CISI: A Tool for Predicting Cross-interaction or Self-interaction of Monoclonal Antibodies Using Sequences

  • Anthony Mackitz Dzisoo
  • Bifang He
  • Rita Karikari
  • Elijah Agoalikum
  • Jian HuangEmail author
Original research article


Monoclonal antibodies (mAbs) are one of the robust classes of therapeutic proteins. Their stability, specificity, and high solubility allow the successful development and commercialization of antibody-based drugs. Though with these characteristics, mAbs projects are often suspended due to self- or cross-interaction of monoclonal antibodies. This is one of the main reasons which causes the development of mAbs into drugs taking forever and expensive. CISI is short for cross-interaction or self-interaction of mAbs. It can be quantified by several assays. The assays such as poly-specificity reagent and cross-interaction chromatography can measure cross-interaction of mAbs. Self-interaction can be assayed through clone self-interaction by biolayer interferometry and affinity-capture self-interaction nanoparticle spectroscopy. To save time and money, we developed a model called CISI which can predict cross-interaction or self-interaction based on tripeptide composition. It showed 88.20% accuracy, 90.22% sensitivity, 86.05% specificity, 0.78 Mathew correlation coefficient, and 0.96 area under the receiver operating characteristic (ROC) curve (AUC) in the leave-one-out cross-validation. CISI is freely available at


Monoclonal antibodies Cross-interaction Self-interaction SVM Developability 



Affinity-capture self-interaction nanoparticle spectroscopy


Area under the receiver operating characteristic curve


Complementarity-determining regions


Cross-interaction chromatography


Cross-interaction and/or self-interaction


Clone self-interaction by bio-layer interferometry


Mathew correlation coefficient


Poly-specificity reagent


Receiver operating characteristic


Support vector machine


Tripeptide composition



The authors are grateful to the anonymous reviewers for their valuable suggestions and comments, which have led to the improvement of this paper. This work was supported by the National Natural Science Foundation of China [grant no. 61571095] and the Fundamental Research Funds for the Central Universities of China [grant no. ZYGX2015Z006].

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.


