3D Similarity Search Research Articles

DMXP: A De Novo Small-Molecule 3D Structure Predictor with Graph Attention Networks.

Generating the three-dimensional (3D) structure of small molecules is crucial in both structure- and ligand-based drug design. Structure-based drug design needs bioactive conformations of compounds for lead identification and optimization. Ligand-based drug design techniques, such as 3D shape similarity search, 3D pharmacophore model, 3D-QSAR, etc., all require high-quality small-molecule ligand conformations to obtain reliable results. Although predicting a small molecular bioactive conformer requires information from the receptor, a crystal structure of the molecule is a proper approximation to its bioactive conformer in a specific receptor because the binding pose of a small molecule in its receptor's binding pockets should be energetically close to the crystal structures. This study presents a de novo small molecular structure predictor (dMXP) with graph attention networks based on crystal data derived from the Cambridge Structural Database (CSD) combined with molecular electrostatic information calculated by density-functional theory (DFT). Two featuring strategies (topological and atomic partial change features) were employed to explore the relation between these features and the 3D crystal structure of a small molecule. These features were then assembled to construct the holistic 3D crystal structure of a molecule. Molecular graphs were encoded using a graph attention mechanism to deal with the issues of the inconsistencies of local substructures contributing to the entire molecular structure. The root-mean-square deviation (RMSDs) of approximately 80% dMXP predicted structures and the native binding poses within receptors are less than 2.0 Å.

Thompson Sampling─An Efficient Method for Searching Ultralarge Synthesis on Demand Databases.

Over the last five years, virtual screening of ultralarge synthesis on-demand libraries has emerged as a powerful tool for hit identification in drug discovery programs. As these libraries have grown to tens of billions of molecules, we have reached a point where it is no longer cost-effective to screen every molecule virtually. To address these challenges, several groups have developed heuristic search methods to rapidly identify the best molecules on a virtual screen. This article describes the application of Thompson sampling (TS), an active learning approach that streamlines the virtual screening of large combinatorial libraries by performing a probabilistic search in the reagent space, thereby never requiring the full enumeration of the library. TS is a general technique that can be applied to various virtual screening modalities, including 2D and 3D similarity search, docking, and application of machine-learning models. In an illustrative example, we show that TS can identify more than half of the top 100 molecules from a docking-based virtual screen of 335 million molecules by evaluating 1% of the data set.

New aryl and acylsulfonamides as state-dependent inhibitors of Nav1.3 voltage-gated sodium channel

Voltage-gated sodium channels (Navs) play an essential role in neurotransmission, and their dysfunction is often a cause of various neurological disorders. The Nav1.3 isoform is found in the CNS and upregulated after injury in the periphery, but its role in human physiology has not yet been fully elucidated. Reports suggest that selective Nav1.3 inhibitors could be used as novel therapeutics to treat pain or neurodevelopmental disorders. Few selective inhibitors of this channel are known in the literature. In this work, we report the discovery of a new series of aryl and acylsulfonamides as state-dependent inhibitors of Nav1.3 channels. Using a ligand-based 3D similarity search and subsequent hit optimization, we identified and prepared a series of 47 novel compounds and tested them on Nav1.3, Nav1.5, and a selected subset also on Nav1.7 channels in a QPatch patch-clamp electrophysiology assay. Eight compounds had an IC50 value of less than 1 μM against the Nav1.3 channel inactivated state, with one compound displaying an IC50 value of 20 nM, whereas activity against the inactivated state of the Nav1.5 channel and Nav1.7 channel was approximately 20-fold weaker. None of the compounds showed use-dependent inhibition of the cardiac isoform Nav1.5 at a concentration of 30 μM. Further selectivity testing of the most promising hits was measured using the two-electrode voltage-clamp method against the closed state of the Nav1.1-Nav1.8 channels, and compound 15b displayed small, yet selective, effects against the Nav1.3 channel, with no activity against the other isoforms. Additional selectivity testing of promising hits against the inactivated state of the Nav1.3, Nav1.7, and Nav1.8 channels revealed several compounds with robust and selective activity against the inactivated state of the Nav1.3 channel among the three isoforms tested. Moreover, the compounds were not cytotoxic at a concentration of 50 μM, as demonstrated by the assay in human HepG2 cells (hepatocellular carcinoma cells). The novel state-dependent inhibitors of Nav1.3 discovered in this work provide a valuable tool to better evaluate this channel as a potential drug target.

