Structure-based optimization of a non-beta-lactam lead results in inhibitors that do not up-regulate beta-lactamase expression in cell culture

Bacterial expression of beta-lactamases is the most widespread resistance mechanism to beta-lactam antibiotics, such as penicillins and cephalosporins. There is a pressing need for novel, non-beta-lactam inhibitors of these enzymes. One previously discovered novel inhibitor of the beta-lactamase AmpC, compound 1, has several favorable properties: it is chemically dissimilar to beta-lactams and is a noncovalent, competitive inhibitor of the enzyme. However, at 26 microM its activity is modest. Using the X-ray structure of the AmpC/1 complex as a template, 14 analogues were designed and synthesized. The most active of these, compound 10, had a K(i) of 1 microM, 26-fold better than the lead. To understand the origins of this improved activity, the structures of AmpC in complex with compound 10 and an analogue, compound 11, were determined by X-ray crystallography to 1.97 and 1.96 A, respectively. Compound 10 was active in cell culture, reversing resistance to the third generation cephalosporin ceftazidime in bacterial pathogens expressing AmpC. In contrast to beta-lactam-based inhibitors clavulanate and cefoxitin, compound 10 did not up-regulate beta-lactamase expression in cell culture but simply inhibited the enzyme expressed by the resistant bacteria. Its escape from this resistance mechanism derives from its dissimilarity to beta-lactam antibiotics.     

Structure-based analysis and optimization of a highly enantioselective catalyst for the strecker reaction

A mechanistic investigation of the asymmetric Strecker reaction catalyzed by a metal-free Schiff base catalyst was conducted. The active site of the catalyst, the relevant stereoisomer of the imine substrate, and the solution structure of the imine-catalyst complex were elucidated using a series of kinetics, structure-activity, and NMR experiments. An unusual bridging interaction between the imine and the urea hydrogens of the catalyst was identified and supported by computation. Rational optimization of catalyst structure based on the mechanistic insight led to an improved catalyst for the asymmetric Strecker reaction.     

Multicanonical basin hopping: a new global optimization method for complex systems

We introduce a new optimization algorithm that combines the basin-hopping method, which can be used to efficiently map out an energy landscape associated with minima, with the multicanonical Monte Carlo method, which encourages the system to move out of energy traps during the computation. As an example of implementing the algorithm for the global minimization of a multivariable system, we consider the Lennard-Jones systems containing 150-185 particles, and find that the new algorithm is more efficient than the original basin-hopping method.     

Lead optimization mapper: automating free energy calculations for lead optimization

Alchemical free energy calculations hold increasing promise as an aid to drug discovery efforts. However, applications of these techniques in discovery projects have been relatively few, partly because of the difficulty of planning and setting up calculations. Here, we introduce lead optimization mapper, LOMAP, an automated algorithm to plan efficient relative free energy calculations between potential ligands within a substantial library of perhaps hundreds of compounds. In this approach, ligands are first grouped by structural similarity primarily based on the size of a (loosely defined) maximal common substructure, and then calculations are planned within and between sets of structurally related compounds. An emphasis is placed on ensuring that relative free energies can be obtained between any pair of compounds without combining the results of too many different relative free energy calculations (to avoid accumulation of error) and by providing some redundancy to allow for the possibility of error and consistency checking and provide some insight into when results can be expected to be unreliable. The algorithm is discussed in detail and a Python implementation, based on both Schrödinger’s and OpenEye’s APIs, has been made available freely under the BSD license.     

Application of belief theory to similarity data fusion for use in analog searching and lead hopping

A wide variety of computational algorithms have been developed that strive to capture the chemical similarity between two compounds for use in virtual screening and lead discovery. One limitation of such approaches is that, while a returned similarity value reflects the perceived degree of relatedness between any two compounds, there is no direct correlation between this value and the expectation or confidence that any two molecules will in fact be equally active. A lack of a common framework for interpretation of similarity measures also confounds the reliable fusion of information from different algorithms. Here, we present a probabilistic framework for interpreting similarity measures that directly correlates the similarity value to a quantitative expectation that two molecules will in fact be equipotent. The approach is based on extensive benchmarking of 10 different similarity methods (MACCS keys, Daylight fingerprints, maximum common subgraphs, rapid overlay of chemical structures (ROCS) shape similarity, and six connectivity-based fingerprints) against a database of more than 150,000 compounds with activity data against 23 protein targets. Given this unified and probabilistic framework for interpreting chemical similarity, principles derived from decision theory can then be applied to combine the evidence from different similarity measures in such a way that both capitalizes on the strengths of the individual approaches and maintains a quantitative estimate of the likelihood that any two molecules will exhibit similar biological activity.     

