Upon binding, proteins undergo conformational changes. These changes often prevent rigid-body docking methods from predicting the 3D structure of a complex from the unbound conformations of its proteins. Handling protein backbone flexibility is a major challenge for docking methodologies, as backbone flexibility adds a huge number of degrees of freedom to the search space, and therefore considerably increases the running time of docking algorithms. Normal mode analysis permits description of protein flexibility as a linear combination of discrete movements (modes). Low-frequency modes usually describe the large-scale conformational changes of the protein. Therefore, many docking methods model backbone flexibility by using only few modes, which have the lowest frequencies. However, studies show that due to molecular interactions, many proteins also undergo local and small-scale conformational changes, which are described by high-frequency normal modes. We presented a new method, FiberDock, for docking refinement which models backbone flexibility by an unlimited number of normal modes. The method iteratively minimizes the structure of the flexible protein along the most relevant modes. The relevance of a mode is calculated according to the correlation between the chemical forces, applied on each atom, and the translation vector of each atom, according to the normal mode. The results show that the method successfully models backbone movements that occur during molecular interactions and considerably improves the accuracy and the ranking of rigid-docking models of protein-protein complexes. Virtual screening is emerging as a productive and cost-effective technology in rational drug design for the identification of novel lead compounds. An important model for virtual screening is the pharmacophore. Pharmacophore is the spatial configuration of essential features that enable a ligand molecule to interact with a specific target receptor. In the absence of a known receptor structure, a pharmacophore can be identified from a set of ligands that have been observed to interact with the target receptor. We presented a novel computational method for pharmacophore detection and virtual screening. The pharmacophore detection module is able to (i) align multiple flexible ligands in a deterministic manner without exhaustive enumeration of the conformational space, (ii) detect subsets of input ligands that may bind to different binding sites or have different binding modes, (iii) address cases where the input ligands have different affinities by defining weighted pharmacophores based on the number of ligands that share them, and (iv) automatically select the most appropriate pharmacophore candidates for virtual screening. The algorithm is highly efficient, allowing a fast exploration of the chemical space by virtual screening of huge compound databases. The performance of PharmaGist was successfully evaluated on a commonly used data set of G-Protein Coupled Receptor alpha1A. Additionally, a large-scale evaluation using the DUD (directory of useful decoys) data set was performed. DUD contains 2950 active ligands for 40 different receptors, with 36 decoy compounds for each active ligand. PharmaGist enrichment rates are comparable with other state-of-the-art tools for virtual screening. The unanimous agreement that cellular processes are (largely) governed by interactions between proteins has led to enormous community efforts culminating in overwhelming information relating to these proteins;to the regulation of their interactions, to the way in which they interact and to the function which is determined by these interactions. These data have been organized in databases and servers. However, to make these really useful, it is essential not only to be aware of these, but in particular to have a working knowledge of which tools to use for a given problem;what are the tool advantages and drawbacks;and no less important how to combine these for a particular goal since usually it is not one tool, but some combination of tool-modules that is needed. We have assembled and organized methods addressing these problems.
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