Discovery of Novel HIV Protease Inhibitors Using Modern Computational Techniques.

The human immunodeficiency virus type 1 (HIV-1) has continued to be a global concern. With the new HIV incidence, the emergence of multi-drug resistance and the untoward side effects of currently used anti-HIV drugs, there is an urgent need to discover more efficient anti-HIV drugs. Modern computational tools have played vital roles in facilitating the drug discovery process. This research focuses on a pharmacophore-based similarity search to screen 111,566,735 unique compounds in the PubChem database to discover novel HIV-1 protease inhibitors (PIs). We used an in silico approach involving a 3D-similarity search, physicochemical and ADMET evaluations, HIV protease-inhibitor prediction (IC50/percent inhibition), rigid receptor–molecular docking studies, binding free energy calculations and molecular dynamics (MD) simulations. The 10 FDA-approved HIV PIs (saquinavir, lopinavir, ritonavir, amprenavir, fosamprenavir, atazanavir, nelfinavir, darunavir, tipranavir and indinavir) were used as reference. The in silico analysis revealed that fourteen out of the twenty-eight selected optimized hit molecules were within the acceptable range of all the parameters investigated. The hit molecules demonstrated significant binding affinity to the HIV protease (PR) when compared to the reference drugs. The important amino acid residues involved in hydrogen bonding and п-п stacked interactions include ASP25, GLY27, ASP29, ASP30 and ILE50. These interactions help to stabilize the optimized hit molecules in the active binding site of the HIV-1 PR (PDB ID: 2Q5K). HPS/002 and HPS/004 have been found to be most promising in terms of IC50/percent inhibition (90.15%) of HIV-1 PR, in addition to their drug metabolism and safety profile. These hit candidates should be investigated further as possible HIV-1 PIs with improved efficacy and low toxicity through in vitro experiments and clinical trial investigations.

Natural Compounds as DPP-4 Inhibitors: 3D-Similarity Search, ADME Toxicity, and Molecular Docking Approaches

Type 2 diabetes mellitus is one of the most common diseases of the 21st century, caused by a sedentary lifestyle, poor diet, high blood pressure, family history, and obesity. To date, there are no known complete cures for type 2 diabetes. To identify bioactive natural products (NPs) to manage type 2 diabetes, the NPs from the ZINC15 database (ZINC-NPs DB) were screened using a 3D shape similarity search, molecular docking approaches, and ADMETox approaches. Frequently, in silico studies result in asymmetric structures as “hit” molecules. Therefore, the asymmetrical FDA-approved diabetes drugs linagliptin (8-[(3R)-3-aminopiperidin-1-yl]-7-but-2-ynyl-3-methyl-1-[(4-methylquinazolin-2-yl)methyl]purine-2,6-dione), sitagliptin ((3R)-3-amino-1-[3-(trifluoromethyl)-6,8-dihydro-5H-[1,2,4]triazolo[4,3-a]pyrazin-7-yl]-4-(2,4,5-trifluorophenyl)butan-1-one), and alogliptin (2-[[6-[(3R)-3-aminopiperidin-1-yl]-3-methyl-2,4-dioxopyrimidin-1-yl]methyl]benzonitrile) were used as queries to virtually screen the ZINC-NPs DB and detect novel potential dipeptidyl peptidase-4 (DPP-4) inhibitors. The most promising NPs, characterized by the best sets of similarity and ADMETox features, were used during the molecular docking stage. The results highlight that 11 asymmetrical NPs out of 224,205 NPs are potential DPP-4 candidates from natural sources and deserve consideration for further in vitro/in vivo tests.