Differential pulse cathodic stripping adsorption voltammetric determination of trace amounts of lead using factorial design for optimization

A sensitive cathodic stripping voltammetric method is developed for determination of lead(II), with adsorptive collection of complexes with Pyrogallol red (PGR) on to a hanging mercury drop electrode. After accumulation of the complex at −0.80 V vs. Ag/AgCl reference electrode, the potential is scanned in a negative direction from −0.20 to −0.50 V with differential pulse method. Then the reduction peak current for the lead(II)-PGR complex is measured at −0.39 V. The influence of reagent and instrumental variables was completely studied by factorial design analysis. The optimum analytical conditions for the determination of lead(II) were established. Under optimum conditions, lead(II) determined in the range of 0.1-30.0 ng ml⁻¹ with a limit of detection of 0.06 ng ml⁻¹. The method is successfully applied to determination of lead(II) in water sample.     

Crystal structure prediction using the minima hopping method

A structure prediction method is presented based on the minima hopping method. To escape local minima, moves on the configurational enthalpy surface are performed by variable cell shape molecular dynamics. To optimize the escape steps the initial atomic and cell velocities are aligned to low curvature directions of the current local minimum. The method is applied to both silicon crystals and well-studied binary Lennard-Jones mixtures. For the latter new putative ground state structures are presented. It is shown that a high success rate is achieved and a reliable prediction of unknown ground state structures is possible.     

Structural unit analysis identifies lead series and facilitates scaffold hopping in combinatorial chemistry

Combinatorial chemistry and high-throughput screening technologies produce huge amounts of data on a regular basis. Sieving through these libraries of compounds and their associated assay data to identify appropriate series for follow-up is a daunting task, which has created a need for computational techniques that can find coherent islands of structure-activity relationships in this sea. Structural unit analysis (SUA) examines an entire data set so as to identify the molecular substructures or fragments that distinguish compounds with high activity from those with average activity. The algorithm is iterative and follows set heuristics in order to generate the structural units. It produces graphs that represent a set of units, which become SUA rules. Finding all of the input structures that match these graphs generates clusters. The Apriori algorithm for association rule mining is adapted to explore all of the combinations of structural units that define useful series. User-defined constraints are applied toward series selection and the refinement of rules. The significance of a series is determined by applying statistical methods appropriate to each data set. Application to the NCI-H23 (DTP Human Tumor Cell Line Screen) database serves to illustrate the process by which structural series are identified. An application of the method to scaffold hopping is then discussed in connection with proprietary screening data from a lead optimization project directed toward the treatment of respiratory tract infections at Bayer Healthcare. SUA was able to successfully identify promising alternative core structures in addition to identifying compounds with above-average activity and selectivity.     

ArQiologist: an integrated decision support tool for lead optimization

This paper describes ArQiologist, a Web-based tool that integrates chemical, analytical, biological, and computational data to facilitate decision support for lead optimization at ArQule. It features an easy-to-use graphical query builder that allows queries to be saved, reused, and shared by researchers. Query results can be viewed with built-in data browsers or exported with structures to external applications such as Microsoft Excel or Spotfire for further analysis.     

Exact-mass library for pesticides using a molecular-feature database

An automated molecular-feature database (MFD) consisting of the exact monoisotopic mass of 100 compounds, at least one exact mass product ion for each compound, and chromatographic retention time were used to identify pesticides in food and water samples. The MFD software compiles a list of accurate mass ions, excludes noise, and compares them with the monoisotopic exact masses in the database. The screening criteria consisted of +/-5 ppm accurate mass window, +/-0.2 min retention time window, and a minimum 1000 counts (signal-to-noise (S/N) ratio of approximately 10:1). The limit of detection for 100 tested compounds varied from <0.01 mg/kg for 72% of the compounds to <0.1 mg/kg for 95% of the compounds. The MFD search was useful for rapid screening and identification of pesticides in food and water, as shown in actual samples. The combined use of accurate mass and chromatographic retention time eliminated false positives in the automated analysis. The major weakness of the MFD is matrix interferences and loss of mass accuracy. Strengths of the MFD include rapid screening of 100 compounds at sensitive levels compared with a manual approach and the ease of use of the library for any accurate mass spectrometer instrumentation capable of routine sub-5-ppm mass accuracy.     

