A fragment library based on Gaussian mixtures predicting favorable molecular interactions

Tailored Approaches in Drug Development and Diagnostics: From Molecular Design to Biological Model Systems

Approaches to increase the efficiency in developing drugs and diagnostics tools, including new drug delivery and diagnostic technologies, are needed for improved diagnosis and treatment of major diseases and health problems such as cancer, inflammatory diseases, chronic wounds, and antibiotic resistance. Development within several areas of research ranging from computational sciences, material sciences, bioengineering to biomedical sciences and bioimaging is needed to realize innovative drug development and diagnostic (DDD) approaches. Here, an overview of recent progresses within key areas that can provide customizable solutions to improve processes and the approaches taken within DDD is provided. Due to the broadness of the area, unfortunately all relevant aspects such as pharmacokinetics of bioactive molecules and delivery systems cannot be covered. Tailored approaches within (i) bioinformatics and computer-aided drug design, (ii) nanotechnology, (iii) novel materials and technologies for drug delivery and diagnostic systems, and (iv) disease models to predict safety and efficacy of medicines under development are focused on. Current developments and challenges ahead are discussed. The broad scope reflects the multidisciplinary nature of the field of DDD and aims to highlight the convergence of biological, pharmaceutical, and medical disciplines needed to meet the societal challenges of the 21st century.

Landscape of protein-small ligand binding modes

Elucidating the mechanisms of specific small-molecule (ligand) recognition by proteins is a long-standing conundrum. While the structures of these molecules, proteins and ligands, have been extensively studied, protein-ligand interactions, or binding modes, have not been comprehensively analyzed. Although methods for assessing similarities of binding site structures have been extensively developed, the methods for the computational treatment of binding modes have not been well established. Here, we developed a computational method for encoding the information about binding modes as graphs, and assessing their similarities. An all-against-all comparison of 20,040 protein-ligand complexes provided the landscape of the protein-ligand binding modes and its relationships with protein- and chemical spaces. While similar proteins in the same SCOP Family tend to bind relatively similar ligands with similar binding modes, the correlation between ligand and binding similarities was not very high (R(2)  = 0.443). We found many pairs with novel relationships, in which two evolutionally distant proteins recognize dissimilar ligands by similar binding modes (757,474 pairs out of 200,790,780 pairs were categorized into this relationship, in our dataset). In addition, there were an abundance of pairs of homologous proteins binding to similar ligands with different binding modes (68,217 pairs). Our results showed that many interesting relationships between protein-ligand complexes are still hidden in the structure database, and our new method for assessing binding mode similarities is effective to find them.

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.

Benzimidazole inhibitors of the protein kinase CHK2: clarification of the binding mode by flexible side chain docking and protein-ligand crystallography

Two closely related binding modes have previously been proposed for the ATP-competitive benzimidazole class of checkpoint kinase 2 (CHK2) inhibitors; however, neither binding mode is entirely consistent with the reported SAR. Unconstrained rigid docking of benzimidazole ligands into representative CHK2 protein crystal structures reveals an alternative binding mode involving a water-mediated interaction with the hinge region; docking which incorporates protein side chain flexibility for selected residues in the ATP binding site resulted in a refinement of the water-mediated hinge binding mode that is consistent with observed SAR. The flexible docking results are in good agreement with the crystal structures of four exemplar benzimidazole ligands bound to CHK2 which unambiguously confirmed the binding mode of these inhibitors, including the water-mediated interaction with the hinge region, and which is significantly different from binding modes previously postulated in the literature.

Probabilistic prediction of contacts in protein-ligand complexes

We introduce a statistical method for evaluating atomic level 3D interaction patterns of protein-ligand contacts. Such patterns can be used for fast separation of likely ligand and ligand binding site combinations out of all those that are geometrically possible. The practical purpose of this probabilistic method is for molecular docking and scoring, as an essential part of a scoring function. Probabilities of interaction patterns are calculated conditional on structural x-ray data and predefined chemical classification of molecular fragment types. Spatial coordinates of atoms are modeled using a Bayesian statistical framework with parametric 3D probability densities. The parameters are given distributions a priori, which provides the possibility to update the densities of model parameters with new structural data and use the parameter estimates to create a contact hierarchy. The contact preferences can be defined for any spatial area around a specified type of fragment. We compared calculated contact point hierarchies with the number of contact atoms found near the contact point in a reference set of x-ray data, and found that these were in general in a close agreement. Additionally, using substrate binding site in cathechol-O-methyltransferase and 27 small potential binder molecules, it was demonstrated that these probabilities together with auxiliary parameters separate well ligands from decoys (true positive rate 0.75, false positive rate 0). A particularly useful feature of the proposed Bayesian framework is that it also characterizes predictive uncertainty in terms of probabilities, which have an intuitive interpretation from the applied perspective.

