De novo drug design using multiobjective evolutionary graphs.

Drug discovery and development is a complex, lengthy process, and failure of a candidate molecule can occur as a result of a combination of reasons, such as poor pharmacokinetics, lack of efficacy, or toxicity. Successful drug candidates necessarily represent a compromise between the numerous, sometimes competing objectives so that the benefits to patients outweigh potential drawbacks and risks. De novo drug design involves searching an immense space of feasible, druglike molecules to select those with the highest chances of becoming drugs using computational technology. Traditionally, de novo design has focused on designing molecules satisfying a single objective, such as similarity to a known ligand or an interaction score, and ignored the presence of the multiple objectives required for druglike behavior. Recently, methods have appeared in the literature that attempt to design molecules satisfying multiple predefined objectives and thereby produce candidate solutions with a higher chance of serving as viable drug leads. This paper describes the Multiobjective Evolutionary Graph Algorithm (MEGA), a new multiobjective optimization de novo design algorithmic framework that can be used to design structurally diverse molecules satisfying one or more objectives. The algorithm combines evolutionary techniques with graph-theory to directly manipulate graphs and perform an efficient global search for promising solutions. In the Experimental Section we present results from the application of MEGA for designing molecules that selectively bind to a known pharmaceutical target using the ChillScore interaction score family. The primary constraints applied to the design are based on the identified structure of the protein target and a known ligand currently marketed as a drug. A detailed explanation of the key elements of the specific implementation of the algorithm is given, including the methods for obtaining molecular building blocks, evolving the chemical graphs, and scoring the designed molecules. Our findings demonstrate that MEGA can produce structurally diverse candidate molecules representing a wide range of compromises of the supplied constraints and thus can be used as an "idea generator" to support expert chemists assigned with the task of molecular design.

Automatic determination of reaction mappings and reaction center information. 1. The imaginary transition state energy approach.

Chemical reactions transform the reactant molecules by deleting existing and forming new bonds. The identification of these so-called reacting bonds is important for studying the reaction mechanism and for applications in metabolomics, e.g. for interpreting substrate labeling experiments. Here, we introduce an approach which suggests the simplest possible reaction center at the heavy atom level, with high accuracy. In contrast to current methods the approach is motivated by a simple theoretical model based on a crude approximation of the reaction energetics, and takes the complete reacting system into account. Finally, it recovers all optimal solutions to the problem while removing all symmetry-related, redundant solutions. We apply the method on the complete KEGG database of biochemical reactions, and compare our approach with previous methods. The resulting reaction centers are represented as imaginary transition states, which are molecule-like representations of reaction mechanisms. We provide the statistics of the calculations on the KEGG database and discuss some examples for the different types of alternative solutions found.

Automatic determination of reaction mappings and reaction center information. 2. Validation on a biochemical reaction database.

The correct identification of the reacting bonds and atoms is a prerequisite for the analysis of the reaction mechanism. We have recently developed a method based on the Imaginary Transition State Energy Minimization approach for automatically determining the reaction center information and the atom-atom mapping numbers. We test here the accuracy of this ITSE approach by comparing the predictions of the method against more than 1500 manually annotated reactions from BioPath, a comprehensive database of biochemical reactions. The results show high agreement between manually annotated mappings and computational predictions (98.4%), with significant discrepancies in only 24 cases out of 1542 (1.6%). This result validates both the computational prediction and the database, at the same time, as the results of the former agree with expert knowledge and the latter appears largely self-consistent, and consistent with a simple principle. In 10 of the discrepant cases, simple chemical arguments or independent literature studies support the predicted reaction center. In five reaction instances the differences in the automatically and manually annotated mappings are described in detail. Finally, in approximately 200 cases the algorithm finds alternate reaction centers, which need to be studied on a case by case basis, as the exact choice of the alternative may depend on the enzyme catalyzing the reaction.

Similarity based docking.

We have recently introduced GMA, a highly efficient method for flexible molecular alignment. Here we show how this approach can be used to improve docking accuracy and efficiency, in cases where a complex structure of a ligand with the target protein is known. In cases where a known ligand exists, yet the complex structure is unknown it is possible to make use of the advantages offered by this approach, by combining it with standard ligand docking.

Inverse in silico screening for identification of kinase inhibitor targets.

Protein kinases are clinically relevant, attractive drug targets for cancer. One major problem with kinase inhibitors is broad promiscuity, causing off-target actions and side effects. In silico prediction of targets of a compound would immensely facilitate and accelerate drug development. Using a virtual "inverse" screening approach, where single compounds are docked into protein structures from a database, we identify among known targets of indirubin derivatives phosphoinositide-dependent kinase 1 (PDK1) as a target of one derivative (6BIO) in particular. This prediction is functionally supported by an in vitro kinase assay, inhibition of intracellular phosphorylation of PDK1-substrates, and inhibition of endothelial cell migration, which highly depends on PDK1. Virtual inverse screening combined with biological tests, thus, is proposed as a valuable tool for the drug discovery process and re-examination of already established kinase inhibitors.

