Structure-based design technology contour and its application to the design of Renin inhibitors

It is well-known that the structure-based design approach has had a measurable impact on the drug discovery process in identifying novel and efficacious therapeutic agents for a variety of disease targets. The de novo design approach has inherent potential to generate novel molecules that best fit into a protein binding site when compared to all of the computational methods applied to structure-based design. In its initial attempts, this approach did not achieve much success due to technical hurdles. More recently, the algorithmic advancements in the methodologies and clever strategies developed to design drug-like molecules have improved the success rate. We describe a state-of-the-art structure-based design technology called Contour and provide details of the algorithmic enhancements we have implemented. Contour was designed to create novel drug-like molecules by assembling synthetically viable fragments in the protein binding site using a high-resolution crystal structure of the protein. The technology consists of a sophisticated growth algorithm and a novel scoring function based on a directional model. The growth algorithm generates molecules by dynamically selecting only those fragments from the fragment library that are complementary to the binding site, and assembling them by sampling the conformational space for each attached fragment. The scoring function embodying the essential elements of the binding interactions aids in the rank ordering of grown molecules and helps identify those that have high probability of exhibiting activity against the protein target of interest. The application of Contour to identify inhibitors against human renin enzyme eventually leading to the clinical candidate VTP-27,999 will be discussed here.     

The multiple roles of computational chemistry in fragment-based drug design

Fragment-based drug discovery (FBDD) represents a change in strategy from the screening of molecules with higher molecular weights and physical properties more akin to fully drug-like compounds, to the screening of smaller, less complex molecules. This is because it has been recognised that fragment hit molecules can be efficiently grown and optimised into leads, particularly after the binding mode to the target protein has been first determined by 3D structural elucidation, e.g. by NMR or X-ray crystallography. Several studies have shown that medicinal chemistry optimisation of an already drug-like hit or lead compound can result in a final compound with too high molecular weight and lipophilicity. The evolution of a lower molecular weight fragment hit therefore represents an attractive alternative approach to optimisation as it allows better control of compound properties. Computational chemistry can play an important role both prior to a fragment screen, in producing a target focussed fragment library, and post-screening in the evolution of a drug-like molecule from a fragment hit, both with and without the available fragment-target co-complex structure. We will review many of the current developments in the area and illustrate with some recent examples from successful FBDD discovery projects that we have conducted.     

Lessons for fragment library design: analysis of output from multiple screening campaigns

Over the past 8 years, we have developed, refined and applied a fragment based discovery approach to a range of protein targets. Here we report computational analyses of various aspects of our fragment library and the results obtained for fragment screening. We reinforce the finding of others that the experimentally observed hit rate for screening fragments can be related to a computationally defined druggability index for the target. In general, the physicochemical properties of the fragment hits display the same profile as the library, as is expected for a truly diverse library which probes the relevant chemical space. An analysis of the fragment hits against various protein classes has shown that the physicochemical properties of the fragments are complementary to the properties of the target binding site. The effectiveness of some fragments appears to be achieved by an appropriate mix of pharmacophore features and enhanced aromaticity, with hydrophobic interactions playing an important role. The analysis emphasizes that it is possible to identify small fragments that are specific for different binding sites. To conclude, we discuss how the results could inform further development and improvement of our fragment library. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Exploring fragment spaces under multiple physicochemical constraints

We present a new algorithm for the enumeration of chemical fragment spaces under constraints. Fragment spaces consist of a set of molecular fragments and a set of rules that specifies how fragments can be combined. Although fragment spaces typically cover an infinite number of molecules, they can be enumerated in case that a physicochemical profile of the requested compounds is given. By using min-max ranges for a number of corresponding properties, our algorithm is able to enumerate all molecules which obey these properties. To speed up the calculation, the given ranges are used directly during the build-up process to guide the selection of fragments. Furthermore, a topology based fragment filter is used to skip most of the redundant fragment combinations. We applied the algorithm to 40 different target classes. For each of these, we generated tailored fragment spaces from sets of known inhibitors and additionally derived ranges for several physicochemical properties. We characterized the target-specific fragment spaces and were able to enumerate the complete chemical subspaces for most of the targets.     

Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets

De novo design can be used to explore vast areas of chemical space in computational lead discovery. As a complement to virtual screening, from‐scratch construction of molecules is not limited to compounds in pre‐existing vendor catalogs. Here, we present an iterative fragment growth method, integrated into the program DOCK, in which new molecules are built using rules for allowable connections based on known molecules. The method leverages DOCK's advanced scoring and pruning approaches and users can define very specific criteria in terms of properties or features to customize growth toward a particular region of chemical space. The code was validated using three increasingly difficult classes of calculations: (1) Rebuilding known X‐ray ligands taken from 663 complexes using only their component parts (focused libraries), (2) construction of new ligands in 57 drug target sites using a library derived from ∼13M drug‐like compounds (generic libraries), and (3) application to a challenging protein‐protein interface on the viral drug target HIVgp41. The computational testing confirms that the de novo DOCK routines are robust and working as envisioned, and the compelling results highlight the potential utility for designing new molecules against a wide variety of important protein targets. © 2017 Wiley Periodicals, Inc.     

Time-efficient flexible superposition of medium-sized molecules

We present an efficient algorithm for the structural alignment of medium-sized organic molecules. The algorithm has been developed for applications in 3D QSAR and in receptor modeling. The method assumes one of the molecules, the reference ligand, to be presented in the conformation that it adopts inside the receptor pocket. The second molecule, the test ligand, is considered to be flexible, and is assumed to be given in an arbitrary low-energy conformation. Ligand flexibility is modeled by decomposing the test ligand into molecular fragments, such that ring systems are completely contained in a single fragment. Conformations of fragments and torsional angles of single bonds are taken from a small finite set, which depends on the fragment and bond, respectively. The algorithm superimposes a distinguished base fragment of the test ligand onto a suitable region of the reference ligand and then attaches the remaining fragments of the test ligand in a step-by-step fashion. During this process, a scoring function is optimized that encompasses bonding terms and terms accounting for steric overlap as well as for similarity of chemical properties of both ligands. The algorithm has been implemented in the FLEXS system. To validate the quality of the produced results, we have selected a number of examples for which the mutual superposition of two ligands is experimentally given by the comparison of the binding geometries known from the crystal structures of their corresponding protein–ligand complexes. On more than two-thirds of the test examples the algorithm produces rms deviations of the predicted versus the observed conformation of the test ligand below 1.5 Å. The run time of the algorithm on a single problem instance is a few minutes on a common-day workstation. The overall goal of this research is to drastically reduce run times, while limiting the inaccuracies of the model and the computation to a tolerable level.     

A comparative study of fragment screening methods on the p38�� kinase: new methods, new insights

The stress-activated kinase p38α was used to evaluate a fragment-based drug discovery approach using the BioFocus fragment library. Compounds were screened by surface plasmon resonance (SPR) on a Biacore(?) T100 against p38α and two selectivity targets. A sub-set of our library was the focus of detailed follow-up analyses that included hit confirmation, affinity determination on 24 confirmed, selective hits and competition assays of these hits with respect to a known ATP binding site inhibitor. In addition, functional activity against p38α was assessed in a biochemical assay using a mobility shift platform (LC3000, Caliper LifeSciences). A selection of fragments was also evaluated using fluorescence lifetime (FLEXYTE(?)) and microscale thermophoresis (Nanotemper) technologies. A good correlation between the data for the different assays was found. Crystal structures were solved for four of the small molecules complexed to p38α. Interestingly, as determined both by X-ray analysis and SPR competition experiments, three of the complexes involved the fragment at the ATP binding site, while the fourth compound bound in a distal site that may offer potential as a novel drug target site. A first round of optimization around the remotely bound fragment has led to the identification of a series of triazole-containing compounds. This approach could form the basis for developing novel and active p38α inhibitors. More broadly, it illustrates the power of combining a range of biophysical and biochemical techniques to the discovery of fragments that facilitate the development of novel modulators of kinase and other drug targets.     

