Chemical fragment spaces for de novo design

Chemical fragment spaces are combinations of molecular fragments and connection rules. They offer the possibility to encode an enormously large number of chemical structures in a very compact format. Fragment spaces are useful both in similarity-based (2D) and structure-based (3D) de novo design applications. We present disconnection and filtering rules leading to several thousand unique, medium size fragments when applied to databases of druglike molecules. We evaluate alternative strategies to select subsets of these fragments, with the aim of maximizing the coverage of known druglike chemical space with a strongly reduced set of fragments. For these evaluations, we use the Ftrees fragment space method. We assess a diversity-oriented selection method based on maximum common substructures and a method biased toward high frequency of occurrence of fragments and find that they are complementary to each other.     

Design and Synthesis of 56 Shape-Diverse 3D Fragments

Fragment-based drug discovery is now widely adopted for lead generation in the pharmaceutical industry. However, fragment screening collections are often predominantly populated with flat, 2D molecules. Herein, we describe a workflow for the design and synthesis of 56 3D disubstituted pyrrolidine and piperidine fragments that occupy under-represented areas of fragment space (as demonstrated by a principal moments of inertia (PMI) analysis). A key, and unique, underpinning design feature of this fragment collection is that assessment of fragment shape and conformational diversity (by considering conformations up to 1.5 kcal mol⁻¹ above the energy of the global minimum energy conformer) is carried out prior to synthesis and is also used to select targets for synthesis. The 3D fragments were designed to contain suitable synthetic handles for future fragment elaboration. Finally, by comparing our 3D fragments with six commercial libraries, it is clear that our collection has high three-dimensionality and shape diversity.     

Peptide‐Guided Assembly of Repeat Protein Fragments

Herein, we present the peptide‐guided assembly of complementary fragments of designed armadillo repeat proteins (dArmRPs) to create proteins that bind peptides not only with high affinity but also with good selectivity. We recently demonstrated that complementary N‐ and C‐terminal fragments of dArmRPs form high‐affinity complexes that resemble the structure of the full‐length protein, and that these complexes bind their target peptides. We now demonstrate that dArmRPs can be split such that the fragments assemble only in the presence of a templating peptide, and that fragment mixtures enrich the combination with the highest affinity for this peptide. The enriched fragment combination discriminates single amino acid variations in the target peptide with high specificity. Our results suggest novel opportunities for the generation of new peptide binders by selection from dArmRP fragment mixtures.     

A fragment energy assembler method for Hartree-Fock calculations of large molecules

We present a fragment energy assembler approach for approximate Hartree-Fock (HF) calculations of macromolecules. In this method, a macromolecule is divided into small fragments with appropriate size, and then each fragment is capped by its neighboring fragments to form a subsystem. The total energy of the target system is evaluated as the sum of the fragment energies of all fragments, which are available from conventional HF calculations on all subsystems. By applying the method to a broad range of molecules, we demonstrate that the present approach could yield satisfactory HF energies for all studied systems.     

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.     

Molecular depth-profiling of polycarbonate with low-energy Cs+ ions

In this work, we explored the possibility of performing molecular depth-profiling by using very low-energy (about 200 eV) monoatomic Cs(+) ions. We show, for the first time, that this simple approach is successful on polymer layers of polycarbonate (PC). Under 200 eV Cs(+) irradiation of PC, a fast decrease of all characteristic negatively charged molecular ion signals is first observed but, rather surprisingly, these signals reach a minimum before rising again. A steady state is reached at which time most specific PC fragments are detected, some with even higher signal intensity (e.g. C(6)H(5)O(-)) than before irradiation. It is believed that the implanted Cs plays a major role in enhancing the negative ionisation of molecular fragments, leading to their easy detection for all the profile, although some material degradation obviously occurs. In the positive ion mode, all molecular fragments of the polymer disappear very rapidly, but clusters combining two Cs atoms and one molecular fragment (e.g. Cs(2)C(6)H(5)O(+)) are detected during the profile, proving that some molecular identification remains possible. In conclusion, this work presents a simple approach to molecular depth-profiling, complementary to cluster ion beam sputtering.     

New fragment weighting scheme for the Bayesian inference network in ligand-based virtual screening

Many of the conventional similarity methods assume that molecular fragments that do not relate to biological activity carry the same weight as the important ones. One possible approach to this problem is to use the Bayesian inference network (BIN), which models molecules and reference structures as probabilistic inference networks. The relationships between molecules and reference structures in the Bayesian network are encoded using a set of conditional probability distributions, which can be estimated by the fragment weighting function, a function of the frequencies of the fragments in the molecule or the reference structure as well as throughout the collection. The weighting function combines one or more fragment weighting schemes. In this paper, we have investigated five different weighting functions and present a new fragment weighting scheme. Later on, these functions were modified to combine the new weighting scheme. Simulated virtual screening experiments with the MDL Drug Data Report (23) and maximum unbiased validation data sets show that the use of new weighting scheme can provide significantly more effective screening when compared with the use of current weighting schemes.     