ER/AR Multi-Conformational Docking Server: A Tool for Discovering and Studying Estrogen and Androgen Receptor Modulators.

The prediction of the estrogen receptor (ER) and androgen receptor (AR) activity of a compound is quite important to avoid the environmental exposures of endocrine-disrupting chemicals. The Estrogen and Androgen Receptor Database (EARDB, http://eardb.schanglab.org.cn/) provides a unique collection of reported ERα, ERβ, or AR protein structures and known small molecule modulators. With the user-uploaded query molecules, molecular docking based on multi-conformations of a single target will be performed. Moreover, the 2D similarity search against known modulators is also provided. Molecules predicted with a low binding energy or high similarity to known ERα, ERβ, or AR modulators may be potential endocrine-disrupting chemicals or new modulators. The server provides a tool to predict the endocrine activity for compounds of interests, benefiting for the ER and AR drug design and endocrine-disrupting chemical identification.

Rapid Identification of Potential Drug Candidates from Multi-Million Compounds' Repositories. Combination of 2D Similarity Search with 3D Ligand/Structure Based Methods and In Vitro Screening.

Rapid in silico selection of target focused libraries from commercial repositories is an attractive and cost-effective approach in early drug discovery. If structures of active compounds are available, rapid 2D similarity search can be performed on multimillion compounds’ databases. This approach can be combined with physico-chemical parameter and diversity filtering, bioisosteric replacements, and fragment-based approaches for performing a first round biological screening. Our objectives were to investigate the combination of 2D similarity search with various 3D ligand and structure-based methods for hit expansion and validation, in order to increase the hit rate and novelty. In the present account, six case studies are described and the efficiency of mixing is evaluated. While sequentially combined 2D/3D similarity approach increases the hit rate significantly, sequential combination of 2D similarity with pharmacophore model or 3D docking enriched the resulting focused library with novel chemotypes. Parallel integrated approaches allowed the comparison of the various 2D and 3D methods and revealed that 2D similarity-based and 3D ligand and structure-based techniques are often complementary, and their combinations represent a powerful synergy. Finally, the lessons we learnt including the advantages and pitfalls of the described approaches are discussed.

Small Molecules of Natural Origin as Potential Anti-HIV Agents: A Computational Approach.

The human immunodeficiency virus type 1 (HIV-1), one of the leading causes of infectious death globally, generates severe damages to people’s immune systems and makes them susceptible to serious diseases. To date, there are no drugs that completely remove HIV from the body. This paper focuses on screening 224,205 natural compounds of ZINC15 NPs subset to identify those with bioactivity similar to non-nucleoside reverse transcriptase inhibitors (NNRTIs) as promising candidates to treat HIV-1. To reach the goal, an in silico approach involving 3D-similarity search, ADMETox, HIV protein-inhibitor prediction, docking, and MM-GBSA free-binding energies was trained. The FDA-approved HIV drugs, efavirenz, etravirine, rilpivirine, and doravirine, were used as queries. The prioritized compounds were subjected to ADMETox, docking, and MM-GBSA studies against HIV-1 reverse transcriptase (RT). Lys101, Tyr181, Tyr188, Trp229, and Tyr318 residues and free-binding energies have proved that ligands can stably bind to HIV-1 RT. Three natural products (ZINC37538901, ZINC38321654, and ZINC67912677) containing oxan and oxolan rings with hydroxyl substituents and one (ZINC2103242) having 3,6,7,8-tetrahydro-2H-pyrido[1,2-a]pyrazine-1,4-dione core exhibited comparable profiles to etravirine and doravirine, with ZINC2103242 being the most promising anti-HIV candidate in terms of drug metabolism and safety profile. These findings may open new avenues to guide the rational design of novel HIV-1 NNRTIs.

Identification of Therapeutic Drug Target of Stenotrophomonas maltophilia Through Subtractive Genomic Approach and in-silico Screening Based on 2D Similarity Filtration and Molecular Dynamic Simulation.

