Structure-based discovery of a novel, noncovalent inhibitor of AmpC beta-lactamase

Large-Scale Pretraining Improves Sample Efficiency of Active Learning-Based Virtual Screening

Virtual screening of large compound libraries to identify potential hit candidates is one of the earliest steps in drug discovery. As the size of commercially available compound collections grows exponentially to the scale of billions, active learning and Bayesian optimization have recently been proven as effective methods of narrowing down the search space. An essential component of those methods is a surrogate machine learning model that predicts the desired properties of compounds. An accurate model can achieve high sample efficiency by finding hits with only a fraction of the entire library being virtually screened. In this study, we examined the performance of a pretrained transformer-based language model and graph neural network in a Bayesian optimization active learning framework. The best pretrained model identifies 58.97% of the top-50,000 compounds after screening only 0.6% of an ultralarge library containing 99.5 million compounds, improving 8% over the previous state-of-the-art baseline. Through extensive benchmarks, we show that the superior performance of pretrained models persists in both structure-based and ligand-based drug discovery. Pretrained models can serve as a boost to the accuracy and sample efficiency of active learning-based virtual screening.

Class C β-Lactamases: Molecular Characteristics

Class C β-lactamases or cephalosporinases can be classified into two functional groups (1, 1e) with considerable molecular variability (≤20% sequence identity). These enzymes are mostly encoded by chromosomal and inducible genes and are widespread among bacteria, including Proteobacteria in particular. Molecular identification is based principally on three catalytic motifs (⁶⁴SXSK, ¹⁵⁰YXN, ³¹⁵KTG), but more than 70 conserved amino-acid residues (≥90%) have been identified, many close to these catalytic motifs. Nevertheless, the identification of a tiny, phylogenetically distant cluster (including enzymes from the genera Legionella, Bradyrhizobium, and Parachlamydia) has raised questions about the possible existence of a C2 subclass of β-lactamases, previously identified as serine hydrolases. In a context of the clinical emergence of extended-spectrum AmpC β-lactamases (ESACs), the genetic modifications observed in vivo and in vitro (point mutations, insertions, or deletions) during the evolution of these enzymes have mostly involved the Ω- and H-10/R2-loops, which vary considerably between genera, and, in some cases, the conserved triplet ¹⁵⁰YXN. Furthermore, the conserved deletion of several amino-acid residues in opportunistic pathogenic species of Acinetobacter, such as A. baumannii, A. calcoaceticus, A. pittii and A. nosocomialis (deletion of residues 304-306), and in Hafnia alvei and H. paralvei (deletion of residues 289-290), provides support for the notion of natural ESACs. The emergence of higher levels of resistance to β-lactams, including carbapenems, and to inhibitors such as avibactam is a reality, as the enzymes responsible are subject to complex regulation encompassing several other genes (ampR, ampD, ampG, etc.). Combinations of resistance mechanisms may therefore be at work, including overproduction or change in permeability, with the loss of porins and/or activation of efflux systems.

A practical guide to large-scale docking

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.

Property-Unmatched Decoys in Docking Benchmarks

Enrichment of ligands versus property-matched decoys is widely used to test and optimize docking library screens. However, the unconstrained optimization of enrichment alone can mislead, leading to false confidence in prospective performance. This can arise by over-optimizing for enrichment against property-matched decoys, without considering the full spectrum of molecules to be found in a true large library screen. Adding decoys representing charge extrema helps mitigate over-optimizing for electrostatic interactions. Adding decoys that represent the overall characteristics of the library to be docked allows one to sample molecules not represented by ligands and property-matched decoys but that one will encounter in a prospective screen. An optimized version of the DUD-E set (DUDE-Z), as well as Extrema and sets representing broad features of the library (Goldilocks), is developed here. We also explore the variability that one can encounter in enrichment calculations and how that can temper one's confidence in small enrichment differences. The new tools and new decoy sets are freely available at http://tldr.docking.org and http://dudez.docking.org.

Do phenothiazines possess antimicrobial and efflux inhibitory properties?

Antibiotic resistance is a global health concern; the rise of drug-resistant bacterial infections is compromising the medical advances that resulted from the introduction of antibiotics at the beginning of the 20th century. Considering that the presence of mutations within individuals in a bacterial population may allow a subsection to survive and propagate in response to selective pressure, as long as antibiotics are used in the treatment of bacterial infections, development of resistance is an inevitable evolutionary outcome. This, combined with the lack of novel antibiotics being released to the clinical market, means the need to develop alternative strategies to treat these resistant infections is critical. We discuss how the use of antibiotic adjuvants can minimise the appearance and impact of resistance. To this effect, several phenothiazine-derived drugs have been shown to potentiate the activities of antibiotics used to treat infections caused by Gram-positive and Gram-negative bacteria. Outside of their role as antipsychotic medications, we review the evidence to suggest that phenothiazines possess inherent antibacterial and efflux inhibitory properties enabling them to potentially combat drug resistance. We also discuss that understanding their mode of action is essential to facilitate the design of new phenothiazine derivatives or novel agents for use as antibiotic adjuvants.

Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases

The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as 'virtual screening' (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance. The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.

Specificity of extended O-aryloxycarbonyl hydroxamates as inhibitors of a class C β-lactamase

Class C β-lactamases have previously been shown to be efficiently inactivated by O-aryloxycarbonyl hydroxamates. O-Phenoxycarbonyl-N-benzyloxycarbonylhydroxylamine (1) and O-phenoxycarbonyl-N-(R)-[(4-amino-4-carboxy-1-butyl)oxycarbonyl]hydroxylamine (2), for example, were found to be effective inactivators. The present paper describes a structure-activity study of these molecules to better define the important structural elements for high inhibitory activity. The results show that a well-positioned hydrophobic element (which may interact with the Tyr221 residue of the enzyme) and a negatively charged element, e.g. a carboxylate group (which may interact with Arg204), are required for high reactivity with the enzyme. The new compounds were found to inactivate by forming a carbonyl cross-linked enzyme (probably Ser64OCONHLys 315) as for 1 rather than the inert hydroxamoyl derivative observed with 2.

