Improving database enrichment through ensemble docking

While it may seem intuitive that using an ensemble of multiple conformations of a receptor in structure-based virtual screening experiments would necessarily yield improved enrichment of actives relative to using just a single receptor, it turns out that at least in the p38 MAP kinase model system studied here, a very large majority of all possible ensembles do not yield improved enrichment of actives. However, there are combinations of receptor structures that do lead to improved enrichment results. We present here a method to select the ensembles that produce the best enrichments that does not rely on knowledge of active compounds or sophisticated analyses of the 3D receptor structures. In the system studied here, the small fraction of ensembles of up to 3 receptors that do yield good enrichments of actives were identified by selecting ensembles that have the best mean GlideScore for the top 1% of the docked ligands in a database screen of actives and drug-like "decoy" ligands. Ensembles of two receptors identified using this mean GlideScore metric generally outperform single receptors, while ensembles of three receptors identified using this metric consistently give optimal enrichment factors in which, for example, 40% of the known actives outrank all the other ligands in the database.     

Topological shape and size of peptides: identification of potential allele specific helper T cell antigenic sites

A database of primary sequences of 28 immunogenic peptides, known to elicit T cell response, derived from five different haplotypes was compiled to identify allele specific helper T cell antigenic sites using a rule based graph-theoretical method. The prediction was based on the identification of allele specific patterns in the form of "topological shape and size" present in the peptides. Indices computed from weighted connected graph models of amino acid side chains and peptides were used in this purpose. The system was trained by 10 Ad and 10 non-Ad restricted peptide sequences, assigned actives and inactives, respectively, chosen randomly from the database, and four Ad and four non-Ad restricted sequences were kept as test peptides. This allowed the system to learn about "topological shape and size" specific for Ad restricted peptides from the differences, if any, they had with the inactive peptides in that respect. The system made 100% correct prediction for the training set peptides and misclassified only one inactive peptide of the test set. The system also identified crucial residues for lambda repressor 12-24 and insulin A-chains. This identification also shows that activity related/crucial residues could be located at varying distances from the peptide terminals. To our knowledge, the method is unique of its kind in the literature and may find application in the rational design of synthetic vaccines and other peptides of immunological importance.     

Definition of new pharmacophores for nonpeptide antagonists of human urotensin-II. Comparison with the 3D-structure of human urotensin-II and URP

Starting from nonpeptide agonists and antagonists of human urotensin-II (hU-II), several pharmacophores were designed and compared to the structure of hU-II. NMR and dynamic studies were realized on hU-II and urotensin-II-related peptide to check the conformation flexibilities of these peptides and the relationships between their potential 3D structures and the pharmacophores. In parallel, a virtual screening was carried out, leading to the discovery of six new derivatives with micromolar affinities. This last result shows the interest of these pharmacophores for the discovery of new ligands.     

ALADDIN: An integrated tool for computer-assisted molecular design and pharmacophore recognition from geometric,steric, and substructure searching of three-dimensional molecular structures

Summary ALADDIN is a computer program for the design or recognition of compounds that meet geometric, steric, and substructural criteria. ALADDIN searches a database of three-dimensional structures, marks atoms that meet substructural criteria, evaluates geometric criteria, and prepares a number of files that are input for molecular modification and coordinate generation as well as for molecular graphics. Properties calculated from the three-dimensional structure are described by either properties calculated from the molecule itself or from the molecule as compared to a reference molecule and associated surfaces. ALADDIN was used to design analogues to probe a bioactive conformation of a small molecule and a peptide, to test alternative superposition rules for receptor mapping of the D2 dopamine receptor, to recognize unexpected D2 dopamine agonist activity of existing compounds, and to design compounds to fit a binding site on a protein of known structure. We have found that series designed by ALADDIN show much more subtle variation in shape than do those designed by traditional methods and that compounds can be designed to be very close matches to the objective.     

Analysis of data fusion methods in virtual screening: similarity and group fusion

In a recent companion paper we have related the operation of simple data fusion rules used in virtual screening to a multiple integral formalism. In this paper we extend these ideas to the analysis of data fusion methods applied to real data. We examine several cases of similarity fusion using different coefficients and different representations and consider the reasons for positive or negative results in terms of the similarity distributions. Results are obtained using the SUM-, MAX- MIN-, and CombMNZ-fusion rules. We also develop a customized fusion rule, which provides an estimate of the optimal possible result for fusing multiple searches of a specific database; this shows that similarity fusion can, in principle, achieve retrieval enhancements even if this is not achieved in practice with current fusion rules. The methods are extended to analyze the comparatively successful results of group fusion with multiple actives, and we provide a rationale for the observed superiority of the MAX-rule over the SUM-rule in this context.     

