基于转运蛋白工程提升微生物菌株耐受性和生物制造效率的研究进展

doi:10.13560/j.cnki.biotech.bull.1985.2023-0750

摘要/Abstract

摘要：

微生物细胞工厂广泛用于生物燃料以及高值化学品和大宗化学品的可持续生产，但是高浓度产物和底物以及多种环境胁迫条件会抑制菌株的发酵效率，降低生产的经济性。因此，增强菌株耐受性对于目的产物的高效和可持续生产至关重要。近年来，利用转运蛋白工程保护菌株免受毒性化合物的损害以提升菌株耐受性的策略日益受到研究者的关注。因此，本文总结了基于微生物转运蛋白工程改造提升菌株耐受性的研究进展，分析了目前微生物转运蛋白研究领域中存在的关键问题，并探讨了基于转运蛋白工程提升微生物菌株耐受性的策略，尤其对人工智能在转运蛋白功能注释、结构模拟和底物-转运蛋白互作预测中的应用进行了总结和展望，以期能够促进微生物在绿色生物制造领域的应用。

关键词: 绿色生物制造, 转运蛋白, 胁迫耐受性, 工业微生物, 发酵效率

Abstract:

Microbial cell factories have been extensively used for sustainable production of biofuels, as well as high value and bulk chemicals. However, high concentration products or substrates, as well as stressful conditions during industrial production, may compromise fermentation efficiency and decreasing economics of production. In this context, microbial stress tolerance is crucial for green and sustainable production of the target products. In recent years, the use of transporters to protect microbial cells from toxic compounds for enhancing strain tolerance has received increasing worldwide attention. This review summarizes the progress of studies on microbial strain tolerance enhancement based on transporter engineering, analyses the current key points in the field of transporter research and discusses strategies to enhance strain tolerance based on transporter manipulation. Especially，the review highlights the applications of artificial intelligence in transporter annotation, structure simulation and substrate-transporter interaction prediction, aiming to promote the application of microorganisms in biological manufacturing.

Key words: green biological manufacturing, membrane transporter, stress tolerance, industrial microorganism, fermentation efficiency

李昕悦, 周明海, 樊亚超, 廖莎, 张风丽, 刘晨光, 孙悦, 张霖, 赵心清. 基于转运蛋白工程提升微生物菌株耐受性和生物制造效率的研究进展[J]. 生物技术通报, 2023, 39(11): 123-136.

LI Xin-yue, ZHOU Ming-hai, FAN Ya-chao, LIAO Sha, ZHANG Feng-li, LIU Chen-guang, SUN Yue, ZHANG Lin, ZHAO Xin-qing. Research Progress in the Improvement of Microbial Strain Tolerance and Efficiency of Biological Manufacturing Based on Transporter Engineering[J]. Biotechnology Bulletin, 2023, 39(11): 123-136.

