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A novel view of modelling interactions between synthetic and biological polymers via docking

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Abstract

Multipoint interactions between synthetic and natural polymers provide a promising platform for many topical applications, including therapeutic blockage of virus-specific targets. Docking may become a useful tool for modelling of such interactions. However, the rigid docking cannot be correctly applied to synthetic polymers with flexible chains. The application of flexible docking to these polymers as whole macromolecule ligands is also limited by too many possible conformations. We propose to solve this problem via stepwise flexible docking. Step 1 is docking of separate polymer components: (1) backbone units (BU), multi-repeated along the chain, and (2) side groups (SG) consisting of functionally active elements (SG F ) and bridges (SG B ) linking SG F with BU. At this step, probable binding sites locations and binding energies for the components are scored. Step 2 is docking of component-integrating models: [BU] m , SG = SG F –SG B , BU–SG, BU–BU(SG)–BU, BU(SG)–[BU] m –BU(SG), and [BU var (SG var )] m . Every modelling level yields new information, including how the linkage of various components influences on the ligand—target contacts positioning, orientation, and binding energy in step-by-step approximation to polymeric ligand motifs. Step 3 extrapolates the docking results to real-scale macromolecules. This approach has been demonstrated by studying the interactions between hetero-SG modified anionic polymers and the N-heptad repeat region tri-helix core of the human immunodeficiency virus type 1 (HIV-1) envelope glycoprotein gp41, the key mediator of HIV-1 fusion during virus entry. The docking results are compared to real polymeric compounds, acting as HIV-1 entry inhibitors in vitro. This study clarifies the optimal macromolecular design for the viral fusion inhibition and drug resistance prevention.

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Notes

  1. The quantity of possible structural conformations for a small molecule reaches 102–3 [10, 11], and this value increases to ≥10(2–3)N in polymeric chains, where N is the number of small molecular monomer residues in the N-mer chain (the degree of polymerisation).

  2. Biopolymers are nano-scale targets, as a rule.

  3. While an antibody is a protein-sized molecule, only small parts of it bind to an antigen directly.

  4. A. G. Bukrinskaya et al. (Ivanovsky Institute of Virology, Moscow, Russia) I. V. Timofeyev et al. (Centre of Virology and Biotechnology “Vector”, Koltsovo, Novosibirsk Region, Russia), E. De Clercq et al. (Rega Institute, Leuven, Belgium), and L. Margolis et al. (National Institute of Child Health and Human Development, NIH, Bethesda, USA) among others.

  5. This assumption has been verified by a test-docking of the Ia derived models (see below) to the both NHR and CHR. The NHR possessed significantly more (than CHR) multiplicity and higher binding energy (1.5–2 fold stronger).

  6. The Dock6 results accept the H-bond formation (within the 3 atoms XD, HD, and XA) if the following two conditions are met: (1) the distance between HD and XA is less than or equal to 2.5 Å; (2) the angle defined by XD, HD, and XA is between 120° and 180°. Several score functions are able to recognize the possibility of H-bonding directly, for example, the G-Score and ChemScore algorithms include direct calculations for the H-bonds contribution in ligand-target binding energy.

  7. Including a solvation effect score.

  8. Different by chemical nature and of location in the macromolecule.

  9. In this study we experimentally modelled the chains up to pentamers by alternating the five furan-like and succinic acid derived units.

  10. Other types of anchors will be reported in future publications.

  11. This correlation degree depends on scoring algorithms of the docking results to a certain extent, and one of the top correlations was obtained with ChemScore function. It can be related to a more adequate scoring of the contributions [70] from not only H-bond forces, and rotation entropy but also from the lipophilic effect, which is typical for hydrophobic Ali -structures of the considered anchors.

  12. With the exception of samples 2 and 3, containing the inefficient anchors CP and CH.

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Acknowledgments

The authors are thankful for the following collaborations: in copolymer synthesis, to Ekaterina Karaseva, et al. (Inst. Petrochem. Synthesis, Moscow); in anti-HIV-1 evaluations, to Marina Bourshtein, Alisa Bukrinskaya, et al. (Virology Inst., Moscow), Igor Timofeyev, Natalia Perminova, et al. (SRC “Vector”, Koltsovo), Erik De Clercq, et al. (Rega Inst. Med. Res., Belgium); in support of the computational modelling, to Alexander Veselovsky, et al. (Inst. Biomed. Chem., Moscow); in provision of equipment and materials, to Olga Alikhanova; and in reviewing this paper and helpful discussion, to Lilia Alkhanova.

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  1. Orekhovich Institute of Biomedical Chemistry, RAMS, Pogodinskaya St. 10/8, 119121, Moscow, Russia

    Vladimir B. Tsvetkov

  2. Topchiev Institute of Petrochemical Synthesis, RAS, Leninsky Prospect 29, 119991, Moscow, Russia

    Vladimir B. Tsvetkov & Alexander V. Serbin

  3. Computing Analysis Center, Health RDF, Boulevard Adm. Ushakova 14-209, 117042, Moscow, Russia

    Vladimir B. Tsvetkov

  4. Biomodulators and Drugs RC, Health RDF, Boulevard Adm. Ushakova 14-209, 117042, Moscow, Russia

    Alexander V. Serbin

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Tsvetkov, V.B., Serbin, A.V. A novel view of modelling interactions between synthetic and biological polymers via docking. J Comput Aided Mol Des 26, 1369–1388 (2012). https://doi.org/10.1007/s10822-012-9621-7

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