Scaffold hopping using clique detection applied to reduced graphs

Similarity-based methods for virtual screening are widely used. However, conventional searching using 2D chemical fingerprints or 2D graphs may retrieve only compounds which are structurally very similar to the original target molecule. Of particular current interest then is scaffold hopping, that is, the ability to identify molecules that belong to different chemical series but which could form the same interactions with a receptor. Reduced graphs provide summary representations of chemical structures and, therefore, offer the potential to retrieve compounds that are similar in terms of their gross features rather than at the atom-bond level. Using only a fingerprint representation of such graphs, we have previously shown that actives retrieved were more diverse than those found using Daylight fingerprints. Maximum common substructures give an intuitively reasonable view of the similarity between two molecules. However, their calculation using graph-matching techniques is too time-consuming for use in practical similarity searching in larger data sets. In this work, we exploit the low cardinality of the reduced graph in graph-based similarity searching. We reinterpret the reduced graph as a fully connected graph using the bond-distance information of the original graph. We describe searches, using both the maximum common induced subgraph and maximum common edge subgraph formulations, on the fully connected reduced graphs and compare the results with those obtained using both conventional chemical and reduced graph fingerprints. We show that graph matching using fully connected reduced graphs is an effective retrieval method and that the actives retrieved are likely to be topologically different from those retrieved using conventional 2D methods.     

Rapid shape-based ligand alignment and virtual screening method based on atom/feature-pair similarities and volume overlap scoring

Shape-based methods for aligning and scoring ligands have proven to be valuable in the field of computer-aided drug design. Here, we describe a new shape-based flexible ligand superposition and virtual screening method, Phase Shape, which is shown to rapidly produce accurate 3D ligand alignments and efficiently enrich actives in virtual screening. We describe the methodology, which is based on the principle of atom distribution triplets to rapidly define trial alignments, followed by refinement of top alignments to maximize the volume overlap. The method can be run in a shape-only mode or it can include atom types or pharmacophore feature encoding, the latter consistently producing the best results for database screening. We apply Phase Shape to flexibly align molecules that bind to the same target and show that the method consistently produces correct alignments when compared with crystal structures. We then illustrate the effectiveness of the method for identifying active compounds in virtual screening of eleven diverse targets. Multiple parameters are explored, including atom typing, query structure conformation, and the database conformer generation protocol. We show that Phase Shape performs well in database screening calculations when compared with other shape-based methods using a common set of actives and decoys from the literature.     

Virtual screening using protein-ligand docking: avoiding artificial enrichment

This study addresses a number of topical issues around the use of protein-ligand docking in virtual screening. We show that, for the validation of such methods, it is key to use focused libraries (containing compounds with one-dimensional properties, similar to the actives), rather than "random" or "drug-like" libraries to test the actives against. We also show that, to obtain good enrichments, the docking program needs to produce reliable binding modes. We demonstrate how pharmacophores can be used to guide the dockings and improve enrichments, and we compare the performance of three consensus-ranking protocols against ranking based on individual scoring functions. Finally, we show that protein-ligand docking can be an effective aid in the screening for weak, fragment-like binders, which has rapidly become a popular strategy for hit identification. All results presented are based on carefully constructed virtual screening experiments against four targets, using the protein-ligand docking program GOLD.     

Naïve Bayes classification using 2D pharmacophore feature triplet vectors

A na?ve Bayes classifier, employed in conjunction with 2D pharmacophore feature triplet vectors describing the molecules, is presented and validated. Molecules are described using a vector where each element in the vector contains the number of times a particular triplet of atom-based features separated by a set of topological distances occurs. Using the feature triplet vectors it is possible to generate na?ve Bayes classifiers that predict whether molecules are likely to be active against a given target (or family of targets). Two retrospective validation experiments were performed using a range of actives from WOMBAT, the Prous Integrity database, and the Arena screening library. The performance of the classifiers was evaluated using enrichment curves, enrichment factors, and the BEDROC metric. The classifiers were found to give significant enrichments for the various test sets.     