Structure-based discovery and in-parallel optimization of novel competitive inhibitors of thymidylate synthase.

BACKGROUND: The substrate sites of enzymes are attractive targets for structure-based inhibitor design. Two difficulties hinder efforts to discover and elaborate new (nonsubstrate-like) inhibitors for these sites. First, novel inhibitors often bind at nonsubstrate sites. Second, a novel scaffold introduces chemistry that is frequently unfamiliar, making synthetic elaboration challenging. RESULTS: In an effort to discover and elaborate a novel scaffold for a substrate site, we combined structure-based screening with in-parallel synthetic elaboration. These techniques were used to find new inhibitors that bound to the folate site of Lactobacillus casei thymidylate synthase (LcTS), an enzyme that is a potential target for proliferative diseases, and is highly studied. The available chemicals directory was screened, using a molecular-docking computer program, for molecules that complemented the three-dimensional structure of this site. Five high-ranking compounds were selected for testing. Activity and docking studies led to a derivative of one of these, dansyltyrosine (Ki 65 microM). Using solid-phase in-parallel techniques 33 derivatives of this lead were synthesized and tested. These analogs are dissimilar to the substrate but bind competitively with it. The most active analog had a Ki of 1.3 microM. The tighter binding inhibitors were also the most specific for LcTS versus related enzymes. CONCLUSIONS: TS can recognize inhibitors that are dissimilar to, but that bind competitively with, the folate substrate. Combining structure-based discovery with in-parallel synthetic techniques allowed the rapid elaboration of this series of compounds. More automated versions of this approach can be envisaged.     

Lead hopping using SVM and 3D pharmacophore fingerprints

The combination of 3D pharmacophore fingerprints and the support vector machine classification algorithm has been used to generate robust models that are able to classify compounds as active or inactive in a number of G-protein-coupled receptor assays. The models have been tested against progressively more challenging validation sets where steps are taken to ensure that compounds in the validation set are chemically and structurally distinct from the training set. In the most challenging example, we simulate a lead-hopping experiment by excluding an entire class of compounds (defined by a core substructure) from the training set. The left-out active compounds comprised approximately 40% of the actives. The model trained on the remaining compounds is able to recall 75% of the actives from the "new" lead series while correctly classifying >99% of the 5000 inactives included in the validation set.     

Scaffold hopping using clique detection applied to reduced graphs

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

A fast exchange algorithm for designing focused libraries in lead optimization

Combinatorial chemistry is widely used in drug discovery. Once a lead compound has been identified, a series of R-groups and reagents can be selected and combined to generate new potential drugs. The combinatorial nature of this problem leads to chemical libraries containing usually a very large number of virtual compounds, far too large to permit their chemical synthesis. Therefore, one often wants to select a subset of "good" reagents for each R-group of reagents and synthesize all their possible combinations. In this research, one encounters some difficulties. First, the selection of reagents has to be done such that the compounds of the resulting sublibrary simultaneously optimize a series of chemical properties. For each compound, a desirability index, a concept proposed by Harrington,(20) is used to summarize those properties in one fitness value. Then a loss function is used as objective criteria to globally quantify the quality of a sublibrary. Second, there are a huge number of possible sublibraries, and the solutions space has to be explored as fast as possible. The WEALD algorithm proposed in this paper starts with a random solution and iterates by applying exchanges, a simple method proposed by Fedorov(13) and often used in the generation of optimal designs. Those exchanges are guided by a weighting of the reagents adapted recursively as the solutions space is explored. The algorithm is applied on a real database and reveals to converge rapidly. It is compared to results given by two other algorithms presented in the combinatorial chemistry literature: the Ultrafast algorithm of D. Agrafiotis and V. Lobanov and the Piccolo algorithm of W. Zheng et al.     

DeepFrag: a deep convolutional neural network for fragment-based lead optimization

Machine learning has been increasingly applied to the field of computer-aided drug discovery in recent years, leading to notable advances in binding-affinity prediction, virtual screening, and QSAR. Surprisingly, it is less often applied to lead optimization, the process of identifying chemical fragments that might be added to a known ligand to improve its binding affinity. We here describe a deep convolutional neural network that predicts appropriate fragments given the structure of a receptor/ligand complex. In an independent benchmark of known ligands with missing (deleted) fragments, our DeepFrag model selected the known (correct) fragment from a set over 6500 about 58% of the time. Even when the known/correct fragment was not selected, the top fragment was often chemically similar and may well represent a valid substitution. We release our trained DeepFrag model and associated software under the terms of the Apache License, Version 2.0.

