A SUPPORT VECTOR-BASED PREDICTIVE MODEL TO REVEAL THE RELATIONSHIPS AMONG ANTIBODY FEATURES AND THEIR EFFECTIVE FUNCTIONS AGAINST HIV

Year 2019, Volume: 3 Issue: 4, 261 - 270, 29.10.2019

Ferdi Saraç

https://doi.org/10.26900/jsp.3.028

Abstract

Despite 4 decades’ effort, an effective HIV-1 vaccine has not been produced owing to the inevitable antigenic diversity of the virus and millions of people around the world have lost their lives due to HIV. Increasing the knowledge of adaptive immune response to vaccination would ultimately lead to an effective HIV cure. Antibodies, which are responsible for protection and fighting against antigens, are vital parts of immune system response. In order to identify discriminative antibodies, which provide protection against HIV, and to disclose the associations between antibody features and their functional outcomes, computational methods, such as feature selection, regression and classification can be used to construct predictive models. Here we used our unsupervised K-Means Based Feature Selection (KBFS) method which is presented in our previous study, to identify functional antibodies that fight against HIV. The accuracy results for the proposed KBFS framework are compared with those presented in a recent study and are also compared with results from four different state-of-the-art unsupervised feature selection methods, namely MCFS, InFS, LapFS, and SPFS, along with the entire feature set. Then, support vector based systems are utilised to predict the associations between antibody features and their functional activities, namely gp120-specific antibody dependent cellular phagocytosis (ADCP), antibody dependent cellular cytotoxicity (ADCC) and cytokine release activities on RV144 vaccine recipients. Pearson Correlation Coefficient (PCC) metric is used to evaluate the prediction accuracy of the predictive models and to be consistent with the previous study. Our SVR based KBFS framework presented higher accuracy than the original study by improving prediction performance 16% for ADCP assay, 200% for the ADCC assay.

Keywords

Feature Selection, RV144, HIV

A SUPPORT VECTOR-BASED PREDICTIVE MODEL TO REVEAL THE RELATIONSHIPS AMONG ANTIBODY FEATURES AND THEIR EFFECTIVE FUNCTIONS AGAINST HIV

Abstract

Despite 4 decades’
effort, an effective HIV-1 vaccine has not been produced owing to the
inevitable antigenic diversity of the virus and millions of people around the
world have lost their lives due to HIV. Increasing the knowledge of adaptive
immune response to vaccination would ultimately lead to an effective HIV cure.
Antibodies, which are responsible for protection and fighting against antigens,
are vital parts of immune system response. In order to identify discriminative
antibodies, which provide protection against HIV, and to disclose the
associations between antibody features and their functional outcomes,
computational methods, such as feature selection, regression and classification
can be used to construct predictive models. Here we used our unsupervised
K-Means Based Feature Selection (KBFS) method which is presented in our
previous study, to identify functional antibodies that fight against HIV. The
accuracy results for the proposed KBFS framework are compared with those presented
in a recent study and are also compared with results from four different
state-of-the-art unsupervised feature selection methods, namely MCFS, InFS,
LapFS, and SPFS, along with the entire feature set. Then, support vector based
systems are utilised to predict the associations between antibody features and
their functional activities, namely gp120-specific antibody dependent cellular
phagocytosis (ADCP), antibody dependent cellular cytotoxicity (ADCC) and
cytokine release activities on RV144 vaccine recipients. Pearson Correlation
Coefficient (PCC) metric is used to evaluate the prediction accuracy of the
predictive models and to be consistent with the previous study. Our SVR based
KBFS framework presented higher accuracy than the original study by improving
prediction performance 16% for ADCP assay, 200% for the ADCC assay. 

