TOHOKU UNIVERSITY Researchers

Research History 2

2023/06 - Present

東北大学大学院工学研究科, 助教
2022/04 - 2023/06

東北大学大学院工学研究科, 特任助教

Education 3

東北大学大学院工学研究科バイオ工学専攻博士課程後期

2019/04 - 2022/03
東北大学大学院工学研究科バイオ工学専攻博士課程前期

2017/04 - 2019/03
東北大学工学部化学・バイオ工学科

2013/04 - 2017/03

Professional Memberships 5

日本抗体学会
日本生物工学会
化学工学会
日本生物物理学会
日本蛋白質科学会

Research Interests 2

Directed evolution
Protein engineering

Research Areas 2

Life sciences / Molecular biology /
Life sciences / Functional biochemistry /

Awards 3

Young Scientist Award

2024/06　The 24th Annual Meeting of the Protein Science Society of Japan　Machine-learning-guided simultaneous molecular evolution for affinity, expression, and stability of antibody fragment
第1回日本生物工学会大会北日本支部若手オンラインシンポジウム優秀ポスター賞

2021/08　日本生物工学会大会北日本支部
生物工学学生優秀賞（飛翔賞）

2019/09　日本生物工学会

Papers 12

Discovery and affinity maturation of antibody fragments from an unfavorably enriched phage display selection by deep sequencing and machine learning

Sakiya Kawada, Yoichi Kurumida, Tomoyuki Ito, Thuy Duong Nguyen, Hafumi Nishi, Hikaru Nakazawa, Yutaka Saito, Tomoshi Kameda, Koji Tsuda, Mitsuo Umetsu

Journal of Bioscience and Bioengineering　2025/08

DOI： 10.1016/j.jbiosc.2025.05.004 　
Structure reveals a regulation mechanism of plant outward-rectifying K+ channel GORK by structural rearrangements in the CNBD-Ankyrin bridge. International-journal

Taro Yamanashi, Yuki Muraoka, Tadaomi Furuta, Tsukasa Kume, Natsuko Sekido, Shunya Saito, Shota Terashima, Takeshi Yokoyama, Yoshikazu Tanaka, Atsushi Miyamoto, Kanane Sato, Tomoyuki Ito, Hikaru Nakazawa, Mitsuo Umetsu, Ellen Tanudjaja, Masaru Tsujii, Ingo Dreyer, Julian I Schroeder, Yasuhiro Ishimaru, Nobuyuki Uozumi

Proceedings of the National Academy of Sciences of the United States of America　122　(30)　e2500070122　2025/07/29

DOI： 10.1073/pnas.2500070122 　

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Guard cells, which regulate stomatal apertures in plants, possess a sophisticated mechanism for regulating turgor pressure. The outward-rectifying "K+out" channel GORK, expressed in guard cells of the plant Arabidopsis thaliana, is a central component that promotes stomatal closure by releasing K+ to the extracellular space, thereby lowering turgor pressure. To date, the structural basis underlying the regulation of the K+ transport activity of GORK is unclear. Using cryo-EM, we determined the structures of the GORK outward-rectifying K+ channel with a resolution of 3.16 to 3.27 Å in five distinct conformations that differ significantly in their C-terminal cyclic nucleotide binding domain (CNBD) and ankyrin repeat (ANK) domain. The C-linker connects the transmembrane domains to the C-terminal domains, i.e., CNBD, CNBD-Ankyrin bridge, and ANK. The structural changes and interactions in the C-linker determine whether the closed state of GORK is closer to the preopen state or in a more removed state from the open state of the channel. In particular, interconversion in the short sequence within the CNBD-Ankyrin bridge plays a decisive role in this determination. This region forms an α-helix in the preopened state, while it adopts a nonhelical structure in further distant closed states. The dynamics of the cytosolic region strongly suggest that the K+ channel activity of GORK is regulated by cytosolic signaling factors during stomatal closure.
A High-Throughput Cell-Free Enzyme Screening System Using Redox-Responsive Hydrogel Beads as Artificial Compartments

Taisei Koga, Yui Okawa, Tomoyuki Ito, Kensei Orita, Kosuke Minamihata, Mitsuo Umetsu, Noriho Kamiya

ACS Synthetic Biology　2025/03/21

DOI： 10.1021/acssynbio.4c00783 　
Design of cyborg proteins by loop region replacement with oligo(ethylene glycol): exploring suitable mutations for cyborg protein construction using machine learning Peer-reviewed

Wijak Yospanya, Akari Matsumura, Yukihiro Imasato, Tomoyuki Itou, Yusuke Aoki, Hikaru Nakazawa, Takashi Matsui, Takeshi Yokoyama, Mihoko Ui, Mitsuo Umetsu, Satoru Nagatoishi, Kouhei Tsumoto, Yoshikazu Tanaka, Kazushi Kinbara