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参考文献 101

[1]	Jiang T, Li CY, Teng YX, et al. Recent advances in improving metabolic robustness of microbial cell factories[J]. Curr Opin Biotechnol, 2020, 66: 69-77. doi: 10.1016/j.copbio.2020.06.006 URL
[2]	Xu Q, Bai CX, Liu YQ, et al. Modulation of acetate utilization in Komagataella phaffii by metabolic engineering of tolerance and metabolism[J]. Biotechnol Biofuels, 2019, 12: 61. doi: 10.1186/s13068-019-1404-0
[3]	Jin J, Jia B, Yuan YJ. Yeast chromosomal engineering to improve industrially-relevant phenotypes[J]. Curr Opin Biotechnol, 2020, 66: 165-170. doi: 10.1016/j.copbio.2020.07.003 URL
[4]	Chen YR, Yang YL, Cai WQ, et al. Research progress of anti-environmental factor stress mechanism and anti-stress tolerance way of Saccharomyces cerevisiae during the brewing process[J]. Crit Rev Food Sci Nutr, 2022:1-16.
[5]	Zhao XQ, Xiong L, Zhang MM, et al. Towards efficient bioethanol production from agricultural and forestry residues: exploration of unique natural microorganisms in combination with advanced strain engineering[J]. Bioresour Technol, 2016, 215: 84-91. doi: 10.1016/j.biortech.2016.03.158 URL
[6]	孙浩. 纤维素乙醇生产菌株Spathaspora passalidarum抗逆性提高策略及纤维素水解液的乙醇发酵[D]. 大连: 大连理工大学, 2022.
	Sun H. Stress resistance strategy and ethanol fermentation of Spathaspora passalidarum in lignocellulosic hydrolysis[D]. Dalian: Dalian University of Technology, 2022.
[7]	Salas-Navarrete PC, de Oca Miranda AIM, Martínez A, et al. Evolutionary and reverse engineering to increase Saccharomyces cerevisiae tolerance to acetic acid, acidic pH, and high temperature[J]. Appl Microbiol Biotechnol, 2022, 106(1): 383-399. doi: 10.1007/s00253-021-11730-z pmid: 34913993
[8]	李翠丽. 高糖胁迫下产甘油假丝酵母细胞质膜抗逆应答机制的初步研究[D]. 汉中: 陕西理工大学, 2019.
	Li CL. Preliminary study on the anti-reverse mechanism of plasma membrane of Candida glycerinogenes under high glucose stress[D]. Hanzhong: Shaanxi University of Technology, 2019.
[9]	Kim HS. Disruption of RIM15 confers an increased tolerance to heavy metals in Saccharomyces cerevisiae[J]. Biotechnol Lett, 2020, 42(7): 1193-1202. doi: 10.1007/s10529-020-02884-3
[10]	李书廷. 全局调控因子工程改进大肠杆菌乙酸钠和异丁醇耐受性及耐受机制研究[D]. 天津: 天津大学, 2020.
	Li ST. Engineering global regulators to improve sodium acetate and isobutanol tolerances and their mechanisms in Escherichia coli[D]. Tianjin: Tianjin University, 2020.
[11]	Matson MM, Cepeda MM, Zhang A, et al. Adaptive laboratory evolution for improved tolerance of isobutyl acetate in Escherichia coli[J]. Metab Eng, 2022, 69: 50-58. doi: 10.1016/j.ymben.2021.11.002 URL
[12]	Yao XR, Liu P, Chen B, et al. Synthetic acid stress-tolerance modules improve growth robustness and lysine productivity of industrial Escherichia coli in fermentation at low pH[J]. Microb Cell Fact, 2022, 21(1): 68. doi: 10.1186/s12934-022-01795-4
[13]	Zhang NN, Shang YY, Wang FE, et al. Influence of prefoldin subunit 4 on the tolerance of Kluyveromyces marxianus to lignocellulosic biomass-derived inhibitors[J]. Microb Cell Fact, 2021, 20(1): 224. doi: 10.1186/s12934-021-01715-y pmid: 34906148
[14]	常瀚文, 郑鑫铃, 骆健美, 等. 抗逆元件及其在高效微生物细胞工厂构建中的应用进展[J]. 生物技术通报, 2020, 36(6): 13-34. doi: 10.13560/j.cnki.biotech.bull.1985.2020-0259
	Chang HW, Zheng XL, Luo JM, et al. Tolerance elements and their application progress on the construction of highly-efficient microbial cell factory[J]. Biotechnol Bull, 2020, 36(6): 13-34.
[15]	Guo L, Pang ZX, Gao C, et al. Engineering microbial cell morphology and membrane homeostasis toward industrial applications[J]. Curr Opin Biotechnol, 2020, 66: 18-26. doi: 10.1016/j.copbio.2020.05.004 URL
[16]	Mutanda I, Sun JZ, Jiang JX, et al. Bacterial membrane transporter systems for aromatic compounds: regulation, engineering, and biotechnological applications[J]. Biotechnol Adv, 2022, 59: 107952. doi: 10.1016/j.biotechadv.2022.107952 URL
[17]	Zhu Y, Zhou C, Wang Y, et al. Transporter engineering for microbial manufacturing[J]. Biotechnol J, 2020, 15(9): e1900494.
[18]	Xia J, Liu CG, Zhao XQ, et al. Contribution of cellulose synthesis, formation of fibrils and their entanglement to the self-flocculation of Zymomonas mobilis[J]. Biotechnol Bioeng, 2018, 115(11): 2714-2725. doi: 10.1002/bit.v115.11 URL
[19]	赵姝一, 袁冰, 王雪晴, 等. 过表达tRNA基因tL(CAA)K提高酿酒酵母乙酸耐受性[J]. 生物工程学报, 2021, 37(12): 4293-4302.
	Zhao SY, Yuan B, Wang XQ, et al. Overexpression of a leucine transfer RNA gene tL(CAA)K improves the acetic acid tolerance of Saccharomyces cerevisiae[J]. Chin J Biotechnol, 2021, 37(12): 4293-4302.
[20]	Nicolaou SA, Gaida SM, Papoutsakis ET. A comparative view of metabolite and substrate stress and tolerance in microbial bioprocessing: from biofuels and chemicals, to biocatalysis and bioremediation[J]. Metab Eng, 2010, 12(4): 307-331. doi: 10.1016/j.ymben.2010.03.004 pmid: 20346409
[21]	Liu J, Lin QL, Chai XY, et al. Enhanced phenolic compounds tolerance response of Clostridium beijerinckii NCIMB 8052 by inactivation of Cbei_3304[J]. Microb Cell Fact, 2018, 17(1): 35. doi: 10.1186/s12934-018-0884-0
[22]	Jiménez-Bonilla P, Zhang J, Wang YF, et al. Enhancing the tolerance of Clostridium saccharoperbutylacetonicum to lignocellulosic-biomass-derived inhibitors for efficient biobutanol production by overexpressing efflux pumps genes from Pseudomonas putida[J]. Bioresour Technol, 2020, 312: 123532. doi: 10.1016/j.biortech.2020.123532 URL
[23]	Kurgan G, Panyon LA, Rodriguez-Sanchez Y, et al. Bioprospecting of native efflux pumps to enhance furfural tolerance in ethanologenic Escherichia coli[J]. Appl Environ Microbiol, 2019, 85(6): e02985-e02918.
[24]	Wan C, Zhang MM, Fang Q, et al. The impact of zinc sulfate addition on the dynamic metabolic profiling of Saccharomyces cerevisiae subjected to long term acetic acid stress treatment and identification of key metabolites involved in the antioxidant effect of zinc[J]. Metallomics, 2015, 7(2): 322-332. doi: 10.1039/C4MT00275J URL
[25]	Zhang MM, Zhang KY, Mehmood MA, et al. Deletion of acetate transporter gene ADY2 improved tolerance of Saccharomyces cerevisiae against multiple stresses and enhanced ethanol production in the presence of acetic acid[J]. Bioresour Technol, 2017, 245: 1461-1468. doi: 10.1016/j.biortech.2017.05.191 URL
[26]	Cámara E, Olsson L, Zrimec J, et al. Data mining of Saccharomyces cerevisiae mutants engineered for increased tolerance towards inhibitors in lignocellulosic hydrolysates[J]. Biotechnol Adv, 2022, 57: 107947. doi: 10.1016/j.biotechadv.2022.107947 URL
[27]	Yi X, Lin L, Mei J, et al. Transporter proteins in Zymomonas mobilis contribute to the tolerance of lignocellulose-derived phenolic aldehyde inhibitors[J]. Bioprocess Biosyst Eng, 2021, 44(9): 1875-1882. doi: 10.1007/s00449-021-02567-x
[28]	王心凝. 酿酒酵母对木质纤维素水解液中抑制物香草醛耐受性机制的解析[D]. 济南: 山东大学, 2017.
	Wang XN. Unravel the tolerance mechanism of Saccharomyces cerevisiae toward vanillin, an inhibitor in lignocellulosic hydrolysates[D]. Jinan: Shandong University, 2017.
[29]	Wang XN, Liang ZZ, Hou J, et al. The absence of the transcription factor Yrr1p, identified from comparative genome profiling, increased vanillin tolerance due to enhancements of ABC transporters expressing, rRNA processing and ribosome biogenesis in Saccharomyces cerevisiae[J]. Front Microbiol, 2017, 8: 367.
[30]	李拥. 土生隐球酵母RTA1脂质转运蛋白在耐酸铝中的功能研究[D]. 昆明: 昆明理工大学, 2022.
	Li Y. The study on the function of RTA1 lipid transporter of Cryptococcus humicola in aluminum and acid tolerance[D]. Kunming: Kunming University of Science and Technology, 2022.
[31]	朱政明, 张娟, 堵国成, 等. 乳酸乳球菌硫胺素转运蛋白ThiT提高酸耐受性的机制[J]. 食品与生物技术学报, 2020, 39(9): 16-24.
	Zhu ZM, Zhang J, Du GC, et al. Mechanism of thiamine transporter thi T improving acid tolerance in Lactococcus lactis NZ9000[J]. J Food Sci Biotechnol, 2020, 39(9): 16-24.
