SuperStar: comparison of CSD and PDB-based interaction fields as a basis for the prediction of protein-ligand interactions

Molecular Interaction Fields Describing Halogen Bond Formable Areas on Protein Surfaces

Molecular interaction fields (MIFs) are three-dimensional interaction maps that describe the intermolecular interactions expected to be formed around target molecules. In this paper, a method for the fast computation of MIFs using the approximation functions of quantum mechanics-level MIFs of small model molecules is proposed. MIF functions of N-methylacetamide with chlorobenzene, bromobenzene, and iodobenzene probes were precisely approximated and used to calculate the MIFs on protein surfaces. This method appropriately reproduced halogen-bond-formable areas around the ligand-binding sites of proteins, where halogen bond formation was suggested in a previous study.

Expanding FTMap for Fragment-Based Identification of Pharmacophore Regions in Ligand Binding Sites

The knowledge of ligand binding hot spots and of the important interactions within such hot spots is crucial for the design of lead compounds in the early stages of structure-based drug discovery. The computational solvent mapping server FTMap can reliably identify binding hot spots as consensus clusters, free energy minima that bind a variety of organic probe molecules. However, in its current implementation, FTMap provides limited information on regions within the hot spots that tend to interact with specific pharmacophoric features of potential ligands. E-FTMap is a new server that expands on the original FTMap protocol. E-FTMap uses 119 organic probes, rather than the 16 in the original FTMap, to exhaustively map binding sites, and identifies pharmacophore features as atomic consensus sites where similar chemical groups bind. We validate E-FTMap against a set of 109 experimentally derived structures of fragment-lead pairs, finding that highly ranked pharmacophore features overlap with the corresponding atoms in both fragments and lead compounds. Additionally, comparisons of mapping results to ensembles of bound ligands reveal that pharmacophores generated with E-FTMap tend to sample highly conserved protein-ligand interactions. E-FTMap is available as a web server at https://eftmap.bu.edu.

Similarities and Differences between Crystal and Enzyme Environmental Effects on the Electron Density of Drug Molecules

The crystal interaction density is generally assumed to be a suitable measure of the polarization of a low-molecular weight ligand inside an enzyme, but this approximation has seldomly been tested and has never been quantified before. In this study, we compare the crystal interaction density and the interaction electrostatic potential for a model compound of loxistatin acid (E64c) with those inside cathepsin B, in solution, and in vacuum. We apply QM/MM calculations and experimental quantum crystallography to show that the crystal interaction density is indeed very similar to the enzyme interaction density. Less than 0.1 e are shifted between these two environments in total. However, this difference has non-negligible consequences for derived properties.

Advances in the Treatment of Explicit Water Molecules in Docking and Binding Free Energy Calculations

Background:

The inclusion of direct effects mediated by water during the ligandreceptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions.

Objective:

In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered.

Results:

Here, we analyse the software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules.

Conclusions:

Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon.

Using more than 801 296 small-molecule crystal structures to aid in protein structure refinement and analysis

The Cambridge Structural Database (CSD) is the worldwide resource for the dissemination of all published three-dimensional structures of small-molecule organic and metal-organic compounds. This paper briefly describes how this collection of crystal structures can be used en masse in the context of macromolecular crystallography. Examples highlight how the CSD and associated software aid protein-ligand complex validation, and show how the CSD could be further used in the generation of geometrical restraints for protein structure refinement.

The use of small-molecule structures to complement protein-ligand crystal structures in drug discovery

Many ligand-discovery stories tell of the use of structures of protein-ligand complexes, but the contribution of structural chemistry is such a core part of finding and improving ligands that it is often overlooked. More than 800 000 crystal structures are available to the community through the Cambridge Structural Database (CSD). Individually, these structures can be of tremendous value and the collection of crystal structures is even more helpful. This article provides examples of how small-molecule crystal structures have been used to complement those of protein-ligand complexes to address challenges ranging from affinity, selectivity and bioavailability though to solubility.

