Guillaume Evrard

The basic reverse Monte Carlo algorithm, as applied primarily for the study of disordered systems, is introduced, using an example of a new reverse Monte Carlo computer code. RMC++ is a new implementation of the RMC algorithm in C++. Its main purpose is to provide the community with a fast, flexible and documented code for RMC simulations, compatible with the rmca distribution. The source code, the documentation and the executable files are made available through the Internet. The flexibility of the code is exemplified by the implementation of a 'molecular move' step in the Metropolis algorithm. This feature, as well as a performance comparison, is illustrated with simulations performed for molecular liquids such as CCl4 and C2Cl4.

DADIMODO is a program for refining atomic models of multidomain proteins or complexes against small-angle X-ray scattering data. Interdomain distance and orientational restraints, such as those derived from NMR measurements, can be included in the optimization process. While domain structures are mainly kept rigid, flexible regions can be user defined. Stepwise generic conformational changes, specified by the user, are applied cyclically in a stochastic optimization algorithm that performs a search in the protein conformation space. The convergence for this genetic algorithm is driven by an adaptable selection pressure. The algorithmic structure guarantees that a physically acceptable full atomic model of the structure is present at all stages of the optimization. A graphical user interface ensures user-friendly handling.

The efficiency of the ligand-building module of ARP/wARP version 6.1 has been assessed through extensive tests on a large variety of protein-ligand complexes from the PDB, as available from the Uppsala Electron Density Server. Ligand building in ARP/wARP involves two main steps: automatic identification of the location of the ligand and the actual construction of its atomic model. The first step is most successful for large ligands. The second step, ligand construction, is more powerful with X-ray data at high resolution and ligands of small to medium size. Both steps are successful for ligands with low to moderate atomic displacement parameters. The results highlight the strengths and weaknesses of both the method of ligand building and the large-scale validation procedure and help to identify means of further improvement.

In reverse Monte Carlo modelling, experimental information (i.e. diffraction data) and a priori information (i.e. constraints introduced in the algorithm) are partly redundant. The extent of this redundancy for ('fixed neighbour') constraints determining the molecular geometry is studied systematically in the typical case of liquid CCl4. Results indicate that data with a very limited momentum transfer range are sufficient for deriving the intermolecular structure of such disordered systems when (intra)molecular geometry and intermolecular distances of closest approach are introduced by the appropriate algorithmic constraints.

The identification and modelling of ligands into macromolecular models is important for understanding molecule's function and for designing inhibitors to modulate its activities. We describe new algorithms for the automated building of ligands into electron density maps in crystal structure determination. Location of the ligand-binding site is achieved by matching numerical shape features describing the ligand to those of density clusters using a "fragmentation-tree" density representation. The ligand molecule is built using two distinct algorithms exploiting free atoms with inter-atomic connectivity and Metropolis-based optimisation of the conformational state of the ligand, producing an ensemble of structures from which the final model is derived. The method was validated on several thousand entries from the Protein Data Bank. In the majority of cases, the ligand-binding site could be correctly located and the ligand model built with a coordinate accuracy of better than 1 Å. We anticipate that the method will be of routine use to anyone modelling ligands, lead compounds or even compound fragments as part of protein functional analyses or drug design efforts.