Keywords

Feature Selection, RV144, HIV

References

• [1] S. Sadanand, T. J. Suscovich, G. Alter, Broadly neutralizing antibodies against hiv: New insights to inform vaccine design, Annual review of medicine 67 (2016) 185–200.
• [2] A. S. Clem, et al., Fundamentals of vaccine immunology, Journal of global infectious diseases 3 (2011) 73.
• [3] G. E. Seabright, K. J. Doores, D. R. Burton, M. Crispin, Protein and glycan mimicry in hiv vaccine design, Journal of molecular biology (2019).
• [4] M. Rolland, P. T. Edlefsen, B. B. Larsen, S. Tovanabutra, E. Sanders- Buell, T. Hertz, C. Carrico, S. Menis, C. A. Magaret, H. Ahmed, et al., Increased hiv-1 vaccine efficacy against viruses with genetic signatures in env v2, Nature 490 (2012) 417–420.
• [5] H. S. Bagalb, Cellular and Molecular Biological Studies of a Retroviral Induced Lymphoma, Transmitted via Breast Milk in a Mouse Model, Ph.D. thesis, University of Toledo, 2008.
• [6] A. Perelson, P. Essunger, D. Ho, Dynamics of hiv-1 and cd4+ lympho- cytes in vivo., AIDS (London, England) 11 (1996) S17–24.
• [7] K. L. Williams, M. Stumpf, N. E. Naiman, S. Ding, M. Garrett, T. Go- billot, D. V ́ezina, K. Dusenbury, N. S. Ramadoss, R. Basom, et al., Identification of hiv gp41-specific antibodies that mediate killing of in- fected cells, PLoS pathogens 15 (2019) e1007572.
• [8] C. Nilsson, S. Aboud, M. Bakari, E. F. Lyamuya, M. L. Robb, M. A. Marovich, P. Earl, B. Moss, C. Ochsenbauer, B. Wahren, et al., Potent functional antibody responses elicited by hiv-i dna priming and boosting with heterologous hiv-1 recombinant mva in healthy tanzanian adults, PloS one 10 (2015) e0118486.Bagalb, H. S. (2008). Cellular and Molecular Biological Studies of a Retroviral Induced Lymphoma, Transmitted via Breast Milk in a Mouse Model (Doctoral dissertation, University of Toledo).
• [9] G. D. Tomaras, B. F. Haynes, Strategies for eliciting hiv-1 inhibitory antibodies, Current Opinion in HIV and AIDS 5 (2010) 421.
• [10] H. L. Robinson, Non-neutralizing antibodies in prevention of hiv infec- tion, Expert opinion on biological therapy 13 (2013) 197–207.
• [11] Y. Guan, M. Pazgier, M. M. Sajadi, R. Kamin-Lewis, S. Al-Darmarki, R. Flinko, E. Lovo, X. Wu, J. E. Robinson, M. S. Seaman, et al., Di- verse specificity and effector function among human antibodies to hiv-1 envelope glycoprotein epitopes exposed by cd4 binding, Proceedings of the National Academy of Sciences 110 (2013) E69–E78.
• [12] R. Ahmad, S. T. Sindhu, E. Toma, R. Morisset, J. Vincelette, J. Menezes, A. Ahmad, Evidence for a correlation between antibody- dependent cellular cytotoxicity-mediating anti-hiv-1 antibodies and prognostic predictors of hiv infection, Journal of clinical immunology 21 (2001) 227–233.
• [13] M. E. Ackerman, A.-S. Dugast, G. Alter, Emerging concepts on the role of innate immunity in the prevention and control of hiv infection, Annual review of medicine 63 (2012) 113–130.
• [14] F. Sarac, V. Uslan, H. Seker, A. Bouridane, Exploration of unsupervised feature selection methods in relation to the prediction of cytokine release effect correlated to antibody features in rv144 vaccines, in: Bioinformatics and Bioengineering (BIBE), 2015 IEEE 15th International Confer- ence on, IEEE, 2015, pp. 1–4.
• [15] M. E Ackerman, G. Alter, Opportunities to exploit non-neutralizing hiv-specific antibody activity, Current HIV research 11 (2013) 365–377.
• [16] S. A. Plotkin, Correlates of protection induced by vaccination, Clinical and Vaccine Immunology 17 (2010) 1055–1065.
• [17] I. Choi, A. W. Chung, T. J. Suscovich, S. Rerks-Ngarm, P. Pitisut- tithum, S. Nitayaphan, J. Kaewkungwal, R. J. O’Connell, D. Francis, M. L. Robb, et al., Machine learning methods enable predictive model- ing of antibody feature: function relationships in rv144 vaccinees, PLoS computational biology 11 (2015) e1004185.
• [18] W. S. Lee, M. S. Parsons, S. J. Kent, M. Lichtfuss, Can hiv-1-specific adcc assist the clearance of reactivated latently infected cells?, Frontiers in immunology 6 (2015).
• [19] F. Sarac, H. Seker, A. Bouridane, Exploration of unsupervised feature selection methods to predict chronological age of individuals by utilising cpg dinucleotics from whole blood, in: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2017.
• [20] D. Cai, C. Zhang, X. He, Unsupervised feature selection for multi- cluster data, in: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010, pp. 333– 342.
• [21] X. He, D. Cai, P. Niyogi, Laplacian score for feature selection, in: Advances in Neural Information Processing Systems, 2005, pp. 507–14.
• [22] Z. Zhao, H. Liu, Spectral feature selection for supervised and unsupervised learning, in: Proceedings of the 24th international conference on Machine learning, ACM, 2007, pp. 1151–1157.
• [23] G. Roffo, S. Melzi, M. Cristani, Infinite feature selection, in: Proceed- ings of the IEEE International Conference on Computer Vision, 2015, pp. 4202–4210.
• [24] H. Drucker, C. J. C. Burges, L. Kaufman, A. J. Smola, V. N. Vapnik, Support Vector Regression Machines, volume 9 of Advances in Neural Information Processing Systems, MIT Press, 1996.
• [25] V. N. Vapnik, An overview of statistical learning theory, Neural Net- works, IEEE Transactions on 10 (1999) 988–999.
• [26] C.-C. Chang, C.-J. Lin, LIBSVM: A library for support vector machines, ACM Transactions on Intelligent Systems and Technology 2 (2011) 1–27.