Infrared spectroscopic characterization of sesamin, a dietary lignan natural product.

Sesamin, a lignan component of sesame seed oil, has shown pharmacologic benefits, such as anti-oxidative and anti-inflammatory qualities. However, the amount of data available to the field is surprisingly sparse, as for instance there is no known spectroscopic characterization of sesamin. This work provides the first experimental infrared spectrum of sesamin. Sesamin powder was subjected to experimental Fourier-transform infrared spectroscopy, and the resulting spectrum was compared to quantum chemical calculations of sesamin's stereoisomers in various hydration states. Major peaks of sesamin were assigned vibrational modes through comparison of computed and observed spectra. Multiple sesamin species may be present in a typical powder sample, coexisting with potential trace hydration.

Structure-Based Identification of Novel Histone Deacetylase 4 (HDAC4) Inhibitors.

Histone deacetylases (HDACs) are important cancer drug targets. Existing FDA-approved drugs target the catalytic pocket of HDACs, which is conserved across subfamilies (classes) of HDAC. However, engineering specificity is an important goal. Herein, we use molecular modeling approaches to identify and target potential novel pockets specific to Class IIA HDAC-HDAC4 at the interface between HDAC4 and the transcriptional corepressor component protein NCoR. These pockets were screened using an ensemble docking approach combined with consensus scoring to identify compounds with a different binding mechanism than the currently known HDAC modulators. Binding was compared in experimental assays between HDAC4 and HDAC3, which belong to a different family of HDACs. HDAC4 was significantly inhibited by compound 88402 but not HDAC3. Two other compounds (67436 and 134199) had IC50 values in the low micromolar range for both HDACs, which is comparable to the known inhibitor of HDAC4, SAHA (Vorinostat). However, both of these compounds were significantly weaker inhibitors of HDAC3 than SAHA and thus more selective, albeit to a limited extent. Five compounds exhibited activity on human breast carcinoma and/or urothelial carcinoma cell lines. The present result suggests potential mechanistic and chemical approaches for developing selective HDAC4 modulators.

Quantifying online citizen science: Dynamics and demographics of public participation in science.

Citizen scientists around the world are collecting data with their smartphones, performing scientific calculations on their home computers, and analyzing images on online platforms. These online citizen science projects are frequently lauded for their potential to revolutionize the scope and scale of data collection and analysis, improve scientific literacy, and democratize science. Yet, despite the attention online citizen science has attracted, it remains unclear how widespread public participation is, how it has changed over time, and how it is geographically distributed. Importantly, the demographic profile of citizen science participants remains uncertain, and thus to what extent their contributions are helping to democratize science. Here, we present the largest quantitative study of participation in citizen science based on online accounts of more than 14 million participants over two decades. We find that the trend of broad rapid growth in online citizen science participation observed in the early 2000s has since diverged by mode of participation, with consistent growth observed in nature sensing, but a decline seen in crowdsourcing and distributed computing. Most citizen science projects, except for nature sensing, are heavily dominated by men, and the vast majority of participants, male and female, have a background in science. The analysis we present here provides, for the first time, a robust 'baseline' to describe global trends in online citizen science participation. These results highlight current challenges and the future potential of citizen science. Beyond presenting our analysis of the collated data, our work identifies multiple metrics for robust examination of public participation in science and, more generally, online crowds. It also points to the limits of quantitative studies in capturing the personal, societal, and historical significance of citizen science.

Origins of glycan selectivity in streptococcal Siglec-like adhesins suggest mechanisms of receptor adaptation.

Bacterial binding to host receptors underlies both commensalism and pathogenesis. Many streptococci adhere to protein-attached carbohydrates expressed on cell surfaces using Siglec-like binding regions (SLBRs). The precise glycan repertoire recognized may dictate whether the organism is a strict commensal versus a pathogen. However, it is currently not clear what drives receptor selectivity. Here, we use five representative SLBRs and identify regions of the receptor binding site that are hypervariable in sequence and structure. We show that these regions control the identity of the preferred carbohydrate ligand using chimeragenesis and single amino acid substitutions. We further evaluate how the identity of the preferred ligand affects the interaction with glycoprotein receptors in human saliva and plasma samples. As point mutations can change the preferred human receptor, these studies suggest how streptococci may adapt to changes in the environmental glycan repertoire.