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.

Bayesian methods in virtual screening and chemical biology

The Naïve Bayesian Classifier, as well as related classification and regression approaches based on Bayes' theorem, has experienced increased attention in the cheminformatics world in recent years. In this contribution, we first review the mathematical framework on which Bayes' methods are built, and then continue to discuss implications of this framework as well as practical experience under which conditions Bayes' methods give the best performance in virtual screening settings. Finally, we present an overview of applications of Bayes' methods to both virtual screening and the chemical biology arena, where applications range from bridging phenotypic and mechanistic space of drug action to the prediction of ligand-target interactions.

Synthetic mRNA splicing modulator compounds with in vivo antitumor activity

We report our progress on the development of new synthetic anticancer lead compounds that modulate the splicing of mRNA. We also report the synthesis and evaluation of new biologically active ester and carbamate analogues. Further, we describe initial animal studies demonstrating the antitumor efficacy of compound 5 in vivo. Additionally, we report the enantioselective and diastereospecific synthesis of a new 1,3-dioxane series of active analogues. We confirm that compound 5 inhibits the splicing of mRNA in cell-free nuclear extracts and in a cell-based dual-reporter mRNA splicing assay. In summary, we have developed totally synthetic novel spliceosome modulators as therapeutic lead compounds for a number of highly aggressive cancers. Future efforts will be directed toward the more complete optimization of these compounds as potential human therapeutics.

Multiple subunit fitting into a low-resolution density map of a macromolecular complex using a gaussian mixture model

Recently, electron microscopy measurement of single particles has enabled us to reconstruct a low-resolution 3D density map of large biomolecular complexes. If structures of the complex subunits can be solved by x-ray crystallography at atomic resolution, fitting these models into the 3D density map can generate an atomic resolution model of the entire large complex. The fitting of multiple subunits, however, generally requires large computational costs; therefore, development of an efficient algorithm is required. We developed a fast fitting program, “gmfit”, which employs a Gaussian mixture model (GMM) to represent approximated shapes of the 3D density map and the atomic models. A GMM is a distribution function composed by adding together several 3D Gaussian density functions. Because our model analytically provides an integral of a product of two distribution functions, it enables us to quickly calculate the fitness of the density map and the atomic models. Using the integral, two types of potential energy function are introduced: the attraction potential energy between a 3D density map and each subunit, and the repulsion potential energy between subunits. The restraint energy for symmetry is also employed to build symmetrical origomeric complexes. To find the optimal configuration of subunits, we randomly generated initial configurations of subunit models, and performed a steepest-descent method using forces and torques of the three potential energies. Comparison between an original density map and its GMM showed that the required number of Gaussian distribution functions for a given accuracy depended on both resolution and molecular size. We then performed test fitting calculations for simulated low-resolution density maps of atomic models of homodimer, trimer, and hexamer, using different search parameters. The results indicated that our method was able to rebuild atomic models of a complex even for maps of 30 Å resolution if sufficient numbers (eight or more) of Gaussian distribution functions were employed for each subunit, and the symmetric restraints were assigned for complexes with more than three subunits. As a more realistic test, we tried to build an atomic model of the GroEL/ES complex by fitting 21-subunit atomic models into the 3D density map obtained by cryoelectron microscopy using the C7 symmetric restraints. A model with low root mean-square deviations (14.7 Å) was obtained as the lowest-energy model, showing that our fitting method was reasonably accurate. Inclusion of other restraints from biological and biochemical experiments could further enhance the accuracy.