[32]	Terra-Matos J, Teixeira MO, Santos-Pereira C, et al. Saccharomyces cerevisiae cells lacking the zinc vacuolar transporter Zrt3 display improved ethanol productivity in lignocellulosic hydrolysates[J]. J Fungi, 2022, 8(1): 78. doi: 10.3390/jof8010078 URL
[33]	Zhu P, Luo R, Li YZ, et al. Metabolic engineering and adaptive evolution for efficient production of l-lactic acid in Saccharomyces cerevisiae[J]. Microbiol Spectr, 2022, 10(6): e0227722. doi: 10.1128/spectrum.02277-22 URL
[34]	Papousková K, Sychrová H. Production of Yarrowia lipolytica Nha2 Na⁺/H⁺ antiporter improves the salt tolerance of Saccharomyces cerevisiae[J]. Folia Microbiol, 2007, 52(6): 600-602. doi: 10.1007/BF02932189 pmid: 18450222
[35]	林婕婷. 嗜盐四联球菌盐胁迫适应性甘氨酸甜菜碱ABC转运系统及精氨酸代谢通路的表达调控研究[D]. 广州: 华南理工大学, 2021.
	Lin JT. The salinity-stress adaptation of Tetragenococcus halophilus revealed by the regulatory mechanism of Glycine betaine ABC transporter and arginine metabolism pathway[D]. Guangzhou: South China University of Technology, 2021.
[36]	Xu Q, Zhai ZY, An HR, et al. The MarR family regulator BmrR is involved in bile tolerance of Bifidobacterium longum BBMN68 via controlling the expression of an ABC transporter[J]. Appl Environ Microbiol, 2019, 85(3): e02453-e02418.
[37]	Sanadhya P, Agarwal P, Khedia J, et al. A low-affinity K⁺ transporter AlHKT2;1 from recretohalophyte Aeluropus lagopoides confers salt tolerance in yeast[J]. Mol Biotechnol, 2015, 57(6): 489-498. doi: 10.1007/s12033-015-9842-9 pmid: 25604033
[38]	Caro G, Bieber J, Ruiz-Castilla FJ, et al. Trk1, the sole potassium-specific transporter in Candida glabrata, contributes to the proper functioning of various cell processes[J]. World J Microbiol Biotechnol, 2019, 35(8): 124. doi: 10.1007/s11274-019-2698-6
[39]	Chen BB, Ling H, Chang MW. Transporter engineering for improved tolerance against alkane biofuels in Saccharomyces cerevisiae[J]. Biotechnol Biofuels, 2013, 6(1): 21. doi: 10.1186/1754-6834-6-21
[40]	Wu JJ, Wang Z, Zhang X, et al. Improving medium chain fatty acid production in Escherichia coli by multiple transporter engineering[J]. Food Chem, 2019, 272: 628-634. doi: 10.1016/j.foodchem.2018.08.102 URL
[41]	曲俊泽. 异源ABC转运蛋白的表达对酿酒酵母萜类化合物合成的影响[D]. 天津: 天津大学, 2020.
	Qu JZ. Effect of heterologous ABC transporter expression on the synthesis of terpenoids in Saccharomyces cerevisiae[D]. Tianjin: Tianjin University, 2020.
[42]	李建. 解脂耶氏酵母对柠檬烯的耐受机理及应用研究[D]. 天津: 天津科技大学, 2021.
	Li J. Molecular mechanism and engineering application of limonene tolerance in Yarrowia lipolytica[D]. Tianjin: Tianjin University of Science & Technology, 2021.
[43]	Pereira R, Mohamed ET, Radi MS, et al. Elucidating aromatic acid tolerance at low pH in Saccharomyces cerevisiae using adaptive laboratory evolution[J]. Proc Natl Acad Sci USA, 2020, 117(45): 27954-27961. doi: 10.1073/pnas.2013044117 pmid: 33106428
[44]	Qiu J, Hou KX, Li Q, et al. Boosting the cannabidiol production in engineered Saccharomyces cerevisiae by harnessing the vacuolar transporter BPT1[J]. J Agric Food Chem, 2022, 70(38): 12055-12064. doi: 10.1021/acs.jafc.2c05468 URL
[45]	Xu X, Williams TC, Divne C, et al. Evolutionary engineering in Saccharomyces cerevisiae reveals a TRK1-dependent potassium influx mechanism for propionic acid tolerance[J]. Biotechnol Biofuels, 2019, 12: 97. doi: 10.1186/s13068-019-1427-6
[46]	Wang L, Li N, Yu SQ, et al. Enhancing caffeic acid production in Escherichia coli by engineering the biosynthesis pathway and transporter[J]. Bioresour Technol, 2023, 368: 128320. doi: 10.1016/j.biortech.2022.128320 URL
[47]	Ma C, Mu QX, Xue YB, et al. One major facilitator superfamily transporter is responsible for propionic acid tolerance in Pseu-domonas putida KT2440[J]. Microb Biotechnol, 2021, 14(2): 386-391. doi: 10.1111/mbt2.v14.2 URL
[48]	Nguyen-Vo TP, Ko S, Ryu H, et al. Systems evaluation reveals novel transporter YohJK renders 3-hydroxypropionate tolerance in Escherichia coli[J]. Sci Rep, 2020, 10(1): 19064. doi: 10.1038/s41598-020-76120-3 pmid: 33149261
[49]	杨佩珊. 基于跨膜蛋白研究提高Lactococcus lactis NZ9000酸胁迫抗性[D]. 无锡: 江南大学, 2019.
	Yang PS. Based on research of transmembrane protein to improve Lactococcus lactis NZ9000 acid stress resistance[D]. Wuxi: Jiangnan University, 2019.
[50]	Wu JL, McAuliffe O, O'Byrne CP. Manganese uptake mediated by the NRAMP-type transporter MntH is required for acid tolerance in Listeria monocytogenes[J]. Int J Food Microbiol, 2023, 399: 110238. doi: 10.1016/j.ijfoodmicro.2023.110238 URL
[51]	Navarrete C, Estrada M, Martínez JL. Debaryomyces hansenii: an old acquaintance for a fresh start in the era of the green biotechnology[J]. World J Microbiol Biotechnol, 2022, 38(6): 99. doi: 10.1007/s11274-022-03280-x
[52]	Mulet JM, Leube MP, Kron SJ, et al. A novel mechanism of ion homeostasis and salt tolerance in yeast: the Hal4 and Hal5 protein kinases modulate the Trk1-Trk2 potassium transporter[J]. Mol Cell Biol, 1999, 19(5): 3328-3337. doi: 10.1128/MCB.19.5.3328 pmid: 10207057
[53]	Zimmermannova O, Salazar A, Sychrova H, et al. Zygosaccharomyces rouxii Trk1 is an efficient potassium transporter providing yeast cells with high lithium tolerance[J]. FEMS Yeast Res, 2015, 15(4): fov029.
[54]	汤岳琴, 王莉, 谢采芸. 一种多重耐受性酿酒酵母菌株及其构建方法和应用.中国: CN113717873A[P], 2021-11-30.
	Tang YQ, Wang L, Xie CY. A multitolerant Saccharomyces cerevisiae strain and its construction method and application. China: CN113717873A[P], 2021-11-30.
[55]	Zhou X, Lu XH, Li XH, et al. Radiation induces acid tolerance of Clostridium tyrobutyricum and enhances bioproduction of butyric acid through a metabolic switch[J]. Biotechnol Biofuels, 2014, 7(1): 22. doi: 10.1186/1754-6834-7-22 URL
[56]	Romano M, Fusco G, Choudhury HG, et al. Structural basis for natural product selection and export by bacterial ABC transporters[J]. ACS Chem Biol, 2018, 13(6): 1598-1609. doi: 10.1021/acschembio.8b00226 pmid: 29757605
[57]	Liu GH, Ding C, Ju Y, et al. Directed evolution of an EamB transporter for improved L-cysteine tolerance and production in Escherichia coli[J]. FEMS Microbiol Lett, 2022, 368(21-24): fnac008.
[58]	Fiedurek J, Trytek M, Szczodrak J. Strain improvement of industrially important microorganisms based on resistance to toxic metabolites and abiotic stress[J]. J Basic Microbiol, 2017, 57(6): 445-459. doi: 10.1002/jobm.v57.6 URL
[59]	van der Hoek SA, Borodina I. Transporter engineering in microbial cell factories: the ins, the outs, and the in-betweens[J]. Curr Opin Biotechnol, 2020, 66: 186-194. doi: 10.1016/j.copbio.2020.08.002 URL
[60]	Yang L, Malla S, Özdemir E, et al. Identification and engineering of transporters for efficient melatonin production in Escherichia coli[J]. Front Microbiol, 2022, 13: 880847. doi: 10.3389/fmicb.2022.880847 URL
[61]	Kell DB. The transporter-mediated cellular uptake and efflux of pharmaceutical drugs and biotechnology products: how and why phospholipid bilayer transport is negligible in real biomembranes[J]. Molecules, 2021, 26(18): 5629. doi: 10.3390/molecules26185629 URL
[62]	Jin K, Xia HZ, Liu YF, et al. Compartmentalization and transporter engineering strategies for terpenoid synthesis[J]. Microb Cell Fact, 2022, 21(1): 92. doi: 10.1186/s12934-022-01819-z pmid: 35599322
[63]	Hu ZH, Li HX, Weng YR, et al. Improve the production of D-limonene by regulating the mevalonate pathway of Saccharomyces cerevisiae during alcoholic beverage fermentation[J]. J Ind Microbiol Biotechnol, 2020, 47(12): 1083-1097. doi: 10.1007/s10295-020-02329-w URL
[64]	张丽丽. 利用转运蛋白提高酵母对萜类化合物耐受性的研究[D]. 天津: 天津科技大学, 2017.
	Zhang LL. The study about using transporter to improve yeast's tolerability to terpenes[D]. Tianjin: Tianjin University of Science & Technology, 2017.
[65]	Hu YT, Zhu ZW, Nielsen J, et al. Heterologous transporter expression for improved fatty alcohol secretion in yeast[J]. Metab Eng, 2018, 45: 51-58. doi: S1096-7176(17)30295-1 pmid: 29183749