Transformation of low-affinity lead compounds into high-affinity protein capture agents

A simple and potentially general approach to the isolation of high-affinity and -specificity protein binding synthetic molecules is presented. A modest affinity lead compound is appended to the end of each molecule in a combinatorial library of oligomeric compounds, such as peptides or peptoids. The library is then screened under conditions too demanding for the lead to support robust binding to the protein target. It was anticipated that this procedure would select for bivalent ligands in which the oligomer library provides both a second binding element as well as an appropriate linker between this element and the lead compound. We report here synthetic ligands for the Mdm2 protein and ubiquitin able to capture their target proteins from dilute solutions in the presence of a large excess of other proteins.     

Altered sequence specificity identified from a library of DNA-binding small molecules

BACKGROUND: The ability to target specific DNA sequences using small molecules has major implications for basic research and medicine. Previous studies revealed that a bis-intercalating molecule containing two 1,4,5,8-napthalenetetracarboxylic diimides separated by a lysine-tris-glycine linker binds to DNA cooperatively, in pairs, with a preference for G + C-rich sequences. Here we investigate the binding properties of a library of bis-intercalating molecules that have partially randomized peptide linkers. RESULTS: A library of bis-intercalating derivatives with varied peptide linkers was screened for sequence specificity using DNase I footprinting on a 231 base pair (bp) restriction fragment. The library mixtures produced footprints that were generally similar to the parent bis-intercalator, which bound within a 15 bp G + C-rich repeat above 125 nM. Nevertheless, subtle differences in cleavage enhancement bands followed by library deconvolution revealed a derivative with novel specificity. A lysine-tris-beta-alanine derivative was found to bind preferentially within a 19 bp palindrome, without substantial loss of affinity. CONCLUSIONS: Synthetically simple changes in the bis-intercalating compounds can produce derivatives with novel sequence specificity. The large size and symmetrical nature of the preferred binding sites suggest that cooperativity may be retained despite modified sequence specificity. Such findings, combined with structural data, could be used to develop versatile DNA ligands of modest molecular weight that target relatively long DNA sequences in a selective manner.     

An ontology for pharmaceutical ligands and its application for in silico screening and library design

Annotation efforts in biosciences have focused in past years mainly on the annotation of genomic sequences. Only very limited effort has been put into annotation schemes for pharmaceutical ligands. Here we propose annotation schemes for the ligands of four major target classes, enzymes, G protein-coupled receptors (GPCRs), nuclear receptors (NRs), and ligand-gated ion channels (LGICs), and outline their usage for in silico screening and combinatorial library design. The proposed schemes cover ligand functionality and hierarchical levels of target classification. The classification schemes are based on those established by the EC, GPCRDB, NuclearDB, and LGICDB. The ligands of the MDL Drug Data Report (MDDR) database serve as a reference data set of known pharmacologically active compounds. All ligands were annotated according to the schemes when attribution was possible based on the activity classification provided by the reference database. The purpose of the ligand-target classification schemes is to allow annotation-based searching of the ligand database. In addition, the biological sequence information of the target is directly linkable to the ligand, hereby allowing sequence similarity-based identification of ligands of next homologous receptors. Ligands of specified levels can easily be retrieved to serve as comprehensive reference sets for cheminformatics-based similarity searches and for design of target class focused compound libraries. Retrospective in silico screening experiments within the MDDR01.1 database, searching for structures binding to dopamine D2, all dopamine receptors and all amine-binding class A GPCRs using known dopamine D2 binding compounds as a reference set, have shown that such reference sets are in particular useful for the identification of ligands binding to receptors closely related to the reference system. The potential for ligand identification drops with increasing phylogenetic distance. The analysis of the focus of a tertiary amine based combinatorial library compared to known amine binding class A GPCRs, peptide binding class A GPCRs, and LGIC ligands constitutes a second application scenario which illustrates how the focus of a combinatorial library can be treated quantitatively. The provided annotation schemes, which bridge chem- and bioinformatics by linking ligands to sequences, are expected to be of key utility for further systematic chemogenomics exploration of previously well explored target families.     

