Membrane Permeating Macrocycles: Design Guidelines from Machine Learning.

The ability to predict cell-permeable candidate molecules has great potential to assist drug discovery projects. Large molecules that lie beyond the Rule of Five (bRo5) are increasingly important as drug candidates and tool molecules for chemical biology. However, such large molecules usually do not cross cell membranes and cannot access intracellular targets or be developed as orally bioavailable drugs. Here, we describe a random forest (RF) machine learning model for the prediction of passive membrane permeation rates developed using a set of over 1000 bRo5 macrocyclic compounds. The model is based on easily calculated chemical features/descriptors as independent variables. Our random forest (RF) model substantially outperforms a multiple linear regression model based on the same features and achieves better performance metrics than previously reported models using the same underlying data. These features include: (1) polar surface area in water, (2) the octanol-water partitioning coefficient, (3) the number of hydrogen-bond donors, (4) the sum of the topological distances between nitrogen atoms, (5) the sum of the topological distances between nitrogen and oxygen atoms, and (6) the multiple molecular path count of order 2. The last three features represent molecular flexibility, the ability of the molecule to adopt different conformations in the aqueous and membrane interior phases, and the molecular "chameleonicity." Guided by the model, we propose design guidelines for membrane-permeating macrocycles. It is anticipated that this model will be useful in guiding the design of large, bioactive molecules for medicinal chemistry and chemical biology applications.

Effect of Changing From Closed-Book to Formulary-Allowed Examinations.

Objective. To determine whether allowing final-year Bachelor of Pharmacy students to use a medicines formulary during examinations modified their learning behaviors and performance, and to investigate students' perceptions about having this resource available during examinations.Methods. Student performance and examination difficulty (as measured by classification of examination questions as high or low according to Bloom's taxonomy of learning) in second semester examinations (formulary allowed) was compared with first semester examinations (closed book) in successive years. Students completed a survey regarding their study and examination approaches and experiences after both semesters.Results. Examinations in semester two had more questions rated higher on Bloom's taxonomy than did examinations in semester one. Data were collected from student surveys for closed book examinations (response rate of 25% and 19% in 2015 and 2016, respectively) and open book examinations (response rate of 22% and 15% in 2015 and 2016, respectively). Students' study approaches, hours studied per week, and anxiety (all self-reported) did not differ between semesters one and two, but students in semester two spent more time studying with a formulary compared with students in semester one. Qualitative analysis of student comments revealed students preferred the formulary-allowed examinations over the closed-book examinations. The majority of students (68%) agreed with the statement: "Knowing that I will have access to the AMH [Australian Medicines Handbook] during the exams allowed me to pay more attention to higher level skills such as analysis and evaluation."Conclusion. When students were allowed to use a formulary for examinations, they studied more using their formulary prior to the examination. Students performed similarly on examinations with a greater proportion of questions addressing higher levels of Bloom's taxonomy and on closed-book examinations that were comparatively less cognitively challenging.

Markov State Model Analysis of Haloperidol Binding to the D3 Dopamine Receptor.

We have developed Markov state models (MSMs) and hidden Markov models (HMMs) that describe the binding of haloperidol to the D3 dopamine receptor. Haloperidol is an antipsychotic drug that binds with nanomolar affinity to the D3 dopamine receptor, where it functions as an inverse agonist. The models were constructed using an adaptive sampling approach from 519 individual molecular dynamics simulations totaling 122 µs of simulated time and encompass the entire drug binding process. They reveal short-lived metastable bound states and two distinct long-lived bound conformations that cannot be separated in affinity using our current methodology. This work extends the use of MSMs and HMMs to study ligand binding, which thus far has been limited to simpler systems.

The influence and manipulation of acid/base properties in drug discovery.

There is a growing awareness of the importance of acid/base properties in medicinal chemistry research. In many drug classes, ionisable groups are present that make critical interactions with the receptor and are essential for potency. Yet the presence of these groups may cause problems with oral bioavailability, pharmacokinetics, or toxicity. Manipulating pKa values during drug development or applying pro-drug techniques are strategies that can overcome potential deficits in a variety of these areas. Knowledge of drug ionisation states coupled with a consideration of pH-specific cellular environments can be used advantageously to target chemoresistance. As modern drug research ventures into drug candidates that exceed the rule of 5, such exploration requires an understanding of drug acid/base properties and how these factors affect ADMET characteristics.

Virtual Screening Against Carbohydrate-Binding Proteins: Evaluation and Application to Bacterial Burkholderia ambifaria Lectin.

