Computational protocol: Inhibitors of Helicobacter pylori Protease HtrA Found by ‘Virtual Ligand’ Screening Combat Bacterial Invasion of Epithelia

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Protocol publication

[…] The virtual ligand was calculated in four steps:The protonation state of the target structure was determined with MOE Protonate3D (MOE version 2007.09 The Molecular Operating Environment, Chemical Computing Group Inc., Montreal, Canada).Potential ligand binding sites were predicted by PocketPicker , . In brief, PocketPicker uses a geometric approach to identify those nodes of a grid (1 Å spacing placed around the protein), which are buried in clefts of the protein surface. These nodes are clustered to disjunct sets using a calculated buriedness value. Each set of nodes is assumed to represent the volume and the shape of a potential ligand binding site.One or more pocket models calculated in the previous step were used as the input for the further processing. The set of residues including a non-hydrogen atom with a minimal distance to one of the nodes of the respective model was calculated. This set is assumed to be the set of interacting pocket residues. The program iterates over all atoms of the set and all nodes of the pocket model and checks for each node/atom pair if one of the rules given in is satisfied. For rules 1 and 2 this was done by calculating the distance d of the optimal position of an interaction partner of the atom and the pocket node under observation (Eq. 1).(1)Dcalc and Acalc are the calculated distance and angle values between the points required by the respective rule and Dopt and Aopt the optimal values given by the rule. The value of d should be zero; since the distribution of the pocket nodes is discrete a tolerance of 0.9 Å was allowed. This value is close to half the maximal distance of two nodes, which is given by (31/2)/2 for the PocketPicker grid, and ensures that at least one node satisfies the rule if the interaction points into the space defined by the pocket model. For rule 3 and 4, the Euclidian distance between the points under investigation was compared to the optimum value (tolerance: 0.5 Å). The coordinates of the corresponding pocket nodes satisfying a rule were stored in separate sets for each interaction type.The given rules were taken from the de novo design program LUDI , and represent a subset of the original LUDI rules. Aromatic carbon atoms were treated as aliphatic/lipophilic.The program LIQUID was used for clustering the nodes in the sets of each interaction type. A local feature density (LFD) was used to determine if a node belongs to a cluster. Using principal component analysis, LIQUID calculates a trivariate Gaussian distribution (trivG) for each cluster that represents so-called ‘fuzzy’ potential pharmacophore points (fPPP). The set of the fPPPs for all interaction types was used to calculate a 120-dimensional correlation vector, the ‘virtual ligand’ (Eq. 2).(2)A and B are interaction types under investigation; d is one of twenty distance intervals with a width of 1 Å (from 0 to 20 Å); i and j are fPPPs of types A or B, respectively.The whole algorithm was implemented in the programming language Java using the Chemistry Development Kit (CDK) . […]

Pipeline specifications

Software tools PocketPicker, WebCDK
Applications Drug design, Protein interaction analysis
Organisms Helicobacter pylori, Homo sapiens, Bacteria
Diseases Bacterial Infections, Neoplasms, Stomach Neoplasms