Ultra-large library docking for discovering new chemotypes

Despite intense interest in expanding chemical space, libraries containing hundreds-of-millions to billions of diverse molecules have remained inaccessible. Here we investigate structure-based docking of 170 million make-on-demand compounds from 130 well-characterized reactions. The resulting library is diverse, representing over 10.7 million scaffolds that are otherwise unavailable. For each compound in the library, docking against AmpC β-lactamase (AmpC) and the D4 dopamine receptor were simulated. From the top-ranking molecules, 44 and 549 compounds were synthesized and tested for interactions with AmpC and the D4 dopamine receptor, respectively. We found a phenolate inhibitor of AmpC, which revealed a group of inhibitors without known precedent. This molecule was optimized to 77 nM, which places it among the most potent non-covalent AmpC inhibitors known. Crystal structures of this and other AmpC inhibitors confirmed the docking predictions. Against the D4 dopamine receptor, hit rates fell almost monotonically with docking score, and a hit-rate versus score curve predicted that the library contained 453,000 ligands for the D4 dopamine receptor. Of 81 new chemotypes discovered, 30 showed submicromolar activity, including a 180-pM subtype-selective agonist of the D4 dopamine receptor.

The Recognition of Unrelated Ligands by Identical Proteins

Unrelated ligands, often found in drug discovery campaigns, can bind to the same receptor, even with the same protein residues. To investigate how this might occur, and whether it might be typically possible to find unrelated ligands for the same drug target, we sought examples of topologically unrelated ligands that bound to the same protein in the same site. Seventy-six pairs of ligands, each bound to the same protein (152 complexes total), were considered, classified into three groups. In the first (31 pairs of complexes), unrelated ligands interacted largely with the same pocket residues through different functional groups. In the second group (39 pairs), the unrelated ligand in each pair engaged different residues, though still within the same pocket. The smallest group (6 pairs) contained ligands with different scaffolds but with shared functional groups interacting with the same residues. We found that there are multiple chemically unrelated but physically similar functional groups that can complement any given local protein pocket; when these functional group substitutions are combined within a single molecule, they lead to topologically unrelated ligands that can each well-complement a site. It may be that many active and orthosteric sites can recognize topologically unrelated ligands.

Probing the Binding Affinity by Jarzynski's Nonequilibrium Binding Free Energy and Rupture Time

Binding affinity of a small ligand to a receptor is the important quantity in drug design, and it might be characterized by different quantities. The most popular one is the binding free energy, which can be estimated by several methods in conventional molecular dynamics simulation. So far in steered molecular dynamics (SMD), one can use either the rupture force or nonequilibrium pulling work as a measure for binding affinity. In this paper, we have shown that the nonequilibrium binding free energy Δ G_neq^Jar, obtained by Jarzynski's equality at a finite pulling speed, has good correlation with experimental data on inhibition constants, implying that this quantity can be used as a good scoring function for binding affinity. A similar correlation has also been disclosed for binding and unbinding free energy barriers. Applying the SMD method to unbinding of 23 small compounds from the binding site of β-lactamase protein, a bacteria-produced enzyme, we have demonstrated that the rupture or unbinding time strongly correlates with experimental data with correlation level R ≈ 0.84. As follows from the Jarzynski's equality, the rupture time depends on the unbinding barrier exponentially. We show that Δ G_neq^Jar, the rupture time, and binding and unbinding free energy barriers are good descriptors for binding affinity. Our observation may be useful for fast screening of potential leads as the SMD simulation is not time-consuming. On the basis of nonequilibrium simulation, we disclosed that, in agreement with the experiment, the binding time is much longer than the unbinding one.

An Efficient Implementation of the Nwat-MMGBSA Method to Rescore Docking Results in Medium-Throughput Virtual Screenings

Nwat-MMGBSA is a variant of MM-PB/GBSA based on the inclusion of a number of explicit water molecules that are the closest to the ligand in each frame of a molecular dynamics trajectory. This method demonstrated improved correlations between calculated and experimental binding energies in both protein-protein interactions and ligand-receptor complexes, in comparison to the standard MM-GBSA. A protocol optimization, aimed to maximize efficacy and efficiency, is discussed here considering penicillopepsin, HIV1-protease, and BCL-XL as test cases. Calculations were performed in triplicates on both classic HPC environments and on standard workstations equipped by a GPU card, evidencing no statistical differences in the results. No relevant differences in correlation to experiments were also observed when performing Nwat-MMGBSA calculations on 4 or 1 ns long trajectories. A fully automatic workflow for structure-based virtual screening, performing from library set-up to docking and Nwat-MMGBSA rescoring, has then been developed. The protocol has been tested against no rescoring or standard MM-GBSA rescoring within a retrospective virtual screening of inhibitors of AmpC β-lactamase and of the Rac1-Tiam1 protein-protein interaction. In both cases, Nwat-MMGBSA rescoring provided a statistically significant increase in the ROC AUCs of between 20 and 30%, compared to docking scoring or to standard MM-GBSA rescoring.

Prediction of Ordered Water Molecules in Protein Binding Sites from Molecular Dynamics Simulations: The Impact of Ligand Binding on Hydration Networks

Water plays a major role in ligand binding and is attracting increasing attention in structure-based drug design. Water molecules can make large contributions to binding affinity by bridging protein–ligand interactions or by being displaced upon complex formation, but these phenomena are challenging to model at the molecular level. Herein, networks of ordered water molecules in protein binding sites were analyzed by clustering of molecular dynamics (MD) simulation trajectories. Locations of ordered waters (hydration sites) were first identified from simulations of high resolution crystal structures of 13 protein–ligand complexes. The MD-derived hydration sites reproduced 73% of the binding site water molecules observed in the crystal structures. If the simulations were repeated without the cocrystallized ligands, a majority (58%) of the crystal waters in the binding sites were still predicted. In addition, comparison of the hydration sites obtained from simulations carried out in the absence of ligands to those identified for the complexes revealed that the networks of ordered water molecules were preserved to a large extent, suggesting that the locations of waters in a protein–ligand interface are mainly dictated by the protein. Analysis of >1000 crystal structures showed that hydration sites bridged protein–ligand interactions in complexes with different ligands, and those with high MD-derived occupancies were more likely to correspond to experimentally observed ordered water molecules. The results demonstrate that ordered water molecules relevant for modeling of protein–ligand complexes can be identified from MD simulations. Our findings could contribute to development of improved methods for structure-based virtual screening and lead optimization.

