Showing papers in "Journal of Computer-aided Molecular Design in 2004"

VEGA--an open platform to develop chemo-bio-informatics applications, using plug-in architecture and script programming.

Abstract: In this paper we present the expandability and flexibility features of the VEGA program (downloadable free of charge at http://www.ddl.unimi.it), for the development of custom applications, using it as a multipurpose graphical environment. VEGA can be customized using both plug-in architecture and script programming. The first is useful to add new features and functions, using homemade routines, written with the VEGA Plug-in Development Kit (SDK). With the second approach it is possible to design scripts in VEGA, using the REBOL language, in order to (1) add new functions or customize existing ones; (2) automate common procedures; and (3) allow network communications, by creating a bridge between VEGA and other applications (or other PCs) through the TCP/IP protocol.

BODIL: a molecular modeling environment for structure-function analysis and drug design

Abstract: BODIL is a molecular modeling environment geared to help the user to quickly identify key features of proteins critical to molecular recognition, especially (1) in drug discovery applications, and (2) to understand the structural basis for function. The program incorporates state-of-the-art graphics, sequence and structural alignment methods, among other capabilities needed in modern structure-function-drug target research. BODIL has a flexible design that allows on-the-fly incorporation of new modules, has intelligent memory management, and fast multi-view graphics. A beta version of BODIL and an accompanying tutorial are available at http://www.abo.fi/fak/mnf/bkf/research/johnson/bodil.html.

Validation of an empirical RNA-ligand scoring function for fast flexible docking using Ribodock.

Abstract: We report the design and validation of a fast empirical function for scoring RNA-ligand interactions, and describe its implementation within RiboDock®, a virtual screening system for automated flexible docking. Building on well-known protein-ligand scoring function foundations, features were added to describe the interactions of common RNA-binding functional groups that were not handled adequately by conventional terms, to disfavour non-complementary polar contacts, and to control non-specific charged interactions. The results of validation experiments against known structures of RNA-ligand complexes compare favourably with previously reported methods. Binding modes were well predicted in most cases and good discrimination was achieved between native and non-native ligands for each binding site, and between native and non-native binding sites for each ligand. Further evidence of the ability of the method to identify true RNA binders is provided by compound selection (`enrichment factor') experiments based around a series of HIV-1 TAR RNA-binding ligands. Significant enrichment in true binders was achieved amongst high scoring docking hits, even when selection was from a library of structurally related, positively charged molecules. Coupled with a semi-automated cavity detection algorithm for identification of putative ligand binding sites, also described here, the method is suitable for the screening of very large databases of molecules against RNA and RNA-protein interfaces, such as those presented by the bacterial ribosome. Abbreviations: ACD – Available Chemicals Directory; AMP – adenosine monophosphate; EF – enrichment factor; FMN – flavin mononucleotide; FRET – fluorescence resonance energy transfer; RMSD – root mean square deviation; TAR – trans-activation response element; Tat – transcriptional activator protein.

3D-QSAR illusions.

Abstract: 3D-QSAR is typically used to construct models (1) to predict activities, (2) to illustrate significant regions, and (3) to provide insight into possible interactions. To the contrary, examples are described herein which make it clear that the predictivity of such models remains elusive, that so-called significant regions are subject to the vagaries of alignment, and that the nature of possible interactions heavily depends on the eye of the beholder. Although great strides have been made in the imaginative use of 3D-descriptors, 3D-QSAR remains largely a retrospective analytical tool. The arbitrary nature of both the alignment paradigm and atom description lends itself to capricious models, which in turn can lead to distorted conclusions. Despite these illusionary pitfalls, predictions can be enhanced when the test set is bounded by the descriptor space represented in the training set. Interpretation of significant interaction regions becomes more meaningful when alignment is constrained by a binding site. Correlations obtained with a variety of atom descriptors suggest choosing useful ones, in particular, in guiding synthetic effort.

Statistical variation in progressive scrambling.