Stenotrophomonas maltophilia is a multi-drug resistant, gram-negative bacterium that causes opportunistic infections and is associated with high morbidity and mortality in severely immunocompromised individuals. The study aimed to find out the drug target and a novel inhibitor for Stenotrophomonas maltophilia. The current study focused on identifying specific drug targets by subtractive genomes analysis to determine the novel inhibitor for the specified target protein by virtual screening, molecular docking, and molecular simulation approach. In this study, we performed a subtractive genomics approach to identify the novel drug target for S.maltophilia. After obtaining the specific target, the next step was to identify inhibitors that include calculating 2D similarity search, molecular docking, and molecular simulation for drug development for S.maltophilia. With an efficient subtractive genomic approach, out of 4386 proteins, five unique targets were found, in which UDP-D-acetylmuramic (murF) was the most remarkable target. Further virtual screening, docking, and dynamics analyses resulted in the identification of seven novel inhibitors. Further, in vitro and in vivo bioassay of the identified novel inhibitors could facilitate effective drug use against S.maltophilia.

Virtual screening and drug repurposing experiments to identify potential novel selective MAO-B inhibitors for Parkinson's disease treatment.

The main study's purpose is to detect novel natural products (NPs) that are potentially selective MAO-B inhibitors and, additionally, to computationally reposition the marketed drugs with a new therapeutic role for Parkinson's disease. To reach the goals, 3D similarity search, docking, ADMETox, and drug repurposing approaches were employed. Thus, an unbiased benchmarking dataset was built including selective and nonselective inhibitors for MAO-B compliant with both ligand- and structure-based virtual screening approaches. A retrospective and prospective mining scenario was applied to SPECS NP and DrugBank databases to detect novel scaffolds with potential benefits for Parkinson's disease patients. Out of the three best selected natural products, cardamomin showed excellently predicted drug-like properties, superior pharmacological profile, and specific interactions with MAO-B active site, indicating a potential selectivity over MAO-B. Two marketed drugs, fenamisal and monobenzone, were proposed as promising candidates repurposed for Parkinson's disease. The application of shape, physicochemical, and electrostatic similarity searches protocol emerged as a plausible solution to explore MAO-B inhibitors selectivity. This protocol might serve as a rewarding tool in early drug discovery and can be extended to other protein targets.

Identification of a novel dual-target scaffold for 3CLpro and RdRp proteins of SARS-CoV-2 using 3D-similarity search, molecular docking, molecular dynamics and ADMET evaluation

The new SARS-CoV-2 coronavirus is the causative agent of the COVID-19 pandemic outbreak that affected whole the world with more than 6 million confirmed cases and over 370,000 deaths. At present, there are no effective treatments or vaccine for this disease, which constitutes a serious global health crisis. As the pandemic still spreading around the globe, it is of interest to use computational methods to identify potential inhibitors for the virus. The crystallographic structures of 3CLpro (PDB: 6LU7) and RdRp (PDB 6ML7) were used in virtual screening of 50000 chemical compounds obtained from the CAS Antiviral COVID19 database using 3D-similarity search and standard molecular docking followed by ranking and selection of compounds based on their binding affinity, computational techniques for the sake of details on the binding interactions, absorption, distribution, metabolism, excretion, and toxicity prediction; we report three 4-(morpholin-4-yl)-1,3,5-triazin-2-amine derivatives; two compounds (2001083-68-5 and 2001083-69-6) with optimal binding features to the active site of the main protease and one compound (833463-19-7) with optimal binding features to the active site of the polymerase for further consideration to fight COVID-19. The structural stability and dynamics of lead compounds at the active site of 3CLpro and RdRp were examined using molecular dynamics (MD) simulation. Essential dynamics demonstrated that the three complexes remain stable during simulation of 20 ns, which may be suitable candidates for further experimental analysis. As the identified leads share the same scaffold, they may serve as promising leads in the development of dual 3CLpro and RdRp inhibitors against SARS-CoV-2. Communicated by Ramaswamy H. Sarma.