Fragment-based de novo ligand design by multiobjective evolutionary optimization

GANDI (Genetic Algorithm-based de Novo Design of Inhibitors) is a computational tool for automatic fragment-based design of molecules within a protein binding site of known structure. A genetic algorithm and a tabu search act in concert to join predocked fragments with a user-supplied list of fragments. A novel feature of GANDI is the simultaneous optimization of force field energy and a term enforcing 2D-similarity to known inhibitor(s) or 3D-overlap to known binding mode(s). Scaffold hopping can be promoted by tuning the relative weights of these terms. The performance of GANDI is tested on cyclin-dependent kinase 2 (CDK2) using a library of about 14 000 fragments and the binding mode of a known oxindole inhibitor to bias the design. Top ranking GANDI molecules are involved in one to three hydrogen bonds with the backbone polar groups in the hinge region of CDK2, an interaction pattern observed in potent kinase inhibitors. Notably, a GANDI molecule with very favorable predicted binding affinity shares a 2-N-phenyl-1,3-thiazole-2,4-diamine moiety with a known nanomolar inhibitor of CDK2. Importantly, molecules with a favorable GANDI score are synthetic accessible. In fact, eight of the 1809 molecules designed by GANDI for CDK2 are found in the ZINC database of commercially available compounds which also contains about 600 compounds with identical scaffolds as those in the top ranking GANDI molecules.     

Design and characterization of libraries of molecular fragments for use in NMR screening against protein targets

We have designed four generations of a low molecular weight fragment library for use in NMR-based screening against protein targets. The library initially contained 723 fragments which were selected manually from the Available Chemicals Directory. A series of in silico filters and property calculations were developed to automate the selection process, allowing a larger database of 1.79 M available compounds to be searched for a further 357 compounds that were added to the library. A kinase binding pharmacophore was then derived to select 174 kinase-focused fragments. Finally, an additional 61 fragments were selected to increase the number of different pharmacophores represented within the library. All of the fragments added to the library passed quality checks to ensure they were suitable for the screening protocol, with appropriate solubility, purity, chemical stability, and unambiguous NMR spectrum. The successive generations of libraries have been characterized through analysis of structural properties (molecular weight, lipophilicity, polar surface area, number of rotatable bonds, and hydrogen-bonding potential) and by analyzing their pharmacophoric complexity. These calculations have been used to compare the fragment libraries with a drug-like reference set of compounds and a set of molecules that bind to protein active sites. In addition, an analysis of the overall results of screening the library against the ATP binding site of two protein targets (HSP90 and CDK2) reveals different patterns of fragment binding, demonstrating that the approach can find selective compounds that discriminate between related binding sites.     

Computational fragment-based screening using RosettaLigand: the SAMPL3 challenge

SAMPL3 fragment based virtual screening challenge provides a valuable opportunity for researchers to test their programs, methods and screening protocols in a blind testing environment. We participated in SAMPL3 challenge and evaluated our virtual fragment screening protocol, which involves RosettaLigand as the core component by screening a 500 fragments Maybridge library against bovine pancreatic trypsin. Our study reaffirmed that the real test for any virtual screening approach would be in a blind testing environment. The analyses presented in this paper also showed that virtual screening performance can be improved, if a set of known active compounds is available and parameters and methods that yield better enrichment are selected. Our study also highlighted that to achieve accurate orientation and conformation of ligands within a binding site, selecting an appropriate method to calculate partial charges is important. Another finding is that using multiple receptor ensembles in docking does not always yield better enrichment than individual receptors. On the basis of our results and retrospective analyses from SAMPL3 fragment screening challenge we anticipate that chances of success in a fragment screening process could be increased significantly with careful selection of receptor structures, protein flexibility, sufficient conformational sampling within binding pocket and accurate assignment of ligand and protein partial charges.     