Drug-like index: a new approach to measure drug-like compounds and their diversity

Combinatorial organic synthesis (combinatorial chemistry or CC) and ultrahigh-throughput screening (UHTS) are speeding up drug discovery by increasing capacity for making and screening large numbers of compounds. However, a key problem is to select the smaller set of "representative" compounds from a virtual library to make or screen. Our approach is to select drug-like as well as structurally diverse compounds. The compounds, which are not very drug-like, are less taken into account or excluded even if they contribute to the diversity of the collection. Hence, the first step in the compound selection is to rank compounds in drug-like "degree". To quantify the drug-like "degree", drug-like index (DLI) is introduced in this paper. A compound's DLI is calculated based upon the knowledge derived from known drugs selected from Comprehensive Medicinal Chemistry (CMC) database. The paper describes the way of this knowledge base is formed and the procedure for selecting drug-like compounds.     

Data shaving: a focused screening approach

The number of compounds available for evaluation as part of the drug discovery process continues to increase. These compounds may exist physically or be stored electronically allowing screening by either actual or virtual means. This growing number of compounds has generated an increasing need for effective strategies to direct screening efforts. Initial efforts toward this goal led to the development of methods to select diverse sets of compounds for screening, methods to cluster actives into related groups of compounds, and tools to select compounds similar to actives of interest for further screening. In this work we extend these earlier efforts to exploit information about inactive compounds to help make rational decisions about which sets of compounds to include as part of a continuing screening campaign, or as part of a focused follow-up effort. This method uses the information from inactive compounds to "shave" off or deprioritize compounds similar to inactives from further consideration. This methodology can be used in two ways: first, to provide a rational means of deciding when sufficient compounds containing certain structural features have been tested and second as a tool to enhance similarity searching around known actives. Similarity searching is improved by deprioritizing compounds predicted to be inactive, due to the presence of structural features associated with inactivity.     

Systematic mutational analysis of an ubiquitin ligase (MDM2)-binding peptide: computational studies

To understand the importance of amino acids that comprise the peptide PMI (p53-MDM2/MDMX inhibitor), a p53-mimicking peptide with high affinity for the ubiquitin ligase MDM2, computational alanine scanning has been carried out using various protocols. This approach is very useful for identifying regions of a peptide that can be mutated to yield peptides that bind to their targets with higher affinities. Computational alanine scanning is a very useful technique that involves mutating each amino acid of the peptide in its complex with its target (MDM2 in the current study) to alanine, running short simulations on the mutated complex and computing the difference in interaction energies between the mutant peptides and the target protein (MDM2 in the current study) relative to the interaction energy of the original (wild-type) peptide and the target protein (MDM2 in the current study). We find that running multiple short simulations yield values of computed binding affinities (enthalpies) that are similar to those obtained from a long simulation and are well correlated with the trends in the data available from experiments that used Surface Plasmon Resonance to obtain dissociation constants. The p53-mimicking peptides contain three amino acids (F19, W23 and L26) that are major determinants of the interactions between the peptides and MDM2 and form an essential motif. We find in the current study that the trends amongst the contributions to experimental binding affinities of the hydrophobic residues F19, W23 and L26 are the best reproduced in all the computational protocols examined here. This study suggests that running such short simulations may provide a rapid method to redesign peptides to obtain high-affinity variants against a target protein. We further observe that modelling an extended conformation at the C-terminus of the helical PMI peptides, in accord with the conformation of the p53-peptide complexed to MDM2, reproduces the trends seen amongst the experimental affinities of the peptides that carry the alanine mutations at their C-termini. This suggests that some of the mutant peptides possibly interconvert between helical and extended states and can bind to MDM2 in either conformation. This novel feature, not obvious from the crystallographic data, if factored into modelling protocols, may yield novel high-affinity peptides. Our findings suggest that such protocols may enable rapid investigations of at least certain types of amino acid mutations, notably from large to small amino acids.     