Molecular evolution of adrenoceptors and dopamine receptors: implications for the binding of catecholamines

We derived homology models for all human catecholamine-binding GPCRs (CABRs; the alpha-1, alpha-2, and beta-adrenoceptors and the D1-type and D2-type dopamine receptor) using the bovine rhodopsin-11-cis-retinal X-ray structure. Interactions were predicted from the endogenous ligands norepinephrine or dopamine and from the binding site and were used to optimize receptor-ligand interactions. Similar binding modes in the complexes agree with a large "binding core" conserved across the CABRs, that is, D3.32, V(I)3.33, T3.37, S5.42, S(A/C)5.43, S5.46, F6.51, F6.52, and W6.48. Model structures and docking simulations suggest that extracellular loop 2 could provide a common attachment point for the ligands' beta-hydroxyl via a hydrogen bond donated by the main-chain NH group of residue xl2.52. The modeled CABRs and docking modes are in good agreement with published experimental studies. Complementarity between the ligand and the binding site suggests that the bovine rhodopsin structure is a suitable template for modeling agonist-bound CABRs.

Model structures of α-2 adrenoceptors in complex with automatically docked antagonist ligands raise the possibility of interactions dissimilar from agonist ligands

Antagonist binding to alpha-2 adrenoceptors (alpha2-ARs) is not well understood. Structural models were constructed for the three human alpha2-AR subtypes based on the bovine rhodopsin X-ray structure. Twelve antagonist ligands (including covalently binding phenoxybenzamine) were automatically docked to the models. A hallmark of agonist binding is the electrostatic interaction between a positive charge on the agonist and the negatively charged side chain of D3.32. For antagonist binding, ion-pair formation would require deviations of the models from the rhodopsin structural template, e.g., a rotation of TM3 to relocate D3.32 more centrally within the binding cavity, and/or creation of new space near TM2/TM7 such that antagonists would be shifted away from TM5. Thus, except for the quinazolines, antagonist ligands automatically docked to the model structures did not form ion-pairs with D3.32. This binding mode represents a valid alternative, whereby the positive charge on the antagonists is stabilized by cation-pi interactions with aromatic residues (e.g., F6.51) and antagonists interact with D3.32 via carboxylate-aromatic interactions. This binding mode is in good agreement with maps derived from a molecular interaction library that predicts favorable atomic contacts; similar interaction environments are seen for unrelated proteins in complex with ligands sharing similarities with the alpha2-AR antagonists.

A novel statistical ligand-binding site predictor: application to ATP-binding sites

Structural genomics initiatives are leading to rapid growth in newly determined protein 3D structures, the functional characterization of which may still be inadequate. As an attempt to provide insights into the possible roles of the emerging proteins whose structures are available and/or to complement biochemical research, a variety of computational methods have been developed for the screening and prediction of ligand-binding sites in raw structural data, including statistical pattern classification techniques. In this paper, we report a novel statistical descriptor (the Oriented Shell Model) for protein ligand-binding sites, which utilizes the distance and angular position distribution of various structural and physicochemical features present in immediate proximity to the center of a binding site. Using the support vector machine (SVM) as the classifier, our model identified 69% of the ATP-binding sites in whole-protein scanning tests and in eukaryotic proteins the accuracy is particularly high. We propose that this feature extraction and machine learning procedure can screen out ligand-binding-capable protein candidates and can yield valuable biochemical information for individual proteins.

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

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.

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.

"Acceptor-donor-acceptor" motifs recognize the Watson-Crick, Hoogsteen and Sugar "donor-acceptor-donor" edges of adenine and adenosine-containing ligands

Nucleotides are among the most extensively exploited chemical moieties in nature and, as a part of a handful of different protein ligands, nucleotides play key roles in energy transduction, enzymatic catalysis and regulation of protein function. We have previously reported that in many proteins with different folds and functions a distinctive adenine-binding motif is involved in the recognition of the Watson-Crick edge of adenine. Here, we show that many proteins do have clear structural motifs that recognize adenosine (and some other nucleotides and nucleotide analogs) not only through the Watson-Crick edge, but also through the sugar and Hoogsteen edges. Each of the three edges of adenosine has a donor-acceptor-donor (DAD) pattern that is often recognized by proteins via a complementary acceptor-donor-acceptor (ADA) motif, whereby three distinct hydrogen bonds are formed: two conventional N-H...O and N-H...N hydrogen bonds, and one weak C-H...O hydrogen bond. The local conformation of the adenine-binding loop is betabetabeta or betabetaalpha and reflects the mode of nucleotide binding. Additionally, we report 21 proteins from five different folds that simultaneously recognize both the sugar edge and the Watson-Crick edge of adenine. In these proteins a unique beta-loop-beta supersecondary structure grasps an adenine-containing ligand between two identical adenine-binding motifs as part of the betaalphabeta-loop-beta fold.

