Papers

35. Xie J, Zhang W, Zhu X, Deng M and Lai L*. Coevolution-based prediction of key allosteric residues for protein function regulation. eLife 2023, 12:e81850. DOI: 10.7554/eLife.81850.

34. Liu Y, Wang S, Li X, Liu Y and Zhu X*. NeuroPpred-SVM: A New Model for Predicting Neuropeptides Based on Embeddings of BERT. Journal of Proteome Research 2023, 22(3): 718–728

33. Liu Y, Liu Y, Wang S and Zhu X*. LBCE‑XGB: A XGBoost Model for Predicting Linear B‑Cell Epitopes Based on BERT Embeddings. Interdisciplinary Sciences: Computational Life Sciences 2023, 15: 293-305

32. Liu Y#, Shen Y#, Wang H, Zhang Y* and Zhu X*. m5Cpred-XS: A New Method for Predicting RNA m5C Sites Based on XGBoost and SHAP. Frontiers in Genetics 2022, 13:853258. DOI: 10.3389/fgene.2022.853258

31. Wang H#, Zhao S#, Cheng Y#, Bi S* and Zhu X*. MTDeepM6A-2S: A two-stage multi-task deep learning method for predicting RNA N6-methyladenosine sites of Saccharomyces cerevisiae. Frontiers in Microbiology 2022, 13: 999506. DOI: 10.3389/fmicb.2022.999506

30. Wang H#, Wang S#, Zhang Y, Bi S* and Zhu X*. A brief review of machine learning methods for RNA methylation sites prediction. Methods 2022, 203:399-421

29. Liu Y#, Liu Y#, Wang G-A, Cheng Y, Bi S* and Zhu X*. BERT-Kgly: A bidirectional encoder representations from transformers (BERT)-based model for predicting lysine glycation site for homo sapiens. Frontiers in Bioinformatics 2022, 2:834153. DOI: 10.3389/fbinf.2022.834153

28. Qiao Y, Zhu X* and Gong H. BERT-Kcr: Prediction of lysine crotonylation sites by a transfer learning method with pre-trained BERT models. Bioinformatics 2022, 38(3): 648-654

27. Chen X#, Xiong Y#, Liu Y, Chen Y, Bi S* and Zhu X*. m5CPred-SVM: a novel method for predicting m5C sites of RNA. BMC Bioinformatics 2020; 21: 489

26. Zhu X*,Liu L,He J,Fang T, Xiong Y, Mitchell J*. iPNHOT: a knowledge-based approach for identifying protein-nucleic acid interaction hot spots. BMC Bioinformatics 2020; 21: 289

25. Zhu X#,He J#,Zhao S#,Tao W, Xiong Y*, Bi S*. A comprehensive comparison and analysis of computational predictors for RNA N6-methyladenosine sites of Saccharomyces cerevisiae.Briefings in Functional Genomics 2019; 18(6): 367-376

24. Fang T, Zhang Z, Sun R, Zhu L, He J, Huang B*, Xiong Y*, Zhu X*. RNAm5CPred: Prediction of RNA 5-Methylcytosine Sites Based on Three Different Kinds of Nucleotide Composition. Molecular Therapy-Nucleic Acids 2019; 18: 739-747

23. Wang X#, Zhu X#, Ye M, Wang Y, Li C, Xiong Y*, Wei DQ*. STS-NLSP: A Network-Based Label Space Partition Method for Predicting the Specificity of Membrane Transporter Substrates Using a Hybrid Feature of Structural and Semantic Similarity Frontiers in Bioengineering and Biotechnology 2019;7:306. Doi: 10.3389/fmicb.2019.00306

22. Xiong Y, Wang Q, Yang J, Zhu X*, Wei DQ*. PredT4SE-Stack: Prediction of Bacterial Type IV Secreted Effectors From Protein Sequences Using a Stacked Ensemble Method. Frontiers in Microbiology 2018; Doi: 10.3389/fmicb.2018.02571

21. Xiong Y, Qiao Y, Kihara D, Zhang HY, Zhu X*, Wei DQ*. Survey of Machine Learning Techniques for Prediction of the Isoform Specificity of Cytochrome P450 Substrates. Current Drug Metabolism 2018; Doi: 10.2174/1389200219666181019094526

20. He J, Fang T, Zhang, Z, Huang B, Zhu X*, Xiong Y*. PseUI: Pseudouridine sites identification based on RNA sequence information. BMC Bioinformatics 2018; Doi: 10.1186/s12859-018-2321-0

19. Liu L#, Xiong Y#, Gao H, Wei DQ, Mitchell J.C.*, Zhu X*. dbAMEPNI: a database of alanine mutagenic effects for protein–nucleic acid interactions. DATABASE-The Journal of Biological Databases and Curation 2018; Doi: 10.1093/database/bay034

18. Xiong Y, Zhu X, Dai H, Wei DQ. Survey of Computational Approaches for Prediction of DNA-Binding Residues on Protein Surfaces Methods in molecular biology( book chapter in Computational Systems Biology) 2018; Doi: 10.1007/978-1-4939-7717-8_13