Identification of Novel Human Serum Albumin (SA) Inhibitors from Scoparia Dulsis for Urolithiasis.

Urolithiasis is the process of forming stones in the kidney, bladder, and/or urinary tract. It has been reported that kidney stones are the third most common disorder among urinary diseases. At present, surgical procedures and Extracorporeal Shock Wave Lithotripsy (ESWL) are commonly employed for the treatment of Urolithiasis. The major drawback of these procedures is the recurrence of stones. This study aimed to identify potential natural inhibitors against human Serum Albumin (SA) from the plant Scoparia Dulsis for Urolithiasis. As protein-ligand interactions play a key role in structure- based drug design, this study screened 26 compounds from Scoparia Dulsis and investigated their binding affinity against SA by using molecular docking. The three dimensional (3D) structure of SA was retrieved from Protein Data Bank (PDB) and docked with PubChem structures of 26 compounds using PyRX docking tool through Autodock Vina. Moreover, a 3D similarity search on the PubChem database was performed to find the analogs of best scored compound and docking studies were performed. Drug-likeness studies were made using Swiss ADME and Lipinski's rule of five was performed for the compounds to evaluate their anti-urolithiatic activity. The results showed that citrusin c (Eugenyl beta-D-glucopyranoside) exhibited best binding energy of -8.1 kcal/mol with SA followed by aphidicolin, apigenin, luteolin and scutellarein. Two compounds (PubChem CID 46186820, PubChem CID 21579141) analogous to citrusin c were selected based on the lowest binding energy. This study, therefore, reveals that these compounds could be promising candidates for further evaluation for Urolithiasis prevention or management.

Partial Least Squares Discriminant Analysis and 3D Similarity Perspective Applied to Analyze Comprehensively the Selectivity of Glycogen Synthase Kinase 3 Inhibitors.

The current work was conducted to investigate the effectiveness of two conceptually distinct in silico ligand-based tools: Partial Least Squares Discriminant Analysis (PLS-DA) and 3D similarity, including shape, physico-chemical and electrostatics to classify target-specific pharmacophores with enrichment power for selective GSK-3 inhibitors against the phylogenetically related CDK-2, CDK-4, CDK-5 and PKC. All virtual screens were performed on four data sets of targets matched pairwise, including selective and nonselective inhibitors for GSK-3. The classification method PLS-DA results revealed that all obtained models are statistically robust according to the cross-validation and response permutation tests. Regarding selective GSK-3 inhibitors differentiation in terms of selectivity (Se), specificity (Sp), and accuracy (ACC), the PLS-DA models for CDK-4/GSK-3, and PKC/GSK-3 datasets are highly efficient discriminative. 3D similarity searches for CDK-4/GSK-3, PKC/GSK-3, and CDK-2/GSK-3 datasets using the most selective reference molecules lead to highest enrichments of selective GSK-3 inhibitors. EON yields excellent early and overall enrichments for ET_ST and ET_combo for most selective query for CDK-4/GSK-3. CDK-5/GSK-3 dataset didn't show consistent statistically significant enrichments for 3D similarity virtual screening. The current methodology is reliable and could be used as a powerful tool for the detection of potentially selective molecules targeting GSK-3.

Design and Selection of Novel C1s Inhibitors by In Silico and In Vitro Approaches.