[66]	Kell DB, Swainston N, Pir P, et al. Membrane transporter engineering in industrial biotechnology and whole cell biocatalysis[J]. Trends Biotechnol, 2015, 33(4): 237-246. doi: 10.1016/j.tibtech.2015.02.001 pmid: 25746161
[67]	Radi MS, SalcedoSora JE, Kim SH, et al. Membrane transporter identification and modulation via adaptive laboratory evolution[J]. Metab Eng, 2022, 72: 376-390. doi: 10.1016/j.ymben.2022.05.004 pmid: 35598887
[68]	Yin JY, You NX, Li FC, et al. State-of-the-art application of artificial intelligence to transporter-centered functional and pharmaceutical research[J]. Curr Drug Metab, 2023, 24(3): 162-174. doi: 10.2174/1389200224666230523155759 pmid: 37226790
[69]	Chen D, Li S, Chen Y. ISTRF: identification of sucrose transporter using random forest[J]. Front Genet, 2022, 13: 1012828. doi: 10.3389/fgene.2022.1012828 URL
[70]	Fiamenghi MB, Bueno JGR, Camargo AP, et al. Machine learning and comparative genomics approaches for the discovery of xylose transporters in yeast[J]. Biotechnol Biofuels Bioprod, 2022, 15(1): 57. doi: 10.1186/s13068-022-02153-7
[71]	Srinivasan P, Smolke CD. Engineering cellular metabolite transport for biosynthesis of computationally predicted tropane alkaloid derivatives in yeast[J]. Proc Natl Acad Sci USA, 2021, 118(25): e2104460118. doi: 10.1073/pnas.2104460118 URL
[72]	Ali Shah SM, Taju SW, Ho QT, et al. GT-Finder: classify the family of glucose transporters with pre-trained BERT language models[J]. Comput Biol Med, 2021, 131: 104259. doi: 10.1016/j.compbiomed.2021.104259 URL
[73]	Alballa M, Butler G. TooT-T: discrimination of transport proteins from non-transport proteins[J]. BMC Bioinformatics, 2020, 21(Suppl 3): 25. doi: 10.1186/s12859-019-3311-6 pmid: 32321420
[74]	Bergman S, Cater RJ, Plante A, et al. Substrate binding-induced conformational transitions in the omega-3 fatty acid transporter MFSD2A[J]. Nat Commun, 2023, 14(1): 3391. doi: 10.1038/s41467-023-39088-y pmid: 37296098
[75]	Mitrovic D, McComas SE, Alleva C, et al. Reconstructing the transport cycle in the sugar porter superfamily using coevolution-powered machine learning[J]. eLife, 2023, 12: e84805. doi: 10.7554/eLife.84805 URL
[76]	Denger A, Helms V. Optimized data set and feature construction for substrate prediction of membrane transporters[J]. J Chem Inf Model, 2022, 62(23): 6242-6257. doi: 10.1021/acs.jcim.2c00850 pmid: 36454173
[77]	Nguyen TTD, Le NQK, Ho QT, et al. Using word embedding technique to efficiently represent protein sequences for identifying substrate specificities of transporters[J]. Anal Biochem, 2019, 577: 73-81. doi: 10.1016/j.ab.2019.04.011 URL
[78]	王茜. 基于深度学习的外膜蛋白拓扑结构预测研究[D]. 长春: 东北师范大学, 2022.
	Wang X. Topology structure prediction of outer membrane protein based on deep learning[D]. Changchun: Northeast Normal University, 2022.
[79]	Saier MH, Reddy VS, Moreno-Hagelsieb G, et al. The transporter classification database(TCDB): 2021 update[J]. Nucleic Acids Res, 2021, 49(D1): D461-D467. doi: 10.1093/nar/gkaa1004 URL
[80]	Yin JY, Sun W, Li FC, et al. VARIDT 1.0: variability of drug transporter database[J]. Nucleic Acids Res, 2020, 48(D1): D1171. doi: 10.1093/nar/gkz878 URL
[81]	Lomize AL, Schnitzer KA, Todd SC, et al. Membranome 3.0: database of single-pass membrane proteins with AlphaFold models[J]. Protein Sci, 2022, 31(5): e4318. doi: 10.1002/pro.4318 pmid: 35481632
[82]	Kozma D, Simon I, Tusnády GE. PDBTM: protein Data Bank of transmembrane proteins after 8 years[J]. Nucleic Acids Res, 2013, 41(Database issue): D524-D529. doi: 10.1093/nar/gks1169 URL
[83]	Ridha F, Kulandaisamy A, Michael Gromiha M. MPAD: a database for binding affinity of membrane protein-protein complexes and their mutants[J]. J Mol Biol, 2023, 435(14): 167870. doi: 10.1016/j.jmb.2022.167870 URL
[84]	Jayasinghe S, Hristova K, White SH. MPtopo: a database of membrane protein topology[J]. Protein Sci, 2001, 10(2): 455-458. pmid: 11266632
[85]	Deshmukh R, Rana N, Liu Y, et al. Soybean transporter database: a comprehensive database for identification and exploration of natural variants in soybean transporter genes[J]. Physiol Plant, 2021, 171(4): 756-770. doi: 10.1111/ppl.13287 pmid: 33231322
[86]	Miao ZY, Li DF, Zhang ZH, et al. Medicago truncatula transporter database: a comprehensive database resource for M. truncatula transporters[J]. BMC Genomics, 2012, 13: 60. doi: 10.1186/1471-2164-13-60
[87]	Morrissey KM, Wen CC, Johns SJ, et al. The UCSF-FDA TransPortal: a public drug transporter database[J]. Clin Pharmacol Ther, 2012, 92(5): 545-546. doi: 10.1038/clpt.2012.44 pmid: 23085876
[88]	Mak L, Marcus D, Howlett A, et al. Metrabase: a cheminformatics and bioinformatics database for small molecule transporter data analysis and(Q)SAR modeling[J]. J Cheminform, 2015, 7: 31. doi: 10.1186/s13321-015-0083-5 URL
[89]	Ozawa N, Shimizu T, Morita R, et al. Transporter database, TP-Search: a web-accessible comprehensive database for research in pharmacokinetics of drugs[J]. Pharm Res, 2004, 21(11): 2133-2134. doi: 10.1023/B:PHAM.0000048207.11160.d0 URL
[90]	Fichant G, Basse MJ, Quentin Y. ABCdb: an online resource for ABC transporter repertories from sequenced archaeal and bacterial genomes[J]. FEMS Microbiol Lett, 2006, 256(2): 333-339. pmid: 16499625
[91]	Podolsky IA, Schauer EE, Seppälä S, et al. Identification of novel membrane proteins for improved lignocellulose conversion[J]. Curr Opin Biotechnol, 2022, 73: 198-204. doi: 10.1016/j.copbio.2021.08.010 URL
[92]	Chen TM, Chen ZM, Li YZ, et al. A novel major facilitator superfamily transporter gene Aokap4 near the kojic acid gene cluster is involved in growth and kojic acid production in Aspergillus oryzae[J]. J Fungi, 2022, 8(8): 885. doi: 10.3390/jof8080885 URL
[93]	Munro LJ, Kell DB. Analysis of a library of Escherichia coli transporter knockout strains to identify transport pathways of antibiotics[J]. Antibiotics, 2022, 11(8): 1129. doi: 10.3390/antibiotics11081129 URL
[94]	Malla S, van der Helm E, Darbani B, et al. A novel efficient L-lysine exporter identified by functional metagenomics[J]. Front Microbiol, 2022, 13: 855736. doi: 10.3389/fmicb.2022.855736 URL
[95]	Gong A, Liu WY, Lin YL, et al. Adaptive laboratory evolution reveals the selenium efflux process to improve selenium tolerance mediated by the membrane sulfite pump in Saccharomyces cerevisiae[J]. Microbiol Spectr, 2023, 11(3): e0132623. doi: 10.1128/spectrum.01326-23 URL
[96]	Jiang Y, Shen Y, Gu LC, et al. Identification and characterization of an efficient d-xylose transporter in Saccharomyces cerevisiae[J]. J Agric Food Chem, 2020, 68(9): 2702-2710. doi: 10.1021/acs.jafc.9b07113 URL
[97]	Wu TJ, Wang YT, Li JG, et al. Dissecting key residues of a C4-dicarboxylic acid transporter to accelerate malate export in Myceliophthora[J]. Appl Microbiol Biotechnol, 2023, 107(2-3): 609-622. doi: 10.1007/s00253-022-12336-9
[98]	Srikant S, Gaudet R, Murray AW. Selecting for altered substrate specificity reveals the evolutionary flexibility of ATP-binding cassette transporters[J]. Curr Biol, 2020, 30(9): 1689-1702.e6. doi: S0960-9822(20)30284-0 pmid: 32220325
[99]	Yeom J, Park JS, Jung SW, et al. High-throughput genetic engineering tools for regulating gene expression in a microbial cell factory[J]. Crit Rev Biotechnol, 2023, 43(1): 82-99. doi: 10.1080/07388551.2021.2007351 URL
[100]	Boyarskiy S, López SD, Kong NW, et al. Transcriptional feedback regulation of efflux protein expression for increased tolerance to and production of n-butanol[J]. Metab Eng, 2016, 33: 130-137. doi: S1096-7176(15)00154-8 pmid: 26656942
[101]	Liu JH, Wang XL, Jin K, et al. In silico prediction and mining of exporters for secretory bioproduction of terpenoids in Saccharomyces cerevisiae[J]. ACS Synth Biol, 2023, 12(3): 863-876. doi: 10.1021/acssynbio.2c00673 URL

