Flexx Docking Software Free [REPACK] Download

We present a new software module, FlexX-Scan, for high-throughput, structure-based virtual screening. FlexX-Scan was developed with the aim to further speed up the virtual screening process. Based on the incremental construction docking tool FlexX (Rarey et al., J Mol Biol 1996;261:470-489), a compact descriptor for representing favorable protein interaction spots within the protein binding site has been developed. The descriptor is calculated using special-purpose clustering techniques applied to the usual interaction points created by FlexX. The algorithm automatically detects a small set of interaction spots in the binding site for positioning ligand functional groups. The parametrizations of the base placement and incremental construction algorithms have been adapted to the new interaction model. We tested the software tool on a diverse set of 200 protein-ligand complexes from the protein database (PDB) (Kramer et al., Proteins 1999;37:228-241). On average, the algorithm proposes about 90 interaction spots per binding site compared to about 1000 interaction dots in FlexX. We observe that the docking solutions of FlexX-Scan have a root-mean-square deviation from the crystal structure similar to the deviation of docking solutions of standard FlexX. For further validation we also performed virtual screening experiments for cyclin-dependent kinase 2, thrombin, angiotensin-converting enzyme, and dihydrofolat reductase. In these experiments, we screened a set of 34,000 random compounds and a number of known actives for each target. With FlexX-Scan, we achieved comparable enrichments to standard FlexX, with an averaged computing time of 5-10 s per compound, depending on parametrization.

We report on a test of FLEXX, a fully automatic docking tool for flexible ligands, on a highly diverse data set of 200 protein-ligand complexes from the Protein Data Bank. In total 46.5% of the complexes of the data set can be reproduced by a FLEXX docking solution at rank 1 with an rms deviation (RMSD) from the observed structure of less than 2 A. This rate rises to 70% if one looks at the entire generated solution set. FLEXX produces reliable results for ligands with up to 15 components which can be docked in 80% of the cases with acceptable accuracy. Ligands with more than 15 components tend to generate wrong solutions more often. The average runtime of FLEXX on this test set is 93 seconds per complex on a SUN Ultra-30 workstation. In addition, we report on "cross-docking" experiments, in which several receptor structures of complexes with identical proteins have been used for docking all cocrystallized ligands of these complexes. In most cases, these experiments show that FLEXX can acceptably dock a ligand into a foreign receptor structure. Finally we report on screening runs of ligands out of a library with 556 entries against ten different proteins. In eight cases FLEXX is able to find the original inhibitor within the top 7% of the total library.

Flexx Docking Software Free Download

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The docking accuracy in a rigid-body approach is much greater for bound complexes than uncomplexed molecules (Shoichet and Kuntz 1991). Even though the observed structural changes between the bound and free forms are small, the difference in accuracy implies that the assumption of rigidity is not fully warranted (Totrov and Abagyan 1994). Also, the difference between the near native structures and others far from native cannot be distinguished, even with simple scoring functions such as measures of surface complementarity (Katchalski-Katzir et al. 1992), solvent accessible surface area (SASA) burial, solvation free energy, electrostatic interaction energy, or the total molecular mechanics energy (Shoichet and Kuntz 1991). Hence, the docking procedures were improved by several groups by allowing for receptor and ligand flexibility.

The entropy loss of a flexible ligand in rigid six body degrees of freedom in an anisotropic environment of the receptor and the change in its internal energy upon binding can greatly affect the binding affinity. Introducing local minimization of a molecular-mechanics energy function such as in the CHARMM package yields only limited improvement (Brooks et al. 2009). Consequently, information regarding the binding site location before the docking processes became very important to increase the docking efficiency. There are several cavity detection programs or online servers that can detect putative active sites within proteins, e.g., GRID (Goodford 1985), POCKET (Levitt and Banaszak 1992), SURFNET (Laskowski 1995), PASS (Putative Active Sites with Spheres) (Brady and Stouten 2000), and MMC (mapping macromolecular topography) (Mezei 2003).

Cell-Dock also performs the global scan using the translational and rotational space of two molecules based on surface complementarity and electrostatics. A paramount difference with FTDock is that the value of the grid size is fixed in a number of cells that reflects grid cell resolution and total span in Angstroms (Pons et al. 2012). Furthermore, to reduce the size of molecules from large compound libraries, shape complementarity was introduced between ligand and protein in MS-DOCK to perform efficient multiple conformation rigid-body docking (Sauton et al. 2008). The contact surface between the ligand and the protein is further optimized by a Gaussian shape fitting function in FLOG (Miller et al. 1994), CLIX (Lawrence and Davis 1992), FRED (McGann et al. 2003), and PAS-Dock (Protein Alpha Shape-Dock) (Tndel et al. 2006) to perform rigid body docking.