Abstract: The two methods most often used to evaluate the robustness and predictivity of partial least squares (PLS) models are cross-validation and response randomization. Both methods may be overly optimistic for data sets that contain redundant observations, however. The kinds of perturbation analysis widely used for evaluating model stability in the context of ordinary least squares regression are only applicable when the descriptors are independent of each other and errors are independent and normally distributed; neither assumption holds for QSAR in general and for PLS in particular. Progressive scrambling is a novel, nonparametric approach to perturbing models in the response space in a way that does not disturb the underlying covariance structure of the data. Here, we introduce adjustments for two of the characteristic values produced by a progressive scrambling analysis - the deprecated predictivity (Q*2s) and standard error of prediction (SDEPs*) - that correct for the effect of introduced perturbation. We also explore the statistical behavior of the adjusted values (Q*2(0) and SDEP0*) and the sensitivity to perturbation (dq2/dryy'2). It is shown that the three statistics are all robust for stable PLS models, in terms of the stochastic component of their determination and of their variation due to sampling effects involved in training set selection.

Interactive essential dynamics

Abstract: Essential dynamics (ED) is a useful method for analyzing trajectories generated by molecular dynamics (MD), but current tools are awkward to use, limiting the usefulness of the technique. This paper describes a new interactive graphical interface for visualization of ED results, including filtering a trajectory on an arbitrary set of eigenvectors and manipulation of a structure's projection along any eigenvector.

An automated PLS search for biologically relevant QSAR descriptors

Abstract: An automated PLS engine, WB-PLS, was applied to 1632 QSAR series with at least 25 compounds per series extracted from WOMBAT (WOrld of Molecular BioAcTivity). WB-PLS extracts a single Y variable per series, as well as pre-computed X variables from a table. The table contained 2D descriptors, the drug-like MDL 320 keys as implemented in the Mesa A&C Fingerprint module, and in-house generated topological-pharmacophore SMARTS counts and fingerprints. Each descriptor type was treated as a block, with or without scaling. Cross-validation, variable importance on projections (VIP) above 0.8 and q 2⩾0.3 were applied for model significance. Among cross-validation methods, leave-one-in-seven-out (CV7) is a better measure of model significance, compared to leave-one-out (measuring redundancy) and leave-half-out (too restrictive). SMARTS counts overlap with 2D descriptors (having a more quantitative nature), whereas MDL keys overlap with in-house fingerprints (both are more qualitative). The SMARTS counts is the most effective descriptor system, when compared to the other three. At the individual level, size-related descriptors and topological indices (in the 2D property space), and branched SMARTS, aromatic and ring atom types and halogens are found to be most relevant according to the VIP criterion.

Measuring CAMD technique performance: a virtual screening case study in the design of validation experiments.

Abstract: The dynamic nature and comparatively young age of computational chemistry is such that novel algorithms continue to be developed at a rapid pace. Such efforts are often wrought at the expense of extensive experimental validations of said techniques, preventing a deeper understanding of their potential utility and limitations. Here we address this issue for ligand-based virtual screening descriptors through design of validation experiments that better reflect the aims of real world application. Applying the newly defined chemotype enrichment approach, a variety of two- and three-dimensional (2D/3D) similarity descriptors have been compared extensively across data sets from four diverse target types. The inhibitors within said data sets contain molecules exhibiting a wide array of substructure functionality, size and flexibility, permitting descriptor comparison in myriad settings. Relative descriptor performance under these conditions is examined, including results obtained using more typical virtual screening validation experiments. Guidelines for optimal application of said descriptors are also discussed in the context of the results obtained, as is the potential utility of fingerprint filtering.

Tomocomd-Cardd, a novel approach for computer-aided ‘rational’ drug design: I. Theoretical and experimental assessment of a promising method for computational screening and in silico design of new anthelmintic compounds

Abstract: In this work, the TOMOCOMD-CARDD approach has been applied to estimate the anthelmintic activity. Total and local (both atom and atom-type) quadratic indices and linear discriminant analysis were used to obtain a quantitative model that discriminates between anthelmintic and non-anthelmintic drug-like compounds. The obtained model correctly classified 90.37% of compounds in the training set. External validation processes to assess the robustness and predictive power of the obtained model were carried out. The QSAR model correctly classified 88.18% of compounds in this external prediction set. A second model was performed to outline some conclusions about the possible modes of action of anthelmintic drugs. This model permits the correct classification of 94.52% of compounds in the training set, and 80.00% of good global classification in the external prediction set. After that, the developed model was used in virtual in silico screening and several compounds from the Merck Index, Negwer's handbook and Goodman and Gilman were identified by models as anthelmintic. Finally, the experimental assay of one organic chemical (G-1) by an in vivo test coincides fairly well (100%) with model predictions. These results suggest that the proposed method will be a good tool for studying the biological properties of drug candidates during the early state of the drug-development process.