采用毛细管电泳技术筛选特异识别蓖麻毒素适配子的研究

采用指数富集配基的系统进化(SELEX)技术从随机寡核苷酸文库中筛选获得特异识别蓖麻毒素靶分子的适配子. 将毛细管电泳技术作为分离手段引入到SELEX筛选中, 利用毛细管电泳高效的分离能力使得筛选周期大大缩短. 酶联免疫和斑点杂交实验结果表明, 仅经4轮筛选即可获得特异识别蓖麻毒素蛋白的寡核苷酸适配子.     

Hammerhead: fast,fully automated docking of flexible ligands to protein binding sites

Background: Molecular docking seeks to predict the geometry and affinity of the binding of a small molecule to a given protein of known structure. Rigid docking has long been used to screen databases of small molecules, because docking techniques that account for ligand flexibility have either been too slow or have required significant human intervention. Here we describe a docking algorithm, Hammerhead, which is a fast, automated tool to screen for the binding of flexible molecules to protein binding sites.Results: We used Hammerhead to successfully dock a variety of positive control ligands into their cognate proteins. The empirically tuned scoring function of the algorithm predicted binding affinities within 1.3 log units of the known affinities for these ligands. Conformations and alignments close to those determined crystallographically received the highest scores. We screened 80 000 compounds for binding to streptavidin, and biotin was predicted as the top-scoring ligand, with other known ligands included among the highest-scoring dockings. The screen ran in a few days on commonly available hardware.Conclusions: Hammerhead is suitable for screening large databases of flexible molecules for binding to a protein of known structure. It correctly docks a variety of known flexible ligands, and it spends an average of only a few seconds on each compound during a screen. The approach is completely automated, from the elucidation of protein binding sites, through the docking of molecules, to the final selection of compounds for assay.     

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.     

Validation of an empirical RNA-ligand scoring function for fast flexible docking using RiboDock®

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.     

In silico fragment-based drug design using a PASS approach

Fragment-based drug design integrates different methods to create novel ligands using fragment libraries focused on particular biological activities. Experimental approaches to the preparation of fragment libraries have some drawbacks caused by the need for target crystallization (X-ray and nuclear magnetic resonance) and careful immobilization (surface plasmon resonance). Molecular modelling (docking) requires accurate data on protein-ligand interactions, which are difficult to obtain for some proteins. The main drawbacks of QSAR application are associated with the need to collect large homogeneous datasets of chemical structures with experimentally determined self-consistent quantitative values (potency). We propose a ligand-based approach to the selection of fragments with positive contribution to biological activity, developed on the basis of the PASS algorithm. The robustness of the PASS algorithm for heterogeneous datasets has been shown earlier. PASS estimates qualitative (yes/no) prediction of biological activity spectra for over 4000 biological activities and, therefore, provides the basis for the preparation of a fragment library corresponding to multiple criteria. The algorithm for fragment selection has been validated using the fractions of intermolecular interactions calculated for known inhibitors of nine enzymes extracted from the Protein Data Bank database. The statistical significance of differences between fractions of intermolecular interactions corresponds, for several enzymes, to the estimated positive and negative contribution of fragments in enzyme inhibition.     

Covalent Lipid Pocket Ligands Targeting p38α MAPK Mutants

A chemical genetic approach is presented to covalently target a unique lipid binding pocket in the protein kinase p38α, whose function is not yet known. Based on a series of cocrystal structures, a library of 2‐arylquinazolines that were decorated with electrophiles were designed and synthesized to covalently target tailored p38α mutants containing artificially introduced cysteine residues. Matching protein–ligand pairs were identified by MS analysis and further validated by MS/MS studies and protein crystallography. The covalent ligands that emerged from this approach showed excellent selectivity towards a single p38α mutant and will be applicable as suitable probes in future studies of biological systems to dissect the function of the lipid pocket by means of pharmacological perturbations.