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

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Abstract

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 http://i.uestc.edu.cn/eli/cgi-bin/cisi.pl.

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Abbreviations

AC-SINS:

Affinity-capture self-interaction nanoparticle spectroscopy

AUC:

Area under the receiver operating characteristic curve

CDR:

Complementarity-determining regions

CIC:

Cross-interaction chromatography

CISI:

Cross-interaction and/or self-interaction

CSI-BLI:

Clone self-interaction by bio-layer interferometry

MCC:

Mathew correlation coefficient

PSR:

Poly-specificity reagent

ROC:

Receiver operating characteristic

SVM:

Support vector machine

TPC:

Tripeptide composition

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Acknowledgements

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].

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Authors and Affiliations

  1. Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Anthony Mackitz Dzisoo, Bifang He, Rita Karikari, Elijah Agoalikum & Jian Huang

  2. School of Medicine, Guizhou University, Guiyang, 550025, China

    Bifang He

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  1. Anthony Mackitz Dzisoo
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  2. Bifang He
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  3. Rita Karikari
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Correspondence to Jian Huang.

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Dzisoo, A.M., He, B., Karikari, R. et al. CISI: A Tool for Predicting Cross-interaction or Self-interaction of Monoclonal Antibodies Using Sequences. Interdiscip Sci Comput Life Sci 11, 691–697 (2019). https://doi.org/10.1007/s12539-019-00330-1

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  • DOI: https://doi.org/10.1007/s12539-019-00330-1

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