The complement system is associated with various diseases such as inflammation or auto-immune diseases. Complement-targeted drugs could provide novel therapeutic intervention against the above diseases. C1s, a serine protease, plays an important role in the CS and could be an attractive target since it blocks the system at an early stage of the complement cascade. Designing C1 inhibitors is particularly challenging since known inhibitors are restricted to a narrow bioactive chemical space in addition selectivity over other serine proteases is an important requirement. The typical architecture of a small molecule inhibitor of C1s contains an amidine (or guanidine) residue, however, the discovery of non-amidine inhibitors might have high value, particularly if novel chemotypes and/or compounds displaying improved selectivity are identified. We applied various virtual screening approaches to identify C1s focused libraries that lack the amidine/guanidine functionalities, then the in silico generated libraries were evaluated by in vitro biological assays. While 3D structure-based methods were not suitable for virtual screening of C1s inhibitors, and a 2D similarity search did not lead to novel chemotypes, pharmacophore model generation allowed us to identify two novel chemotypes with submicromolar activities. In three screening rounds we tested altogether 89 compounds and identified 20 hit compounds (<10 μM activities; overall hit rate: 22.5%). The highest activity determined was 12 nM (1,2,4-triazole), while for the newly identified chemotypes (1,3-benzoxazin-4-one and thieno[2,3-d][1,3]oxazin-4-one) it was 241 nM and 549 nM, respectively.

Ligand-Based Pharmacophore Modeling Using Novel 3D Pharmacophore Signatures.

Pharmacophore modeling is a widely used strategy for finding new hit molecules. Since not all protein targets have available 3D structures, ligand-based approaches are still useful. Currently, there are just a few free ligand-based pharmacophore modeling tools, and these have a lot of restrictions, e.g., using a template molecule for alignment. We developed a new approach to 3D pharmacophore representation and matching which does not require pharmacophore alignment. This representation can be used to quickly find identical pharmacophores in a given set. Based on this representation, a 3D pharmacophore ligand-based modeling approach to search for pharmacophores which preferably match active compounds and do not match inactive ones was developed. The approach searches for 3D pharmacophore models starting from 2D structures of available active and inactive compounds. The implemented approach was successfully applied for several retrospective studies. The results were compared to a 2D similarity search, demonstrating some of the advantages of the developed 3D pharmacophore models. Also, the generated 3D pharmacophore models were able to match the 3D poses of known ligands from their protein-ligand complexes, confirming the validity of the models. The developed approach is available as an open-source software tool: http://www.qsar4u.com/pages/pmapper.php and https://github.com/meddwl/psearch.

Portraying the selectivity of GSK-3 inhibitors towards CDK-2 by 3D similarity and molecular docking

The striking structural resemblance between adenosine triphosphate (ATP) binding sites of glycogen synthase kinase-3 (GSK-3) and cyclin-dependent kinase-2 (CDK-2) raises numerous off-target selectivity problems in lead-identification processes that may jeopardize their progress into safe and effective drugs. The structural disparities between GSK-3 and CDK-2 in terms of inhibitors chemical space and binding site characteristics were investigated computationally by ligand-based (3D-similarity search) and structure-based (molecular docking) methods to reproduce the selectivity trend of indirubin derivatives. We attempted to assess distinctive key selectivity features of GSK-3 over CDK-2 with focus on indirubins and to provide a cascade virtual screening approach capable to identify suitable de novo GSK-3 selective scaffolds. Seven inhibitors with higher predicted interaction energies against GSK-3 compared to the highly active reference inhibitor were proposed. Concerted effects between 3D similarity search and docking afforded an exhaustive characterization of the binding site interactions. In spite of inherent challenges and limitations, the workflow developed hereby can be applied to other GSK-3 inhibitors, which display similar inhibitory profile against CDK-2, to rationally design potentially selective scaffolds.

In-silico guided discovery of novel CCR9 antagonists.

Antagonism of CCR9 is a promising mechanism for treatment of inflammatory bowel disease, including ulcerative colitis and Crohn's disease. There is limited experimental data on CCR9 and its ligands, complicating efforts to identify new small molecule antagonists. We present here results of a successful virtual screening and rational hit-to-lead campaign that led to the discovery and initial optimization of novel CCR9 antagonists. This work uses a novel data fusion strategy to integrate the output of multiple computational tools, such as 2D similarity search, shape similarity, pharmacophore searching, and molecular docking, as well as the identification and incorporation of privileged chemokine fragments. The application of various ranking strategies, which combined consensus and parallel selection methods to achieve a balance of enrichment and novelty, resulted in 198 virtual screening hits in total, with an overall hit rate of 18%. Several hits were developed into early leads through targeted synthesis and purchase of analogs.