Comprehensive classification and diversity assessment of atomic contacts in protein-small ligand interactions

Elucidating the molecular mechanisms of selective ligand recognition by proteins is a long-standing problem in drug discovery. Rapid increase in the availability of three-dimensional protein structural data indicates that a data-driven approach for finding the rules that govern protein-ligand interactions is increasingly attractive. However, this approach is not straightforward because of the complexity of molecular interactions and our inadequate understanding of the diversity of molecular interactions that occur during ligand recognition. Thus, we aimed to provide a comprehensive classification of the spatial arrangements of ligand atoms based on the local coordinates of each interacting "protein fragment" consisting of three atoms with covalent bonds in each amino acid. We used a pattern recognition technique based on the Gaussian mixture model and found 13,519 patterns in the spatial arrangements of interacting ligand atoms, each of which was described as a Gaussian function of the local coordinates. Some typical well-known interaction patterns such as hydrogen bonds were ubiquitous in several hundred protein families, whereas others were only observed in a few specific protein families. After removing protein sequence redundancy from the data set, we found that 63.4% of ligand atoms interacted via one or more interaction patterns and that 25.7% of ligand atoms interacted without patterns, whereas the remainder had no direct interactions. The top 3115 major patterns included 90% of the interacting pairs of residues and ligand atoms with patterns, while the top 6229 included all of them.

A PRIORI CONTACT PREFERENCES IN MOLECULAR RECOGNITION

A molecular interaction library modeling favorable non-bonded interactions between atoms and molecular fragments is considered. In this paper, we represent the structure of the interaction library by a network diagram, which demonstrates that the underlying prediction model obtained for a molecular fragment is multi-layered. We clustered the molecular fragments into four groups by analyzing the pairwise distances between the molecular fragments. The distances are represented as an unrooted tree, in which the molecular fragments fall into four groups according to their function. For each fragment group, we modeled a group-specific a priori distribution with a Dirichlet distribution. The group-specific Dirichlet distributions enable us to derive a large population of similar molecular fragments that vary only in their contact preferences. Bayes' theorem then leads to a population distribution of the posterior probability vectors referred to as a "Dickey–Savage"-density. Two known methods for approximating multivariate integrals are applied to obtain marginal distributions of the Dickey–Savage density. The results of the numerical integration methods are compared with the simulated marginal distributions. By studying interactions between the protein structure of cyclohydrolase and its ligand guanosine-5′-triphosphate, we show that the marginal distributions of the posterior probabilities are more informative than the corresponding point estimates.

Preparation and refinement of model protein-ligand complexes

The formation of ligand-protein complexes are critical for the correct functioning of a cell. The prediction of these interactions is important for our understanding of how the cell works and for the development of new drug molecules. Homology modeling is a method for predicting the structure of a protein based on a crystal structure template. Once a model of the protein is complete, a ligand-docking algorithm predicts the ligand-protein model interaction by searching for the best steric and energetically favorable fit. A refinement of the ligand-binding pocket improves the predicted interactions by considering the flexible nature of the ligand-binding pocket. In this chapter, we describe, from first principles, methods to identify and prepare the ligand-binding pocket in a protein model, to dock the ligand, and refine the resulting complex.

Ligand-binding site prediction of proteins based on known fragment-fragment interactions

Motivation: The identification of putative ligand-binding sites on proteins is important for the prediction of protein function. Knowledge-based approaches using structure databases have become interesting, because of the recent increase in structural information. Approaches using binding motif information are particularly effective. However, they can only be applied to well-known ligands that frequently appear in the structure databases.