蛋白名称 Protein name	转运蛋白类型 Type of transporter	蛋白来源 Source of protein	宿主 Host	耐受类型 Tolerance type	改进策略 Strategy	结果 Result	参考文献 Reference
木质纤维素水解液胁迫Stress of lignocellulosic hydrolysate
ZMO0799, ZMO0800, ZMO1288, ZMO1856, ZMO0282, ZMO0798	ATP-binding Cassette（ABC）Superfamily	Zymomonas mobilis	Z. mobilis	酚醛 Phenolic aldehyde	过表达，敲除Overexpression, knockout	六种转运蛋白被认为与酚醛耐受性相关	[27]
Pdr5, Snq2	ABC Superfamily	S. cerevisiae	S. cerevisiae	香草醛 Vanilline	过表达 Overexpression	菌体生长加快，菌株延滞期缩短	[28]
Pdr5, Yor1, Snq2	ABC Superfamily	S. cerevisiae	S. cerevisiae	香草醛 Vanilline	过表达 Overexpression	菌株香兰素耐受性提升	[29]
酸胁迫Acid stress
Ady2	Acetate Uptake Transporter Family	S. cerevisiae	S. cerevisiae	乙酸 Acetic acid	敲除 Knockout	菌株在乙酸，过氧化氢，甲酸胁迫下的生长性能提升，ADY2敲除突变体在3.6 g/L乙酸胁迫条件下的乙醇生产强度提高了14.66%	[25]
Rta1	Lipid-translocating Exporter Family	Cryptococcus humicola	S. cerevisiae	酸 Acid	异源表达 Heterologous expression	重组菌株在酸铝胁迫下延滞期比对照减少12 h	[30]
ThiT	Vitamin Uptake Transporter Family	L. lactisNZ9000	L. lactisNZ9000	酸 Acid	过表达 Overexpression	菌株在酸胁迫下最大存活率提升16.2倍	[31]
Zrt3	Zinc-Iron Permease Family	S. cerevisiae	S. cerevisiae	乙酸 Acetic acid	敲除 Knockout	对乙酸介导的调节性细胞死亡具有高度抗性，液泡功能障碍减少	[32]
Jen1	Major Facilitator Superfamily（MFS）	S. cerevisiae	S. cerevisiae	L-乳酸 L-lactic acid	过表达 Overexpression	菌株乳酸产量从43.6 g/L提升到51.4 g/L	[33]
盐胁迫Salt stress
Nha2	Monovalent Cation: Proton Antiporter-1 Family	Y. lipolytica	S. cerevisiae	钠盐 Na⁺	异源表达 Heterologous expression	酿酒酵母中的Na⁺耐受性显著提升	[34]
BusA	ABC Superfamily	Tetragenococcus halophilus	E. coli	高浓度盐 High salinity	异源表达 Heterologous expression	大肠杆菌MKH13的盐耐受能力显著提升	[35]
BmrA, BmrB	ABC Superfamily	Bifidobacterium longumBBMN68	L. lactisNZ9000	胆汁盐 Bile salt	异源表达 Heterologous expression	菌株对牛胆汁（0.10%）的耐受性提高20.77倍	[36]
HKT2;1	K⁺Transporter（Trk）Family	Aeluropus lagopoides	A. lagopoides	钾盐 K⁺	异源表达Heterologous expression	使K⁺吸收缺陷（WΔ6）和Na⁺敏感酵母突变体（G19）能够在>1 mmol/L KCl浓度下生长	[37]
Trk1	Trk Family	Candida glabrata	S. cerevisiae	高浓度盐 High salinity	异源表达 Heterologous expression	细胞盐离子耐受性提升	[38]
产物胁迫Product stress
Abc2, Abc3	ABC Superfamily	Y. lipolytica	S. cerevisiae	烷烃 Alkane	异源表达 Heterologous expression	酿酒酵母对癸烷和十一烷的耐受性显著增加，ABC2转运蛋白将酿酒酵母对癸烷的耐受限度提高约80倍	[39]
AcrE, MdtE, MdtC, Cmr	Membrane Fusion Protei Family; MFS	E. coli	E. coli	脂肪酸 Fatty acid	过表达 Overexpression	菌株中链脂肪酸滴度增加两倍以上	[40]
Abc2, Abc3	ABC Superfamily	Grosmannia ciavigera, Y. Iipoiytica	S. cerevisiae	桧烯，青篙二烯Hinokiene, Pennyroyalene	异源表达 Heterologous expression	ABC3的表达使桧烯的产量提高34.5%，表达ABC2使青蒿二烯产量提高24.6%，外排率提高16.0%	[41]
YALI0F19492g	Cor413im1 Family	Y. lipolytica	Y. lipolytica	柠檬烯 Limonene	过表达 Overexpression	显著提高了菌株的柠檬烯耐受性	[42]
Esbp6	MFS	S. cerevisiae	S. cerevisiae	香豆酸Coumaric acid	过表达 Overexpression	菌株对芳香酸的耐受性提升，菌株的香豆酸分泌显著改善	[43]
Bpt1	ABC Superfamily	S. cerevisiae	S. cerevisiae	大麻二酚 Cannabidiol	过表达 Overexpression	大麻二酚产量达到6.92 mg/L，较出发菌株产量提高100倍	[44]
Trk1	Trk Family	S. cerevisiae	S. cerevisiae	丙酸Propionic acid	适应性实验室进化 Adaptive laboratory evolution	菌株对丙酸和多种有机酸的耐受能力提升	[45]
YcjP	ABC Superfamily	E. coli	E. coli	咖啡酸Caffeic acid	过表达 Overexpression	菌株的咖啡酸滴度达到 775.7 mg/L（对照589.9 mg/L）	[46]