Similarity search combined with docking and molecular dynamics for novel hAChE inhibitor scaffolds.

The main purpose of this study was to address the performance of virtual screening methods based on ligands and the protein structure of acetylcholinesterase (AChE) in order to retrieve novel human AChE (hAChE) inhibitors. In addition, a protocol was developed to identify novel hit compounds and propose new promising AChE inhibitors from the ZINC database with 10 million commercially available compounds. In this sense, 3D similarity searches using rapid overlay of chemical structures and similarity analysis through comparison of electrostatic overlay of docked hits were used to retrieve AChE inhibitors from collected databases. Molecular dynamics simulation of 100ns was carried out to study the best docked compounds from similarity searches. Some key residues were identified as crucial for the dual binding mode of inhibitor with the interaction site. All results indicated the relevant use of EON and docking strategy for identifying novel hit compounds as promising potential anticholinesterase candidates, and seven new structures were selected as potential hAChE inhibitors. Graphical abstract Compound N01 in the 4M0E hAChE crystallography structure from docking results. Yellow dashed lines Hydrogen bonds, blue dashed lines π-stacking interactions, green dashed lines cation-π interactions.

Spatial attributes of the four-helix bundle group of bacteriocins – The high-resolution structure of BacSp222 in solution

BacSp222 is a multifunctional bacteriocin produced by Staphylococcus pseudintermedius strain 222, an opportunistic pathogen of domestic animals. At micromolar concentrations, BacSp222 kills Gram-positive bacteria and is cytotoxic toward mammalian cells, while at nanomolar doses, it acts as an immunomodulatory factor, enhancing nitric oxide release in macrophage-like cell lines. The bacteriocin is a cationic, N-terminally formylated, 50-amino-acid-long linear peptide that is rich in tryptophan residues.In this study, the solution structure of BacSp222 was determined and compared to the currently known structures of similar bacteriocins. BacSp222 was isolated from a liquid culture medium in a uniformly 13C- and 15N-labeled form, and NMR data were collected. The structure was calculated based on NMR-derived constraints and consists of a rigid and tightly packed globular bundle of four alpha-helices separated by three short turns.Although the amino acid sequence of BacSp222 has no significant similarity to any known peptide or protein, a 3D structure similarity search indicates a close relation to other four-helix bundle-motif bacteriocins, such as aureocin A53, lacticin Q and enterocins 7A/7B. Assuming similar functions, biology, structure and physicochemical properties, we propose to distinguish the four-helix bundle bacteriocins as a new Type A in subclass IId of bacteriocins, containing linear, non-pediocin-like peptides.

Identification of Privileged Antichlamydial Natural Products by a Ligand-Based Strategy.

The obligate intracellular pathogen Chlamydia pneumoniae remains a difficult target for antimicrobial therapy. Owing to the permeability barrier placed by bacterial and host vacuolar membranes, as well as the propensity of the bacterium for persistent infections, treatment failures are common. Despite the urgent need for new antichlamydial compounds, their discovery is challenged by the technically demanding assay procedures and lack of validated targets. An alternative strategy of using naturally occurring compounds and their derivatives against C. pneumoniae is presented. The strategy consists of the application of ligand-based virtual screening to a natural product library of 502 compounds with the ChemGPS-NP chemography tool followed by in vitro antichlamydial assays. The reference set used for the 2D similarity search was constructed of 19 known antichlamydial compounds of plant origin. Based on the similarity screen, 53 virtual hits were selected for in vitro testing. Six compounds (leads) were identified that cause ≥50% C. pneumoniae growth inhibition and showed no impact on host cell viability. The leads fall into completely new antichlamydial chemotypes, one of them being mycophenolic acid (IC50 value 0.3 μM). The outcome indicates that using this flipped, target-independent strategy is useful for facilitating the antimicrobial lead discovery against challenging microbes.