Results: We have developed a new method for predicting the binding sites of chemically diverse ligands, by using information about the interactions between fragments. The selection of the fragment size is important. If the fragments are too small, then the patterns derived from the binding motifs cannot be used, since they are many-body interactions, while using larger fragments limits the application to well-known ligands. In our method, we used the main and side chains for proteins, and three successive atoms for ligands, as fragments. After superposition of the fragments, our method builds the conformations of ligands and predicts the binding sites. As a result, our method could accurately predict the binding sites of chemically diverse ligands, even though the Protein Data Bank currently contains a large number of nucleotides. Moreover, a further evaluation for the unbound forms of proteins revealed that our building up procedure was robust to conformational changes induced by ligand binding.

Availability: Our method, named ‘BUMBLE’, is available at http://bumble.hgc.jp/

Contact:kasahara@cb.k.u-tokyo.ac.jp

Supplementary information:Supplementary Material is available at Bioinformatics online.

Environmental effects on charge densities of biologically active molecules: do molecule crystal environments indeed approximate protein surroundings?

In the present paper, we investigate whether crystal and enzyme environments influence the electron density (ED) of active compounds in a similar manner. This supposition is essential for high-resolution X-ray studies, which use the EDs obtained from crystals of the pure active compound as approximations for the ED of the active compound in its complex with the target enzyme. The EDs of such complexes determine the molecular recognition process between the targeted enzyme and active compound and are, hence, extremely useful tools for rational drug design. The approximation of such EDs by data obtained from crystals of the pure active compound is needed since high-resolution X-ray experiments of the target-ligand complexes are still extremely demanding. Quantum mechanical/molecular mechanical (QM/MM) and pure QM calculations are employed to determine the EDs of two inhibitors, the reversible trans-4-(aminomethyl)cyclohexane-1-carboxylic acid (AMCHA) and the irreversible E64c in four different environments (the enzyme-inhibitor complex, crystals of the pure compounds, a continuum solvation model, and the gas phase). Our investigation shows that the environment inside of the crystal of the pure active compound generally influences the ED of an active compound in a very similar way as the enzyme surrounding in the complex between the active compound and target enzyme. However, this does not hold any more if the geometrical arrangement of the inhibitor in the enzyme differs significantly from that in the crystal. While EDs computed for gas-phase environments deviate strongly from those in crystal and protein surroundings, polar solvent environments provide rather similar electron distributions. Thus, such continuum solvation models are very well suited to compute density databases which are to be employed for the determination of the ED of macromolecules.

Minimal pharmacophoric elements and fragment hopping, an approach directed at molecular diversity and isozyme selectivity. Design of selective neuronal nitric oxide synthase inhibitors

Fragment hopping, a new fragment-based approach for de novo inhibitor design focusing on ligand diversity and isozyme selectivity, is described. The core of this approach is the derivation of the minimal pharmacophoric element for each pharmacophore. Sites for both ligand binding and isozyme selectivity are considered in deriving the minimal pharmacophoric elements. Five general-purpose libraries are established: the basic fragment library, the bioisostere library, the rules for metabolic stability, the toxicophore library, and the side chain library. These libraries are employed to generate focused fragment libraries to match the minimal pharmacophoric elements for each pharmacophore and then to link the fragment to the desired molecule. This method was successfully applied to neuronal nitric oxide synthase (nNOS), which is implicated in stroke and neurodegenerative diseases. Starting with the nitroarginine-containing dipeptide inhibitors we developed previously, a small organic molecule with a totally different chemical structure was designed, which showed nanomolar nNOS inhibitory potency and more than 1000-fold nNOS selectivity. The crystallographic analysis confirms that the small organic molecule with a constrained conformation can exactly mimic the mode of action of the dipeptide nNOS inhibitors. Therefore, a new peptidomimetic strategy, referred to as fragment hopping, which creates small organic molecules that mimic the biological function of peptides by a pharmacophore-driven strategy for fragment-based de novo design, has been established as a new type of fragment-based inhibitor design. As an open system, the newly established approach efficiently incorporates the concept of early "ADME/Tox" considerations and provides a basic platform for medicinal chemistry-driven efforts.