On the ease of predicting the thermodynamic properties of beta-cyclodextrin inclusion complexes.

BACKGROUND: In this study we investigated the predictability of three thermodynamic quantities related to complex formation. As a model system we chose the host-guest complexes of beta-cyclodextrin (beta-CD) with different guest molecules. A training dataset comprised of 176 beta-CD guest molecules with experimentally determined thermodynamic quantities was taken from the literature. We compared the performance of three different statistical regression methods - principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression combined with forward feature selection (SVMR/FSS) - with respect to their ability to generate predictive quantitative structure property relationship (QSPR) models for DeltaG degrees, DeltaH degrees and DeltaS degrees on the basis of computed molecular descriptors. RESULTS: We found that SVMR/FFS marginally outperforms PLSR and PCR in the prediction of DeltaG degrees, with PLSR performing slightly better than PCR. PLSR and PCR proved to be more stable in a nested cross-validation protocol. Whereas DeltaG degrees can be predicted in good agreement with experimental values, none of the methods led to comparably good predictive models for DeltaH degrees. In using the methods outlined in this study, we found that DeltaS degrees appears almost unpredictable. In order to understand the differences in the ease of predicting the quantities, we performed a detailed analysis. As a result we can show that free energies are less sensitive (than enthalpy or entropy) to the small structural variations of guest molecules. This property, as well as the lower sensitivity of DeltaG degrees to experimental conditions, are possible explanations for its greater predictability. CONCLUSION: This study shows that the ease of predicting DeltaG degrees cannot be explained by the predictability of either DeltaH degrees or DeltaS degrees. Our analysis suggests that the poor predictability of TDeltaS degrees and, to a lesser extent, DeltaH degrees has to do with a stronger dependence of these quantities on the structural details of the complex and only to a lesser extent on experimental error.

Improved cyclodextrin-based receptors for camptothecin by inverse virtual screening.

We report the computer-aided optimization of a synthetic receptor for a given guest molecule, based on inverse virtual screening of receptor libraries. As an example, a virtual set of beta-cyclodextrin (beta-CD) derivatives was generated as receptor candidates for the anticancer drug camptothecin. We applied the two docking tools AutoDock and GlamDock to generate camptothecin complexes of every candidate receptor. Scoring functions were used to rank all generated complexes. From the 10 % top-ranking candidates nine were selected for experimental validation. They were synthesized by reaction of heptakis-[6-deoxy-6-iodo]-beta-CD with a thiol compound to form the hepta-substituted beta-CDs. The stabilities of the camptothecin complexes obtained from solubility measurements of five of the nine CD derivatives were significantly higher than for any other CD derivative known from literature. The remaining four CD derivatives were insoluble in water. In addition, corresponding mono-substituted CD derivatives were synthesized that also showed improved binding constants. Among them the 9-H-purine derivative was the best, being comparable to the investigated hepta-substituted beta-CDs. Since the measured binding free energies correlated satisfactorily with the calculated scores, the applied scoring functions appeared to be appropriate for the selection of promising candidates for receptor synthesis.

GlamDock: development and validation of a new docking tool on several thousand protein-ligand complexes.

In this study, we present GlamDock, a new docking tool for flexible ligand docking. GlamDock (version 1.0) is based on a simple Monte Carlo with minimization procedure. The main features of the method are the energy function, which is a continuously differentiable empirical potential, and the definition of the search space, which combines internal coordinates for the conformation of the ligand, with a mapping-based description of the rigid body translation and rotation. First, we validate GlamDock on a standard benchmark, a set of 100 protein-ligand complexes, which allows comparative evaluation to existing docking tools. The results on this benchmark show that GlamDock is at least comparable in efficiency and accuracy to the best existing docking tools. The main focus of this work is the validation on the scPDB database of protein-ligand complexes. The size of this data set allows a thorough analysis of the dependencies of docking accuracy on features of the protein-ligand system. In particular, it allows a two-dimensional analysis of the results, which identifies a number of interesting dependencies that are generally lost or even misinterpreted in the one-dimensional approach. The overall result that GlamDock correctly predicts the complex structure in practically half of the cases in the scPDB is important not only for screening ligands against a particular protein but even more so for inverse screening, that is, the identification of the correct targets for a particular ligand.

Flavonoids affect actin functions in cytoplasm and nucleus.