Bacterial adhesion to human epithelia via lectins constitutes a therapeutic opportunity to prevent infection. Specifically, BambL (the lectin from Burkholderia ambifaria) is implicated in cystic fibrosis, where lectin-mediated bacterial adhesion to fucosylated lung epithelia is suspected to play an important role. We employed structure-based virtual screening to identify inhibitors of BambL-saccharide interaction with potential therapeutic value. To enable such discovery, a virtual screening protocol was iteratively developed via 194 retrospective screening protocols against 4 bacterial lectins (BambL, BC2L-A, FimH, and LecA) with known ligands. Specific attention was given to the rigorous evaluation of retrospective screening, including calculation of analytical errors for enrichment metrics. The developed virtual screening workflow used crystallographic constraints, pharmacophore filters, and a final manual selection step. The protocol was applied to BambL, predicting 15 active compounds from virtual libraries of approximately 7 million compounds. Experimental validation using fluorescence polarization confirmed micromolar inhibitory activity for two compounds, which were further characterized by isothermal titration calorimetry and surface plasmon resonance. Subsequent testing against LecB from Pseudomonas aeruginosa demonstrated binding specificity of one of the hit compounds. This report demonstrates the utility of virtual screening protocols, integrating ligand-based pharmacophore filtering and structure-based constraints, in the search for bacterial lectin inhibitors.

Structural basis for antibody targeting of the broadly expressed microbial polysaccharide poly-N-acetylglucosamine.

In response to the widespread emergence of antibiotic-resistant microbes, new therapeutic agents are required for many human pathogens. A non-mammalian polysaccharide, poly-N-acetyl-d-glucosamine (PNAG), is produced by bacteria, fungi, and protozoan parasites. Antibodies that bind to PNAG and its deacetylated form (dPNAG) exhibit promising in vitro and in vivo activities against many microbes. A human IgG1 mAb (F598) that binds both PNAG and dPNAG has opsonic and protective activities against multiple microbial pathogens and is undergoing preclinical and clinical assessments as a broad-spectrum antimicrobial therapy. Here, to understand how F598 targets PNAG, we determined crystal structures of the unliganded F598 antigen-binding fragment (Fab) and its complexes with N-acetyl-d-glucosamine (GlcNAc) and a PNAG oligosaccharide. We found that F598 recognizes PNAG through a large groove-shaped binding site that traverses the entire light- and heavy-chain interface and accommodates at least five GlcNAc residues. The Fab-GlcNAc complex revealed a deep binding pocket in which the monosaccharide and a core GlcNAc of the oligosaccharide were almost identically positioned, suggesting an anchored binding mechanism of PNAG by F598. The Fab used in our structural analyses retained binding to PNAG on the surface of an antibiotic-resistant, biofilm-forming strain of Staphylococcus aureus Additionally, a model of intact F598 binding to two pentasaccharide epitopes indicates that the Fab arms can span at least 40 GlcNAc residues on an extended PNAG chain. Our findings unravel the structural basis for F598 binding to PNAG on microbial surfaces and biofilms.

Free Energy Methods in Drug Design: Prospects of "Alchemical Perturbation" in Medicinal Chemistry.

Underpinning all drug discovery projects is the interaction between a drug and its target, usually a protein. Thus, improved methods for predicting the magnitude of protein-ligand interactions have the potential to improve the efficiency of drug development. In this review, we describe the principles of free energy methods used for the calculation of protein-ligand binding free energies, the challenges associated with these methods, and recent advances developed to address these difficulties. We then present case studies from 2005 to 2017, each demonstrating that alchemical free energy methods can assist rational drug design projects. We conclude that alchemical methods are becoming a feasible reality in medicinal chemistry research due to improved computational resources and algorithms and that alchemical free energy predictions methods are close to becoming a mainstream tool for medicinal chemists.

Molecular Simulations of Carbohydrates with a Fucose-Binding Burkholderia ambifaria Lectin Suggest Modulation by Surface Residues Outside the Fucose-Binding Pocket.

Burkholderia ambifaria is an opportunistic respiratory pathogen belonging to the Burkholderia cepacia complex, a collection of species responsible for the rapidly fatal cepacia syndrome in cystic fibrosis patients. A fucose-binding lectin identified in the B. ambifaria genome, BambL, is able to adhere to lung tissue, and may play a role in respiratory infection. X-ray crystallography has revealed the bound complex structures for four fucosylated human blood group epitopes (blood group B, H type 1, H type 2, and Lex determinants). The present study employed computational approaches, including docking and molecular dynamics (MD), to extend the structural analysis of BambL-oligosaccharide complexes to include four additional blood group saccharides (A, Lea, Leb, and Ley) and a library of blood-group-related carbohydrates. Carbohydrate recognition is dominated by interactions with fucose via a hydrogen-bonding network involving Arg15, Glu26, Ala38, and Trp79 and a stacking interaction with Trp74. Additional hydrogen bonds to non-fucose residues are formed with Asp30, Tyr35, Thr36, and Trp74. BambL recognition is dominated by interactions with fucose, but also features interactions with other parts of the ligands that may modulate specificity or affinity. The detailed computational characterization of the BambL carbohydrate-binding site provides guidelines for the future design of lectin inhibitors.

