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A Role for Both Conformational Selection and Induced Fit in Ligand Binding by the LAO Protein

Author

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  • Daniel-Adriano Silva
  • Gregory R Bowman
  • Alejandro Sosa-Peinado
  • Xuhui Huang

Abstract

Molecular recognition is determined by the structure and dynamics of both a protein and its ligand, but it is difficult to directly assess the role of each of these players. In this study, we use Markov State Models (MSMs) built from atomistic simulations to elucidate the mechanism by which the Lysine-, Arginine-, Ornithine-binding (LAO) protein binds to its ligand. We show that our model can predict the bound state, binding free energy, and association rate with reasonable accuracy and then use the model to dissect the binding mechanism. In the past, this binding event has often been assumed to occur via an induced fit mechanism because the protein's binding site is completely closed in the bound state, making it impossible for the ligand to enter the binding site after the protein has adopted the closed conformation. More complex mechanisms have also been hypothesized, but these have remained controversial. Here, we are able to directly observe roles for both the conformational selection and induced fit mechanisms in LAO binding. First, the LAO protein tends to form a partially closed encounter complex via conformational selection (that is, the apo protein can sample this state), though the induced fit mechanism can also play a role here. Then, interactions with the ligand can induce a transition to the bound state. Based on these results, we propose that MSMs built from atomistic simulations may be a powerful way of dissecting ligand-binding mechanisms and may eventually facilitate a deeper understanding of allostery as well as the prediction of new protein-ligand interactions, an important step in drug discovery. Author Summary: Protein-ligand interactions are crucial to chemistry, biology and medicine. Many studies have been conducted to probe the mechanism of protein-ligand binding, leading to the development of the induced fit and conformational selection models. Unfortunately, experimentally probing the atomistic details of protein-ligand binding mechanisms is challenging. Computer simulations have the potential to provide a detailed picture of molecular recognition events. In this study, we construct kinetic network models from atomistic simulations to elucidate the mechanism by which the LAO protein binds to its ligand. Because the LAO protein completely encompasses its substrate in the bound state, it has generally been assumed that it operates via an induced fit mechanism. We find that both the conformational selection and induced fit mechanisms play important roles in LAO binding. Furthermore, we have identified a number of parallel pathways for binding, all of which pass through a single gatekeeper state, which we refer to as the encounter complex state because the protein is partially closed and only weakly interacting with its substrate.

Suggested Citation

  • Daniel-Adriano Silva & Gregory R Bowman & Alejandro Sosa-Peinado & Xuhui Huang, 2011. "A Role for Both Conformational Selection and Induced Fit in Ligand Binding by the LAO Protein," PLOS Computational Biology, Public Library of Science, vol. 7(5), pages 1-11, May.
  • Handle: RePEc:plo:pcbi00:1002054
    DOI: 10.1371/journal.pcbi.1002054
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    References listed on IDEAS

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    1. Chun Tang & Charles D. Schwieters & G. Marius Clore, 2007. "Open-to-closed transition in apo maltose-binding protein observed by paramagnetic NMR," Nature, Nature, vol. 449(7165), pages 1078-1082, October.
    2. Faruck Morcos & Santanu Chatterjee & Christopher L McClendon & Paul R Brenner & Roberto López-Rendón & John Zintsmaster & Maria Ercsey-Ravasz & Christopher R Sweet & Matthew P Jacobson & Jeffrey W Pen, 2010. "Modeling Conformational Ensembles of Slow Functional Motions in Pin1-WW," PLOS Computational Biology, Public Library of Science, vol. 6(12), pages 1-13, December.
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    1. Polydefkis Diamantis & Oliver T Unke & Markus Meuwly, 2017. "Migration of small ligands in globins: Xe diffusion in truncated hemoglobin N," PLOS Computational Biology, Public Library of Science, vol. 13(3), pages 1-22, March.
    2. Lin-Tai Da & Fátima Pardo Avila & Dong Wang & Xuhui Huang, 2013. "A Two-State Model for the Dynamics of the Pyrophosphate Ion Release in Bacterial RNA Polymerase," PLOS Computational Biology, Public Library of Science, vol. 9(4), pages 1-9, April.
    3. Shuo Gu & Daniel-Adriano Silva & Luming Meng & Alexander Yue & Xuhui Huang, 2014. "Quantitatively Characterizing the Ligand Binding Mechanisms of Choline Binding Protein Using Markov State Model Analysis," PLOS Computational Biology, Public Library of Science, vol. 10(8), pages 1-11, August.
    4. Patrick G Blachly & César A F de Oliveira & Sarah L Williams & J Andrew McCammon, 2013. "Utilizing a Dynamical Description of IspH to Aid in the Development of Novel Antimicrobial Drugs," PLOS Computational Biology, Public Library of Science, vol. 9(12), pages 1-13, December.
    5. Hanlun Jiang & Fu Kit Sheong & Lizhe Zhu & Xin Gao & Julie Bernauer & Xuhui Huang, 2015. "Markov State Models Reveal a Two-Step Mechanism of miRNA Loading into the Human Argonaute Protein: Selective Binding followed by Structural Re-arrangement," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-21, July.

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