Development and evaluation of data-driven designed tags (DDTs) for controlling protein solubility

被引:7
|
作者
Hirose, Shuichi [1 ]
Kawamura, Yoshifumi [2 ]
Mori, Masatoshi [2 ]
Yokota, Kiyonobu [1 ]
Noguchi, Tamotsu [1 ]
Goshima, Naoki [3 ]
机构
[1] Natl Inst Adv Ind Sci & Technol, CBRC, Tokyo, Japan
[2] JBiC, Tokyo, Japan
[3] Natl Inst Adv Ind Sci & Technol, BIRC, Tokyo, Japan
关键词
ESCHERICHIA-COLI; RECOMBINANT PROTEINS; DISORDERED REGIONS; FUSION; EXPRESSION; SEQUENCES; PURIFICATION; PREDICTION; FACILITATE; PATTERNS;
D O I
10.1016/j.nbt.2010.08.012
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Production of proteins is an important issue in protein science and pharmaceutical studies. Numerous protein expression systems using living cells and cell-free methods have been developed to date. In these systems, a promising strategy for improving the success rate of obtaining soluble proteins is the attachment of various tags into target proteins based on empirical rules. This paper presents a method for the production of data-driven designed tags (DDTs) based on highly frequent sequence property patterns in an experimentally assessed protein solubility dataset in a wheat germ cell-free system. We constructed seven proteins combined with 12 kinds of DDTs (six for enhancing solubility and six for insolubility) at the N-terminal region as tags. Then we investigated their behavior using SDS-PAGE. Results show that three and four proteins respectively showed a trend toward solubilization and insolubilization, which indicates the possibility that the theoretically designed sequence can control protein solubility.
引用
收藏
页码:225 / 231
页数:7
相关论文
共 50 条
  • [1] Improving protein solubility and activity by introducing small peptide tags designed with machine learning models
    Han, Xi
    Ning, Wenbo
    Ma, Xiaoqiang
    Wang, Xiaonan
    Zhou, Kang
    METABOLIC ENGINEERING COMMUNICATIONS, 2020, 11
  • [2] Robust development of data-driven models for methane and hydrogen mixture solubility in brine
    Saleem, Kashif
    Kumar, Abhinav
    Prasad, K. D. V.
    Alkhayyat, Ahmad
    Ramachandran, T.
    Dey, Protyay
    Kaur, Navdeep
    Sivaranjani, R.
    Sapaev, I. B.
    Mottaghi, Mehrdad
    GEOMECHANICS AND GEOPHYSICS FOR GEO-ENERGY AND GEO-RESOURCES, 2025, 11 (01)
  • [3] Data-driven computational protein design
    Frappier, Vincent
    Keating, Amy E.
    CURRENT OPINION IN STRUCTURAL BIOLOGY, 2021, 69 : 63 - 69
  • [4] Data-driven models for protein interaction and design
    Zhu, Xiaolei
    Ericksen, Spencer S.
    Demerdash, Omar N. A.
    Mitchell, Julie C.
    PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, 2013, 81 (12) : 2221 - 2228
  • [5] Mechanistic and data-driven modeling of protein glycosylation
    Shek, Coral Fung
    Kotidis, Pavlos
    Betenbaugh, Michael
    CURRENT OPINION IN CHEMICAL ENGINEERING, 2021, 32
  • [6] Development of Data-Driven System in Materials Integration
    Inoue, Junya
    Okada, Masato
    Nagao, Hiromichi
    Yokota, Hideo
    Adachi, Yoshitaka
    MATERIALS TRANSACTIONS, 2020, 61 (11) : 2058 - 2066
  • [7] Development of surface texture evaluation system for highly sparse data-driven machining domains
    Raju, Umamaheswara R. S.
    Ramesh, R.
    Rohit Varma, K.
    INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2020, 33 (09) : 859 - 868
  • [8] Data-Driven Materials Research and Development for Functional Coatings
    Xu, Kai
    Xiao, Xuelian
    Wang, Linjing
    Lou, Ming
    Wang, Fangming
    Li, Changheng
    Ren, Hui
    Wang, Xue
    Chang, Keke
    ADVANCED SCIENCE, 2024, 11 (42)
  • [9] Development of data-driven models for the flow cytometric crossmatch
    Weimer, Eric T.
    Newhall, Katherine A.
    HUMAN IMMUNOLOGY, 2019, 80 (12) : 983 - 989
  • [10] MetaCity: Data-driven sustainable development of complex cities
    Zhang, Yunke
    Lin, Yuming
    Zheng, Guanjie
    Liu, Yu
    Sukiennik, Nicholas
    Xu, Fengli
    Xu, Yongjun
    Lu, Feng
    Wang, Qi
    Lai, Yuan
    Tian, Li
    Li, Nan
    Fang, Dongping
    Wang, Fei
    Zhou, Tao
    Li, Yong
    Zheng, Yu
    Wu, Zhiqiang
    Guo, Huadong
    INNOVATION, 2025, 6 (02):