Structure-Based Design and Discovery of New M2 Receptor Agonists

Muscarinic receptor agonists are characterized by apparently strict restraints on their tertiary or quaternary amine and their distance to an ester or related center. On the basis of the active state crystal structure of the muscarinic M₂ receptor in complex with iperoxo, we explored potential agonists that lacked the highly conserved functionalities of previously known ligands. Using structure-guided pharmacophore design followed by docking, we found two agonists (compounds 3 and 17), out of 19 docked and synthesized compounds, that fit the receptor well and were predicted to form a hydrogen-bond conserved among known agonists. Structural optimization led to compound 28, which was 4-fold more potent than its parent 3. Fortified by the discovery of this new scaffold, we sought a broader range of chemotypes by docking 2.2 million fragments, which revealed another three micromolar agonists unrelated either to 28 or known muscarinics. Even pockets as tightly defined and as deeply studied as that of the muscarinic reveal opportunities for the structure-based design and the discovery of new chemotypes.

FLIP: An assisting software in structure based drug design using fingerprint of protein-ligand interaction profiles

With the growing number of labor-intensive data in the pharmaceutical industries and public domain for protein-ligand complexes, a significant challenge is still remaining in managing and leveraging this vast information. Here, a standalone application is presented for analysis, organization, and illustration of structural data and molecular interactions for exploiting 3D-structures into simple 1D fingerprints. The utility of the approach was shown in unraveling a feasible solution for post-processing of docking results in parallel with providing fruitful analysis for users in order to investigate molecular interactions. Remarkably, all interaction possibilities including (hydrogen bond, water-bridged, electrostatic, and hydrophobic as well as π- π and cation-π interactions) are supported both in the form of fingerprints and compelling reports. These investigations are mainly considered based on right orientation, location, and geometry of the interacting pairs rather than the acquisition of the energy terms. The reasonable efficiency of our application in different models was comparable to recent methods It is clearly presented that FLIP provides a faster way to generate usable fingerprints for ligand and protein binding modes. FLIP is free for academic use and is available at: http://zistrayan.com/development/download/flip/package.zip.

Discovery of new GPCR ligands to illuminate new biology

Although a plurality of drugs target G-protein-coupled receptors (GPCRs), most have emerged from classical medicinal chemistry and pharmacology programs and resemble one another structurally and functionally. Though effective, these drugs are often promiscuous. With the realization that GPCRs signal via multiple pathways, and with the emergence of crystal structures for this family of proteins, there is an opportunity to target GPCRs with new chemotypes and confer new signaling modalities. We consider structure-based and physical screening methods that have led to the discovery of new reagents, focusing particularly on the former. We illustrate their use against previously untargeted or orphan GPCRs, against allosteric sites, and against classical orthosteric sites that selectively activate one downstream pathway over others. The ligands that emerge are often chemically novel, which can lead to new biological effects.

Fragment-Based Discovery and Optimization of Enzyme Inhibitors by Docking of Commercial Chemical Space

Fragment-based lead discovery has emerged as a leading drug development strategy for novel therapeutic targets. Although fragment-based drug discovery benefits immensely from access to atomic-resolution information, structure-based virtual screening has rarely been used to drive fragment discovery and optimization. Here, molecular docking of 0.3 million fragments to a crystal structure of cancer target MTH1 was performed. Twenty-two predicted fragment ligands, for which analogs could be acquired commercially, were experimentally evaluated. Five fragments inhibited MTH1 with IC₅₀ values ranging from 6 to 79 μM. Structure-based optimization guided by predicted binding modes and analogs from commercial chemical libraries yielded nanomolar inhibitors. Subsequently solved crystal structures confirmed binding modes predicted by docking for three scaffolds. Structure-guided exploration of commercial chemical space using molecular docking gives access to fragment libraries that are several orders of magnitude larger than those screened experimentally and can enable efficient optimization of hits to potent leads.

Search for non-lactam inhibitors of mtb β-lactamase led to its open shape in apo state: new concept for antibiotic design

Mtb β-lactamase (BlaC) is extremely efficient in hydrolyzing ß-lactam antibiotics which renders/leads to protection and/or resistance to this bug. There is a compelling need to develop new non-lactam inhibitors which can bind and inhibit BlaC, but cannot be hydrolyzed, thus neutralizing this survival mechanism of Mtb. Using the crystal structure of BlaC we screened 750000 purchasable compounds from ZINC Database for their theoretical affinity to the enzyme’s active site. 32 of the best hits of the compounds having tetra-, tri- and thiadi-azole moiety were tested in vitro, and 4 efficiently inhibited the enzymatic activity of recombinant BlaC. Characterization of the shape of BlaC−/+ inhibitors by small angle X-ray scattering (SAXS) brought forth that BlaC adopts: (1) an open shape (radius of gyration of 2.3 nm compared to 1.9 nm of crystal structures) in solution; (2) closed shape similar to observed crystal structure(s) in presence of effective inhibitor; and (3) a closed shape which opens up when a hydrolysable inhibitor is present in solution. New BlaC inhibitors were: 1-(4-(pyridin-3-yl)-thiazol-2-ylamino)-2-(7,8,9-triaza-bicyclo[4.3.0]nona-1(6),2,4,8-tetraen-7-yl)-ethanone; 8-butyl-3-((5-(pyridin-2-yl)-4H-1,2,4-triazol-3-ylamino)-formyl)-8-aza-bicyclo[4.3.0]nona-1(6),2,4-triene-7,9-dione; 1-(3-((5-(5-bromo-thiophen-2-yl)-1,3,4-oxadiazol-2-yl)-methoxy)-phenyl)-1H-1,2,3,4-tetraazole; and 1-(2,3-dimethyl-phenylamino)-2-(2-(1-(2-methoxy-5-methyl-phenyl)-1H-1,2,3,4-tetraazol-5-ylsulfanyl)-acetylamino)-ethanone. The open-close shape of BlaC questions the physiological significance of the closed shape known for BlaC−/+ inhibitors and paves new path for structure aided design of novel inhibitors.