Bulletin of the Chemical Society of Japan　97　(9)　2024/09/02
Publisher: Oxford University Press (OUP)
DOI： 10.1093/bulcsj/uoae090 　

ISSN： 0009-2673

eISSN： 1348-0634

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Abstract We synthesized a “cyborg protein,” wherein a synthetic molecule partially substitutes the main peptide chain by linking 2 protein domains with a synthetic oligomer. Green fluorescent protein (GFP) served as the model for constructing the cyborg proteins. We prepared circularly permuted GFP (cpGFP) with new termini between β10 and β11, where the original N- and C-termini were linked by a cleavable peptide loop. The cyborg GFP was constructed from cpGFP by linking the β10 and β11 with oligo(ethylene glycol) (OEG) using maleimide–cysteine couplings, followed by the enzymatic cleavage of the N- and C-termini linking loop by thrombin. With the help of machine learning, we were able to obtain the cpGFP mutants that significantly alter the fluorescence intensity (53% increase) by thrombin treatment, which splits cpGFP into 2 fragments (fragmented GFP), and by heat shock. When the cyborg GFP was constructed using this mutant, the fluorescence intensity increased by 13% after heat treatment, similar to cpGFP (33% increase), and the behavior was significantly different from that of the fragmented GFP. This result suggests the possibility that the OEG chain in the cyborg protein plays a similar role to the peptide in the main chain of the protein.
Extensive antibody search with whole spectrum black-box optimization Peer-reviewed

Andrejs Tučs, Tomoyuki Ito, Yoichi Kurumida, Sakiya Kawada, Hikaru Nakazawa, Yutaka Saito, Mitsuo Umetsu, Koji Tsuda

Scientific Reports　14　(1)　552　2024/01/04
Publisher: Springer Science and Business Media LLC
DOI： 10.1038/s41598-023-51095-z 　

eISSN： 2045-2322

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Abstract In designing functional biological sequences with machine learning, the activity predictor tends to be inaccurate due to shortage of data. Top ranked sequences are thus unlikely to contain effective ones. This paper proposes to take prediction stability into account to provide domain experts with a reasonable list of sequences to choose from. In our approach, multiple prediction models are trained by subsampling the training set and the multi-objective optimization problem, where one objective is the average activity and the other is the standard deviation, is solved. The Pareto front represents a list of sequences with the whole spectrum of activity and stability. Using this method, we designed VHH (Variable domain of Heavy chain of Heavy chain) antibodies based on the dataset obtained from deep mutational screening. To solve multi-objective optimization, we employed our sequence design software MOQA that uses quantum annealing. By applying several selection criteria to 19,778 designed sequences, five sequences were selected for wet-lab validation. One sequence, 16 mutations away from the closest training sequence, was successfully expressed and found to possess desired binding specificity. Our whole spectrum approach provides a balanced way of dealing with the prediction uncertainty, and can possibly be applied to extensive search of functional sequences.
Recent research advances on non-linear phenomena in various biosystems Peer-reviewed

Yutaka Tamaru, Shuji Nakanishi, Kenya Tanaka, Mitsuo Umetsu, Hikaru Nakazawa, Aruto Sugiyama, Tomoyuki Ito, Naofumi Shimokawa, Masahiro Takagi

Journal of Bioscience and Bioengineering　136　(2)　75-86　2023/08
Publisher: Elsevier BV
DOI： 10.1016/j.jbiosc.2023.03.012 　

ISSN： 1389-1723
Synthesis of epitope‐targeting nanobody based on native protein–protein interactions for FtsZ filamentation suppressor Peer-reviewed

Hikaru Nakazawa, Taiji Katsuki, Takashi Matsui, Atsushi Tsugita, Takeshi Yokoyama, Tomoyuki Ito, Sakiya Kawada, Yoshikazu Tanaka, Mitsuo Umetsu

Biotechnology Journal　2023/07/26
Publisher: Wiley
DOI： 10.1002/biot.202300039 　

ISSN： 1860-6768

eISSN： 1860-7314

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Abstract Phage display and biopanning are powerful tools for generating binding molecules for a specific target. However, the selection process based only on binding affinity provides no assurance for the antibody's affinity to the target epitope. In this study, we propose a molecular‐evolution approach guided by native protein–protein interactions to generate epitope‐targeting antibodies. The binding‐site sequence in a native protein was grafted into a complementarity‐determining region (CDR) in the nanobody, and a nonrelated CDR loop (in the grafted nanobody) was randomized to create a phage display library. In this construction of nanobodies by integrating graft and evolution technology (CAnIGET method), suitable grafting of the functional sequence added functionality to the nanobody, and the molecular‐evolution approach enhanced the binding function to inhibit the native protein–protein interactions. To apply for biological tool with growth screening, model nanobodies with an affinity for filamenting temperature‐sensitive mutant Z (FtsZ) from Staphylococcus aureus were constructed and completely inhibited the polymerization of FtsZ as a function. Consequently, the expression of these nanobodies drastically decreased the cell division rate. We demonstrate the potential of the CAnIGET method with the use of native protein–protein interactions for steady epitope‐specific evolutionary engineering.
Selection of target-binding proteins from the information of weakly enriched phage display libraries by deep sequencing and machine learning Peer-reviewed

