纪宏超课题组

纪宏超课题组

Ji Hongchao Lab

　　课题组长

纪宏超，副研究员，硕士生导师，中南大学理学博士。2022年加入深圳农业基因组所担任课题组长。长期致力于计算机科学与化学生物学的交叉研究。以生物信息学为主要手段，开发一系列用于植物、作物代谢组学数据分析的新算法、新技术，并围绕“未知小分子结构与功能预测”这一核心科学问题展开研究工作。

获得国家自然科学基金青年项目、深圳市优秀科技人才培养青年项目等项目资助，在Nature Communications、Cell Chemical Biology、Analytical Chemistry、Briefings in Bioinformatics等国际学术期刊上发表论文20余篇。申请国家发明专利2项，PCT国际专利1项。担任Briefings in Bioinformatics, Journal of Chemical Information and Modeling, Analytical and Bioanalytical Chemistry, Chemometrics and Intelligent Laboratory Systems等期刊审稿人，Metabolites期刊客座编辑。

课题组每年计划招收硕士及联合培养博士研究生1-2名，长期招聘具有计算机科学、生物信息学背景的博士后、科研助理。联系方式：jihongchao@caas.cn

　　工作经历

2022.05 - 至今           中国农业科学院深圳农业基因组研究所   副研究员

2020.07 – 2022.05     南方科技大学                                        博士后

　　

教育经历

2015.09 – 2020.06  中南大学 化学化工学院 理学博士

2011.09 - 2015.07  中南大学 化学化工学院 工学学士　

研究方向

1）化学信息学与人工智能算法开发及其在植物代谢组学的应用

2）植物、作物代谢组学质谱数据解析算法与软件开发

3）植物、作物中未知小分子代谢物结构注释与功能解析

研究进展

重要进展成果1：针对复杂体系质谱分析，率先提出了基于机器学习的策略的特征提取、质量控制、校准及模式识别等子模块，并整合为数据处理软件；有效减少噪声干扰，对特征信号，尤其是低强度特征进行准确的定量。

重要进展成果2：针对未知小分子结构注释，开发了基于深度学习模型的从已知预测未知的解决思路，挖掘化合物结构与质谱碎片、色谱保留时间等分析化学性质的关联性，开发了一系列用于注释未知小分子化合物二维结构的方法。

重要进展成果3：针对潜在药物小分子靶标解析，率先提出了机器学习与热转变实验相结合的策略。大幅提高了实验通量，单次实验可以解析15 - 30个待筛化合物在细胞内的作用靶点，将筛选效率提高 15 - 60 倍。

代表论著

2024:

(1) Li, H., Fotouhi, N., Liu, F., Ji, H.,* Wu, Q.* Early detection of dark-affected plant mechanical responses using enhanced electrical signals. Plant Methods. 2024, 22 (1) 49.

(2) Xue, J., Wang, B., Ji, H., Li, W.* RT-Transformer: retention time prediction for metabolite annotation to assist in metabolite identification. Bioinformatics. 2024, 40(3) btae084.

(3) Wu, Q., Zheng, J., Sui, X., Fu, C., Cui, X., Liao, B., Ji, H., Luo, Y., He, A., Lu, X., Xue, X., Tan, C.S.H.*,. Tian, R.*, High-throughput drug target discovery using a fully automated proteomics sample preparation platform. Chem. Sci. 15 (8) 2833-2847.

2023:

(4) Ji, H.^#; Lu, X.^#; Zhao, S.; Wang, Q.; Bin, L.; Huber, K. V. M.; Luo, R.; Tian, R.; Tan, C. S. H. Target deconvolution with matrix-augmented pooling strategy reveals cell-specific drug-protein interactions. Cell Chem. Biol. 2023, 30(11) 1478-1487. (Cell子刊)

(5) Yang, Q.^#; Ji, H.^#; Xu, Z.; Li, Y.; Wang, P.; Sun, J.; Fan, X.; Zhang, H.; Lu, H.; Zhang, Z. Ultra-Fast and Accurate Electron Ionization Mass Spectrum Matching for Compound Identification with Million-Scale in-Silico Library. Nat. Commun. 2023, 14 (1), 3722. (Nature子刊, Nature Index期刊)

(6) Song, Y., Chang, S., Tian, J., Pan, W., Feng L,.* Ji, H.,* A Comprehensive Comparative Analysis of Deep Learning Based Feature Representations for Molecular Taste Prediction. Foods. 2023, 12(18), 3386,

2014 - 2022:

(7) Ji, H.*; Tian, J. Deep Denoising Autoencoder-Assisted Continuous Scoring of Peak Quality in High-Resolution LC−MS Data. Chemo. Intell. Lab. 2022, 231, 104694.

(8) Ji, H.; Lu, X.; Zheng, Z.; Sun, S.; Tan, C.S.H. ProSAP: A GUI Software Tool for Statistical Analysis and Assessment of Thermal Stability Data. Brief. Bioinform. 2022, 23 (3), bbac057.