Protein-Ligand Empirical Interaction Components for Virtual Screening

A major shortcoming of empirical scoring functions is that they often fail to predict binding affinity properly. Removing false positives of docking results is one of the most challenging works in structure-based virtual screening. Postdocking filters, making use of all kinds of experimental structure and activity information, may help in solving the issue. We describe a new method based on detailed protein-ligand interaction decomposition and machine learning. Protein-ligand empirical interaction components (PLEIC) are used as descriptors for support vector machine learning to develop a classification model (PLEIC-SVM) to discriminate false positives from true positives. Experimentally derived activity information is used for model training. An extensive benchmark study on 36 diverse data sets from the DUD-E database has been performed to evaluate the performance of the new method. The results show that the new method performs much better than standard empirical scoring functions in structure-based virtual screening. The trained PLEIC-SVM model is able to capture important interaction patterns between ligand and protein residues for one specific target, which is helpful in discarding false positives in postdocking filtering.

Structure-based discovery of opioid analgesics with reduced side effects

Morphine is an alkaloid from the opium poppy used to treat pain. The potentially lethal side effects of morphine and related opioids-which include fatal respiratory depression-are thought to be mediated by μ-opioid-receptor (μOR) signalling through the β-arrestin pathway or by actions at other receptors. Conversely, G-protein μOR signalling is thought to confer analgesia. Here we computationally dock over 3 million molecules against the μOR structure and identify new scaffolds unrelated to known opioids. Structure-based optimization yields PZM21-a potent Gi activator with exceptional selectivity for μOR and minimal β-arrestin-2 recruitment. Unlike morphine, PZM21 is more efficacious for the affective component of analgesia versus the reflexive component and is devoid of both respiratory depression and morphine-like reinforcing activity in mice at equi-analgesic doses. PZM21 thus serves as both a probe to disentangle μOR signalling and a therapeutic lead that is devoid of many of the side effects of current opioids.

An evolutionarily conserved allosteric site modulates beta-lactamase activity

Declining efficiency of antibiotic-inhibitor combinatorial therapies in treating beta-lactamase mediated resistance necessitates novel inhibitor development. Allosteric inhibition offers an alternative to conventional drugs that target the conserved active site. Here, we show that the evolutionarily conserved PWP triad located at the N-terminus of the H10 helix directly interacts with the allosteric site in TEM-1 beta-lactamase and regulates its activity. While point mutations in the PWP triad preserve the overall secondary structures around the allosteric site, they result in a more open and dynamic global structure with decreased chemical stability and increased aggregation propensity. These mutant enzymes with a less compact hydrophobic core around the allosteric site displayed significant activity loss. Detailed sequence and structure conservation analyses revealed that the PWP triad is an evolutionarily conserved motif unique to class A beta-lactamases aligning its allosteric site and hence is an effective potential target for enzyme regulation and selective drug design.

Docking Screens for Novel Ligands Conferring New Biology

It is now plausible to dock libraries of 10 million molecules against targets over several days or weeks. When the molecules screened are commercially available, they may be rapidly tested to find new leads. Although docking retains important liabilities (it cannot calculate affinities accurately nor even reliably rank order high-scoring molecules), it can often can distinguish likely from unlikely ligands, often with hit rates above 10%. Here we summarize the improvements in libraries, target quality, and methods that have supported these advances, and the open access resources that make docking accessible. Recent docking screens for new ligands are sketched, as are the binding, crystallographic, and in vivo assays that support them. Like any technique, controls are crucial, and key experimental ones are reviewed. With such controls, docking campaigns can find ligands with new chemotypes, often revealing the new biology that may be docking's greatest impact over the next few years.

Docking and Linking of Fragments To Discover Jumonji Histone Demethylase Inhibitors

Development of tool molecules that inhibit Jumonji demethylases allows for the investigation of cancer-associated transcription. While scaffolds such as 2,4-pyridinedicarboxylic acid (2,4-PDCA) are potent inhibitors, they exhibit limited selectivity. To discover new inhibitors for the KDM4 demethylases, enzymes overexpressed in several cancers, we docked a library of 600,000 fragments into the high-resolution structure of KDM4A. Among the most interesting chemotypes were the 5-aminosalicylates, which docked in two distinct but overlapping orientations. Docking poses informed the design of covalently linked fragment compounds, which were further derivatized. This combined approach improved affinity by ∼ 3 log-orders to yield compound 35 (Ki = 43 nM). Several hybrid inhibitors were selective for KDM4C over the related enzymes FIH, KDM2A, and KDM6B while lacking selectivity against the KDM3 and KDM5 subfamilies. Cocrystal structures corroborated the docking predictions. This study extends the use of structure-based docking from fragment discovery to fragment linking optimization, yielding novel KDM4 inhibitors.

Benchmarking Data Sets for the Evaluation of Virtual Ligand Screening Methods: Review and Perspectives

Virtual screening methods are commonly used nowadays in drug discovery processes. However, to ensure their reliability, they have to be carefully evaluated. The evaluation of these methods is often realized in a retrospective way, notably by studying the enrichment of benchmarking data sets. To this purpose, numerous benchmarking data sets were developed over the years, and the resulting improvements led to the availability of high quality benchmarking data sets. However, some points still have to be considered in the selection of the active compounds, decoys, and protein structures to obtain optimal benchmarking data sets.

Pharmacophore modeling using site-identification by ligand competitive saturation (SILCS) with multiple probe molecules

Receptor-based pharmacophore modeling is an efficient computer-aided drug design technique that uses the structure of the target protein to identify novel leads. However, most methods consider protein flexibility and desolvation effects in a very approximate way, which may limit their use in practice. The Site-Identification by Ligand Competitive Saturation (SILCS) assisted pharmacophore modeling protocol (SILCS-Pharm) was introduced recently to address these issues, as SILCS naturally takes both protein flexibility and desolvation effects into account by using full molecular dynamics simulations to determine 3D maps of the functional group-affinity patterns on a target receptor. In the present work, the SILCS-Pharm protocol is extended to use a wider range of probe molecules including benzene, propane, methanol, formamide, acetaldehyde, methylammonium, acetate and water. This approach removes the previous ambiguity brought by using water as both the hydrogen-bond donor and acceptor probe molecule. The new SILCS-Pharm protocol is shown to yield improved screening results, as compared to the previous approach based on three target proteins. Further validation of the new protocol using five additional protein targets showed improved screening compared to those using common docking methods, further indicating improvements brought by the explicit inclusion of additional feature types associated with the wider collection of probe molecules in the SILCS simulations. The advantage of using complementary features and volume constraints, based on exclusion maps of the protein defined from the SILCS simulations, is presented. In addition, reranking using SILCS-based ligand grid free energies is shown to enhance the diversity of identified ligands for the majority of targets. These results suggest that the SILCS-Pharm protocol will be of utility in rational drug design.