Validation tools for variable subset regression.

Abstract: Variable selection is applied frequently in QSAR research. Since the selection process influences the characteristics of the finally chosen model, thorough validation of the selection technique is very important. Here, a validation protocol is presented briefly and two of the tools which are part of this protocol are introduced in more detail. The first tool, which is based on permutation testing, allows to assess the inflation of internal figures of merit (such as the cross-validated prediction error). The other tool, based on noise addition, can be used to determine the complexity and with it the stability of models generated by variable selection. The obtained statistical information is important in deciding whether or not to trust the predictive abilities of a specific model. The graphical output of the validation tools is easily accessible and provides a reliable impression of model performance. Among others, the tools were employed to study the influence of leave-one-out and leave-multiple-out cross-validation on model characteristics. Here, it was confirmed that leave-multiple-out cross-validation yields more stable models. To study the performance of the entire validation protocol, it was applied to eight different QSAR data sets with default settings. In all cases internal and external model performance was good, indicating that the protocol serves its purpose quite well.

Model of full-length HIV-1 integrase complexed with viral DNA as template for anti-HIV drug design.

Abstract: We report structural models of the full-length integrase enzyme (IN) of the human immunodeficiency virus type 1 (HIV-1) and its complex with viral and human DNA. These were developed by means of molecular modeling techniques using all available experimental evidence, including X-ray crystallographic and NMR structures of portions of the full-length protein. Special emphasis was placed on obtaining a model of the enzyme's active site with the viral DNA apposed to it, based on the hypothesis that such a model would allow structure-based design of inhibitors that retain activity in vivo. This was because bound DNA might be present in vivo after 3'-processing but before strand transfer. These structural models were used to study the potential binding modes of various diketo-acid HIV-1 IN inhibitors (many of them preferentially inhibiting strand transfer) for which no experimentally derived complexed structures are available. The results indicate that the diketo-acid IN inhibitors probably chelate the metal ion in the catalytic site and also prevent the exposure of the 3'-processed end of the viral DNA to human DNA.

Evaluation and application of multiple scoring functions for a virtual screening experiment.

Abstract: In order to identify novel chemical classes of factor Xa inhibitors, five scoring functions (FlexX, DOCK, GOLD, ChemScore and PMF) were engaged to evaluate the multiple docking poses generated by FlexX. The compound collection was composed of confirmed potent factor Xa inhibitors and a subset of the LeadQuest® screening compound library. Except for PMF the other four scoring functions succeeded in reproducing the crystal complex (PDB code: 1FAX). During virtual screening the highest hit rate (80%) was demonstrated by FlexX at an energy cutoff of −40 kJ/mol, which is about 40-fold over random screening (2.06%). Limited results suggest that presenting more poses of a single molecule to the scoring functions could deteriorate their enrichment factors. A series of promising scaffolds with favorable binding scores was retrieved from LeadQuest. Consensus scoring by pair-wise intersection failed to enrich the hit rate yielded by single scorings (i.e. FlexX). We note that reported successes of consensus scoring in hit rate enrichment could be artificial because their comparisons were based on a selected subset of single scoring and a markedly reduced subset of double or triple scoring. The findings presented in this report are based upon a single biological system and support further studies.

Variable selection and model validation of 2D and 3D molecular descriptors

Abstract: We have found that molecular shape and electrostatics, in conjunction with 2D structural fingerprints, are important variables in discriminating classes of active and inactive compounds. The subject of this paper is how to explore the selection of these variables and identify their relative importance in quantitative structure-activity relationships (QSAR) analysis. We show the use of these variables in a form of similarity searching with respect to a crystal structure of a known bound ligand. This analysis is then validated through k-fold cross-validation of enrichments via several common classifiers. Additionally, we show an effective methodology using the variables in hypothesis generation; namely, when the crystal structure of a bound ligand is not known.

Generation of multiple pharmacophore hypotheses using multiobjective optimisation techniques.