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.     

Fragment-based lead generation: identification of seed fragments by a highly efficient fragment screening technology

For the detection of the precise and unambiguous binding of fragments to a specific binding site on the target protein, we have developed a novel reporter displacement binding assay technology. The application of this technology for the fragment screening as well as the fragment evolution process with a specific modelling based design strategy is demonstrated for inhibitors of the protein kinase p38alpha. In a fragment screening approach seed fragments were identified which were then used to build compounds from the deep-pocket towards the hinge binding area of the protein kinase p38alpha based on a modelling approach. BIRB796 was used as a blueprint for the alignment of the fragments. The fragment evolution of these deep-pocket binding fragments towards the fully optimized inhibitor BIRB796 included the modulation of the residence time as well as the affinity. The goal of our study was to evaluate the robustness and efficiency of our novel fragment screening technology at high fragment concentrations, compare the screening data with biochemical activity data and to demonstrate the evolution of the hit fragments with fast kinetics, into slow kinetic inhibitors in an in silico approach.     

An evaluation of selected global (Q)SARs/expert systems for the prediction of skin sensitisation potential

Skin sensitisation potential is an endpoint that needs to be assessed within the framework of existing and forthcoming legislation. At present, skin sensitisation hazard is normally identified using in vivo test methods, the favoured approach being the local lymph node assay (LLNA). This method can also provide a measure of relative skin sensitising potency which is essential for assessing and managing human health risks. One potential alternative approach to skin sensitisation hazard identification is the use of (Quantitative) structure activity relationships ((Q)SARs) coupled with appropriate documentation and performance characteristics. This represents a major challenge. Current thinking is that (Q)SARs might best be employed as part of a battery of approaches that collectively provide information on skin sensitisation hazard. A number of (Q)SARs and expert systems have been developed and are described in the literature. Here we focus on three models (TOPKAT, Derek for Windows and TOPS-MODE), and evaluate their performance against a recently published dataset of 211 chemicals. The current strengths and limitations of one of these models is highlighted, together with modifications that could be made to improve its performance. Of the models/expert systems evaluated, none performed sufficiently well to act as a standalone tool for hazard identification.     

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.     

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.     

An evaluation of selected global (Q)SARs/expert systems for the prediction of skin sensitisation potential

Skin sensitisation potential is an endpoint that needs to be assessed within the framework of existing and forthcoming legislation. At present, skin sensitisation hazard is normally identified using in vivo test methods, the favoured approach being the local lymph node assay (LLNA). This method can also provide a measure of relative skin sensitising potency which is essential for assessing and managing human health risks. One potential alternative approach to skin sensitisation hazard identification is the use of (Quantitative) structure activity relationships ((Q)SARs) coupled with appropriate documentation and performance characteristics. This represents a major challenge. Current thinking is that (Q)SARs might best be employed as part of a battery of approaches that collectively provide information on skin sensitisation hazard. A number of (Q)SARs and expert systems have been developed and are described in the literature. Here we focus on three models (TOPKAT, Derek for Windows and TOPS-MODE), and evaluate their performance against a recently published dataset of 211 chemicals. The current strengths and limitations of one of these models is highlighted, together with modifications that could be made to improve its performance. Of the models/expert systems evaluated, none performed sufficiently well to act as a standalone tool for hazard identification.     

Identification of photolabeled peptides for the acceptor substrate binding domain of beta 1,4-galactosyltransferase.

We successfully applied a carbene-generating N-acetylglucosamine derivative carrying a biotinyl group to the radioisotope-free identification of peptides within bovine UDP-galactose: N-acetylglucosamine beta 1,4-galactosyltransferase (GalT, EC 2.4.1.38) catalytic domain. Owing to the low yield of cross-linking, conventional photoaffinity labeling experiments usually encounter a thorny problem in attempting to isolate labeled components from very complex mixtures. A biotin tag introduced with our photoaffinity probe enabled us to separate the photolabeled protein from a large amount of coexisting unlabeled GalT. The introduction of biotin was also useful for the radioisotope-free detection of a labeled protein based on a highly sensitive chemiluminescent technique. We developed a novel poly(vinylidene difluoride) membrane for the identification of labeled peptides in a simple dot blot assay. Using this membrane, we successfully identified biotinyl peptides among a number of HPLC separated fragments derived from the protease digestion of photolabeled GalT proteins. The sequence analysis revealed that the biotin tag was incorporated within a tryptic GalT fragment of Y197-R208. Our approach yields, for the first time, information on the acceptor substrate binding-site fragment in this enzyme, that has been difficult to obtain using other approaches. These data are consistent with previous suggestions concerning the GalT acceptor site and clearly demonstrate the effectiveness of our approach for rapid identification of photolabeled peptides.