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Proteochemometric model for predicting the inhibition of penicillin-binding proteins

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Abstract

Neisseria gonorrhoeae infection threatens to become an untreatable sexually transmitted disease in the near future owing to the increasing emergence of N. gonorrhoeae strains with reduced susceptibility and resistance to the extended-spectrum cephalosporins (ESCs), i.e. ceftriaxone and cefixime, which are the last remaining option for first-line treatment of gonorrhea. Alteration of the penA gene, encoding penicillin-binding protein 2 (PBP2), is the main mechanism conferring penicillin resistance including reduced susceptibility and resistance to ESCs. To predict and investigate putative amino acid mutations causing β-lactam resistance particularly for ESCs, we applied proteochemometric modeling to generalize N. gonorrhoeae susceptibility data for predicting the interaction of PBP2 with therapeutic β-lactam antibiotics. This was afforded by correlating publicly available data on antimicrobial susceptibility of wild-type and mutant N. gonorrhoeae strains for penicillin-G, cefixime and ceftriaxone with 50 PBP2 protein sequence data using partial least-squares projections to latent structures. The generated model revealed excellent predictability (R 2 = 0.91, Q 2 = 0.77, Q 2Ext  = 0.78). Moreover, our model identified amino acid mutations in PBP2 with the highest impact on antimicrobial susceptibility and provided information on physicochemical properties of amino acid mutations affecting antimicrobial susceptibility. Our model thus provided insight into the physicochemical basis for resistance development in PBP2 suggesting its use for predicting and monitoring novel PBP2 mutations that may emerge in the future.

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Acknowledgments

S.N., under the supervision of V.P., was supported by the Royal Golden Jubilee Ph.D. scholarship (No. PHD/0195/2550) jointly funded by Mahidol University and the Thailand Research Fund. CN and JESW were supported by a joint grant from the Swedish Research Links program (No. C0610701) funded by the Swedish Research Council. The research was also supported in part by the Office of the Higher Education Commission and Mahidol University under the National Research Universities Initiative. Partial support from the annual budget grant of Mahidol University is also acknowledged.

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Authors and Affiliations

  1. Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand

    Sunanta Nabu, Chanin Nantasenamat & Wiwat Owasirikul

  2. Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand

    Sunanta Nabu, Chanin Nantasenamat, Ratana Lawung, Chartchalerm Isarankura-Na-Ayudhya & Virapong Prachayasittikul

  3. Department of Radiological Technology, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand

    Wiwat Owasirikul

  4. Center of Medical Laboratory Service, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand

    Ratana Lawung

  5. Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden

    Sunanta Nabu, Maris Lapins & Jarl E. S. Wikberg

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  1. Sunanta Nabu
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Nabu, S., Nantasenamat, C., Owasirikul, W. et al. Proteochemometric model for predicting the inhibition of penicillin-binding proteins. J Comput Aided Mol Des 29, 127–141 (2015). https://doi.org/10.1007/s10822-014-9809-0

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