The cationic small molecule GW4869 is cytotoxic to high phosphatidylserine-expressing myeloma cells.

We have discovered that a small cationic molecule, GW4869, is cytotoxic to a subset of myeloma cell lines and primary myeloma plasma cells. Biochemical analysis revealed that GW4869 binds to anionic phospholipids such as phosphatidylserine - a lipid normally confined to the intracellular side of the cell membrane. However, interestingly, phosphatidylserine was expressed on the surface of all myeloma cell lines tested (n = 12) and 9/15 primary myeloma samples. Notably, the level of phosphatidylserine expression correlated well with sensitivity to GW4869. Inhibition of cell surface phosphatidylserine exposure with brefeldin A resulted in resistance to GW4869. Finally, GW4869 was shown to delay the growth of phosphatidylserine-high myeloma cells in vivo. To the best of our knowledge, this is the first example of using a small molecule to target phosphatidylserine on malignant cells. This study may provide the rationale for the development of phosphatidylserine-targeting small molecules for the treatment of surface phosphatidylserine-expressing cancers.

Antibody recognition of aberrant glycosylation on the surface of cancer cells.

Carbohydrate-binding antibodies and carbohydrate-based vaccines are being actively pursued as targeted immunotherapies for a broad range of cancers. Recognition of tumor-associated carbohydrates (glycans) by antibodies is predominantly towards terminal epitopes on glycoproteins and glycolipids on the surface of cancer cells. Crystallography along with complementary experimental and computational methods have been extensively used to dissect antibody recognition of glycan epitopes commonly found in cancer. We provide an overview of the structural biology of antibody recognition of tumor-associated glycans and propose potential rearrangements of these targets in the membrane that could dictate the complex biological activities of these antibodies against cancer cells.

Student Engagement with a Flipped Classroom Teaching Design Affects Pharmacology Examination Performance in a Manner Dependent on Question Type.

Objective: To investigate the relationship between student engagement with the key elements of a flipped classroom approach (preparation and attendance), their attitudes to learning, including strategy development, and their performance on two types of examination questions (knowledge recall and providing rational predictions when faced with novel scenarios). Methods. This study correlated student engagement with the flipped classroom and student disposition to learning with student ability to solve novel scenarios in examinations. Results. Students who both prepared for and attended classes performed significantly better on examination questions that required analysis of novel scenarios compared to students who did not prepare and missed classes. However, there was no difference for both groups of students on examination questions that required knowledge and comprehension. Student motivation and use of strategies correlated with higher examination scores on questions requiring novel scenario analysis. Conclusion. There is a synergistic relationship between class preparation and attendance. The combination of preparation and attendance was positively correlated to assessment type; the relationship was apparent for questions requiring students to solve novel problems but not for questions requiring knowledge or comprehension.

Ligand Binding Pathways of Clozapine and Haloperidol in the Dopamine D2 and D3 Receptors.

The binding of a small molecule ligand to its protein target is most often characterized by binding affinity and is typically viewed as an on/off switch. The more complex reality is that binding involves the ligand passing through a series of intermediate states between the solution phase and the fully bound pose. We have performed a set of 29 unbiased molecular dynamics simulations to model the binding pathways of the dopamine receptor antagonists clozapine and haloperidol binding to the D2 and D3 dopamine receptors. Through these simulations we have captured the binding pathways of clozapine and haloperidol from the extracellular vestibule to the orthosteric binding site and thereby, we also predict the bound pose of each ligand. These are the first long time scale simulations of haloperidol or clozapine binding to dopamine receptors. From these simulations, we have identified several important stages in the binding pathway, including the involvement of Tyr7.35 in a "handover" mechanism that transfers the ligand between the extracellular vestibule and Asp3.32. We have also performed interaction and cluster analyses to determine differences in binding pathways between the D2 and D3 receptors and identified metastable states that may be of use in drug design.

Antibody-Carbohydrate Recognition from Docked Ensembles Using the AutoMap Procedure.