Molecular mechanisms of ligand-receptor interactions in transmembrane domain V of the alpha2A-adrenoceptor

1. The structural determinants of catechol hydroxyl interactions with adrenergic receptors were examined using 12 alpha2-adrenergic agonists and a panel of mutated human alpha2A-adrenoceptors. The alpha2ASer201 mutant had a Cys --> Ser201 (position 5.43) amino-acid substitution, and alpha2ASer201Cys200 and alpha2ASer201Cys204 had Ser --> Cys200 (5.42) and Ser --> Cys204 (5.46) substitutions, respectively, in addition to the Cys --> Ser201 substitution. 2. Automated docking methods were used to predict the receptor interactions of the ligands. Radioligand-binding assays and functional [35S]GTPgammaS-binding assays were performed using transfected Chinese hamster ovary cells to experimentally corroborate the predicted binding modes. 3. The hydroxyl groups of phenethylamines were found to have different effects on ligand affinity towards the activated and resting forms of the wild-type alpha2A-adrenoceptor. Substitution of Ser200 or Ser204 with cysteine caused a deterioration in the capability of catecholamines to activate the alpha2A-adrenoceptor. The findings indicate that (i) Cys201 plays a significant role in the binding of catecholamine ligands and UK14,304 (for the latter, by a hydrophobic interaction), but Cys201 is not essential for receptor activation; (ii) Ser200 interacts with the meta-hydroxyl group of phenethylamine ligands, affecting both catecholamine binding and receptor activation; while (iii) substituting Ser204 with a cysteine interferes both with the binding of catecholamine ligands and with receptor activation, due to an interaction between Ser204 and the para-hydroxyl group of the catecholic ring.

A consensus-binding structure for adenine at the atomic level permits searching for the ligand site in a wide spectrum of adenine-containing complexes

Attempts to derive structural features of ligand-binding sites have traditionally involved seeking commonalities at the residue level. Recently, structural studies have turned to atomic interactions of small molecular fragments to extract common binding-site properties. Here, we explore the use of larger ligand elements to derive a consensus binding structure for the ligand as a whole. We superimposed multiple molecular structures from a nonredundant set of adenosine-5'-triphosphate (ATP) protein complexes, using the adenine moiety as template. Clustered binding-site atoms of compatible atomic classes forming attractive contacts with the adenine probe were extracted. A set of atomic clusters characterizing the adenine binding pocket was then derived. Among the clusters are three vertices representing the interactions of adenine atom N6 with its protein-binding niche. These vertices, together with atom C6 of the purine ring system, complete the set of four vertices for the pyramid-like structure of the N6 anchor atom. Also, the sequence relationship for the adenine-binding loop interacting with the C2-N6 end of the conjugated ring system is expanded to include a third hydrophilic cluster interacting with atom N1. A search procedure involving interatomic distances between cluster centers was formulated and applied to seek putative binding sites in test cases. The results show that a consensus network of clusters, based on an adenine probe and an ATP-complexed training set of proteins, is sufficient to recognize the experimental cavity for adenine in a wide spectrum of ligand-protein complexes.

Ligand binding: functional site location, similarity and docking

Computational methods for the detection and characterisation of protein ligand-binding sites have increasingly become an area of interest now that large amounts of protein structural information are becoming available prior to any knowledge of protein function. There have been particularly interesting recent developments in the following areas: first, functional site detection, whereby protein evolutionary information has been used to locate binding sites on the protein surface; second, functional site similarity, whereby structural similarity and three-dimensional templates can be used to compare and classify and potentially locate new binding sites; and third, ligand docking, which is being used to find and validate functional sites, in addition to having more conventional uses in small-molecule lead discovery.

Conserved positions for ribose recognition: importance of water bridging interactions among ATP, ADP and FAD-protein complexes

Analysis of the spatial arrangement of protein and water atoms that form polar interactions with ribose has been performed for a structurally non-redundant dataset of ATP, ADP and FAD-protein complexes. The 26 ligand-protein structures were separated into two groups corresponding to the most populated furanose ring conformations (N and S-domains). Four conserved positions were found for S-domain protein-ligand complexes and five for N-domain complexes. Multiple protein folds and secondary structural elements were represented at a single conserved position. The following novel points were revealed: (i) Two complementary positions sometimes combine to describe a putative atomic spatial location for a specific conserved binding spot. (ii) More than one third of the interactions scored were water-mediated. Thus, conserved spatial positions rich in water atoms are a significant feature of ribose-protein complexes.