An integrated approach to knowledge-driven structure-based virtual screening.

In many practical applications of structure-based virtual screening (VS) ligands are already known. This circumstance requires that the obtained hits need to satisfy initial made expectations i.e., they have to fulfill a predefined binding pattern and/or lie within a predefined physico-chemical property range. Based on the RApid Index-based Screening Engine (RAISE) approach, we introduce CRAISE-a user-controllable structure-based VS method. It efficiently realizes pharmacophore-guided protein-ligand docking to assess the library content but thereby concentrates only on molecules that have a chance to fulfill the given binding pattern. In order to focus only on hits satisfying given molecular properties, library profiles can be utilized to simultaneously filter compounds. CRAISE was evaluated on a range of strict to rather relaxed hypotheses with respect to its capability to guide binding-mode predictions and VS runs. The results reveal insights into a guided VS process. If a pharmacophore model is chosen appropriately, a binding mode below 2 Å is successfully reproduced for 85% of well-prepared structures, enrichment is increased up to median AUC of 73%, and the selectivity of the screening process is significantly enhanced leading up to seven times accelerated runtimes. In general, CRAISE supports a versatile structure-based VS approach allowing to assess hypotheses about putative ligands on a large scale.

Facing the challenges of structure-based target prediction by inverse virtual screening.

Computational target prediction for bioactive compounds is a promising field in assessing off-target effects. Structure-based methods not only predict off-targets, but, simultaneously, binding modes, which are essential for understanding the mode of action and rationally designing selective compounds. Here, we highlight the current open challenges of computational target prediction methods based on protein structures and show why inverse screening rather than sequential pairwise protein-ligand docking methods are needed. A new inverse screening method based on triangle descriptors is introduced: iRAISE (inverse Rapid Index-based Screening Engine). A Scoring Cascade considering the reference ligand as well as the ligand and active site coverage is applied to overcome interprotein scoring noise of common protein-ligand scoring functions. Furthermore, a statistical evaluation of a score cutoff for each individual protein pocket is used. The ranking and binding mode prediction capabilities are evaluated on different datasets and compared to inverse docking and pharmacophore-based methods. On the Astex Diverse Set, iRAISE ranks more than 35% of the targets to the first position and predicts more than 80% of the binding modes with a root-mean-square deviation (RMSD) accuracy of <2.0 Å. With a median computing time of 5 s per protein, large amounts of protein structures can be screened rapidly. On a test set with 7915 protein structures and 117 query ligands, iRAISE predicts the first true positive in a ranked list among the top eight ranks (median), i.e., among 0.28% of the targets.

Searching for recursively defined generic chemical patterns in nonenumerated fragment spaces.

Retrieving molecules with specific structural features is a fundamental requirement of today's molecular database technologies. Estimates claim the chemical space relevant for drug discovery to be around 106° molecules. This figure is many orders of magnitude larger than the amount of molecules conventional databases retain today and will store in the future. An elegant description of such a large chemical space is provided by the concept of fragment spaces. A fragment space comprises fragments that are molecules with open valences and describes rules how to connect these fragments to products. Due to the combinatorial nature of fragment spaces, a complete enumeration of its products is intractable. We present an algorithm to search fragment spaces for generic chemical patterns as present in the SMARTS chemical pattern language. Our method allows specification of the chemical surrounding of an atom in a query and, therefore, enables a chemically intuitive search. During the search, the costly enumeration of products is avoided. The result is a fragment space that exactly describes all possible molecules that contain the user-defined pattern. We evaluated the algorithm in three different drug development use-cases and performed a large scale statistical analysis with 738 SMARTS patterns on three public available fragment spaces. Our results show the ability of the algorithm to explore the chemical space around known active molecules, to analyze fragment spaces for the presence of likely toxic molecules, and to identify complex macromolecular structures under additional structural constraints. By searching the fragment space in its nonenumerated form, spaces covering up to 10¹9 molecules can be examined in times ranging between 47 s and 19 min depending on the complexity of the query pattern.

Fast protein binding site comparison via an index-based screening technology.

We present TrixP, a new index-based method for fast protein binding site comparison and function prediction. TrixP determines binding site similarities based on the comparison of descriptors that encode pharmacophoric and spatial features. Therefore, it adopts the efficient core components of TrixX, a structure-based virtual screening technology for large compound libraries. TrixP expands this technology by new components in order to allow a screening of protein libraries. TrixP accounts for the inherent flexibility of proteins employing a partial shape matching routine. After the identification of structures with matching pharmacophoric features and geometric shape, TrixP superimposes the binding sites and, finally, assesses their similarity according to the fit of pharmacophoric properties. TrixP is able to find analogies between closely and distantly related binding sites. Recovery rates of 81.8% for similar binding site pairs, assisted by rejecting rates of 99.5% for dissimilar pairs on a test data set containing 1331 pairs, confirm this ability. TrixP exclusively identifies members of the same protein family on top ranking positions out of a library consisting of 9802 binding sites. Furthermore, 30 predicted kinase binding sites can almost perfectly be classified into their known subfamilies.

In Pursuit of Fully Flexible Protein-Ligand Docking: Modeling the Bilateral Mechanism of Binding.

Modern structure-based drug design aims at accounting for the intrinsic flexibility of therapeutic relevant targets. Over the last few years a considerable amount of docking approaches that encounter this challenging problem has emerged. Here we provide the readership with an overview of established methods for fully flexible protein-ligand docking and current developments in the field. All methods are based on one of two fundamental models which describe the dynamic behavior of proteins upon ligand binding. Methods for ensemble docking (ED) model the protein conformational change before the ligand is placed, whereas induced-fit docking (IFD) optimizes the protein structure afterwards. A third category of docking approaches is formed by recent approaches that follow both concepts. This categorization allows to comprehensively discover strengths and weaknesses of the individual processes and to extract information for their applicability in real world docking scenarios.