Computational development of rubromycin-based lead compounds for HIV-1 reverse transcriptase inhibition

Pedro J Silva; Carlos E. P. Bernardo

doi:10.7287/peerj.preprints.348v1

Computational development of rubromycin-based lead compounds for HIV-1 reverse transcriptase inhibition

Pedro J Silva ¹, Carlos E. P. Bernardo²

1 Universidade Fernando Pessoa, Porto, Portugal

2 REQUIMTE, Universidade Fernando Pessoa, Porto, Portugal

DOI: 10.7287/peerj.preprints.348v1

Published: 2014-04-01
Accepted: 2014-04-01

Subject Areas: Biochemistry, Biophysics, Computational Biology
Keywords: molecular dynamics, docking, computer-aided drug design

Copyright: © 2014 Silva et al.
Licence: This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.

Cite this article: Silva PJ, Bernardo CEP. 2014. Computational development of rubromycin-based lead compounds for HIV-1 reverse transcriptase inhibition. PeerJ PrePrints 2:e348v1 https://doi.org/10.7287/peerj.preprints.348v1

Abstract

The binding of several rubromycin-based ligands to HIV1-reverse transcriptase was analyzed using molecular docking and molecular dynamics simulations. MM-PBSA analysis and examination of the trajectories allowed the identification of several promising compounds with predicted high affinity towards reverse transcriptase mutants which have proven resistant to current drugs. Important insights on the complex interplay of factors determining the ability of ligands to selectively target each mutant have been obtained.

Supplemental Information

Correlations in the distances between K219, D67, L289 and N137 throughout the2nd half of the simulations of wild-type and mutant RT bound to ligand 46

DOI: 10.7287/peerj.preprints.348v1/supp-5

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Correlations in the distances between K219, D67, L289 and N137 throughout the2nd half of the simulations of wild-type and mutant RT bound to ligand 36

DOI: 10.7287/peerj.preprints.348v1/supp-6

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Correlations in the distances between K219, D67, L289 and N137 throughout the2nd half of the simulations of wild-type and mutant RT bound to ligand 37

DOI: 10.7287/peerj.preprints.348v1/supp-7

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Correlations in the distances between K219, D67, L289 and N137 throughout the2nd half of the simulations of wild-type and mutant RT bound to ligand 38

DOI: 10.7287/peerj.preprints.348v1/supp-8

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Correlations in the distances between K219, D67, L289 and N137 throughout the2nd half of the simulations of wild-type and mutant RT bound to ligand 13

DOI: 10.7287/peerj.preprints.348v1/supp-9

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Correlations in the distances between K219, D67, L289 and N137 throughout the2nd half of the simulations of wild-type and mutant RT bound to ligand 45

DOI: 10.7287/peerj.preprints.348v1/supp-10

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Correlations in the distances between K219, D67, L289 and N137 throughout the2nd half of the simulations of wild-type and mutant RT bound to ligand 27

DOI: 10.7287/peerj.preprints.348v1/supp-11

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Correlations in the distances between K219, D67, L289 and N137 throughout the2nd half of the simulations of wild-type and mutant RT bound to gamma-rubromycin

DOI: 10.7287/peerj.preprints.348v1/supp-12

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Correlations in the distances between K219, D67, L289 and N137 throughout the2nd half of the simulations of wild-type and mutant RT bound to rilpivirine

DOI: 10.7287/peerj.preprints.348v1/supp-13

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Supplemental Information

Optimized geometries of all moleucles used in the docking stage of the work

AutoDock 4.2.3 docking energies of all tested gamma-rubromycin derivatives

Stable H-bonds between ligands and WT and mutated reverse transcriptase

Energetic contributions to binding, for all mutants and ligands

Correlations in the distances between K219, D67, L289 and N137 throughout the2nd half of the simulations of wild-type and mutant RT bound to ligand 46

Correlations in the distances between K219, D67, L289 and N137 throughout the2nd half of the simulations of wild-type and mutant RT bound to ligand 36

Correlations in the distances between K219, D67, L289 and N137 throughout the2nd half of the simulations of wild-type and mutant RT bound to ligand 37

Correlations in the distances between K219, D67, L289 and N137 throughout the2nd half of the simulations of wild-type and mutant RT bound to ligand 38

Correlations in the distances between K219, D67, L289 and N137 throughout the2nd half of the simulations of wild-type and mutant RT bound to ligand 13

Correlations in the distances between K219, D67, L289 and N137 throughout the2nd half of the simulations of wild-type and mutant RT bound to ligand 45

Correlations in the distances between K219, D67, L289 and N137 throughout the2nd half of the simulations of wild-type and mutant RT bound to ligand 27

Correlations in the distances between K219, D67, L289 and N137 throughout the2nd half of the simulations of wild-type and mutant RT bound to gamma-rubromycin

Correlations in the distances between K219, D67, L289 and N137 throughout the2nd half of the simulations of wild-type and mutant RT bound to rilpivirine

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