17. Qiao Y#, Xiong Y#, Gao H, Zhu X*，Chen P*. Protein-protein interface hot spots prediction based on a hybrid feature selection strategy. BMC Bioinformatics 2018; 19(1):14. Doi: 10.1186/s12859-018-2009-5

16. Bai LY, Dai H, Xu Q, Junaid M, Peng SL, Zhu X, Xiong Y*, Wei DQ*. Prediction of effective drug combinations by an improved naïve bayesian algorithm. Int. J. Mol. Sci. 2018, 19(2), 467; doi:10.3390/ijms19020467

15. Zeng L, Shin WH, Zhu X, Park SH, Park C, Tao WA, Kihara D. Dicovery of NAD-binding proteins inthe E.coli proteome using combined energetic-based and structural-bioinformatics-based approach.J. Proteome Res ., 2017, 16(2): 470-480

14. Sukumar S, Zhu X, Ericksen SS, Mitchell JC. DBSI server: DNA binding site identifier. Bioinformatics 2016, 32(18): 2853-2855

13. Xia J, Yue Z, Di Y, Zhu X*, Zheng C. Predicting hot spots in protein interfaces based on protrusion index, pseudo hydrophobicity and electron-ion interaction pseudopotential features. Oncotarget 2016; 7(14): 18065-18075

12. Zhu X, Shin WH, Kim H, Kihara D. Combined approach of Patch-Surfer and PL-PatchSurfer for Protein-Ligand binding prediction in CSAR 2013 and 2014, Journal of Chemical Information and Modeling 2016; 56(6): 1088-1099

11. Zhu X, Xiong Y, Kihara D. Large-scale binding ligand prediction by improved patch-based method Patch-Surfer2.0. Bioinformatics 2015; 31(5): 707-713

10. Shin WH, Zhu X, Bures MG, and Kihara, D. Three-Dimensional Compound Comparison Methods and Their Application in Drug Discovery. Molecules 2015, 20(7): 12841-12862

9. Hu B, Zhu X, Monroe L, Bures MG, Kihara D. PL-PatchSurfer: A Novel Molecular Local Surface-Based Method for Exploring Protein-Ligand Interactions. International Journal of Molecular Science 2014, 15: 15122-15145

8. Zhu X#, Ericksen SS#, Mitchell JC. DBSI: DNA Binding Site Identifier. Nucleic Acids Research. 2013, 41(16): e160

7. Zhu X, Demerdash ONA, Ericksen SS, Mitchell JC. Data driven models for protein interaction and design. Proteins: Structure, Function, and Bioinformatics. 2013, 81(12):2221-8

6. Moretti R, Fleishman SJ, Agius R, Torchala M, Bates PA, Kastritis PL, Rodrigues JP, Trellet M, Bonvin AM, Cui M, Rooman M, Gillis D, Dehouck Y, Moal I, Romero-Durana M, Perez-Cano L, Pallara C, Jimenez B, Fernandez-Recio J, Flores S, Pacella M, Praneeth Kilambi K, Gray JJ, Popov P, Grudinin S, Esquivel-Rodríguez J, Kihara D, Zhao N, Korkin D, Zhu X, Demerdash ON, Mitchell JC, Kanamori E, Tsuchiya Y, Nakamura H, Lee H, Park H, Seok C, Sarmiento J, Liang S, Teraguchi S, Standley DM, Shimoyama H, Terashi G, Takeda-Shitaka M, Iwadate M, Umeyama H, Beglov D, Hall DR, Kozakov D, Vajda S, Pierce BG, Hwang H, Vreven T, Weng Z, Huang Y, Li H, Yang X, Ji X, Liu S, Xiao Y, Zacharias M, Qin S, Zhou HX, Huang SY, Zou X, Velankar S, Janin J, Wodak SJ, Baker D. Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions. Proteins: Structure, Function, and Bioinformatics. 2013, 81(11):1980-7

5. Xiong Y#, Zhu X#, Kihara D(#Equal contribution). Ligand binding site identification. In silico Drug Discovery and Design Techniques. M. Lill (eds), Future Science, London, UK, Chapter 16, pp. 204-220 (2013).

4. Zhu X, Mitchell JC. KFC2: a knowledge-based hot spot prediction method based on interface solvation, atomic density, and plasticity features. Proteins: Structure, Function, and Bioinformatics. 2011, 79(9): 2671-83

3. Zhu X, Lai L. A novel method for enzyme design. J Comput Chem. 2009, 30(2): 256-67

2. Liu S, Liu S, Zhu X, Liang H, Cao A, Chang Z, Lai L. Nonnatural protein-protein interaction-pair design by key residues grafting. Proc Natl Acad Sci USA. 2007, 104(13): 5330-5

1. Liu S, Pei J, Chen H, Zhu X, Liu Z, Ma W, He F, Lai L. Modeling of the SARS coronavirus main proteinase and conformational flexibility of the active site. Beijing Da Xue Xue Bao. 2003, 35 Suppl: 62-5