Peptide and protein identification by matrix-assisted laser desorption ionization (MALDI) and MALDI-post-source decay time-of-flight mass spectrometry

The potential of matrix-assisted laser desorption ionization (MALDI) and MALDI-post-source decay (PSD) time-of-flight mass spectrometry for the characterization of peptides and proteins is discussed. Recent instrumental developments provide for levels of sensitivity and accuracy that make these techniques major analytical tools for proteome analysis. New software developments employing protein database searches have greatly enhanced the fields of application of MALDI-PSD. Peptides and proteins can be easily identified even if only a partial sequence information is determined. Derivatization procedures have been optimized for MALDI-PSD to increase the structural information and to obtain a complete peptide sequence even in critical cases. They are fast, simple and can be performed on target. MALDI-PSD is also a very powerful tool to characterize or elucidate post-translational or chemically induced modifications. In association with database searches, proteins issued from electrophoretic gels can be identified after specific enzymatic cleavages and peptide mapping.     

New methods for ligand-based virtual screening: use of data fusion and machine learning to enhance the effectiveness of similarity searching

Similarity searching using a single bioactive reference structure is a well-established technique for accessing chemical structure databases. This paper describes two extensions of the basic approach. First, we discuss the use of group fusion to combine the results of similarity searches when multiple reference structures are available. We demonstrate that this technique is notably more effective than conventional similarity searching in scaffold-hopping searches for structurally diverse sets of active molecules; conversely, the technique will do little to improve the search performance if the actives are structurally homogeneous. Second, we make the assumption that the nearest neighbors resulting from a similarity search, using a single bioactive reference structure, are also active and use this assumption to implement approximate forms of group fusion, substructural analysis, and binary kernel discrimination. This approach, called turbo similarity searching, is notably more effective than conventional similarity searching.     

Temperature selectivity effects in reversed-phase liquid chromatography due to conformation differences between helical and non-helical peptides

In order to characterize the effect of temperature on the retention behaviour and selectivity of separation of polypeptides and proteins in reversed-phase high-performance liquid chromatography (RP-HPLC), the chromatographic properties of four series of peptides, with different peptide conformations, have been studied as a function of temperature (5-80 degrees C). The secondary structure of model peptides was based on either the amphipathic alpha-helical peptide sequence Ac-EAEKAAKEX(D/L)EKAAKEAEK-amide, (position X being in the centre of the hydrophobic face of the alpha-helix), or the random coil peptide sequence Ac-X(D/L)LGAKGAGVG-amide, where position X is substituted by the 19 L- or D-amino acids and glycine. We have shown that the helical peptide analogues exhibited a greater effect of varying temperature on elution behaviour compared to the random coil peptide analogues, due to the unfolding of alpha-helical structure with the increase of temperature during RP-HPLC. In addition, temperature generally produced different effects on the separations of peptides with different L- or D-amino acid substitutions within the groups of helical or non-helical peptides. The results demonstrate that variations in temperature can be used to effect significant changes in selectivity among the peptide analogues despite their very high degree of sequence homology. Our results also suggest that a temperature-based approach to RP-HPLC can be used to distinguish varying amino acid substitutions at the same site of the peptide sequence. We believe that the peptide mixtures presented here provide a good model for studying temperature effects on selectivity due to conformational differences of peptides, both for the rational development of peptide separation optimization protocols and a probe to distinguish between peptide conformations.     

Can one derive the conformational preference of a beta-peptide from its CD spectrum?

CD spectroscopy is often used to elucidate the secondary structure of peptides built from non-natural amino acids such as beta-amino acids. The interpretation of such CD spectra is not always unambiguous. Here, we present a case where two beta-hexapeptides, a dimethyl-beta-hexapeptide indicated as DM-BHP (A) and its nonmethylated analogue indicated as BHP (B), exhibit similar CD spectra, whereas they are expected to differ in secondary structure. The structural properties of both peptides were studied by molecular dynamics simulation, and from the resulting trajectories, the corresponding CD spectra were calculated. Starting from a fully extended conformation, BHP is observed to form a 3(14)-helix, while DM-BHP remains unfolded. However, even though these two peptides hardly share any conformations, their calculated CD spectra are alike and show the same features as the experimentally measured ones. Our results imply that a particular CD pattern can be induced by spatially different structures, which makes it difficult to derive the conformational preference of a peptide from its CD spectrum alone. To gain more insight into the relationship between the preferred conformation of a peptide and its CD spectrum, more accurate methods to calculate the CD spectrum for a given conformation are required.