蛋白名称 Protein name	转运蛋白类型 Type of transporter	蛋白来源 Source of protein	宿主 Host	耐受类型 Tolerance type	改进策略 Strategy	结果 Result	参考文献 Reference
木质纤维素水解液胁迫Stress of lignocellulosic hydrolysate
ZMO0799, ZMO0800, ZMO1288, ZMO1856, ZMO0282, ZMO0798	ATP-binding Cassette（ABC）Superfamily	Zymomonas mobilis	Z. mobilis	酚醛 Phenolic aldehyde	过表达，敲除Overexpression, knockout	六种转运蛋白被认为与酚醛耐受性相关	[27]
Pdr5, Snq2	ABC Superfamily	S. cerevisiae	S. cerevisiae	香草醛 Vanilline	过表达 Overexpression	菌体生长加快，菌株延滞期缩短	[28]
Pdr5, Yor1, Snq2	ABC Superfamily	S. cerevisiae	S. cerevisiae	香草醛 Vanilline	过表达 Overexpression	菌株香兰素耐受性提升	[29]
酸胁迫Acid stress
Ady2	Acetate Uptake Transporter Family	S. cerevisiae	S. cerevisiae	乙酸 Acetic acid	敲除 Knockout	菌株在乙酸，过氧化氢，甲酸胁迫下的生长性能提升，ADY2敲除突变体在3.6 g/L乙酸胁迫条件下的乙醇生产强度提高了14.66%	[25]
Rta1	Lipid-translocating Exporter Family	Cryptococcus humicola	S. cerevisiae	酸 Acid	异源表达 Heterologous expression	重组菌株在酸铝胁迫下延滞期比对照减少12 h	[30]
ThiT	Vitamin Uptake Transporter Family	L. lactisNZ9000	L. lactisNZ9000	酸 Acid	过表达 Overexpression	菌株在酸胁迫下最大存活率提升16.2倍	[31]
Zrt3	Zinc-Iron Permease Family	S. cerevisiae	S. cerevisiae	乙酸 Acetic acid	敲除 Knockout	对乙酸介导的调节性细胞死亡具有高度抗性，液泡功能障碍减少	[32]
Jen1	Major Facilitator Superfamily（MFS）	S. cerevisiae	S. cerevisiae	L-乳酸 L-lactic acid	过表达 Overexpression	菌株乳酸产量从43.6 g/L提升到51.4 g/L	[33]
盐胁迫Salt stress
Nha2	Monovalent Cation: Proton Antiporter-1 Family	Y. lipolytica	S. cerevisiae	钠盐 Na⁺	异源表达 Heterologous expression	酿酒酵母中的Na⁺耐受性显著提升	[34]
BusA	ABC Superfamily	Tetragenococcus halophilus	E. coli	高浓度盐 High salinity	异源表达 Heterologous expression	大肠杆菌MKH13的盐耐受能力显著提升	[35]
BmrA, BmrB	ABC Superfamily	Bifidobacterium longumBBMN68	L. lactisNZ9000	胆汁盐 Bile salt	异源表达 Heterologous expression	菌株对牛胆汁（0.10%）的耐受性提高20.77倍	[36]
HKT2;1	K⁺Transporter（Trk）Family	Aeluropus lagopoides	A. lagopoides	钾盐 K⁺	异源表达Heterologous expression	使K⁺吸收缺陷（WΔ6）和Na⁺敏感酵母突变体（G19）能够在>1 mmol/L KCl浓度下生长	[37]
Trk1	Trk Family	Candida glabrata	S. cerevisiae	高浓度盐 High salinity	异源表达 Heterologous expression	细胞盐离子耐受性提升	[38]
产物胁迫Product stress
Abc2, Abc3	ABC Superfamily	Y. lipolytica	S. cerevisiae	烷烃 Alkane	异源表达 Heterologous expression	酿酒酵母对癸烷和十一烷的耐受性显著增加，ABC2转运蛋白将酿酒酵母对癸烷的耐受限度提高约80倍	[39]
AcrE, MdtE, MdtC, Cmr	Membrane Fusion Protei Family; MFS	E. coli	E. coli	脂肪酸 Fatty acid	过表达 Overexpression	菌株中链脂肪酸滴度增加两倍以上	[40]
Abc2, Abc3	ABC Superfamily	Grosmannia ciavigera, Y. Iipoiytica	S. cerevisiae	桧烯，青篙二烯Hinokiene, Pennyroyalene	异源表达 Heterologous expression	ABC3的表达使桧烯的产量提高34.5%，表达ABC2使青蒿二烯产量提高24.6%，外排率提高16.0%	[41]
YALI0F19492g	Cor413im1 Family	Y. lipolytica	Y. lipolytica	柠檬烯 Limonene	过表达 Overexpression	显著提高了菌株的柠檬烯耐受性	[42]
Esbp6	MFS	S. cerevisiae	S. cerevisiae	香豆酸Coumaric acid	过表达 Overexpression	菌株对芳香酸的耐受性提升，菌株的香豆酸分泌显著改善	[43]
Bpt1	ABC Superfamily	S. cerevisiae	S. cerevisiae	大麻二酚 Cannabidiol	过表达 Overexpression	大麻二酚产量达到6.92 mg/L，较出发菌株产量提高100倍	[44]
Trk1	Trk Family	S. cerevisiae	S. cerevisiae	丙酸Propionic acid	适应性实验室进化 Adaptive laboratory evolution	菌株对丙酸和多种有机酸的耐受能力提升	[45]
YcjP	ABC Superfamily	E. coli	E. coli	咖啡酸Caffeic acid	过表达 Overexpression	菌株的咖啡酸滴度达到 775.7 mg/L（对照589.9 mg/L）	[46]

