Abstract
The development of comprehensive measurement technology for biomolecules and the development of computer hardware and algorithms, enhanced by human genome research, have greatly contributed to the advancement of predictive biology. However, to make that happen, it was essential to have a common concept of sharing data in a standard format. In this article, I would like to briefly review how such concepts were developed in Japan for the foundation of today’s biological sciences.
Keywords: Signaling network, Protein folding, Trajectories, Computational simulation, Mathematical modeling
I am writing this commentary on my way back to Osaka from the International Conference on Systems Biology (ICSB) 2022 in Berlin. I have been working in this relatively new research field for over 20 years. Many wonderful talks were given on topics such as the microbiome, gene regulation, cancer genomics, mathematical modeling, machine learning, and artificial intelligence (AI). One of the most appealing keynote lectures was on AlphaFold (Jumper et al. 2021) by Dr. Alexander Pritzel of DeepMind. In his presentation, he repeatedly referred to the Protein Data Bank (PDB), and I could not help but think about the efforts and ideas of Dr. Haruki Nakamura, who intended to host the PDB Japan (PDBj) at the Institute for Protein Research (IPR), Osaka University, even when the human genome was not completely identified. At that time, I believe Japanese scientists underestimated the importance of accumulating and sharing protein structural data for the coming age of data science, as well as the ability to predict protein structures from amino acid sequences. When Dr. Nakamura launched the PDBj, I was a researcher at the Genome Sciences Center (GSC), RIKEN Yokohama campus.
The GSC was founded by Dr. Akiyoshi Wada, who was the first to advocate the need for an automated DNA sequencing machine (Wada et al. 1983). He was also the doctoral advisor for Dr. Nakamura at the University of Tokyo. GSC is a kind of systems biology center dedicated to the comprehensive understanding of the central dogma and the identification of the strategies of life, based on the large-scale experimental and computational analysis of the human genome, mouse transcriptome, protein structure, and plant and mouse phenotypes. Dr. Wada believed in the significance of reproducible data in a standard format that could be shared within the scientific community. These concepts served as the inspiration for the DNA sequencer. If automated machines were used to identify nucleic acid sequences with high accuracy and without human error, the resulting data would be widely shareable and a priceless asset for mankind. Dr. Nakamura must have been inspired by the common ideas of genomics and protein sciences for open sciences. He would have foreseen that the universality of protein data and the value of individual data would foster the development of future science.
I have an interesting episode of my own. When I started a small project on mathematical modeling of cancer signaling networks and published our first paper (Hatakeyama et al. 2003), only a few people realized the significance of our work. However, one day I was invited as a lecturer to a course on biological simulation in Kobe, and I recently discovered that Dr. Nakamura was the one who recommended me. I believe that Dr. Nakamura discovered a connection between computer simulations of cancer signaling networks and protein structures (Fig. 1). Although we discovered that the valley-like trajectories of protein folding (Wu Kim et al. 2020) and signaling networks (Rukhlenko et al. 2022) resemble the Waddington’s epigenetic landscape (Ferrell 2012) in cellular regulation, many people, except for Dr. Nakamura, did not recognize these similarities 20 years ago.
Fig. 1.
Landscapes of the transitions of protein folding and cellular development. (a) Free energy landscape of protein folding. Adapted and modified from Kim et al. (Wu Kim et al. 2020). (b) Developmental landscape of cells regulated by gene networks. Adapted and modified from Sáez et al. (Sáez et al. 2022)
To understand the specificities that arise from signaling networks, we initiated a project on the mathematical modeling of biological networks. We believe that signaling dynamics resulting from multiple protein interactions are important for cellular decision-making (Nakakuki et al. 2010; Shinohara et al. 2014; Imoto et al. 2022). We aimed to discover universal rules for these complex phenomena through wet-lab experiments and computational analysis and then apply these rules to the prediction of unobserved events in cell fate determination. Data accumulation, identification of general principles, and the development of predictive computational models are fundamental to the systems biology approach, and the relationship between PDB and AlphaFold can be viewed as a successful illustration.
As Dr. Nakamura predicted the future research directions of protein science, IPR seeks to strengthen the computational and theoretical aspects of protein science, providing new insights into computer-aided protein design and understanding biological systems at the atomic level. IPR also seeks to understand the origin of life as a system of protein networks. However, this research cannot be completed solely by IPR researchers and requires the participation of many collaborators.
Acknowledgements
I thank Dr Keita Iida for discussion for the protein folding and cellular development landscapes and figure presentation.
Funding
M.O. is supported by the Uehara Memorial Foundation.
Declarations
Consent to participate
The author agreed to participate the publication.
Consent for publication
The author agreed to publish the manuscript.
Competing interests
The author declares no competing interests.
Footnotes
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References
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