Covalent docking predicts substrates for haloalkanoate dehalogenase superfamily phosphatases

Enzyme function prediction remains an important open problem. Though structure-based modeling, such as metabolite docking, can identify substrates of some enzymes, it is ill-suited to reactions that progress through a covalent intermediate. Here we investigated the ability of covalent docking to identify substrates that pass through such a covalent intermediate, focusing particularly on the haloalkanoate dehalogenase superfamily. In retrospective assessments, covalent docking recapitulated substrate binding modes of known cocrystal structures and identified experimental substrates from a set of putative phosphorylated metabolites. In comparison, noncovalent docking of high-energy intermediates yielded nonproductive poses. In prospective predictions against seven enzymes, a substrate was identified for five. For one of those cases, a covalent docking prediction, confirmed by empirical screening, and combined with genomic context analysis, suggested the identity of the enzyme that catalyzes the orphan phosphatase reaction in the riboflavin biosynthetic pathway of Bacteroides.

Fluorescent TEM-1 β-lactamase with wild-type activity as a rapid drug sensor for in vitro drug screening

We report the development of a novel fluorescent drug sensor from the bacterial drug target TEM-1 β-lactamase through the combined strategy of Val²¹⁶→Cys²¹⁶ mutation and fluorophore labelling for in vitro drug screening. The Val²¹⁶ residue in TEM-1 is replaced with a cysteine residue, and the environment-sensitive fluorophore fluorescein-5-maleimide is specifically attached to the Cys²¹⁶ residue in the V216C mutant for sensing drug binding at the active site. The labelled V216C mutant has wild-type catalytic activity and gives stronger fluorescence when β-lactam antibiotics bind to the active site. The labelled V216C mutant can differentiate between potent and impotent β-lactam antibiotics and can distinguish active-site binders from non-binders (including aggregates formed by small molecules in aqueous solution) by giving characteristic time-course fluorescence profiles. Mass spectrometric, molecular modelling and trypsin digestion results indicate that drug binding at the active site is likely to cause the fluorescein label to stay away from the active site and experience weaker fluorescence quenching by the residues around the active site, thus making the labelled V216C mutant to give stronger fluorescence in the drug-bound state. Given the ancestor's role of TEM-1 in the TEM family, the fluorescent TEM-1 drug sensor represents a good model to demonstrate the general combined strategy of Val²¹⁶→Cys²¹⁶ mutation and fluorophore labelling for fabricating tailor-made fluorescent drug sensors from other clinically significant TEM-type β-lactamase variants for in vitro drug screening.

Increasing chemical space coverage by combining empirical and computational fragment screens

Most libraries for fragment-based drug discovery are restricted to 1,000-10,000 compounds, but over 500,000 fragments are commercially available and potentially accessible by virtual screening. Whether this larger set would increase chemotype coverage, and whether a computational screen can pragmatically prioritize them, is debated. To investigate this question, a 1281-fragment library was screened by nuclear magnetic resonance (NMR) against AmpC β-lactamase, and hits were confirmed by surface plasmon resonance (SPR). Nine hits with novel chemotypes were confirmed biochemically with KI values from 0.2 to low mM. We also computationally docked 290,000 purchasable fragments with chemotypes unrepresented in the empirical library, finding 10 that had KI values from 0.03 to low mM. Though less novel than those discovered by NMR, the docking-derived fragments filled chemotype holes from the empirical library. Crystal structures of nine of the fragments in complex with AmpC β-lactamase revealed new binding sites and explained the relatively high affinity of the docking-derived fragments. The existence of chemotype holes is likely a general feature of fragment libraries, as calculation suggests that to represent the fragment substructures of even known biogenic molecules would demand a library of minimally over 32,000 fragments. Combining computational and empirical fragment screens enables the discovery of unexpected chemotypes, here by the NMR screen, while capturing chemotypes missing from the empirical library and tailored to the target, with little extra cost in resources.

Targeting class A and C serine β-lactamases with a broad-spectrum boronic acid derivative

Production of β-lactamases (BLs) is the most widespread resistance mechanism adopted by bacteria to fight β-lactam antibiotics. The substrate spectrum of BLs has become increasingly broad, posing a serious health problem. Thus, there is an urgent need for novel BL inhibitors. Boronic acid transition-state analogues are able to reverse the resistance conferred by class A and C BLs. We describe a boronic acid analogue possessing interesting and potent broad-spectrum activity vs class A and C serine-based BLs. Starting from benzo(b)thiophene-2-boronic acid (BZBTH2B), a nanomolar non-β-lactam inhibitor of AmpC that can potentiate the activity of a third-generation cephalosporin against AmpC-producing resistant bacteria, we designed a novel broad-spectrum nanomolar inhibitor of class A and C BLs. Structure-based drug design (SBDD), synthesis, enzymology data, and X-ray crystallography results are discussed. We clarified the inhibitor binding geometry responsible for broad-spectrum activity vs serine-active BLs using double mutant thermodynamic cycle studies.

Fragment-based inhibitor discovery against β-lactamase

The production of β-lactamase is one of the primary resistance mechanisms used by Gram-negative bacterial pathogens to counter β-lactam antibiotics, such as penicillins, cephalosporins and carbapenems. There is an urgent need to develop novel β-lactamase inhibitors in response to ever evolving β-lactamases possessing an expanded spectrum of β-lactam hydrolyzing activity. Whereas traditional high-throughput screening has proven ineffective against serine β-lactamases, fragment-based approaches have been successfully employed to identify novel chemical matter, which in turn has revealed much about the specific molecular interactions possible in the active site of serine and metallo β-lactamases. In this review, we summarize recent progress in the field, particularly: the identification of novel inhibitor chemotypes through fragment-based screening; the use of fragment-protein structures to understand key features of binding hot spots and inform the design of improved leads; lessons learned and new prospects for β-lactamase inhibitor development using fragment-based approaches.