Monte Carlo methods accept or reject the random changes of the thermodynamic accessible states by using Metropolis criteria (Metropolis and Ulam 1949). The configurations with increase in temperature T will be accepted by slow cooling through so-called simulated annealing (Kirkpatrick et al. 1983). The changes in conformations are quite large, allowing the ligand to cross the energy barriers on the potential energy surface. This technique of conformational searches combined with the potentials of molecular affinity gives an efficient method of substrate docking with known structures (Goodsell and Olson 1990). Along with affinity potentials, distance constraints were added as soft potentials in simulated annealing (Yue 1990).

Further, the algorithms were developed to build ligands directly in the binding site in flexible-docking and design strategy. One of these was the de novo design of peptide inhibitors using a library of low-energy conformations of isolated amino acid residues as building blocks (Moon and Howe 1991). Subsequently, this method was extended to a non-peptide ligand design using functional groups or single atoms using GroupBuild and LEGEND (Nishibata and Itai 1993; Rotstein and Murcko 1993a, b). Goodford introduced the idea of using functional groups (water, methyl group, amine nitrogen, carboxy oxygen, and hydroxyl) as molecular probes to map the binding site of a macromolecule (Goodford 1985). Thus, the energy contour surfaces for the various probes differentiate regions of attraction between the probe and protein. The procedure is well suited to multiple-copy techniques (Miranker and Karplus 1991). The goal of fragment-assembly approaches, pioneered by Lewis and Dean (1989a, b), is to connect the individual molecular fragments into a single viable molecule. The CLIX program attempts to make a pair of favorable interactions in the binding site of the protein with a pair of chemical substitutions (Lawrence and Davis 1992). LUDI places molecular fragments to form hydrogen bonds with the enzyme so that the hydrophobic pockets are filled. These fragments are then linked together with suitable spacers (Bhm 1992). The linked-fragment approach of Verlinde and coworkers are based on shape descriptors (Verlinde et al. 1992). Caflisch and coworkers used MCSS (maximal common substructure search) against HIV protease to map a binding site and constructed peptide inhibitors by building bonds to connect the various minima they found (Caflisch et al. 1993). In HOOK, MCSS is also used in the mapping stage, but the minima are connected by a database of molecular scaffolds for possible connectors (Eisen et al. 1994). FlexX uses a tree-search technique for placing the ligand into the active site, incrementally starting with the base fragment (Rarey et al. 1996).

Further results against 164 targets show that ICM and Glide produce the lowest average RMSDs of 1.08 and 2.37 matching with the native ligands, while GOLD and FlexX fared worse, with RMSDs of 2.80 and 3.98 , respectively. At the RMSD cutoff of 2.0 , ICM and Glide showed success rates of 91 and 63%, respectively, by classifying 149 out of 164 and 104 out of 164 compounds correctly within this threshold. GOLD also performed reasonably well by classifying 91 from 164 (55%), while FlexX performed less well with 70 from 164, a percentage of 42%. ICM and Glide again performed well at the more stringent RMSD cutoff of 1.0 , correctly docking 93 out of 164 and 81 out of 164, leading to success rates of 57 and 49%, respectively. GOLD was successful in 64 out of 164 cases, for a success rate of 39%, and FlexX was successful with a rate of 26% (42 out of 164) (Chen et al. 2006). With eight protein targets, 50% of the ligands were placed well for five targets by at least one program. Indeed, 90% of the ligands could be docked with the correct orientation and 100% could be docked in the correct location for several protein targets (Warren et al. 2006). The RMSD-based evaluations against 116 complexes of 13 types revealed that no docking program was significantly superior to GOLD. Thirteen complexes found solutions with an RMSD of 2 or better only by GOLD, and no solution was found by either AutoDock or DOCK alone. The sizes of the binding sites for the complexes that were successfully solved only by GOLD were widely distributed, from 2253 to 7900 , and represented the various protein types (Onodera et al. 2007).

Comparative docking evaluation of 1 C-crys (A) and 2 N-crys (B). The black bars indicate the percentage of the total poses with RMSD below 2 and the grey bars indicate the percentage of the total poses with RMSDs below 4 . Data used for this presentation are based on Mulliken charges obtained at HF/6-31+G* level for AutoDock and Glide, rigid docking mode for Glide, and BFP of PP with PA1 for FlexX. For Surflex docking, default settings without fragment placement were used for 1 C-crys and PP placement combined with multistart 5 option for 2 N-crys. e24fc04721