Carbohydrate-protein recognition is vital to many processes in health and disease. In particular, elucidation of the structural basis of carbohydrate binding is important to the development of oligosaccharides and oligosaccharide mimetics as vaccines for infectious diseases and cancer. Computational structural techniques are valuable for the study of carbohydrate-protein recognition due to the challenges associated with experimental determination of carbohydrate-protein complexes. AutoMap is a computer program that we have developed to study protein-ligand recognition. AutoMap determines the interactions taking place in a set of highly ranked poses obtained from molecular docking and processes these to identify the protein residues most likely to be involved in interactions. In this protocol, we describe the use of AutoMap and illustrate its suitability for studying antibody recognition of the Lewis Y tetrasaccharide, which is a potential cancer vaccine antigen.

Molecular Characterisation of the Haemagglutinin Glycan-Binding Specificity of Egg-Adapted Vaccine Strains of the Pandemic 2009 H1N1 Swine Influenza A Virus.

The haemagglutinin (HA) glycan binding selectivity of H1N1 influenza viruses is an important determinant for the host range of the virus and egg-adaption during vaccine production. This study integrates glycan binding data with structure-recognition models to examine the impact of the K123N, D225G and Q226R mutations (as seen in the HA of vaccine strains of the pandemic 2009 H1N1 swine influenza A virus). The glycan-binding selectivity of three A/California/07/09 vaccine production strains, and purified recombinant A/California/07/09 HAs harboring these mutations was examined via a solid-phase ELISA assay. Wild-type A/California/07/09 recombinant HA bound specifically to α2,6-linked sialyl-glycans, with no affinity for the α2,3-linked sialyl-glycans in the array. In contrast, the vaccine virus strains and recombinant HA harboring the Q226R HA mutation displayed a comparable pattern of highly specific binding to α2,3-linked sialyl-glycans, with a negligible affinity for α2,6-linked sialyl-glycans. The D225G A/California/07/09 recombinant HA displayed an enhanced binding affinity for both α2,6- and α2,3-linked sialyl-glycans in the array. Notably its α2,6-glycan affinity was generally higher compared to its α2,3-glycan affinity, which may explain why the double mutant was not naturally selected during egg-adaption of the virus. The K123N mutation which introduces a glycosylation site proximal to the receptor binding site, did not impact the α2,3/α2,6 glycan selectivity, however, it lowered the overall glycan binding affinity of the HA; suggesting glycosylation may interfere with receptor binding. Docking models and 'per residues' scoring were employed to provide a structure-recognition rational for the experimental glycan binding data. Collectively, the glycan binding data inform future vaccine design strategies to introduce the D225G or Q226R amino acid substitutions into recombinant H1N1 viruses.

Structural biology of antibody recognition of carbohydrate epitopes and potential uses for targeted cancer immunotherapies.

Monoclonal antibodies represent the most successful class of biopharmaceuticals for the treatment of cancer. Mechanisms of action of therapeutic antibodies are very diverse and reflect their ability to engage in antibody-dependent effector mechanisms, internalize to deliver cytotoxic payloads, and display direct effects on cells by lysis or by modulating the biological pathways of their target antigens. Importantly, one of the universal changes in cancer is glycosylation and carbohydrate-binding antibodies can be produced to selectively recognize tumor cells over normal tissues. A promising group of cell surface antibody targets consists of carbohydrates presented as glycolipids or glycoproteins. In this review, we outline the basic principles of antibody-based targeting of carbohydrate antigens in cancer. We also present a detailed structural view of antibody recognition and the conformational properties of a series of related tissue-blood group (Lewis) carbohydrates that are being pursued as potential targets of cancer immunotherapy.

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review.

Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.

The carbohydrate-binding promiscuity of Euonymus europaeus lectin is predicted to involve a single binding site.

Euonymus europaeus lectin (EEL) is a carbohydrate-binding protein derived from the fruit of the European spindle tree. EEL was first identified for its erythrocyte agglutinating properties and specificity for B and H blood groups. However, a detailed molecular picture of the structural basis of carbohydrate recognition by EEL remains to be developed. In this study, we performed fluorescence titrations of a range of carbohydrates against EEL. Binding of EEL to a wide range of carbohydrates was observed, including a series of blood group-related carbohydrates, mannosides, chitotriose and sialic acid. Affinity was strongest for carbohydrates with H-related structures and the B trisaccharide. A homology model of EEL was produced from templates identified using the HHPred server, which employs hidden Markov models (HMMs) to identify templates. The HMM approach identified that the best templates for EEL were proteins featuring a ricin B-like (R-type) fold. Separate templates were used to model the core and binding site regions of the lectin. Through the use of constrained docking and spatial comparison with a template ligand, binding modes for the carbohydrate ligands were predicted. A relationship between the experimental binding energies and the computed binding energies of the selected docked poses was determined and optimized. Collectively, our results suggest that EEL utilizes a single site for recognition of carbohydrates terminating in a variety of monosaccharides.