Based on the identification of actin as a target protein for the flavonol quercetin, the binding affinities of quercetin and structurally related flavonoids were determined by flavonoid-dependent quenching of tryptophan fluorescence from actin. Irrespective of differences in the hydroxyl pattern, similar Kd values in the 20 microM range were observed for six flavonoids encompassing members of the flavonol, isoflavone, flavanone, and flavane group. The potential biological relevance of the flavonoid/actin interaction in the cytoplasm and the nucleus was addressed using an actin polymerization and a transcription assay, respectively. In contrast to the similar binding affinities, the flavonoids exert distinct and partially opposing biological effects: although flavonols inhibit actin functions, the structurally related flavane epigallocatechin promotes actin activity in both test systems. Infrared spectroscopic evidence reveals flavonoid-specific conformational changes in actin which may mediate the different biological effects. Docking studies provide models of flavonoid binding to the known small molecule-binding sites in actin. Among these, the mostly hydrophobic tetramethylrhodamine-binding site is a prime candidate for flavonoid binding and rationalizes the high efficiency of quenching of the two closely located fluorescent tryptophans. The experimental and theoretical data consistently indicate the importance of hydrophobic, rather than H-bond-mediated, actin-flavonoid interactions. Depending on the rigidity of the flavonoid structures, different functionally relevant conformational changes are evoked through an induced fit.

Graph-based molecular alignment (GMA).

We describe a combined 2D/3D approach for the superposition of flexible chemical structures, which is based on recent progress in the efficient identification of common subgraphs and a gradient-based torsion space optimization algorithm. The simplicity of the approach is reflected in its generality and computational efficiency: the suggested approach neither requires precalculated statistics on the conformations of the molecules nor does it make simplifying assumptions on the topology of the molecules being compared. Furthermore, graph-based molecular alignment produces alignments that are consistent with the chemistry of the molecules as well as their general structure, as it depends on both the local connectivities between atoms and the overall topology of the molecules. We validate this approach on benchmark sets taken from the literature and show that it leads to good results compared to computationally and algorithmically more involved methods. The results suggest that, for most practical purposes, graph-based molecular alignment is a viable alternative to molecular field alignment with respect to structural superposition and leads to structures of comparable quality in a fraction of the time.

Fully automated flexible docking of ligands into flexible synthetic receptors using forward and inverse docking strategies.

The prediction of the structure of host-guest complexes is one of the most challenging problems in supramolecular chemistry. Usual procedures for docking of ligands into receptors do not take full conformational freedom of the host molecule into account. We describe and apply a new docking approach which performs a conformational sampling of the host and then sequentially docks the ligand into all receptor conformers using the incremental construction technique of the FlexX software platform. The applicability of this approach is validated on a set of host-guest complexes with known crystal structure. Moreover, we demonstrate that due to the interchangeability of the roles of host and guest, the docking process can be inverted. In this inverse docking mode, the receptor molecule is docked around its ligand. For all investigated test cases, the predicted structures are in good agreement with the experiment for both normal (forward) and inverse docking. Since the ligand is often smaller than the receptor and, thus, its conformational space is more restricted, the inverse docking approach leads in most cases to considerable speed-up. By having the choice between two alternative docking directions, the application range of the method is significantly extended. Finally, an important result of this study is the suitability of the simple energy function used here for structure prediction of complexes in organic media.

A simple approach for protein name identification: prospects and limits.

BACKGROUND: Significant parts of biological knowledge are available only as unstructured text in articles of biomedical journals. By automatically identifying gene and gene product (protein) names and mapping these to unique database identifiers, it becomes possible to extract and integrate information from articles and various data sources. We present a simple and efficient approach that identifies gene and protein names in texts and returns database identifiers for matches. It has been evaluated in the recent BioCreAtIvE entity extraction and mention normalization task by an independent jury. METHODS: Our approach is based on the use of synonym lists that map the unique database identifiers for each gene/protein to the different synonym names. For yeast and mouse, synonym lists were used as provided by the organizers who generated them from public model organism databases. The synonym list for fly was generated directly from the corresponding organism database. The lists were then extensively curated in largely automated procedure and matched against MEDLINE abstracts by exact text matching. Rule-based and support vector machine-based post filters were designed and applied to improve precision. RESULTS: Our procedure showed high recall and precision with F-measures of 0.897 for yeast and 0.764/0.773 for mouse in the BioCreAtIvE assessment (Task 1B) and 0.768 for fly in a post-evaluation. CONCLUSION: The results were close to the best over all submissions. Depending on the synonym properties it can be crucial to consider context and to filter out erroneous matches. This is especially important for fly, which has a very challenging nomenclature for the protein name identification task. Here, the support vector machine-based post filter proved to be very effective.

Evaluation of a fast implicit solvent model for molecular dynamics simulations.

A solvation term based on the solvent accessible surface area (SASA) is combined with the CHARMM polar hydrogen force field for the efficient simulation of peptides and small proteins in aqueous solution. Only two atomic solvation parameters are used: one is negative for favoring the direct solvation of polar groups and the other positive for taking into account the hydrophobic effect on apolar groups. To approximate the water screening effects on the intrasolute electrostatic interactions, a distance-dependent dielectric function is used and ionic side chains are neutralized. The use of an analytical approximation of the SASA renders the model extremely efficient (i.e., only about 50% slower than in vacuo simulations). The limitations and range of applicability of the SASA model are assessed by simulations of proteins and structured peptides. For the latter, the present study and results reported elsewhere show that with the SASA model it is possible to sample a significant amount of folding/unfolding transitions, which permit the study of the thermodynamics and kinetics of folding at an atomic level of detail.