Classification and comparison of ligand-binding sites derived from grid-mapped knowledge-based potentials

We describe the application of knowledge-based potentials implemented in the MOE program to compare the ligand-binding sites of several proteins. The binding probabilities for a polar and a hydrophobic probe are calculated on a grid to allow easy comparison of binding sites of superimposed related proteins. The method is fast and simple enough to simultaneously use structural information of multiple proteins of a target family. The method can be used to rapidly cluster proteins into subfamilies according to the similarity of hydrophobic and polar fields of their ligand-binding sites. Regions of the binding site which are common within a protein family can be identified and analysed for the design of family-targeted libraries or those which differ for improvement of ligand selectivity. The field-based hierarchical clustering is demonstrated for three protein families: the ligand-binding domains of nuclear receptors, the ATP-binding sites of protein kinases and the substrate binding sites of proteases. More detailed comparisons are presented for serine proteases of the chymotrypsin family, for the peroxisome proliferator-activated receptor subfamily of nuclear receptors and for progesterone and androgen receptor. The results are in good accordance with structure-based analysis and highlight important differences of the binding sites, which have been also described in the literature.

Virtual screening for inhibitors of human aldose reductase

The inhibition of aldose reductase (AR) provides an interesting strategy to prevent the complications of chronic diabetes. Although a large number of different AR inhibitors are known, very few of these compounds exhibit sufficient efficacy in clinical trials. We performed a virtual screening based on the ultrahigh resolution crystal structure of the inhibitor IDD594 in complex with human AR. AR operates on a large scale of structurally different substrates. To achieve this pronounced promiscuity, the enzyme can adapt rather flexibly to its substrates. Likewise, it has a similar adaptability for the binding of inhibitors. We applied a protocol of consecutive hierarchical filters to search the Available Chemicals Directory. In the first selection step, putative ligands were chosen that exhibit functional groups to anchor the anion-binding pocket of AR. Subsequently, a pharmacophore model based on the binding geometry of IDD594 and the mapping of the binding pocket in terms of putative "hot spots" of binding was applied as a second consecutive filter. In a third and final filtering step, the remaining candidate molecules were flexibly docked into the binding pocket of IDD594 with FlexX and ranked according to their estimated DrugScore values. Out of 206 compounds selected by this search and complemented by a cluster analysis and visual inspection, 9 compounds were selected and subjected to biological testing. Of these, 6 compounds showed IC50 values in the micromolar range. According to the proposed binding mode, the two inhibitors BTB02809 (IC50 = 2.4 +/- 0.5 microM) and JFD00882 (IC50 = 4.1 +/- 1.0 microM) both place a nitro group into the hydrophobic specificity pocket of human AR in an orientation coinciding with the position of the bromine atom of IDD594. The interaction of this Br with Thr113 has been identified as a key feature that is responsible for selectivity enhancement.

A Bayesian molecular interaction library

We describe a library of molecular fragments designed to model and predict non-bonded interactions between atoms. We apply the Bayesian approach, whereby prior knowledge and uncertainty of the mathematical model are incorporated into the estimated model and its parameters. The molecular interaction data are strengthened by narrowing the atom classification to 14 atom types, focusing on independent molecular contacts that lie within a short cutoff distance, and symmetrizing the interaction data for the molecular fragments. Furthermore, the location of atoms in contact with a molecular fragment are modeled by Gaussian mixture densities whose maximum a posteriori estimates are obtained by applying a version of the expectation-maximization algorithm that incorporates hyperparameters for the components of the Gaussian mixtures. A routine is introduced providing the hyperparameters and the initial values of the parameters of the Gaussian mixture densities. A model selection criterion, based on the concept of a 'minimum message length' is used to automatically select the optimal complexity of a mixture model and the most suitable orientation of a reference frame for a fragment in a coordinate system. The type of atom interacting with a molecular fragment is predicted by values of the posterior probability function and the accuracy of these predictions is evaluated by comparing the predicted atom type with the actual atom type seen in crystal structures. The fact that an atom will simultaneously interact with several molecular fragments forming a cohesive network of interactions is exploited by introducing two strategies that combine the predictions of atom types given by multiple fragments. The accuracy of these combined predictions is compared with those based on an individual fragment. Exhaustive validation analyses and qualitative examples (e.g., the ligand-binding domain of glutamate receptors) demonstrate that these improvements lead to effective modeling and prediction of molecular interactions.