模型 Model	功能 Function	AI算法 AI method	特征提取方法 Feature extraction method	数据集 Data set	年份Year	参考文献Reference
转运蛋白分类和功能注释Classification and functional annotation of transporters
ISTRF	鉴定蔗糖转运蛋白	随机森林 Random Forest	k-separated-bigrams-PSSM	382个SUT蛋白（蔗糖转运蛋白）和911个非SUT蛋白	2022	[69]
Fiamenghi’s model	鉴定木糖转运蛋白	梯度提升决策树 Gradient Boosting Decision Tree Classifier	HMM and PSSM profiles	396个蛋白质（其中25个能够转运木糖）	2022	[70]
Srinivasan’s model	鉴定托烷生物碱（TA）转运蛋白	监督分类器 Supervised Classifier Models	基因的序列信息	15个TA和15 333个非TA基因	2021	[71]
GT-Finder	葡萄糖转运蛋白家族的分类	BERT语言模型 Pre-trained BERT Language Models	使用预训练的BERT模型生成上下文相关的词嵌入	510个GLUT、225个SGLT和190个SWEET葡萄糖转运蛋白	2021	[72]
TooT-T	鉴别转运蛋白与非转运蛋白	支持向量机 Support Vector Machine	位置特异性迭代氨基酸组成、成对氨基酸组成和伪氨基酸组成	900个转运蛋白与660个非转运蛋白	2020	[73]
膜转运蛋白的结构预测Structure prediction of transporters
Bergman’s model	研究底物结合诱导的ω-3脂肪酸转运蛋白MFSD2A中的构象转变	深度神经网络分类算法 Deep Neural Network（DNN）Classification Algorithm	将每个轨迹帧转换为适合输入到DNN的视觉表示	分子动力学模拟的数据，4 000个轨迹帧（步幅160 ps）表示OFS集合和4 000帧（步幅为160 ps）表示选择的OcS状态集合	2023	[74]
Mitrovic’s model	研究糖转运蛋白超家族转运周期中的构象变化	卷积神经网络 Convolutional Neural Network	通过协同进化得分过滤实验接触图	实验确定的结构的接触图	2023	[75]
底物与转运蛋白相互作用预测Prediction of the interactions between substrates and transporters
Denger’s model	预测大肠杆菌、拟南芥、酿酒酵母以及人类膜转运蛋白的底物	支持向量机 Support Vector Machine	氨基酸组成、PSSM	具有已知底物的膜转运蛋白的数据集来自手动策划的Swiss-Prot数据库	2022	[76]
Nguyen’s model	鉴定转运蛋白的底物特异性	神经网络 Neural Network	使用词嵌入向量作为蛋白质的特征	1 197个转运蛋白，其中73个氨基酸转运蛋白、221个电子转运蛋白、88个氢离子转运蛋白、78个脂质转运蛋白、455个蛋白质/mRNA转运蛋白，84个糖转运蛋白，198种其他转运蛋白和1 050种膜蛋白	2019	[77]