Automating crystallographic structure solution and refinement of protein-ligand complexes

High-throughput drug-discovery and mechanistic studies often require the determination of multiple related crystal structures that only differ in the bound ligands, point mutations in the protein sequence and minor conformational changes. If performed manually, solution and refinement requires extensive repetition of the same tasks for each structure. To accelerate this process and minimize manual effort, a pipeline encompassing all stages of ligand building and refinement, starting from integrated and scaled diffraction intensities, has been implemented in Phenix. The resulting system is able to successfully solve and refine large collections of structures in parallel without extensive user intervention prior to the final stages of model completion and validation.

On 3LEZ, a deep-sea halophilic protein with in vitro class-a β-lactamase activity: molecular-dynamics, docking, and reactivity simulations

This work shows that a deep-sea protein, 3LEZ, with known in vitro β-lactamase activity, proved stable, substantially in the conformation detected by X-ray diffraction of the crystal, when subjected to molecular-dynamics (MD) simulations under conditions compatible with shallow seas. Docking simulations showed that the β-lactamase active site S85 of 3LEZ (S70 in Ambler numbering) is the preferential binding pocket for not only β-lactam antibiotics and inhibitors, but, surprisingly, also for a wide variety of other biologically active compounds in various chemical classes, including marine metabolites. In line with the in vitro β-lactamase activity, a) affinities on docking β-lactam antibiotics and inhibitors onto 3LEZ were found to roughly parallel published K(m) and K(i) values, obtained from MichaelisMenten kinetics under room conditions, and b) DFT calculations agreed with experiments that the irreversible reaction of the β-lactamase inhibitor clavulanic acid with the whole S85 catalytic center of 3LEZ is spontaneous. These observations must be viewed in the light that a) the compounds in other chemical classes showed comparable affinities, and, in some cases, even higher than β-lactams, for the S85 active site, b) K(m) and K(i) data are not available at the high hydrostatic pressure of the deep sea, where 3LEZ is believed to have evolved, c) an inverse order of affinities for the β-lactams, with respect to both experimentation and simulations at room conditions, was observed from comparative docking simulations with 3LEZ derived from MD under high hydrostatic pressure. Although MD requires a general assessment for high hydrostatic pressure before c) above is given the same weight as all other observations, this work questions the conclusion that the in vitro determined β-lactamase activity represents the ecological role of 3LEZ.

Structure-based discovery of antagonists of nuclear receptor LRH-1

Liver receptor homolog 1 (nuclear receptor LRH-1, NR5A2) is an essential regulator of gene transcription, critical for maintenance of cell pluripotency in early development and imperative for the proper functions of the liver, pancreas, and intestines during the adult life. Although physiological hormones of LRH-1 have not yet been identified, crystallographic and biochemical studies demonstrated that LRH-1 could bind regulatory ligands and suggested phosphatidylinositols as potential hormone candidates for this receptor. No synthetic antagonists of LRH-1 are known to date. Here, we identify the first small molecule antagonists of LRH-1 activity. Our search for LRH-1 modulators was empowered by screening of 5.2 million commercially available compounds via molecular docking followed by verification of the top-ranked molecules using in vitro direct binding and transcriptional assays. Experimental evaluation of the predicted ligands identified two compounds that inhibit the transcriptional activity of LRH-1 and diminish the expression of the receptor's target genes. Among the affected transcriptional targets are co-repressor SHP (small heterodimer partner) as well as cyclin E1 (CCNE1) and G0S2 genes that are known to regulate cell growth and proliferation. Treatments of human pancreatic (AsPC-1), colon (HT29), and breast adenocarcinoma cells T47D and MDA-MB-468 with the LRH-1 antagonists resulted in the receptor-mediated inhibition of cancer cell proliferation. Our data suggest that specific antagonists of LRH-1 could be used as specific molecular probes for elucidating the roles of the receptor in different types of malignancies.

Biochemical characterization of CTX-M-15 from Enterobacter cloacae and designing a novel non-β-lactam-β-lactamase inhibitor

The worldwide dissemination of CTX-M type β-lactamases is a threat to human health. Previously, we have reported the spread of bla_CTX-M-15 gene in different clinical strains of Enterobacteriaceae from the hospital settings of Aligarh in north India. In view of the varying resistance pattern against cephalosporins and other β-lactam antibiotics, we intended to understand the correlation between MICs and catalytic activity of CTX-M-15. In this study, steady-state kinetic parameters and MICs were determined on E. coli DH5α transformed with bla_CTX-M-15 gene that was cloned from Enterobacter cloacae (EC-15) strain of clinical background. The effect of conventional β-lactamase inhibitors (clavulanic acid, sulbactam and tazobactam) on CTX-M-15 was also studied. We have found that tazobactam is the best among these inhibitors against CTX-M-15. The inhibition characteristic of tazobactam is defined by its very low IC₅₀ value (6 nM), high affinity (K_i = 0.017 µM) and better acylation efficiency (k₊₂/K′ = 0.44 µM⁻¹s⁻¹). It forms an acyl-enzyme covalent complex, which is quite stable (k₊₃ = 0.0057 s⁻¹). Since increasing resistance has been reported against conventional β-lactam antibiotic-inhibitor combinations, we aspire to design a non-β-lactam core containing β-lactamase inhibitor. For this, we screened ZINC database and performed molecular docking to identify a potential non-β-lactam based inhibitor (ZINC03787097). The MICs of cephalosporin antibiotics in combination with this inhibitor gave promising results. Steady-state kinetics and molecular docking studies showed that ZINC03787097 is a reversible inhibitor which binds non-covalently to the active site of the enzyme through hydrogen bonds and hydrophobic interactions. Though, it’s IC₅₀ (180 nM) is much higher than tazobactam, it has good affinity for CTX-M-15 (K_i = 0.388 µM). This study concludes that ZINC03787097 compound can be used as seed molecule to design more efficient non-β-lactam containing β-lactamase inhibitor that could evade pre-existing bacterial resistance mechanisms.

Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking

A key metric to assess molecular docking remains ligand enrichment against challenging decoys. Whereas the directory of useful decoys (DUD) has been widely used, clear areas for optimization have emerged. Here we describe an improved benchmarking set that includes more diverse targets such as GPCRs and ion channels, totaling 102 proteins with 22886 clustered ligands drawn from ChEMBL, each with 50 property-matched decoys drawn from ZINC. To ensure chemotype diversity, we cluster each target’s ligands by their Bemis–Murcko atomic frameworks. We add net charge to the matched physicochemical properties and include only the most dissimilar decoys, by topology, from the ligands. An online automated tool (http://decoys.docking.org) generates these improved matched decoys for user-supplied ligands. We test this data set by docking all 102 targets, using the results to improve the balance between ligand desolvation and electrostatics in DOCK 3.6. The complete DUD-E benchmarking set is freely available at http://dude.docking.org.

Structure-based ligand discovery for the protein-protein interface of chemokine receptor CXCR4

G-protein-coupled receptors (GPCRs) are key signaling molecules and are intensely studied. Whereas GPCRs recognizing small-molecules have been successfully targeted for drug discovery, protein-recognizing GPCRs, such as the chemokine receptors, claim few drugs or even useful small molecule reagents. This reflects both the difficulties that attend protein-protein interface inhibitor discovery, and the lack of structures for these targets. Imminent structure determination of chemokine receptor CXCR4 motivated docking screens for new ligands against a homology model and subsequently the crystal structure. More than 3 million molecules were docked against the model and then against the crystal structure; 24 and 23 high-scoring compounds from the respective screens were tested experimentally. Docking against the model yielded only one antagonist, which resembled known ligands and lacked specificity, whereas the crystal structure docking yielded four that were dissimilar to previously known scaffolds and apparently specific. Intriguingly, several were potent and relatively small, with IC(50) values as low as 306 nM, ligand efficiencies as high as 0.36, and with efficacy in cellular chemotaxis. The potency and efficiency of these molecules has few precedents among protein-protein interface inhibitors, and supports structure-based efforts to discover leads for chemokine GPCRs.

Catalytic versatility and backups in enzyme active sites: the case of serum paraoxonase 1

The origins of enzyme specificity are well established. However, the molecular details underlying the ability of a single active site to promiscuously bind different substrates and catalyze different reactions remain largely unknown. To better understand the molecular basis of enzyme promiscuity, we studied the mammalian serum paraoxonase 1 (PON1) whose native substrates are lipophilic lactones. We describe the crystal structures of PON1 at a catalytically relevant pH and of its complex with a lactone analogue. The various PON1 structures and the analysis of active-site mutants guided the generation of docking models of the various substrates and their reaction intermediates. The models suggest that promiscuity is driven by coincidental overlaps between the reactive intermediate for the native lactonase reaction and the ground and/or intermediate states of the promiscuous reactions. This overlap is also enabled by different active-site conformations: the lactonase activity utilizes one active-site conformation whereas the promiscuous phosphotriesterase activity utilizes another. The hydrolysis of phosphotriesters, and of the aromatic lactone dihydrocoumarin, is also driven by an alternative catalytic mode that uses only a subset of the active-site residues utilized for lactone hydrolysis. Indeed, PON1's active site shows a remarkable level of networking and versatility whereby multiple residues share the same task and individual active-site residues perform multiple tasks (e.g., binding the catalytic calcium and activating the hydrolytic water). Overall, the coexistence of multiple conformations and alternative catalytic modes within the same active site underlines PON1's promiscuity and evolutionary potential.

MM-GB/SA rescoring of docking poses

The critical issues in docking include the prediction of the correct binding pose and the accurate estimation of the corresponding binding affinity. Different docking methodologies have all been successful in reproducing the crystallographic binding modes, but struggle when predicting the corresponding binding affinities. The rescoring of docking poses using the MM-GB/SA technique has emerged as an important computational approach in structure-based lead optimization as it provides for congeneric molecules, clearly superior correlations with experimental data to those obtained with typical docking scoring functions. Although the technique has been collectively referred as MM-GB/SA, there are in fact many flavors in the literature. Here we describe the details of our MM-GB/SA scoring protocol, highlighting not only its strengths but also the limitations.

Anopheles gambiae odorant binding protein crystal complex with the synthetic repellent DEET: implications for structure-based design of novel mosquito repellents

Insect odorant binding proteins (OBPs) are the first components of the olfactory system to encounter and bind attractant and repellent odors emanating from various sources for presentation to olfactory receptors, which trigger relevant signal transduction cascades culminating in specific physiological and behavioral responses. For disease vectors, particularly hematophagous mosquitoes, repellents represent important defenses against parasitic diseases because they effect a reduction in the rate of contact between the vectors and humans. OBPs are targets for structure-based rational approaches for the discovery of new repellent or other olfaction inhibitory compounds with desirable features. Thus, a study was conducted to characterize the high resolution crystal structure of an OBP of Anopheles gambiae, the African malaria mosquito vector, in complex with N,N-diethyl-m-toluamide (DEET), one of the most effective repellents that has been in worldwide use for six decades. We found that DEET binds at the edge of a long hydrophobic tunnel by exploiting numerous non-polar interactions and one hydrogen bond, which is perceived to be critical for DEET's recognition. Based on the experimentally determined affinity of AgamOBP1 for DEET (K (d) of 31.3 μΜ) and our structural data, we modeled the interactions for this protein with 29 promising leads reported in the literature to have significant repellent activities, and carried out fluorescence binding studies with four highly ranked ligands. Our experimental results confirmed the modeling predictions indicating that structure-based modeling could facilitate the design of novel repellents with enhanced binding affinity and selectivity.

Synthesis and evaluation of new carbonic anhydrase inhibitors

A series of new sulfamide derivatives have been synthesized, their structures were confirmed by (1)H NMR and ESI-MS. Some target compounds were assessed by the tool of Dock6, and inhibition effects of all the new compounds on carbonic anhydrase II have been investigated. In addition, some compounds have been investigated for their antihypoxic effects in mice. Results indicated that nine target compounds exhibit as effectively as acetazolamide and 10 compounds have more potent inhibition effects on carbonic anhydrase II than acetazolamide. Three of them (I-8, I-18 and I'-3) can prolong markedly the survival time of mice in hypoxia, which are worth carrying out further studies.

