LAUSR

The third wave of AI (artificial intelligence) has been boosted by the developments of high-performance computing technologies and big data, both of which were absent in the first and second AI waves. The same would be also true for the studies in bioinformatics or biological data science. DNA sequence data from next-generation sequencing represent such a recent explosive increase of biological data, which has enlarged the quantitative gap between sequence and structural data. However, the advances of structure/interaction analysis techniques, as represented by cryo-EM (electron microscopy), are steadily narrowing the gap by accumulating the structural data of biological supramolecules. Consequently, this trend demands concurrent improvements in bioinformatics methods, and it prompted us to plan a symposium to review recent progress in structural bioinformatics in the BSJ2019 (57th Annual Meeting of the Biophysical Society of Japan). The symposium session titled “Challenges of bioinformatics for the era of molecular structure big-data” was started on the morning of September 25, 2019 at the Seagaia Convention Center. The session was opened by the keynote speech “Big Data Science at AMED-BINDS” from Dr. Haruki Nakamura (Japan Agency for Medical Research and Development). The objective of the BINDS (Basis for Supporting Innovative Drug Discovery and Life Science Research) program is to establish an innovative platform to accelerate the therapeutic applications of early-stage drug discovery and medical technology advances by providing and sharing key technological infrastructures and technical/scientific supports from expert researchers in respective fields. In his talk, the mission of BINDS was introduced with a special emphasis on the notion that every basic life science research would be eventually related to medicine. The next talk, “Prediction of protein residue contacts and protein-ligand interactions with deep neural networks”, was from Dr. Kentaro Tomii (National Institute of Advanced Industrial Science and Technology (AIST)). He introduced a novel machine learning method to predict protein–ligand interactions. In this method, a GNN (graph neural network) for ligand molecules and a CNN (convolutional neural network) for protein sequences were combined, and the results showed the presented method out-performed existing methods (Tsubaki et al. 2019). He also introduced a recent application of the method for residue–residue contact predictions based on MSA (multiple sequence alignment) (Fukuda and Tomii 2020). Such efforts, including a neural network used in analyzing protein dynamics (Tsuchiya et al. 2019), are reported in this issue (Tsuchiya and Tomii 2020). Dr. Hidetoshi Kono (National Institutes for Quantum and Radiological Science and Technology) introduced the recent study on structural modeling of overlapping dinucleosome in his talk, “Integrated approach of experimental data and computer modeling and simulation for understanding chromatin structure and dynamics.” The crystal structure of overlapping dinucleosome revealed considerable deviations from SANS/ SAXS (small angle neutron/X-ray scattering) data especially at high-resolution areas. He and his colleagues have generated a structural library of the overlapping dinucleosome through molecular modeling and simulation including histone tail regions, which were invisible by crystal structure analysis. They successfully find the structures which are well fitting to the SANS/SAXS data from the library and suggest possible conformational change and dynamics in solution (Matsumoto et al. 2020). A recent modeling study of HP1-bound nucleosome was also described in his talk (Kumar and Kono 2020). Dr. Takeshi Kawabata (Osaka University) first introduced the databases EMPIAR-PDBj and BSM-Arc in his talk, “EM informatics: archiving raw 2D images and fitting atomic * Tsuyoshi Shirai [email protected]