(9) Ji, H.; Deng, H.; Lu, H.; Zhang, Z. Predicting a Molecular Fingerprint from an Electron Ionization Mass Spectrum with Deep Neural Networks. Anal. Chem. 2020, 92 (13), 8649–8653. (Nature Index期刊)

(10) Ji, H.; Zhang, Z.; Lu, H. TarMet: A Reactive GUI Tool for Efficient and Confident Quantification of MS Based Targeted Metabolic and Stable Isotope Tracer Analysis. Metabolomics 2018, 14 (5), 68.

(11) Ji, H.; Zeng, F.; Xu, Y.; Lu, H.; Zhang, Z. KPIC2: An Effective Framework for Mass Spectrometry-Based Metabolomics Using Pure Ion Chromatograms. Anal. Chem. 2017, 89 (14), 7631–7640. (Nature Index期刊)

(12) Ji, H.; Xu, Y.; Lu, H.; Zhang, Z. Deep MS/MS-Aided Structural-Similarity Scoring for Unknown Metabolite Identification. Anal. Chem. 2019, 91 (9), 5629–5637. (Nature Index期刊)

(13) Ji, H.; Lu, H.; Zhang, Z. Pure Ion Chromatogram Extraction: Via Optimal k -Means Clustering. RSC Adv. 2016, 6 (62), 56977–56985.

(14) Yang, Q.; Ji, H.; Lu, H.; Zhang, Z. Prediction of Liquid Chromatographic Retention Time with Graph Neural Networks to Assist in Small Molecule Identification. Anal. Chem. 2021, 93 (4), 2200–2206.

(15) Yang, Q.; Ji, H.; Fan, X.; Zhang, Z.; Lu, H. Retention Time Prediction in Hydrophilic Interaction Liquid Chromatography with Graph Neural Network and Transfer Learning. J. Chromatogr. A 2021, 1656.

(16) Wu, Q.; Xu, Y.; Ji, H.; Wang, Y.; Zhang, Z.; Lu, H. Enhancing Coverage in LC–MS-Based Untargeted Metabolomics by a New Sample Preparation Procedure Using Mixed-Mode Solid-Phase Extraction and Two Derivatizations. Anal Bioanal Chem 2019, 411 (23), 6189–6202.

(17) Zhu, H.; Chen, Y.; Liu, C.; Wang, R.; Zhao, G.; Hu, B.; Ji, H.; Zhang, Z.-M.; Lu, H. Feature Extraction for LC-MS via Hierarchical Density Clustering. Chromatographia 2019, 82 (10), 1449–1457.

(18) Fu, C.; Wu, Q.; Zhang, Z.; Xia, Z.; Ji, H.; Lu, H.; Wang, Y. UPLC-ESI-IT-TOF-MS Metabolomic Study of the Therapeutic Effect of Xuefu Zhuyu Decoction on Rats with Traumatic Brain Injury. Journal of Ethnopharmacology 2019, 245, 112149.

(19) Li, W.; Ye, H.; Liu, G.; Ji, H.; Zhou, Y.; Han, K. The Role of Graphene Coating on Cordierite-Supported Pd Monolithic Catalysts for Low-Temperature Combustion of Toluene. Cuihua Xuebao/Chinese Journal of Catalysis 2018, 39 (5), 946–954.

(20) He, Y.; Zhang, Z. ; Ma, P.; Ji, H. .; Lu, H. GC-MS Profiling of Leukemia Cells: An Optimized Preparation Protocol for the Intracellular Metabolome. Anal. Methods 2018, 10 (10), 1266–1274.

(21) Zeng, F.; Ji, H.; Zhang, Z.; Luo, J.; Lu, H. ; Wang, Y. Metabolic Profiling Putatively Identifies Plasma Biomarkers of Male Infertility Using UPLC-ESI-IT-TOFMS. RSC Adv. 2018, 8 (46), 25974–25982.

(22) Wang, R.; Ji, H.; Ma, P.; Zeng, H.; Xu, Y.; Zhang, Z.-M.; Lu, H.-M. Fast Pure Ion Chromatograms Extraction Method for LC-MS. Chemom. Intell. Lab. Syst. 2017, 170, 68–74.

(23) Lin, Z.; Vicente Gonçalves, C. M.; Dai, L.; Lu, H.; Huang, J.; Ji, H.; Wang, D.; Yi, L.; Liang, Y. Exploring Metabolic Syndrome Serum Profiling Based on Gas Chromatography Mass Spectrometry and Random Forest Models. Analytica Chimica Acta 2014, 827, 22–27.

代表性专利

(1) 纪宏超; 化合物的化学结构确定方法、装置及终端设备, 2023-12-7, 中国, 202311690470.0 (申请)

(2) 纪宏超; 陈顺兴; 一种提高化合物与蛋白质相互作用实验通量的方法, 2022-6-7, 中国, 202210638301.1 (申请)

(3) Ji Hongchao; Soon Heng Tan; Method for Improving Throughput of Compound-Protein Interaction, 2023-6-5, 美国, PCT/CN2023/098376 (申请)

纪宏超课题组更新于2024年4月