Abstract: Pharmacophore methods provide a way of establishing a structure activity relationship for a series of known active ligands. Often, there are several plausible hypotheses that could explain the same set of ligands and, in such cases, it is important that the chemist is presented with alternatives that can be tested with different synthetic compounds. Existing pharmacophore methods involve either generating an ensemble of conformers and considering each conformer of each ligand in turn or exploring conformational space on-the-fly. The ensemble methods tend to produce a large number of hypotheses and require considerable effort to analyse the results, whereas methods that vary conformation on-the-fly typically generate a single solution that represents one possible hypothesis, even though several might exist. We describe a new method for generating multiple pharmacophore hypotheses with full conformational flexibility being explored on-the-fly. The method is based on multiobjective evolutionary algorithm techniques and is designed to search for an ensemble of diverse yet plausible overlays which can then be presented to the chemist for further investigation.

Model based on GRID-derived descriptors for estimating CYP3A4 enzyme stability of potential drug candidates.

Abstract: A number of computational approaches are being proposed for an early optimization of ADME (absorption, distribution, metabolism and excretion) properties to increase the success rate in drug discovery. The present study describes the development of an in silico model able to estimate, from the three-dimensional structure of a molecule, the stability of a compound with respect to the human cytochrome P450 (CYP) 3A4 enzyme activity. Stability data were obtained by measuring the amount of unchanged compound remaining after a standardized incubation with human cDNA-expressed CYP3A4. The computational method transforms the three-dimensional molecular interaction fields (MIFs) generated from the molecular structure into descriptors (VolSurf and Almond procedures). The descriptors were correlated to the experimental metabolic stability classes by a partial least squares discriminant procedure. The model was trained using a set of 1800 compounds from the Pharmacia collection and was validated using two test sets: the first one including 825 compounds from the Pharmacia collection and the second one consisting of 20 known drugs. This model correctly predicted 75% of the first and 85% of the second test set and showed a precision above 86% to correctly select metabolically stable compounds. The model appears a valuable tool in the design of virtual libraries to bias the selection toward more stable compounds.

The effect of tightly bound water molecules on the structural interpretation of ligand-derived pharmacophore models

Abstract: The importance of the consideration of water molecules in the structural interpretation of ligand-derived pharmacophore models is explored. We compare and combine results from recently introduced methods for bound-water molecule identification in protein binding sites and ligand-superposition-based pharmacophore derivation, for the interpretation of ligand-derived pharmacophore models. In the analysis of thymidine kinase (HSV-1) and poly (ADP-ribose) polymerase (PARP), the concurrent application of both methods leads to an agreement in the prediction of tightly bound water molecules as key pharmacophoric points in the binding site of these proteins. This agreement has implications for approaching binding site analysis and consensus drug design, as it highlights how pharmacophore-based models of binding sites can include interaction features not only with protein groups but also with bound water molecules.

Three-dimensional quantitative structure-activity and structure-selectivity relationships of dihydrofolate reductase inhibitors.

Abstract: Three-dimensional quantitative structure-activity relationship (3D-QSAR) modelling using comparative molecular similarity indices analysis (CoMSIA) was applied to a series of 406 structurally diverse dihydrofolate reductase (DHFR) inhibitors from Pneumocystis carinii (pc) and rat liver (rl). X-ray crystal structures of three inhibitors bound to pcDHFR were used for defining the alignment rule. For pcDHFR, a QSAR model containing 6 components was selected using leave-10%-out cross-validation (n= 240, q2 = 0.65), while a 4-component model was selected for rlDHFR (n= 237, q2 = 0.63); both include steric, electrostatic and hydrophobic contributions. The models were validated using a large test set, designed to maximise its diversity and to verify the predictive accuracy of models for extrapolation. The pcDHFR model has r2 = 0.60 and mean absolute error (MAE) = 0.57 for the test set after removing 4 outliers, and the rlDHFR model has r2 = 0.60 and MAE = 0.69 after removing 4 test set outliers. In addition, classification models predicting selectivity for pcDHFR over rlDHFR were developed using soft independent modelling by class analogy (SIMCA), with a selectivity ratio of 2 (IC50,rlDHFR/ IC50,pcDHFR) used for delimiting classes. A 5-component model including steric and electrostatic contributions has cross-validated and test set classification rates of 0.67 and 0.68 for selective inhibitors, and 0.85 and 0.72 for unselective inhibitors. The predictive accuracy of models, together with the identification of important contributions in QSAR and classification models, offer the possibility of designing potent selective inhibitors and estimating their activity prior to synthesis.

Human topoisomerase I poisoning: docking protoberberines into a structure-based binding site model.