  1. 1.
    Jacobs SA et al (2010) Cross-interaction chromatography: a rapid method to identify highly soluble monoclonal antibody candidates. Pharm Res 27(1):65–71CrossRefGoogle Scholar
  2. 2.
    Geng SB et al (2016) Measurements of monoclonal antibody self-association are correlated with complex biophysical properties. Mol Pharm 13(5):1636–1645CrossRefGoogle Scholar
  3. 3.
    Sun T et al (2013) High throughput detection of antibody self-interaction by bio-layer interferometry. MAbs 5(6):838–841CrossRefGoogle Scholar
  4. 4.
    Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3(8):711–715CrossRefGoogle Scholar
  5. 5.
    Waldmann TA (2003) Immunotherapy: past, present and future. Nat Med 9(3):269–277CrossRefGoogle Scholar
  6. 6.
    Li N et al (2017) PSBinder: a web service for predicting polystyrene surface-binding peptides. Biomed Res Int 2017:5761517PubMedPubMedCentralGoogle Scholar
  7. 7.
    He B et al (2016) SABinder: a web service for predicting streptavidin-binding peptides. Biomed Res Int 2016:9175143PubMedPubMedCentralGoogle Scholar
  8. 8.
    Zhang Y, Liu T, Chen L, Yang J, Yin J, Zhang Y, Yun Z, Xu H, Ning L, Guo F, Jiang Y, Lin H, Wang D, Huang Y, Huang J, Wren J (2019) RIscoper: a tool for RNA-RNA interaction extraction from the literature. Bioinformatics. CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Tang Q et al (2015) NIEluter: predicting peptides eluted from HLA class I molecules. J Immunol Methods 422:22–27CrossRefGoogle Scholar
  10. 10.
    Feng C-Q, Zhang Z-Y, Zhu X-J, Lin Y, Chen W, Tang H, Lin H, Hancock J (2019) iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators. Bioinformatics 35(9):1469–1477CrossRefGoogle Scholar
  11. 11.
    Chennamsetty N et al (2009) Aggregation-prone motifs in human immunoglobulin G. J Mol Biol 391(2):404–413CrossRefGoogle Scholar
  12. 12.
    Lauer TM et al (2012) Developability index: a rapid in silico tool for the screening of antibody aggregation propensity. J Pharm Sci 101(1):102–157CrossRefGoogle Scholar
  13. 13.
    Wang X et al (2009) Potential aggregation prone regions in biotherapeutics: a survey of commercial monoclonal antibodies. MAbs 1(3):254–267CrossRefGoogle Scholar
  14. 14.
    Sormanni P et al (2017) Rapid and accurate in silico solubility screening of a monoclonal antibody library. Sci Rep 7:8200CrossRefGoogle Scholar
  15. 15.
    Seeliger D et al (2015) Boosting antibody developability through rational sequence optimization. Mabs 7(3):505–515CrossRefGoogle Scholar
  16. 16.
    Sydow JF et al (2014) Structure-based prediction of asparagine and aspartate degradation sites in antibody variable regions. PLoS One 9(6):e100736CrossRefGoogle Scholar
  17. 17.
    Agrawal NJ et al (2016) Computational tool for the early screening of monoclonal antibodies for their viscosities. Mabs 8(1):43–48CrossRefGoogle Scholar
  18. 18.
    van der Kant R et al (2017) Prediction and reduction of the aggregation of monoclonal antibodies. J Mol Biol 429(8):1244–1261CrossRefGoogle Scholar
  19. 19.
    Jain T, Sun T, Durand S, Hall A, Houston NR, Nett JH, Sharkey B, Bobrowicz B, Caffry I, Yu Y, Cao Y, Lynaugh H, Brown M, Baruah H, Gray LT, Krauland EM, Xu Y, Vásquez M, Wittrup KD (2017) Biophysical properties of the clinical-stage antibody landscape. Proc Nat Acad Sci 114(5):944–949CrossRefGoogle Scholar
  20. 20.
    Ru B et al (2014) PhD7Faster: predicting clones propagating faster from the Ph.D.-7 phage display peptide library. J Bioinform Comput Biol 12(1):1450005CrossRefGoogle Scholar
  21. 21.
    Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27CrossRefGoogle Scholar
  22. 22.
    Chen W, Lv H, Nie F, Lin H, Hancock J (2019) i6mA-Pred: identifying DNA N6-methyladenine sites in the rice genome. Bioinformatics. CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Su Z-D, Huang Y, Zhang Z-Y, Zhao Y-W, Wang D, Chen W, Chou K-C, Lin H, Hancock J (2018) iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC. Bioinformatics 34(24):4196–4204PubMedGoogle Scholar
  24. 24.
    Yang H et al. (2018) iRSpot-Pse6NC: identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC. Int J Biol Sci 14(8):883–891CrossRefGoogle Scholar
  25. 25.
    Kang J, Fang Y, Yao P, Li N, Tang Q, Huang J (2019) NeuroPP: a tool for the prediction of neuropeptide precursors based on optimal sequence composition. Interdisc Sci Comput Life Sci 11(1):108–114CrossRefGoogle Scholar
  26. 26.
    Obuchowski NA (2003) Receiver operating characteristic curves and their use in radiology. Radiology 229(1):3–8CrossRefGoogle Scholar
  27. 27.
    Bradley AP (1997) The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recogn 30(7):1145–1159CrossRefGoogle Scholar
  28. 28.
    Fellouse FA et al (2006) Tyrosine plays a dominant functional role in the paratope of a synthetic antibody derived from a four amino acid code. J Mol Biol 357(1):100–114CrossRefGoogle Scholar
  29. 29.
    Fellouse FA et al (2005) Molecular recognition by a binary code. J Mol Biol 348(5):1153–1162CrossRefGoogle Scholar

Copyright information

© International Association of Scientists in the Interdisciplinary Areas 2019

Authors and Affiliations

  1. 1.Center for Informational BiologyUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.School of MedicineGuizhou UniversityGuiyangChina

Personalised recommendations