Daisuke Kihara

Active Mentor - currently hosting PULSe students for laboratory rotations and recruiting PULSe students into the laboratory; serves on preliminary exam committees

Current Research Interests:

Protein Bioinformatics. Our lab develops and applies computational tools for building protein 3D structure models, protein complexes, structure modeling for cryo-electron microscope density data, drug screening, and protein function prediction. We use various machine learning and computational biophysics approaches in the tools.

Selected Publications:

Detecting protein and DNA/RNA structures in cryo-EM maps of intermediate resolution using deep learning. Wang X., Alnabati E., Aderinwale T.W., Subramaniya S.R.M.V., Terashi G., & Kihara D. (2021). Nature Communications. 12: 2302. PMCID : PMC8052361.

VESPER: Global and local cryo-EM map alignment using local density vectors. Han, X., Terashi, G., Christoffer, C., & Kihara, D. (2021). Nature Communications 12 : 2090. PMCID : PMC8027200.

Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge. Lawson, CL., Kryshtafovych A., Kihara D., et al. (2021). Nature Methods, 18: 156-164.

LZerD webserver for pairwise and multiple protein-protein docking. Christoffer, C., Chen, S., Bharadwaj, V., Aderinwale, T., Kumar, V., Hormati, M., & Kihara, D. (2021). Nucleic Acid Research, May 8;gkab336. doi: 10.1093/nar/gkab336.

Protein contact map refinement for improving structure prediction using generative adversarial networks. Subramanya, S.R.M.V., Terashi, G., Jain A., Kagaya, Y., & Kihara, D. (2021). Bioinformatics, Mar 31:btab220. doi: 10.1093/bioinformatics/btab220

Protein docking model evaluation by 3D deep convolutional neural networks. Wang, X., Terashi, G., Christoffer, C.W., & Kihara, D. (2020). Bioinformatics, 36: 2113-2118.

Phylo-PFP: Improved automated protein function prediction using phylogenetic distance of distantly related sequences. Jain A. & Kihara D. (2019) Bioinformatics. 35: 753-759.

Protein secondary structure detection in intermediate resolution cryo-EM maps using deep learning. Subramanya, S.R.M.V., Terashi, G., & Kihara, D. (2019). Nature Methods, 16: 911-917.

De novo main-chain modeling for EM maps using MAINMAST. Terashi G. & Kihara D. (2018). Nature Communications, 9: 1618.

Structure and inhibition of EV-D68, a virus that causes respiratory illness in children. Y. Liu, J. Sheng, A. Fokine, G. Meng, W.-H. Shin, F. Long, R.J. Kuhn, D. Kihara, & M.G. Rossmann, Science 347: 71-74 (2015)