DeepFrag is a machine-learning model designed to assist with lead optimization. It recommends appropriate fragment additions given the 3D structures of a protein receptor and bound small-molecule ligand.     

Scaffold hopping in drug discovery using inductive logic programming

In chemoinformatics, searching for compounds which are structurally diverse and share a biological activity is called scaffold hopping. Scaffold hopping is important since it can be used to obtain alternative structures when the compound under development has unexpected side-effects. Pharmaceutical companies use scaffold hopping when they wish to circumvent prior patents for targets of interest. We propose a new method for scaffold hopping using inductive logic programming (ILP). ILP uses the observed spatial relationships between pharmacophore types in pretested active and inactive compounds and learns human-readable rules describing the diverse structures of active compounds. The ILP-based scaffold hopping method is compared to two previous algorithms (chemically advanced template search, CATS, and CATS3D) on 10 data sets with diverse scaffolds. The comparison shows that the ILP-based method is significantly better than random selection while the other two algorithms are not. In addition, the ILP-based method retrieves new active scaffolds which were not found by CATS and CATS3D. The results show that the ILP-based method is at least as good as the other methods in this study. ILP produces human-readable rules, which makes it possible to identify the three-dimensional features that lead to scaffold hopping. A minor variant of a rule learnt by ILP for scaffold hopping was subsequently found to cover an inhibitor identified by an independent study. This provides a successful result in a blind trial of the effectiveness of ILP to generate rules for scaffold hopping. We conclude that ILP provides a valuable new approach for scaffold hopping.     

Constructing virtual combinatorial fragment libraries based upon MDL Drug Data Report database

Structural analysis of known drugs or drug-like compounds provides important information for drug design. The 142553 drug molecules in the MDL Drug Data Report database were analyzed, and then the common structural features were extracted. According to the common structural features, drug molecules were segmented into 32017 fragments, including 13642 ring fragments, 10076 linker fragments, and 8299 side chain fragments. These fragments were further used to establish three types of virtual combinatorial fragment libraries: a basic framework library containing 13574 rings; a linker library of 8051 linkers and a pharmacophore library of 34244 fragments combined by rings and side chains. After energy minimization, all fragments in the above three libraries maintain reasonable geometrical features and spatial conformations, and would be useful for building a virtual combinatorial database and de novo drug design.     

Structure-based drug discovery for Plasmodium falciparum

X-ray crystallography is a technique which is finding increasing utility in the effort to find new antimalarial drugs. This is in spite of the serious difficulties often encountered in obtaining sufficient quantities of protein to crystallize. This review provides an overview of the Plasmodium falciparum proteins which have been crystallized with bound inhibitors and the methodology employed in the heterologous expression of these proteins. Lactate dehydrogenase, plasmepsin II, and triosphosphate isomerase are the most advanced targets of structure-based drug design, but nine other P. falciparum proteins have been crystallized with inhibitors as well, and this is clearly an area which is moving very quickly. Some consideration will also be given to the limitations of structure-based drug discovery with respect to known antimalarial drugs.     

Chromatographic separation for domoic acid using a fragment imprinted polymer

A method for real-time visualisation of reactions performed in-capillary by the technique of electrophoretically mediated microanalysis (EMMA) is described, using a two dimensional imaging detection system. The UV absorbance detector is based on a complementary metal oxide semiconductor (CMOS) active pixel sensor. Imaging of analyte peaks absorbing at 200 nm and migrating over length of 14 mm in the capillary dimension allowed measurement of velocities and lengths of reactant and product zones. By contrast with use of single point detection, velocities of species generated by reaction anywhere within the capillary are readily measured with CMOS imaging: this is of particular benefit for EMMA experiments where reaction occurs during zone overlap. For the oxidation of glutathione by hydrogen peroxide, reaction times were varied over the range 0.5-20 s by changing voltages for electrokinetic injection and zone migration, and reactant and product peak areas were obtained for kinetic analysis of the reaction. The use of EMMA conditions with CMOS imaging allows the whole process of reaction, separation and quantification to be carried out in nanolitre volumes on-capillary in a single run on a time scale of less than 5 min.