Piperazinobenzodiazepinones: New Encephalitic Alphavirus Inhibitors via Ring Expansion of 2-Dichloromethylquinazolinones.

Venezuelan and eastern equine encephalitis viruses are disease-causing, neuropathic pathogens with no approved treatment options in humans. While expanding the pharmacophoric model of antialphaviral amidines prepared via a quinazolinone rearrangement, we discovered that diamine-treated, 2-dihalomethylquinolinones unexpectedly afforded ring-expanded piperazine-fused benzodiazepinones. Notably, this new chemotype (19 examples) showed potent, submicromolar inhibition of virus-induced cell death, >7-log reduction of viral yield, and tractable structure-activity relationships across both viruses. Antiviral activity was confirmed in primary human neuronal cells. A mechanistic rationale for product formation is proposed, and key structural elements were comparatively modeled between a similarly substituted antiviral amidine and piperazinobenzodiazepinone prototypes to guide future antiviral development.

Using Big Data Analytics to "Back Engineer" Protein Conformational Selection Mechanisms.

In the living cells, proteins bind small molecules (or "ligands") through a "conformational selection" mechanism, where a subset of protein structures are capable of binding the small molecules well while most other protein structures are not capable of such binding. The present work uses machine learning approaches to identify, in a very large amount of protein:ligand complexes, what protein properties are associated with their capacity to bind small molecules. In order to do so, we calculate 40 physicochemical properties on about 1.5 millions of protein conformations: ligand and protein conformations. This work describes a machine learning approach to identify the unique physico-chemical descriptors of a protein that maximize the prediction rate of potential protein molecular conformations for the test case proteins ADORA2A (Adenosine A2a Receptor), ADRB2 (Adrenoceptor Beta 2) and OPRK1 (Opioid Receptor Kappa 1). We find adequate machine learning techniques can increase by an order of magnitude the identification of "binding protein conformations" in an otherwise very large ensemble of protein conformations, compared to random selection of protein conformations. This opens the door to the systematic identification of such "binding conformations" for proteins and provides a big data approach to the conformational selection mechanism.

Novel Big Data-Driven Machine Learning Models for Drug Discovery Application.

Most contemporary drug discovery projects start with a 'hit discovery' phase where small chemicals are identified that have the capacity to interact, in a chemical sense, with a protein target involved in a given disease. To assist and accelerate this initial drug discovery process, 'virtual docking calculations' are routinely performed, where computational models of proteins and computational models of small chemicals are evaluated for their capacities to bind together. In cutting-edge, contemporary implementations of this process, several conformations of protein targets are independently assayed in parallel 'ensemble docking' calculations. Some of these protein conformations, a minority of them, will be capable of binding many chemicals, while other protein conformations, the majority of them, will not be able to do so. This fact that only some of the conformations accessible to a protein will be 'selected' by chemicals is known as 'conformational selection' process in biology. This work describes a machine learning approach to characterize and identify the properties of protein conformations that will be selected (i.e., bind to) chemicals, and classified as potential binding drug candidates, unlike the remaining non-binding drug candidate protein conformations. This work also addresses the class imbalance problem through advanced machine learning techniques that maximize the prediction rate of potential protein molecular conformations for the test case proteins ADORA2A (Adenosine A2a Receptor) and OPRK1 (Opioid Receptor Kappa 1), and subsequently reduces the failure rates and hastens the drug discovery process.

Big Data analytics for improved prediction of ligand binding and conformational selection.

This research introduces new machine learning and deep learning approaches, collectively referred to as Big Data analytics techniques that are unique to address the protein conformational selection mechanism for protein:ligands complexes. The novel Big Data analytics techniques presented in this work enables efficient data processing of a large number of protein:ligand complexes, and provides better identification of specific protein properties that are responsible for a high probability of correct prediction of protein:ligand binding. The GPCR proteins ADORA2A (Adenosine A2a Receptor), ADRB2 (Adrenoceptor Beta 2), OPRD1 (Opioid receptor Delta 1) and OPRK1 (Opioid Receptor Kappa 1) are examined in this study using Big Data analytics techniques, which can efficiently process a huge ensemble of protein conformations, and significantly enhance the prediction of binding protein conformation (i.e., the protein conformations that will be selected by the ligands for binding) about 10-38 times better than its random selection counterpart for protein conformation selection. In addition to providing a Big Data approach to the conformational selection mechanism, this also opens the door to the systematic identification of such "binding conformations" for proteins. The physico-chemical features that are useful in predicting the "binding conformations" are largely, but not entirely, shared among the test proteins, indicating that the biophysical properties that drive the conformation selection mechanism may, to an extent, be protein-specific for the protein properties used in this work.

