Experimental versus predicted affinities for ligand binding to estrogen receptor: iterative selection and rescoring of docked poses systematically improves the correlation

Insulin purification-Innovation continuum via synthesis of fundamentals, technology, and modeling

Advancement in all disciplines (art, science, education, and engineering) requires a careful balance of disruption and advancement of classical techniques. Often technologies are created with a limited understanding of fundamental principles and are prematurely abandoned. Over time, knowledge improves, new opportunities are identified, and technology is reassessed in a different light leading to a renaissance. Recovery of biological products is currently experiencing such a renaissance. Crystallization is one example of an elegant and ancient technology that has been applied in many fields and was employed to purify insulins from naturally occurring sources. Crystallization can also be utilized to determine protein structures. However, a multitude of parameters can impact protein crystallization and the "hit rate" for identifying protein crystals is relatively low, so much so that the development of a crystallization process is often viewed as a combination of art and science even today. Supplying the worldwide requirement for insulin (and associated variants) requires significant advances in process intensification to support scale of production and to minimize the overall cost to enable broader access. Expanding beyond insulin, the increasing complexity and diversity of biologics agents challenge the current purification methodologies. To harness the full potential of biologics, there is a need to fully explore a broader range of purification technologies, including nonchromatographic approaches. This impetus requires one to challenge and revisit the classical techniques including crystallization, chromatography, and filtration from a different vantage point and with a new set of tools, including molecular modeling. Fortunately, computational biophysics tools now exist to provide insights into mechanisms of protein/ligand interactions and molecular assembly processes (including crystallization) that can be used to support de novo process development. For example, specific regions or motifs of insulins and ligands can be identified and used as targets to support crystallization or purification development. Although the modeling tools have been developed and validated for insulin systems, the same tools can be applied to more complex modalities and to other areas including formulation, where the issue of aggregation and concentration-dependent oligomerization could be mechanistically modeled. This paper will illustrate a case study juxtaposing historical approaches to insulin downstream processes to a recent production process highlighting the application and evolution of technologies. Insulin production from Escherichia coli via inclusion bodies is an elegant example since it incorporates virtually all the unit operations associated with protein production-recovery of cells, lysis, solubilization, refolding, purification, and crystallization. The case study will include an example of an innovative application of existing membrane technology to combine three-unit operations into one, significantly reducing solids handling and buffer consumption. Ironically, a new separations technology was developed over the course of the case study that could further simplify and intensify the downstream process, emphasizing and highlighting the ever-accelerating pace of innovation in downstream processing. Molecular biophysics modeling was also employed to enhance the mechanistic understanding of the crystallization and purification processes.

Effect-directed analysis of androgenic compounds from sewage sludges in China

The safety of municipal sewage sludge has raised great concerns because of the accumulation of large-scale endocrine disrupting chemicals in the sludge during wastewater treatment. The presence of contaminants in sludge can cause secondary pollution owing to inappropriate disposal mechanisms, posing potential risks to the environment and human health. Effect-directed analysis (EDA), involving an androgen receptor (AR) reporter gene bioassay, fractionation, and suspect and nontarget chemical analysis, were applied to identify causal AR agonists in sludge; 20 of the 30 sludge extracts exhibited significant androgenic activity. Among these, the extracts from Yinchuan, Kunming, and Shijiazhuang, which held the most polluted AR agonistic activities were prepared for extensive EDA, with the dihydrotestosterone (DHT)-equivalency of 2.5 - 4.5 ng DHT/g of sludge. Seven androgens, namely boldione, androstenedione, testosterone, megestrol, progesterone, and testosterone isocaproate, were identified in these strongest sludges together, along with testosterone cypionate, first reported in sludge media. These identified androgens together accounted for 55 %, 87 %, and 52 % of the effects on the sludge from Yinchuan, Shijiazhuang, and Kunming, respectively. This study elucidates the causative androgenic compounds in sewage sludge and provides a valuable reference for monitoring and managing androgens in wastewater treatment.

