Dataset Information

Computational Prediction of the Mode of Binding of Antitumor Lankacidin C to Tubulin.

ABSTRACT: Lankacidin C, which is an antibiotic produced by the organism Streptomyces rochei, shows considerable antitumor activity. The mechanism of its antitumor activity remained elusive for decades until it was recently shown to overstabilize microtubules by binding at the taxol binding site of tubulin, causing mitotic arrest followed by apoptosis. However, the exact binding mode of lankacidin C inside the tubulin binding pocket remains unknown, an issue that impedes proper structure-based design, modification, and optimization of the drug. Here, we have used computational methods to predict the most likely binding mode of lankacidin C to tubulin. We employed ensemble-based docking in different software packages, supplemented with molecular dynamics simulation and subsequent binding-energy prediction. The molecular dynamics simulations performed on lankacidin C were collectively 1.1 ?s long. Also, a multiple-trajectory approach was performed to assess the stability of different potential binding modes. The identified binding mode could serve as an ideal starting point for structural modification and optimization of lankacidin C to enhance its affinity to the tubulin binding site and therefore improve its antitumor activity.

SUBMITTER: Ayoub AT

PROVIDER: S-EPMC6648929 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Computational Prediction of the Mode of Binding of Antitumor Lankacidin C to Tubulin.

Ayoub Ahmed Taha AT Elrefaiy Mohamed Ali MA Arakawa Kenji K

ACS omega 20190228 2

Lankacidin C, which is an antibiotic produced by the organism <i>Streptomyces rochei</i>, shows considerable antitumor activity. The mechanism of its antitumor activity remained elusive for decades until it was recently shown to overstabilize microtubules by binding at the taxol binding site of tubulin, causing mitotic arrest followed by apoptosis. However, the exact binding mode of lankacidin C inside the tubulin binding pocket remains unknown, an issue that impedes proper structure-based desig ...[more]

PMID: 31459641

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Project description:BACKGROUND: Protein-DNA interactions are essential for fundamental biological activities including DNA transcription, replication, packaging, repair and rearrangement. Proteins interacting with DNA can be classified into two categories of binding mechanisms - sequence-specific and non-specific binding. Protein-DNA specific binding provides a mechanism to recognize correct nucleotide base pairs for sequence-specific identification. Protein-DNA non-specific binding shows sequence independent interaction for accelerated targeting by interacting with DNA backbone. Both sequence-specific and non-specific binding residues contribute to their roles for interaction. RESULTS: The proposed framework has two stage predictors: DNA-binding residues prediction and binding mode prediction. In the first stage - DNA-binding residues prediction, the predictor for DNA specific binding residues achieves 96.45% accuracy with 50.14% sensitivity, 99.31% specificity, 81.70% precision, and 62.15% F-measure. The predictor for DNA non-specific binding residues achieves 89.14% accuracy with 53.06% sensitivity, 95.25% specificity, 65.47% precision, and 58.62% F-measure. While combining prediction results of sequence-specific and non-specific binding residues with OR operation, the predictor achieves 89.26% accuracy with 56.86% sensitivity, 95.63% specificity, 71.92% precision, and 63.51% F-measure. In the second stage, protein-DNA binding mode prediction achieves 75.83% accuracy while using support vector machine with multi-class prediction. CONCLUSION: This article presents the design of a sequence based predictor aiming to identify sequence-specific and non-specific binding residues in a transcription factor with DNA binding-mechanism concerned. The protein-DNA binding mode prediction was introduced to help improve DNA-binding residues prediction. In addition, the results of this study will help with the design of binding-mechanism concerned predictors for other families of proteins interacting with DNA.

Project description:Microtubules are multistranded polymers in eukaryotic cells that support key cellular functions such as chromosome segregation, motor-based cargo transport, and maintenance of cell polarity. Microtubules self-assemble via "dynamic instability," in which the dynamic plus ends switch stochastically between alternating phases of polymerization and depolymerization. A key question in the field is what are the atomistic origins of this switching, i.e., what is different between the GTP- and GDP-tubulin states that enables microtubule growth and shortening, respectively? More generally, a major challenge in biology is how to connect theoretical frameworks across length- and timescales, from atoms to cellular behavior. In this study, we describe a multiscale model by linking atomistic molecular dynamics (MD), molecular Brownian dynamics (BD), and cellular-level thermokinetic modeling of microtubules. Here, we investigated the underlying interaction energy when tubulin dimers associate laterally by performing all-atom MD simulations. We found that the lateral potential energy is not significantly different among three nucleotide states of tubulin, GTP, GDP, and GMPCPP and is estimated to be ≅ -11 kBT. Furthermore, using MD potential energy in our BD simulations of tubulin dimers confirms that the lateral bond is weak on its own, with a mean lifetime of ∼0.1 μs, implying that the longitudinal bond is required for microtubule assembly. We conclude that nucleotide-dependent lateral-bond strength is not the key mediator microtubule dynamic instability, implying that GTP acts elsewhere to exert its stabilizing influence on microtubule polymer. Furthermore, the estimated lateral-bond strength (ΔGlat0≅ -5 kBT) is well-aligned with earlier estimates based on thermokinetic modeling and light microscopy measurements. Thus, we have computationally connected atomistic-level structural information, obtained by cryo-electron microscopy, to cellular-scale microtubule assembly dynamics using a combination of MD, BD, and thermokinetic models to bridge from Ångstroms to micrometers and from femtoseconds to minutes.

Dataset Information

Computational Prediction of the Mode of Binding of Antitumor Lankacidin C to Tubulin.

Publications

Computational Prediction of the Mode of Binding of Antitumor Lankacidin C to Tubulin.

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