Tomoyuki Ito, Thuy Duong Nguyen, Yutaka Saito, Yoichi Kurumida, Hikaru Nakazawa, Sakiya Kawada, Hafumi Nishi, Koji Tsuda, Tomoshi Kameda, Mitsuo Umetsu

mAbs　15　(1)　2023/01/22
Publisher: Informa UK Limited
DOI： 10.1080/19420862.2023.2168470 　

ISSN： 1942-0862

eISSN： 1942-0870
機械学習が導く進化分子工学の新しいフェーズ

Mitsuo Umetsu, Tomoyuki Ito

100　(11)　593-595　2022/11

DOI： 10.34565/seibutsukogaku.100.11_593 　
Machine-Learning-Guided Library Design Cycle for Directed Evolution of Enzymes: The Effects of Training Data Composition on Sequence Space Exploration Peer-reviewed

Yutaka Saito, Misaki Oikawa, Takumi Sato, Hikaru Nakazawa, Tomoyuki Ito, Tomoshi Kameda, Koji Tsuda, Mitsuo Umetsu

ACS Catalysis　11　(23)　14615-14624　2021/11/19
Publisher: American Chemical Society (ACS)
DOI： 10.1021/acscatal.1c03753 　

ISSN： 2155-5435

eISSN： 2155-5435
Combination Informatic and Experimental Approach for Selecting Scaffold Proteins for Development as Antibody Mimetics Peer-reviewed

Tomoyuki Ito, Hafumi Nishi, Tomoshi Kameda, Mayu Yoshida, Reito Fukazawa, Sakiya Kawada, Hikaru Nakazawa, Mitsuo Umetsu

Chemistry Letters　50　(11)　1867-1871　2021/11/05
Publisher: The Chemical Society of Japan
DOI： 10.1246/cl.210443 　

ISSN： 0366-7022

eISSN： 1348-0715
Compact Seahorse‐Shaped T Cell–Activating Antibody for Cancer Therapy Peer-reviewed

Hiroto Fujii, Yoshikazu Tanaka, Hikaru Nakazawa, Aruto Sugiyama, Noriyoshi Manabe, Akira Shinoda, Nobutaka Shimizu, Takamitsu Hattori, Katsuhiro Hosokawa, Takuma Sujino, Tomoyuki Ito, Teppei Niide, Ryutaro Asano, Izumi Kumagai, Mitsuo Umetsu

Advanced Therapeutics　1　(3)　1700031-1700031　2018/06/05
Publisher: Wiley
DOI： 10.1002/adtp.201700031 　

ISSN： 2366-3987

eISSN： 2366-3987

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Misc. 3

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BIOSCIENCE & INDUSTRY　82　(1)　52-53　2024/01
微生物発現と構造安定性を考慮した機械学習支援による抗体断片の親和性成熟

伊藤智之, 河田早矢, 中澤光, 村上明一, 梅津光央

第75回日本生物工学会大会 Topics of 2023　16-17　2023/09
Application of Next-Generation Sequencing Analysis in the Directed Evolution for Creating Antibody Mimic

Tomoyuki Ito, Hafumi Nishi, Thuy Duong Nguyen, Yutaka Saito, Tomoshi Kameda, Hikaru Nakazawa, Koji Tsuda, Mitsuo Umetsu

BIOPHYSICAL JOURNAL　120　(3)　87A-87A　2021/02

ISSN： 0006-3495

eISSN： 1542-0086

Research Projects 1

Evolutionary Molecular Engineering Guided by Machine Learning: Smart Maturation Process for Cancer Therapeutic Antibodies

Umetsu Mitsuo

Offer Organization: Japan Society for the Promotion of Science

System: Grants-in-Aid for Scientific Research

Category: Grant-in-Aid for Scientific Research (A)

Institution: Tohoku University

2020/04/01 - 2024/03/31

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In this study, we have developed a technology that can predict amino acid sequences with optimized multiple functions and properties by advancing evolutionary molecular engineering, which can indicate the direction of evolution from information on small variants using machine learning. We developed a process to accelerate the development of antibody drugs that can simultaneously optimize the properties of antibody by creating a predictor for camelid heavy-chain antibody variable region fragment using machine learning with the expression level, target binding, structural stability, humaneness, and other properties of about 100 variants as training data.