Abstract: Using the X-ray crystal structure of the human topoisomerase I (top1) – DNA cleavable complex and the Sybyl software package, we have developed a general model for the ternary cleavable complex formed with four protoberberine alkaloids differing in the substitution on the terminal phenyl rings and covering a broad range of the top1-poisoning activities. This model has the drug intercalated with its planar chromophore between the −1 and +1 base pairs flanking the cleavage site, with the nonplanar portion pointing into the minor groove. The ternary complexes were geometry-optimized and relative interaction energies, computed by using the Tripos force field, were found to rank in correct order the biological potency of the compounds; in addition, the model is also consistent with the top1-poisoning inactivity of berberine, a major prototype of the protoberberine alkaloids. The model might serve as a rational basis for elaboration of the most active compound as a lead structure, in order to develop more potent top1 poisons as next generation anti-cancer drugs.

Linear and nonlinear functions on modeling of aqueous solubility of organic compounds by two structure representation methods.

Abstract: Several quantitative models for the prediction of aqueous solubility of organic compounds were developed based on a diverse dataset with 2084 compounds by using multi-linear regression analysis and backpropagation neural networks. The compounds were described by two different structure representation methods: (1) with 18 topological descriptors; and (2) with 32 radial distribution function codes representing the 3D structure of a molecule and eight additional descriptors. The dataset was divided into a training and a test set based on Kohonen's self-organizing neural network. Good prediction results were obtained for backpropagation neural network models: with 18 topological descriptors, for the 936 compounds in the test set, a correlation coefficient of 0.92, and a standard deviation of 0.62 were achieved; with 3D descriptors, for the 866 compounds in the test set, a correlation coefficient of 0.90, and a standard deviation of 0.73 were achieved. The models were also tested by using another dataset, and the relationship of the two datasets was examined by Kohonen's self-organizing neural network.

Homology modeling, force field design, and free energy simulation studies to optimize the activities of histone deacetylase inhibitors.

Abstract: As an effort to develop therapeutics for cancer treatments, a number of effective histone deacetylase inhibitors with structural diversity have been discovered. To gain insight into optimizing the activity of an identified lead compound, a computational protocol sequentially involving homology modeling, docking experiments, molecular dynamics simulation, and free energy perturbation calculations was applied for rationalizing the relative activities of known histone deacetylase inhibitors. With the newly developed force field parameters for the coordination environment of the catalytic zinc ion in hand, the computational strategy proved to be successful in predicting the rank orders for 12 derivatives of three hydroxamate-based inhibitor scaffolds with indole amide, pyrrole, and sulfonamide moieties. The results showed that the free energy of an inhibitor in aqueous solution should be an important factor in determining the binding free energy. Hence, in order to enhance the inhibitory activity by adding or substituting a chemical group, the increased stabilization in solution due to the structural changes must be overcome by a stronger enzyme-inhibitor interaction. It was also found that to optimize inhibitor potency, the hydrophobic head of an inhibitor should be elongated or enlarged so that it can interact with Pro29 and His28 that are components of the flexible loop at the top of the active site.

QSAR and classification models of a novel series of COX-2 selective inhibitors: 1, 5-diarylimidazoles based on support vector machines

Abstract: The support vector machine, which is a novel algorithm from the machine learning community, was used to develop quantitation and classification models which can be used as a potential screening mechanism for a novel series of COX-2 selective inhibitors. Each compound was represented by calculated structural descriptors that encode constitutional, topological, geometrical, electrostatic, and quantum-chemical features. The heuristic method was then used to search the descriptor space and select the descriptors responsible for activity. Quantitative modelling results in a nonlinear, seven-descriptor model based on SVMs with root mean-square errors of 0.107 and 0.136 for training and prediction sets, respectively. The best classification results are found using SVMs: the accuracy for training and test sets is 91.2% and 88.2%, respectively. This paper proposes a new and effective method for drug design and screening.

Docking studies on NSAID/COX-2 isozyme complexes using contact statistics analysis.

Abstract: The selective inhibition of COX-2 isozymes should lead to a new generation of NSAIDs with significantly reduced side effects; e.g. celecoxib (Celebrex®) and rofecoxib (Vioxx®). To obtain inhibitors with higher selectivity it has become essential to gain additional insight into the details of the interactions between COX isozymes and NSAIDs. Although X-ray structures of COX-2 complexed with a small number of ligands are available, experimental data are missing for two well-known selective COX-2 inhibitors (rofecoxib and nimesulide) and docking results reported are controversial. We use a combination of a traditional docking procedure with a new computational tool (Contact Statistics analysis) that identifies the best orientation among a number of solutions to shed some light on this topic.

