Design and applications of reduced point charge models of proteins

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From the formalism given in references [1,2], the smoothed analytical charge density (CD) distribution function of an atom ρa,s(r) can be expressed as:

where s is the smoothing factor and qa is, e.g., the Amber99 [3] atomic charge.
A topological analysis procedure is applied to locate extrema in smoothed CD distribution functions. The approach is applied to the design of amino acid (AA) reduced point charge (RPC) templates (Figure 1).
Figure 1. RPC model of Tyrosin as obtained from a smoothed Amber99-based CD distribution function.
To generate charge values, various fitting conditions are selected, i.e., from either electrostatic Coulomb potential or forces, considering reference grid points located within various distances from the protein atoms, with or without separate treatment of main and side chain charges.
Full protein RPC descriptions are generated through a superposition algorithm of the AA templates onto the protein structure. The program GROMACS [4,5] is used to generate molecular dynamics (MD) trajectories of the solvated proteins modelled using the various RPC models. Point charges that are not located on atoms are considered as virtual sites with a null mass and radius.
Applications are carried out on Ubiquitin systems to assess the RPC models [6-8]. All models involve a partial loss in the protein secondary and lead to a less structured solute solvation shell. Various stable conformations of a protein can be generated more rapidly than with the all-atom point charge representation. The model built by fitting charges on Coulomb forces calculated at grid points ranging between 1.4 and 2.0 times the van der Waals radius of the atoms, with a separate treatment of main chain and side chain charges, appears to best approximate all-atom MD trajectories due to a better approximation of the Coulomb-14 interactions and short-range forces.

[1] Leherte L, Vercauteren DP: Coarse point charge models for proteins from smoothed molecular electrostatic potentials. J Chem Theory Comput 2009, 5:3279-3298.
[2] Leherte L, Vercauteren DP: Charge density distributions derived from smoothed electrostatic potential functions: design of protein reduced point charge models. J Comput-Aided Mol Des 2011, 25 :913-930.
[3] Wang J, Cieplak P, Kollman PA: How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? J Comput Chem 2000, 21:1049-1074.
[4] Hess B, Kutzner C, van der Spoel D, Lindahl E: GROMACS 4:  Algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 2008, 4:435-447.
[5] Pronk S, Páll S, Schulz R, Larsson P, Bjelkmar P, Apostolov R, Shirts MR, Smith JC, Kasson PM, van der Spoel D, Hess B, Lindahl E: GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics 2013, 29:845-854.
[6] Leherte L, Vercauteren DP: Evaluation of reduced point charge models of proteins through Molecular dynamics simulations: Application to the Vps27 UIM-1–Ubiquitin complex. J Molec Graphics Model 2014, 47:44-61.
[7] Leherte L, Vercauteren DP: Comparison of reduced point charge models of proteins: Molecular dynamics simulations of Ubiquitin. Sci China Chem 2014, 57:1340-1354.
[8] Leherte L: Reduced point charge models of proteins: Assessment based on molecular dynamics simulations. Mol Simulat (in press), doi 10.1080/08927022.2015.1044452.

Original languageEnglish
Publication statusPublished - Sept 2015
Event10th European Conference on Computational Chemistry - Fulda, Germany
Duration: 31 Aug 20153 Sept 2015


Symposium10th European Conference on Computational Chemistry


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