模型 Model	功能 Function	AI算法 AI method	特征提取方法 Feature extraction method	数据集 Data set	年份Year	参考文献Reference
转运蛋白分类和功能注释Classification and functional annotation of transporters
ISTRF	鉴定蔗糖转运蛋白	随机森林 Random Forest	k-separated-bigrams-PSSM	382个SUT蛋白（蔗糖转运蛋白）和911个非SUT蛋白	2022	[69]
Fiamenghi’s model	鉴定木糖转运蛋白	梯度提升决策树 Gradient Boosting Decision Tree Classifier	HMM and PSSM profiles	396个蛋白质（其中25个能够转运木糖）	2022	[70]
Srinivasan’s model	鉴定托烷生物碱（TA）转运蛋白	监督分类器 Supervised Classifier Models	基因的序列信息	15个TA和15 333个非TA基因	2021	[71]
GT-Finder	葡萄糖转运蛋白家族的分类	BERT语言模型 Pre-trained BERT Language Models	使用预训练的BERT模型生成上下文相关的词嵌入	510个GLUT、225个SGLT和190个SWEET葡萄糖转运蛋白	2021	[72]
TooT-T	鉴别转运蛋白与非转运蛋白	支持向量机 Support Vector Machine	位置特异性迭代氨基酸组成、成对氨基酸组成和伪氨基酸组成	900个转运蛋白与660个非转运蛋白	2020	[73]
膜转运蛋白的结构预测Structure prediction of transporters
Bergman’s model	研究底物结合诱导的ω-3脂肪酸转运蛋白MFSD2A中的构象转变	深度神经网络分类算法 Deep Neural Network（DNN）Classification Algorithm	将每个轨迹帧转换为适合输入到DNN的视觉表示	分子动力学模拟的数据，4 000个轨迹帧（步幅160 ps）表示OFS集合和4 000帧（步幅为160 ps）表示选择的OcS状态集合	2023	[74]
Mitrovic’s model	研究糖转运蛋白超家族转运周期中的构象变化	卷积神经网络 Convolutional Neural Network	通过协同进化得分过滤实验接触图	实验确定的结构的接触图	2023	[75]
底物与转运蛋白相互作用预测Prediction of the interactions between substrates and transporters
Denger’s model	预测大肠杆菌、拟南芥、酿酒酵母以及人类膜转运蛋白的底物	支持向量机 Support Vector Machine	氨基酸组成、PSSM	具有已知底物的膜转运蛋白的数据集来自手动策划的Swiss-Prot数据库	2022	[76]
Nguyen’s model	鉴定转运蛋白的底物特异性	神经网络 Neural Network	使用词嵌入向量作为蛋白质的特征	1 197个转运蛋白，其中73个氨基酸转运蛋白、221个电子转运蛋白、88个氢离子转运蛋白、78个脂质转运蛋白、455个蛋白质/mRNA转运蛋白，84个糖转运蛋白，198种其他转运蛋白和1 050种膜蛋白	2019	[77]