Interactions of peptide mimics of hyaluronic acid with the receptor for hyaluronan mediated motility (RHAMM)

Abstract: Using the hyaluronic acid (HA) binding region of the receptor for hyaluronan-mediated motility (RHAMM) as a model, a molecular perspective for peptide mimicry of the natural ligand was established by comparing the interaction sites of HA and unnatural peptide-ligands to RHAMM. This was accomplished by obtaining a series of octapeptide-ligands through screening experiments that bound to the HA binding domains of RHAMM (amino acids 517-576) and could be displaced by HA. These molecules were computationally docked onto a three-dimensional NMR based model of RHAMM. The NMR model showed that RHAMM(517-576) was a set of three helices, two of which contained the HA binding domains (HABDs) flanking a central groove. The structure was stabilized by hydrophobic interactions from four pairs of Val and Ile side chains extending into the groove. The presence of solvent exposed, positively charged side chains spaced 11 A apart matched the spacing of negative charges on HA. Docking experiments using flexible natural and artificial ligands demonstrated that HA and peptide-mimetics preferentially bound to the second helix that contains HABD-2. Three salt bridges between HA carboxylates and Lys548, Lys553 and Lys560 and two hydrophobic interactions involving Val538 and Val559 were predicted to stabilize the RHAMM-HA complex. The high affinity peptides and HA utilized the same charged residues, with additional contacts to other basic residues. However, hydrophobic contacts do not contribute to affinity for peptide ligand-RHAMM complexes. These results offer insight into how selectivity is achieved in the binding of HA to RHAMM, and how peptide competitors may compete for binding with HA on a single hyaladherin.

Tuning of hydrogen bond strength using substituents on phenol and aniline: a possible ligand design strategy.

Abstract: Using Density Functional Theory, the hydrogen bonding energy is calculated for the interaction of phenol and aniline with four model compounds representing the protein backbone and various amino acid site chain residues. The models are methanol, protonated methylamine, formaldehyde and acetate anion. The H-bond energies for the uncharged species are ∼2.5kcalmol−1, whereas the charged model compounds bind with much higher energies of ∼20kcalmol−1. The effect of para-substitution on the hydrogen bond energies is determined. Substitution has little effect on the H-bond energy of the neutral complexes (<2kcalmol−1), but for the positively and negatively charged systems substitution drastically alters the binding energies, e.g., 14.3kcalmol−1 for para-NO2. In the context of protein–ligand binding, relatively small changes in binding energy can cause large changes in affinity due to their exponential relationship. This means that for –NO2 an enormous change of 10 orders of magnitude for the affinity constant is predicted. These calculations allow prediction of H-bonds, using different substituents, in order to fine-tune and optimize ligand–protein interactions in the search for drug candidates.

Prediction of inter-residue contacts map based on genetic algorithm optimized radial basis function neural network and binary input encoding scheme.

Abstract: Inter-residue contacts map prediction is one of the most important intermediate steps to the protein folding problem. In this paper, we focus on the problem of protein inter-residue contacts map prediction based on neural network technique. Firstly, we use a genetic algorithm (GA) to optimize the radial basis function widths and hidden centers of a radial basis function neural network (RBFNN), then a novel binary encoding scheme is employed to train the network for the purpose of learning and predicting the inter-residue contacts patterns of protein sequences got from the protein data bank (PDB). The experimental evidence indicates the utility of our proposed encoding strategy and GA optimized RBFNN. Moreover, the simulation results demonstrate that the network got a better performance for these proteins, whose residue length falls into the area of (100, 300), and the predicted accuracy with a contact threshold of 7 A scores higher than the other 3 values with 5, 6, and 8 A .

ProteinShop: a tool for interactive protein manipulation and steering.

Abstract: We describe ProteinShop, a new visualization tool that streamlines and simplifies the process of determining optimal protein folds. ProteinShop may be used at different stages of a protein structure prediction process. First, it can create protein configurations containing secondary structures specified by the user. Second, it can interactively manipulate protein fragments to achieve desired folds by adjusting the dihedral angles of selected coil regions using an Inverse Kinematics method. Last, it serves as a visual framework to monitor and steer a protein structure prediction process that may be running on a remote machine. ProteinShop was used to create initial configurations for a protein structure prediction method developed by a team that competed in CASP5. ProteinShop's use accelerated the process of generating initial configurations, reducing the time required from days to hours. This paper describes the structure of ProteinShop and discusses its main features.

