Molecular Recognition in the Case of Flexible Targets

Ivetac, Anthony; McCammon, J. Andrew

doi:10.2174/138161211796355056

Cited by 49 publications

(41 citation statements)

References 81 publications

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“…One of the key characteristics of DMEs (CYPs, SULTs) is that they are promiscuous, showing a remarkable plasticity of the active site to adapt its conformation to diverse ligands [12,14,17,35,36]. Therefore, we explored the flexibility of SULTs by molecular dynamics (MD) simulations in order to elucidate the molecular mechanisms involved in ligand binding and to identify the most suitable multiple receptor conformations for ligand binding prediction [29,37–39]. Then, we employed a structure-based docking-scoring approach [34,40,41] to probe ligand binding and finally we combined the predicted interaction energies with a QSAR methodology.…”

Section: Introductionmentioning

confidence: 99%

In Silico Mechanistic Profiling to Probe Small Molecule Binding to Sulfotransferases

et al. 2013

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Drug metabolizing enzymes play a key role in the metabolism, elimination and detoxification of xenobiotics, drugs and endogenous molecules. While their principal role is to detoxify organisms by modifying compounds, such as pollutants or drugs, for a rapid excretion, in some cases they render their substrates more toxic thereby inducing severe side effects and adverse drug reactions, or their inhibition can lead to drug–drug interactions. We focus on sulfotransferases (SULTs), a family of phase II metabolizing enzymes, acting on a large number of drugs and hormones and showing important structural flexibility. Here we report a novel in silico structure-based approach to probe ligand binding to SULTs. We explored the flexibility of SULTs by molecular dynamics (MD) simulations in order to identify the most suitable multiple receptor conformations for ligand binding prediction. Then, we employed structure-based docking-scoring approach to predict ligand binding and finally we combined the predicted interaction energies by using a QSAR methodology. The results showed that our protocol successfully prioritizes potent binders for the studied here SULT1 isoforms, and give new insights on specific molecular mechanisms for diverse ligands’ binding related to their binding sites plasticity. Our best QSAR models, introducing predicted protein-ligand interaction energy by using docking, showed accuracy of 67.28%, 78.00% and 75.46%, for the isoforms SULT1A1, SULT1A3 and SULT1E1, respectively. To the best of our knowledge our protocol is the first in silico structure-based approach consisting of a protein-ligand interaction analysis at atomic level that considers both ligand and enzyme flexibility, along with a QSAR approach, to identify small molecules that can interact with II phase dug metabolizing enzymes.

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Section: Introductionmentioning

confidence: 99%

In Silico Mechanistic Profiling to Probe Small Molecule Binding to Sulfotransferases

et al. 2013

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“…21–25 An important advance in accounting for target flexibility has been to dock the same ligand library to multiple different static conformations of the same target. 26,27 Approaches to generating target conformational ensembles have included using multiple different experimental atomic-resolution structures of the same protein 28,29 or multiple different conformations generated by in silico methods such as MD or Monte Carlo simulations.…”

Section: Introductionmentioning

confidence: 99%

Balancing target flexibility and target denaturation in computational fragment‐based inhibitor discovery

2012

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Accounting for target flexibility and selecting “hot spots” most likely to be able to bind an inhibitor continue to be challenges in the field of structure-based drug design, especially in the case of protein-protein interactions. Computational fragment-based approaches employing molecular dynamics (MD) simulations are a promising emerging technology having the potential to address both of these challenges. However, the optimal MD conditions permitting sufficient target flexibility while also avoiding fragment-induced target denaturation remain ambiguous. Using one such technology (SILCS: Site Identification by Ligand Competitive Saturation), conditions were identified to either prevent denaturation or identify and exclude trajectories in which subtle but important denaturation was occurring. The target system employed was the well-characterized protein cytokine IL-2, which is involved in a protein-protein interface and, in its un-liganded crystallographic form, lacks surface pockets that can serve as small-molecule binding sites. Nonetheless, small-molecule inhibitors have previously been discovered that bind to two “cryptic” binding sites that emerge only in the presence of ligand binding, highlighting the important role of IL-2 flexibility. Using the above conditions, SILCS with hydrophobic fragments was able to identify both sites based on favorable fragment binding while avoiding IL-2 denaturation. An important additional finding was that acetonitrile, a water-miscible fragment, fails to identify either site yet can induce target denaturation, highlighting the importance of fragment choice.

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“…4−8 This inherent protein flexibility presents an important complication for computer-based approaches in drug discovery, as it is effectively harder to hit a moving target. 9,10 …”

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confidence: 99%

Induced Fit or Conformational Selection? The Role of the Semi-closed State in the Maltose Binding Protein

2011

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A full characterization of the thermodynamic forces underlying ligand-associated conformational changes in proteins is essential for understanding and manipulating diverse biological processes, including transport, signaling, and enzymatic activity. Recent experiments on the maltose binding protein (MBP) have provided valuable data about the different conformational states implicated in the ligand recognition process; however, a complete picture of the accessible pathways and the associated changes in free energy remains elusive. Here we describe results from advanced accelerated molecular dynamics (aMD) simulations, coupled with adaptively biased force (ABF) and thermodynamic integration (TI) free energy methods. The combination of approaches allows us to track the ligand recognition process on the microsecond time scale and provides a detailed characterization of the protein’s dynamic and the relative energy of stable states. We find that an induced-fit (IF) mechanism is most likely and that a mechanism involving both a conformational selection (CS) step and an IF step is also possible. The complete recognition process is best viewed as a “Pac Man” type action where the ligand is initially localized to one domain and naturally occurring hinge-bending vibrations in the protein are able to assist the recognition process by increasing the chances of a favorable encounter with side chains on the other domain, leading to a population shift. This interpretation is consistent with experiments and provides new insight into the complex recognition mechanism. The methods employed here are able to describe IF and CS effects and provide formally rigorous means of computing free energy changes. As such, they are superior to conventional MD and flexible docking alone and hold great promise for future development and applications to drug discovery.

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Molecular Recognition in the Case of Flexible Targets

Cited by 49 publications

References 81 publications

In Silico Mechanistic Profiling to Probe Small Molecule Binding to Sulfotransferases

In Silico Mechanistic Profiling to Probe Small Molecule Binding to Sulfotransferases

Balancing target flexibility and target denaturation in computational fragment‐based inhibitor discovery

Induced Fit or Conformational Selection? The Role of the Semi-closed State in the Maltose Binding Protein

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