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Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape.

ABSTRACT: Molecular docking is a computational approach for predicting the most probable position of ligands in the binding sites of macromolecules and constitutes the cornerstone of structure-based computer-aided drug design. Here, we present a new algorithm called Attracting Cavities that allows molecular docking to be performed by simple energy minimizations only. The approach consists in transiently replacing the rough potential energy hypersurface of the protein by a smooth attracting potential driving the ligands into protein cavities. The actual protein energy landscape is reintroduced in a second step to refine the ligand position. The scoring function of Attracting Cavities is based on the CHARMM force field and the FACTS solvation model. The approach was tested on the 85 experimental ligand-protein structures included in the Astex diverse set and achieved a success rate of 80% in reproducing the experimental binding mode starting from a completely randomized ligand conformer. The algorithm thus compares favorably with current state-of-the-art docking programs.

SUBMITTER: Zoete V

PROVIDER: S-EPMC4738475 | biostudies-other | 2016 Feb

REPOSITORIES: biostudies-other

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Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape.

Zoete Vincent V Schuepbach Thierry T Bovigny Christophe C Chaskar Prasad P Daina Antoine A Röhrig Ute F UF Michielin Olivier O

Journal of computational chemistry 20151112 4

Molecular docking is a computational approach for predicting the most probable position of ligands in the binding sites of macromolecules and constitutes the cornerstone of structure-based computer-aided drug design. Here, we present a new algorithm called Attracting Cavities that allows molecular docking to be performed by simple energy minimizations only. The approach consists in transiently replacing the rough potential energy hypersurface of the protein by a smooth attracting potential drivi ...[more]

PMID: 26558715

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Project description:Calculations of electrostatic potential and solvation free energy of macromolecules are essential for understanding the mechanism of many biological processes. In the classical implicit solvent Poisson-Boltzmann (PB) model, the macromolecule and water are modeled as two-dielectric media with a sharp border. However, the dielectric property of interior cavities and ion-channels is difficult to model realistically in a two-dielectric setting. In fact, the detection of water molecules in a protein cavity remains to be an experimental challenge. This introduces an uncertainty, which affects the subsequent solvation free energy calculation. In order to compensate this uncertainty, a novel super-Gaussian dielectric PB model is introduced in this work, which devices an inhomogeneous dielectric distribution to represent the compactness of atoms and characterizes empty cavities via a gap dielectric value. Moreover, the minimal molecular surface level set function is adopted so that the dielectric profile remains to be smooth when the protein is transferred from water phase to vacuum. An important feature of this new model is that as the order of super-Gaussian function approaches the infinity, the dielectric distribution reduces to a piecewise constant of the two-dielectric model. Mathematically, an effective dielectric constant analysis is introduced in this work to benchmark the dielectric model and select optimal parameter values. Computationally, a pseudo-time alternative direction implicit (ADI) algorithm is utilized for solving the super-Gaussian PB equation, which is found to be unconditionally stable in a smooth dielectric setting. Solvation free energy calculation of a Kirkwood sphere and various proteins is carried out to validate the super-Gaussian model and ADI algorithm. One macromolecule with both water filled and empty cavities is employed to demonstrate how the cavity uncertainty in protein structure can be bypassed through dielectric modeling in biomolecular electrostatic analysis.

Project description:Lipopolysaccharide (LPS) is the major pathogen-associated molecular pattern of Gram-negative bacterial infections, and includes smooth (S-LPS) and rough (R-LPS) chemotypes. Upon activation by LPS through CD14, TLR4/MD-2 heterodimers sequentially induce two waves of intracellular signaling for macrophage activation: the MyD88-dependent pathway from the plasma membrane and, following internalization, the TRIF-dependent pathway from endosomes. We sought to better define the role of scavenger receptors CD36 and CD204/SR-A as accessory LPS receptors that can contribute to pro-inflammatory and microbicidal activation of macrophages. We have found that CD36 differently regulates activation of mouse macrophages by S-LPS versus R-LPS. The ability of CD36 to substitute for CD14 in loading R-LPS, but not S-LPS onto TLR4/MD-2 allows CD14-independent macrophage responses to R-LPS. Conversely, S-LPS, but not R-LPS effectively stimulates CD14 binding to CD36, which favors S-LPS transfer from CD14 onto TLR4/MD-2 under conditions of low CD14 occupancy with S-LPS in serum-free medium. In contrast, in the presence of serum, CD36 reduces S-LPS binding to TLR4/MD-2 and the subsequent MyD88-dependent signaling, by mediating internalization of S-LPS/CD14 complexes. Additionally, CD36 positively regulates activation of TRIF-dependent signaling by both S-LPS and R-LPS, by promoting TLR4/MD-2 endocytosis. In contrast, we have found that SR-A does not function as a S-LPS receptor. Thus, by co-operating with CD14 in both R- and S-LPS loading onto TLR4/MD-2, CD36 can enhance the sensitivity of tissue-resident macrophages in detecting infections by Gram-negative bacteria. However, in later phases, following influx of serum to the infection site, the CD36-mediated negative regulation of MyD88-dependent branch of S-LPS-induced TLR4 signaling might constitute a mechanism to prevent an excessive inflammatory response, while preserving the adjuvant effect of S-LPS for adaptive immunity.

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Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape.

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Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape.

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