The small-molecule topology generator PRODRG is described, which takes input from existing coordinates or various two-dimensional formats and automatically generates coordinates and molecular topologies suitable for X-ray refinement of protein–ligand complexes. Test results are described for automatic generation of topologies followed by energy minimization for a subset of compounds from the Cambridge Structural Database, which shows that, within the limits of the empirical GROMOS87 force field used, structures with good geometries are generated. X-ray refinement in X-­PLOR/CNS, REFMAC and SHELX using PRODRG-generated topologies produces results comparable to refinement with topologies from the standard libraries. However, tests with distorted starting coordinates show that PRODRG topologies perform better, both in terms of ligand geometry and of crystallographic R factors.

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PRODRG: a tool for high-throughput crystallography of protein–ligand complexes

Real-world observable physical and chemical characteristics are increasingly being calculated from the 3D structures of biomolecules. Methods for calculating pK(a) values, binding constants of ligands, and changes in protein stability are readily available, but often the limiting step in computational biology is the conversion of PDB structures into formats ready for use with biomolecular simulation software. The continued sophistication and integration of biomolecular simulation methods for systems- and genome-wide studies requires a fast, robust, physically realistic and standardized protocol for preparing macromolecular structures for biophysical algorithms. As described previously, the PDB2PQR web server addresses this need for electrostatic field calculations (Dolinsky et al., Nucleic Acids Research, 32, W665-W667, 2004). Here we report the significantly expanded PDB2PQR that includes the following features: robust standalone command line support, improved pK(a) estimation via the PROPKA framework, ligand parameterization via PEOE_PB charge methodology, expanded set of force fields and easily incorporated user-defined parameters via XML input files, and improvement of atom addition and optimization code. These features are available through a new web interface (http://pdb2pqr.sourceforge.net/), which offers users a wide range of options for PDB file conversion, modification and parameterization.

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PDB2PQR: expanding and upgrading automated preparation of biomolecular structures for molecular simulations

Structural Basis of the Drug-Binding Specificity of Human Serum Albumin.

The drug discovery process has been deeply transformed recently by the use of computational ligand-based or structure-based methods, helping the lead compounds identification and optimization, and finally the delivery of new drug candidates more quickly and at lower cost. Structure-based computational methods for drug discovery mainly involve ligand-protein docking and rapid binding free energy estimation, both of which require force field parameterization for many drug candidates. Here, we present a fast force field generation tool, called SwissParam, able to generate, for arbitrary small organic molecule, topologies, and parameters based on the Merck molecular force field, but in a functional form that is compatible with the CHARMM force field. Output files can be used with CHARMM or GROMACS. The topologies and parameters generated by SwissParam are used by the docking software EADock2 and EADock DSS to describe the small molecules to be docked, whereas the protein is described by the CHARMM force field, and allow them to reach success rates ranging from 56 to 78%. We have also developed a rapid binding free energy estimation approach, using SwissParam for ligands and CHARMM22/27 for proteins, which requires only a short minimization to reproduce the experimental binding free energy of 214 ligand-protein complexes involving 62 different proteins, with a standard error of 2.0 kcal mol(-1), and a correlation coefficient of 0.74. Together, these results demonstrate the relevance of using SwissParam topologies and parameters to describe small organic molecules in computer-aided drug design applications, together with a CHARMM22/27 description of the target protein. SwissParam is available free of charge for academic users at www.swissparam.ch.

SwissParam: a fast force field generation tool for small organic molecules.

One of the conclusions drawn at the CASP4 meeting in Asilomar was that applying various force fields during refinement of template-based models tends to move predictions in the wrong direction, away from the experimentally determined coordinates. We have derived an all-atom force field aimed at protein and nucleotide optimization in vacuo (NOVA), which has been specifically designed to avoid this problem. NOVA resembles common molecular dynamics force fields but has been automatically parameterized with two major goals: (i) not to make high resolution X-ray structures worse and (ii) to improve homology models built by WHAT IF. Force-field parameters were not required to be physically correct; instead, they were optimized with random Monte Carlo moves in force-field parameter space, each one evaluated by simulated annealing runs of a 50-protein optimization set. Errors inherent to the approximate force-field equation could thus be canceled by errors in force-field parameters. Compared with the optimization set, the force field did equally well on an independent validation set and is shown to move in silico models closer to reality. It can be applied to modeling applications as well as X-ray and NMR structure refinement. A new method to assign force-field parameters based on molecular trees is also presented. A NOVA server is freely accessible at http://www.yasara.com/servers

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Increasing the precision of comparative models with YASARA NOVA--a self-parameterizing force field.

A software package is described that operates on small molecules observed in the PDB collection of protein structures. Molecular topology files for many molecular modeling programs can be generated automatically. The three-dimensional coordinates of small molecules can be converted to molecular descriptor strings that encode them uniquely in order to enable small-molecule recognition, despite high variability in atom and molecule nomenclature. From this descriptor a plausible 3D structure can be regenerated using energy minimisation. Alternatively, an ensemble of structures can be generated using a distance-geometry-based algorithm.

M. Hendlich

Papers

PRODRG, a program for generating molecular topologies and unique molecular descriptors from coordinates of small molecules.