Docking and scoring of metallo-β-lactamases inhibitors

Abstract: The performance of the AutoDock, GOLD and FlexX docking programs was evaluated for docking of dicarboxylic acid inhibitors into metallo-β-lactamases (MBLs). GOLD provided the best overall performance, with RMSDs between experimental and docked structures of 1.8–2.6 A and a good correlation between the experimentally determined MBL-inhibitor affinities and the GOLD scores. GOLD was selected for a test including a broad spectrum of inhibitors for which experimental MBL-inhibitor binding affinities are available. This study revealed that (1) for most compound classes (dicarboxylic acids, tetrazoles, sulfonylhydrazones, and peptide-like compounds) there is a good correlation between the experimentally determined MBL-inhibitor affinities and the GOLD scores, (2) the correlation only holds within a given class, that is, scores of compounds from different classes cannot be directly compared, (3) for some compound classes (e.g. small sulphur compounds) there is no direct correlation between the experimentally determined MBL-inhibitor affinities and the GOLD scores. Using partial least squares methods, a model with R2=0.82 and Q2=0.78 for the training set was obtained based on the GOLD score and descriptors associated with binding of the IMP-1 inhibitors to the enzyme. The external Q2 for the test set is 0.73. This final model for prediction of IMP-1 MBL-inhibitor affinity handled all known classes of MBL-inhibitors, except small sulphur compounds.

GRIND-derived pharmacophore model for a series of α-tropanyl derivative ligands of the sigma-2 receptor*

Abstract: A pharmacophore model for the sigma-2 receptor was derived using GRIND (GRid INdependent Descriptors) descriptors arising from a 3D-level procedure whose main prerogative is that it does not require ligand alignment. PLS models for sigma-2 affinity (sigma-2 model: r2=0.83, q2=0.63) and sigma-1/sigma-2 selectivity (r2=0.72, q2=0.46) were derived using a series of α-tropanyl derivatives. The models provide pictures of the virtual receptor site (VRS) significant enough to attain a qualitative pharmacophoric representation of the sigma receptor. They give the internal geometrical relationships within two hydrophobic areas (hydrophobic-1 and -2) and a H-bond donor receptor region with which ligands establish non-covalent bonds.

Quantitative structure-activity relationships of mutagenic activity from quantum topological descriptors: triazenes and halogenated hydroxyfuranones (mutagen-X) derivatives

Abstract: The mutagenic activity of 23 triazenes and, in a different set, of 24 halogenated hydroxyfuranones (MX derivatives) is quantitatively related to new features of contemporary molecular wave functions. Nowadays affordable computers are powerful enough to rapidly generate geometry-optimised ab initio wave functions at HF/3-21G*, HF/6-31G* and B3LYP/6-311+G(2d,p) level for all molecules. The bonds of a common molecular skeleton are described by their ab initio bond lengths and local properties provided by the theory of quantum chemical topology (QCT). The chemometric analysis involves two types: one to generate a statistically validated quantitative model, and one to isolate the active center. In the former a genetic algorithm (GA) selects bond descriptors in order to optimise the cross-validation error, q2, followed by a full partial least squares (PLS) analysis, which also yields randomisation statistics. In the latter type principal components (PCs) are constructed from the original bond descriptors and their variables important to the projection (VIPs) are plotted in a histogram. This analysis suggests a preferred mechanistic pathway for the initial hydroxylation of the triazenes, an issue that has remained ambiguous so far. In the case of the hydroxyfuranones the proposed method aids the elucidation of a mechanistic ambivalence.

The de novo design of median molecules within a property range of interest

Abstract: In this paper an application is presented of the median molecule workflow to the de novo design of novel molecular entities with a property profile of interest. Median molecules are structures that are optimised to be similar to a set of existing molecules of interest as an approach for lead exploration and hopping. An overview of this workflow is provided together with an example of an instance using the similarity to camphor and menthol as objectives. The methodology of the experiments is defined and the workflow is applied to designing novel molecules for two physical property datasets: mean molecular polarisability and aqueous solubility. This paper concludes with a discussion of the characteristics of this method.