Design, synthesis, crystal structures, and antimicrobial activity of sulfonamide boronic acids as β-lactamase inhibitors

We investigated a series of sulfonamide boronic acids that resulted from the merging of two unrelated AmpC β-lactamase inhibitor series. The new boronic acids differed in the replacement of the canonical carboxamide, found in all penicillin and cephalosporin antibiotics, with a sulfonamide. Surprisingly, these sulfonamides had a highly distinct structure-activity relationship from the previously explored carboxamides, high ligand efficiencies (up to 0.91), and K(i) values down to 25 nM and up to 23 times better for smaller analogues. Conversely, K(i) values were 10-20 times worse for larger molecules than in the carboxamide congener series. X-ray crystal structures (1.6-1.8 Å) of AmpC with three of the new sulfonamides suggest that this altered structure-activity relationship results from the different geometry and polarity of the sulfonamide versus the carboxamide. The most potent inhibitor reversed β-lactamase-mediated resistance to third generation cephalosporins, lowering their minimum inhibitory concentrations up to 32-fold in cell culture.

Current challenges in antimicrobial chemotherapy: focus on ß-lactamase inhibition

The use of the three classical beta-lactamase inhibitors (clavulanic acid, tazobactam and sulbactam) in combination with beta-lactam antibacterials is currently the most successful strategy to combat beta-lactamase-mediated resistance. However, these inhibitors are efficient in inactivating only class A beta-lactamases and the efficiency of the inhibitor/antibacterial combination can be compromised by several mechanisms, such as the production of naturally resistant class B or class D enzymes, the hyperproduction of AmpC or even the production of evolved inhibitor-resistant class A enzymes. Thus, there is an urgent need for the development of novel inhibitors. For serine active enzymes (classes A, C and D), derivatives of the beta-lactam ring such as 6-beta-halogenopenicillanates, beta-lactam sulfones, penems and oxapenems, monobactams or trinems seem to be potential starting points to design efficient molecules (such as AM-112 and LK-157). Moreover, a promising non-beta-lactam molecule, NXL-104, is now under clinical development. In contrast, an ideal inhibitor of metallo-beta-lactamases (class B) remains to be found, despite the huge number of potential molecules already described (biphenyl tetrazoles, cysteinyl peptides, mercaptocarboxylates, succinic acid derivatives, etc.). The search for such an inhibitor is complicated by the absence of a covalent intermediate in their catalytic mechanisms and the fact that beta-lactam derivatives often behave as substrates rather than as inhibitors. Currently, the most promising broad-spectrum inhibitors of class B enzymes are molecules presenting chelating groups (thiols, carboxylates, etc.) combined with an aromatic group. This review describes all the types of molecules already tested as potential beta-lactamase inhibitors and thus constitutes an update of the current status in beta-lactamase inhibitor discovery.

Structure-based discovery of A2A adenosine receptor ligands

The recent determination of X-ray structures of pharmacologically relevant GPCRs has made these targets accessible to structure-based ligand discovery. Here we explore whether novel chemotypes may be discovered for the A_2A adenosine receptor, based on complementarity to its recently determined structure. The A_2A adenosine receptor signals in the periphery and the CNS, with agonists explored as anti-inflammatory drugs and antagonists explored for neurodegenerative diseases. We used molecular docking to screen a 1.4 million compound database against the X-ray structure computationally and tested 20 high-ranking, previously unknown molecules experimentally. Of these 35% showed substantial activity with affinities between 200 nM and 9 μM. For the most potent of these new inhibitors, over 50-fold specificity was observed for the A_2A versus the related A₁ and A₃ subtypes. These high hit rates and affinities at least partly reflect the bias of commercial libraries toward GPCR-like chemotypes, an issue that we attempt to investigate quantitatively. Despite this bias, many of the most potent new ligands were novel, dissimilar from known ligands, providing new lead structures for modulation of this medically important target.

SiMMap: a web server for inferring site-moiety map to recognize interaction preferences between protein pockets and compound moieties

The protein–ligand interacting mechanism is essential to biological processes and drug discovery. The SiMMap server statistically derives site-moiety map with several anchors, which describe the relationship between the moiety preferences and physico-chemical properties of the binding site, from the interaction profiles between query target protein and its docked (or co-crystallized) compounds. Each anchor includes three basic elements: a binding pocket with conserved interacting residues, the moiety composition of query compounds and pocket–moiety interaction type (electrostatic, hydrogen bonding or van der Waals). We provide initial validation of the site-moiety map on three targets, thymidine kinase, and estrogen receptors of antagonists and agonists. Experimental results show that an anchor is often a hot spot and the site-moiety map can help to assemble potential leads by optimal steric, hydrogen bonding and electronic moieties. When a compound highly agrees with anchors of site-moiety map, this compound often activates or inhibits the target protein. We believe that the site-moiety map is useful for drug discovery and understanding biological mechanisms. The SiMMap web server is available at http://simfam.life.nctu.edu.tw/.

Three decades of beta-lactamase inhibitors

Since the introduction of penicillin, beta-lactam antibiotics have been the antimicrobial agents of choice. Unfortunately, the efficacy of these life-saving antibiotics is significantly threatened by bacterial beta-lactamases. beta-Lactamases are now responsible for resistance to penicillins, extended-spectrum cephalosporins, monobactams, and carbapenems. In order to overcome beta-lactamase-mediated resistance, beta-lactamase inhibitors (clavulanate, sulbactam, and tazobactam) were introduced into clinical practice. These inhibitors greatly enhance the efficacy of their partner beta-lactams (amoxicillin, ampicillin, piperacillin, and ticarcillin) in the treatment of serious Enterobacteriaceae and penicillin-resistant staphylococcal infections. However, selective pressure from excess antibiotic use accelerated the emergence of resistance to beta-lactam-beta-lactamase inhibitor combinations. Furthermore, the prevalence of clinically relevant beta-lactamases from other classes that are resistant to inhibition is rapidly increasing. There is an urgent need for effective inhibitors that can restore the activity of beta-lactams. Here, we review the catalytic mechanisms of each beta-lactamase class. We then discuss approaches for circumventing beta-lactamase-mediated resistance, including properties and characteristics of mechanism-based inactivators. We next highlight the mechanisms of action and salient clinical and microbiological features of beta-lactamase inhibitors. We also emphasize their therapeutic applications. We close by focusing on novel compounds and the chemical features of these agents that may contribute to a "second generation" of inhibitors. The goal for the next 3 decades will be to design inhibitors that will be effective for more than a single class of beta-lactamases.