubmed citations.
biorxiv.org/content/10.6...
Try it: queryome.app

DAQ-Score: Automatic AI-based Cryo-EM Structure Model Validation! YouTube video by Kihara Bioinformatics Lab

A quick tutorial on the DAQ structure validation score for protein models derived from cryo-EM. DAQ evaluates amino acid–level accuracy in your model. Consider including a DAQ validation report in your next publication!
www.youtube.com/watch?v=YRAT...

December Update of DAQ-Score DB! Quality evaluations of protein models from cryo-EM. Now includes evaluation of 258,956 protein chains.
Check at daqdb.kiharalab.org
It's very easy to compute DAQ for your protein model. Add the validation result in your publication: em.kiharalab.org/algorithm/da...

Distance-AF improves predicted protein structure models by AlphaFold2 with user-specified distance constraints Distance-AF is a computational method that enhances AlphaFold2 by integrating user-specified distance constraints, enabling accurate protein structures prediction on different applications. It demonst...

Blog of our recent method, Distance-AF by the first author, Yuanyuan Zhang: "Distance-AF improves predicted protein structure models by AlphaFold2 with user-specified distance constraints". Original paper is published in Communications Biology.
communities.springernature.com/posts/distan...

Structural Insights and Functional Dynamics of β-Lactoglobulin Fibrils Amyloid fibrils from β-lactoglobulin (β-LG), a major whey protein, have attracted interest for nanotechnology due to their biocompatibility, tunable surface chemistry, and ability to bind functional m...

New paper in collaboration with Jinghui Luo lab at PSI Paul Scherrer Inst. "Structural Insights and Functional Dynamics of β-Lactoglobulin Fibrils", Nano Letters.
We used DeepMainmast and DAQ for structure modeling.
pubs.acs.org/doi/full/10....

October release of DAQ Score DB of cryo-EM str validation! Now includes 255,199 protein chains from 17,283 EMDB maps. Fig is an example with an entire helix having a residue shift.
daqdb.kiharalab.org
Easy to use DAQ for structure validation in your paper: em.kiharalab.org

New paper released! "Distance-AF improves predicted protein structure models by AlphaFold2 with user-specified distance constraints" Yuanyuan Zhang, Zicong Zhang, Y Kagaya, G Terashi, B Zhao, Y Xiong & D Kihara, Communications Biology.
www.nature.com/articles/s42...

New paper from our lab! Introducing EQAFold: "AlphaFold model quality self‐assessment improvement via deep graph learning" Jacob Verburgt, Zicong Zhang & D. Kihara, Protein Science. onlinelibrary.wiley.com/doi/10.1002/...

PNCC Cryo-EM modeling and validation workshop on Sep 3-5, 2025. Register now! tinyurl.com/Cryo-EMModel...

Joon Hong Park received the RCSB poster Prize Award for his presentation at Meeting of the American Crystallographic Association. "EMSuite server: Advanced tools for cryo-EM str modeling, validation and refinement". Congratulations!!
The server: em.kiharalab.org

Summer update of DAQ-Score DB, cryo-EM model quality assessment! Now includes assessment for 251,375 PDB chains from 16,852 EM maps. CCC+Overlap value is now displayed at each entry. daqdb.kiharalab.org

Joon Hong Park presenting his poster at American Crystallographic Association (ACA) 2025 meeting. It is about our cryo-EM structure modeling server.
Visit the server for easy and accurate modeling at em.kiharalab.org

Distpepfold, our new peptide docking method is available as a source code and Google Colab notebook!
Paper: pubs.acs.org/doi/10.1021/...
Github: github.com/kiharalab/Di...
Google Colab: colab.research.google.com/drive/1Q1ecU...

Using machine learning to speed up discovery for drug delivery and disease treatment A new computational tool developed with support from the U.S. National Science Foundation could greatly speed up determining the 3D structure of RNAs, a…

NuFold, a new computational tool from the @kiharalab.bsky.social at @purduecs.bsky.social, accelerates 3D RNA structure and function discovery, opening new possibilities for faster development of RNA-based therapeutics and technologies. 🧪 www.nsf.gov/news/using-m...