Distilling the essential features of a protein surface for improving protein-ligand docking, scoring, and virtual screening

For the successful identification and docking of new ligands to a protein target by virtual screening, the essential features of the protein and ligand surfaces must be captured and distilled in an efficient representation. Since the running time for docking increases exponentially with the number of points representing the protein and each ligand candidate, it is important to place these points where the best interactions can be made between the protein and the ligand. This definition of favorable points of interaction can also guide protein structure-based ligand design, which typically focuses on which chemical groups provide the most energetically favorable contacts. In this paper, we present an alternative method of protein template and ligand interaction point design that identifies the most favorable points for making hydrophobic and hydrogen-bond interactions by using a knowledge base. The knowledge-based protein and ligand representations have been incorporated in version 2.0 of SLIDE and resulted in dockings closer to the crystal structure orientations when screening a set of 57 known thrombin and glutathione S-transferase (GST) ligands against the apo structures of these proteins. There was also improved scoring enrichment of the dockings, meaning better differentiation between the chemically diverse known ligands and a approximately 15,000-molecule dataset of randomly-chosen small organic molecules. This approach for identifying the most important points of interaction between proteins and their ligands can equally well be used in other docking and design techniques. While much recent effort has focused on improving scoring functions for protein-ligand docking, our results indicate that improving the representation of the chemistry of proteins and their ligands is another avenue that can lead to significant improvements in the identification, docking, and scoring of ligands.

A new test set for validating predictions of protein-ligand interaction

We present a large test set of protein-ligand complexes for the purpose of validating algorithms that rely on the prediction of protein-ligand interactions. The set consists of 305 complexes with protonation states assigned by manual inspection. The following checks have been carried out to identify unsuitable entries in this set: (1) assessing the involvement of crystallographically related protein units in ligand binding; (2) identification of bad clashes between protein side chains and ligand; and (3) assessment of structural errors, and/or inconsistency of ligand placement with crystal structure electron density. In addition, the set has been pruned to assure diversity in terms of protein-ligand structures, and subsets are supplied for different protein-structure resolution ranges. A classification of the set by protein type is available. As an illustration, validation results are shown for GOLD and SuperStar. GOLD is a program that performs flexible protein-ligand docking, and SuperStar is used for the prediction of favorable interaction sites in proteins. The new CCDC/Astex test set is freely available to the scientific community (http://www.ccdc.cam.ac.uk).

Identification and mapping of small-molecule binding sites in proteins: computational tools for structure-based drug design

The number of protein structures is currently increasing at an impressive rate. The growing wealth of data calls for methods to efficiently exploit structural information for medicinal and pharmaceutical purposes. Given the three-dimensional (3D) structure of a validated protein target, the identification of functionally relevant binding sites and the analysis ('mapping') of these sites with respect to molecular recognition properties are important initial tasks in structure-based drug design. To address these tasks, a variety of computational tools have been developed. Approaches to identify binding pockets include geometric analyses of protein surfaces, comparisons of protein structures, similarity searches in databases of protein cavities, and docking scans to reveal areas of high ligand complementarity. In the context of binding-site analysis, powerful data mining tools help to retrieve experimental information about related protein-ligand complexes. To identify interaction hot spots, various potential functions and knowledge-based approaches are available for mapping binding regions. The results may subsequently be used to guide virtual screenings for new ligands via pharmacophore searches or docking simulations.