Dissecting the Enantioselective Neurotoxicity of Isocarbophos: Chiral Insight from Cellular, Molecular, and Computational Investigations

Chiral organophosphorus pollutants are found abundantly in the environment, but the neurotoxicity risks of these asymmetric chemicals to human health have not been fully assessed. Using cellular, molecular, and computational toxicology methods, this story is to explore the static and dynamic toxic actions and its stereoselective differences of chiral isocarbophos toward SH-SY5Y nerve cells mediated by acetylcholinesterase (AChE) and further dissect the microscopic basis of enantioselective neurotoxicity. Cell-based assays indicate that chiral isocarbophos exhibits strong enantioselectivity in the inhibition of the survival rates of SH-SY5Y cells and the intracellular AChE activity, and the cytotoxicity of (S)-isocarbophos is significantly greater than that of (R)-isocarbophos. The inhibitory effects of isocarbophos enantiomers on the intracellular AChE activity are dose-dependent, and the half-maximal inhibitory concentrations (IC₅₀) of (R)-/(S)-isocarbophos are 6.179/1.753 μM, respectively. Molecular experiments explain the results of cellular assays, namely, the stereoselective toxic actions of isocarbophos enantiomers on SH-SY5Y cells are stemmed from the differences in bioaffinities between isocarbophos enantiomers and neuronal AChE. In the meantime, the modes of neurotoxic actions display that the key amino acid residues formed strong noncovalent interactions are obviously different, which are related closely to the molecular structural rigidity of chiral isocarbophos and the conformational dynamics and flexibility of the substrate binding domain in neuronal AChE. Still, we observed that the stable "sandwich-type π-π stacking" fashioned between isocarbophos enantiomers and aromatic Trp-86 and Tyr-337 residues is crucial, which notably reduces the van der Waals' contribution (ΔG_vdW) in the AChE-(S)-isocarbophos complexes and induces the disparities in free energies during the enantioselective neurotoxic conjugations and thus elucidating that (S)-isocarbophos mediated by synaptic AChE has a strong toxic effect on SH-SY5Y neuronal cells. Clearly, this effort can provide experimental insights for evaluating the neurotoxicity risks of human exposure to chiral organophosphates from macroscopic to microscopic levels.

Kinetically selective and potent inhibitors of HDAC8

Histone deacetylase 8 (HDAC8) is an established and validated target for T-cell lymphoma and childhood neuroblastoma. The active site binding pocket of HDAC8 is highly conserved among all zinc-containing representatives of the histone deacetylase (HDAC) family. This explains that most HDACs are unselectively recognized by similar inhibitors featuring a zinc binding group (ZBG), a hydrophobic linker and a head group. In the light of this difficulty, the creation of isoenzyme-selectivity is one of the major challenges in the development of HDAC inhibitors. In a series of trifluoromethylketone inhibitors of HDAC8 compound 10 shows a distinct binding mechanism and a dramatically increased residence time (RT) providing kinetic selectivity against HDAC4. Combining the binding kinetics results with computational docking and binding site flexibility analysis suggests that 10 occupies the conserved catalytic site as well as an adjacent transient sub-pocket of HDAC8.

Structure activity relationship (SAR) and quantitative structure activity relationship (QSAR) studies showed plant flavonoids as potential inhibitors of dengue NS2B-NS3 protease

Background

Due to dengue virus disease, half of the world population is at severe health risk. Viral encoded NS2B-NS3 protease complex causes cleavage in the nonstructural region of the viral polyprotein. The cleavage is essentially required for fully functional viral protein. It has already been reported that if function of NS2B-NS3 complex is disrupted, viral replication is inhibited. Therefore, the NS2B-NS3 is a well-characterized target for designing antiviral drug.

Results

In this study docking analysis was performed with active site of dengue NS2B-NS3 protein with selected plant flavonoids. More than 100 flavonoids were used for docking analysis. On the basis of docking results 10 flavonoids might be considered as the best inhibitors of NS2B-NS3 protein. The interaction studies showed resilient interactions between ligand and receptor atoms. Furthermore, QSAR and SAR studies were conducted on the basis of NS2B-NS3 protease complex docking results. The value of correlation coefficient (r) 0.95 shows that there was a good correlation between flavonoid structures and selected properties.

Conclusion

We hereby suggest that plant flavonoids could be used as potent inhibitors of dengue NS2B-NS3 protein and can be used as antiviral agents against dengue virus. Out of more than hundred plant flavonoids, ten flavonoid structures are presented in this study. On the basis of best docking results, QSAR and SAR studies were performed. These flavonoids can directly work as anti-dengue drug or with little modifications in their structures.

Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening

Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure-based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine-learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been introduced. These machine-learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert-selected structural features can be strongly improved by a machine-learning approach based on nonlinear regression allied with comprehensive data-driven feature selection. Furthermore, the performance of classical SFs does not grow with larger training datasets and hence this performance gap is expected to widen as more training data becomes available in the future. Other topics covered in this review include predicting the reliability of a SF on a particular target class, generating synthetic data to improve predictive performance and modeling guidelines for SF development. WIREs Comput Mol Sci 2015, 5:405-424. doi: 10.1002/wcms.1225 For further resources related to this article, please visit the WIREs website.

Towards predictive docking at aminergic G-protein coupled receptors

G protein-coupled receptors (GPCRs) are hard to crystallize. However, attempts to predict their structure have boomed as a result of advancements in crystallographic techniques. This trend has allowed computer-aided molecular modeling of GPCRs. We analyzed the performance of four molecular modeling programs in pose evaluation of re-docked antagonists / inverse agonists to 11 original crystal structures of aminergic GPCRs using an induced fit-docking procedure. AutoDock and Glide were used for docking. AutoDock binding energy function, GlideXP, Prime MM-GB/SA, and YASARA binding function were used for pose scoring. Root mean square deviation (RMSD) of the best pose ranged from 0.09 to 1.58 Å, and median RMSD of the top 60 poses ranged from 1.47 to 3.83 Å. However, RMSD of the top pose ranged from 0.13 to 7.33 Å and ranking of the best pose ranged from the 1st to 60th out of 60 poses. Moreover, analysis of ligand-receptor interactions of top poses revealed substantial differences from interactions found in crystallographic structures. Bad ranking of top poses and discrepancies between top docked poses and crystal structures render current simple docking methods unsuitable for predictive modeling of receptor-ligand interactions. Prime MM-GB/SA optimized for 3NY9 by multiple linear regression did not work well at 3NY8 and 3NYA, structures of the same receptor with different ligands. However, 9 of 11 trajectories of molecular dynamics simulations by Desmond of top poses converged with trajectories of crystal structures. Key interactions were properly detected for all structures. This procedure also worked well for cross-docking of tested β2-adrenergic antagonists. Thus, this procedure represents a possible way to predict interactions of antagonists with aminergic GPCRs.

The Augmenting Effects of Desolvation and Conformational Energy Terms on the Predictions of Docking Programs against mPGES-1

In this study we introduce a rescoring method to improve the accuracy of docking programs against mPGES-1. The rescoring method developed is a result of extensive computational study in which different scoring functions and molecular descriptors were combined to develop consensus and rescoring methods. 127 mPGES-1 inhibitors were collected from literature and were segregated into training and external test sets. Docking of the 27 training set compounds was carried out using default settings in AutoDock Vina, AutoDock, DOCK6 and GOLD programs. The programs showed low to moderate correlation with the experimental activities. In order to introduce the contributions of desolvation penalty and conformation energy of the inhibitors various molecular descriptors were calculated. Later, rescoring method was developed as empirical sum of normalised values of docking scores, LogP and Nrotb. The results clearly indicated that LogP and Nrotb recuperate the predictions of these docking programs. Further the efficiency of the rescoring method was validated using 100 test set compounds. The accurate prediction of binding affinities for analogues of the same compounds is a major challenge for many of the existing docking programs; in the present study the high correlation obtained for experimental and predicted pIC₅₀ values for the test set compounds validates the efficiency of the scoring method.

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.

Synthesis and receptor binding in trans-CD ring-fused A-CD estrogens: comparison with the cis-fused isomers

Ligands which selectively activate only one of the estrogen receptors, ERα or ERβ, are current pharmaceutical targets. Previously, we have reported on substituted cis A-CD ligands in which the B-ring of the steroidal structure has been removed and cis refers the stereochemistry of the CD ring junction as compared to trans in estradiol. These compounds often showed good potency and selectivity for ERβ. Here we report the synthesis and binding affinities for a similar series of trans A-CD ligands, and compare them to the cis-series. Counterintuitively, trans A-CD ligands, which are structurally more closely related to the natural ligand estradiol, show weaker binding and less β-selectivity than their cis-counterparts.