Turning crowds into communities: The collectives of online citizen science.

Over the past two decades, a number of digital platforms have been developed with the aim of engaging citizens in scientific research projects. The success of these platforms depends in no small part on their ability to attract and retain participants, turning diffuse crowds of users into active and productive communities. This article investigates how the collectives of online citizen science are formed and governed, and identifies two ideal-types of government, either based on self-interest or on universal norms of science. Based on an ethnography of three citizen science platforms and a series of interviews with their managers, we show how different technologies - rhetorical, of the self, social, and ontological - can be diversely combined to configure these collectives. We suggest that the shift from individual projects to platforms is a defining moment for online citizen science, during which the technologies that sustain the collectives are standardized and automatized in ways that make the crowd appear to be a natural community.

Sequential Dynamics of Stearoyl-CoA Desaturase-1(SCD1)/Ligand Binding and Unbinding Mechanism: A Computational Study.

Stearoyl-CoA desaturase-1 (SCD1 or delta-9 desaturase, D9D) is a key metabolic protein that modulates cellular inflammation and stress, but overactivity of SCD1 is associated with diseases, including cancer and metabolic syndrome. This transmembrane endoplasmic reticulum protein converts saturated fatty acids into monounsaturated fatty acids, primarily stearoyl-CoA into oleoyl-CoA, which are critical products for energy metabolism and membrane composition. The present computational molecular dynamics study characterizes the molecular dynamics of SCD1 with substrate, product, and as an apoprotein. The modeling of SCD1:fatty acid interactions suggests that: (1) SCD1:CoA moiety interactions open the substrate-binding tunnel, (2) SCD1 stabilizes a substrate conformation favorable for desaturation, and (3) SCD1:product interactions result in an opening of the tunnel, possibly allowing product exit into the surrounding membrane. Together, these results describe a highly dynamic series of SCD1 conformations resulting from the enzyme:cofactor:substrate interplay that inform drug-discovery efforts.

Cross-reactive immunogenicity of group A streptococcal vaccines designed using a recurrent neural network to identify conserved M protein linear epitopes.

The M protein of group A streptococci (Strep A) is a major virulence determinant and protective antigen. The N-terminal sequence of the protein defines the more than 200 M types of Strep A and also contains epitopes that elicit opsonic antibodies, some of which cross-react with heterologous M types. Current efforts to develop broadly protective M protein-based vaccines are directed at identifying potential cross-protective epitopes located in the N-terminal regions of cluster-related M proteins for use as vaccine antigens. In this study, we have used a comprehensive approach using the recurrent neural network ABCpred and IEDB epitope conservancy analysis tools to predict 16 residue linear B-cell epitopes from 117 clinically relevant M types of Strep A (~88% of global Strep A infections). To examine the immunogenicity of these epitope-based vaccines, nine peptides that together shared ≥60% sequence identity with 37 heterologous M proteins were incorporated into two recombinant hybrid protein vaccines, in which the epitopes were repeated 2 or 3 times, respectively. The combined immune responses of immunized rabbits showed that the vaccines elicited significant levels of antibodies against all nine vaccine epitopes present in homologous N-terminal 1-50 amino acid synthetic M peptides, as well as cross-reactive antibodies against 16 of 37 heterologous M peptides predicted to contain similar epitopes. The epitope-specificity of the cross-reactive antibodies was confirmed by ELISA inhibition assays and functional opsonic activity was assayed in HL-60-based bactericidal assays. The results provide important information for the future design of broadly protective M protein-based Strep A vaccines.

Calcium signaling on Jurkat T cells induced by microbeads coated with novel peptide ligands specific to human CD3ε.

CD3ε is expressed on T lymphocytes as a part of the T cell receptor (TCR)-CD3 complex. Together with other CD3 molecules, CD3ε is responsible for the activation of T cells via transducing the event of antigen recognition by the TCR into intracellular signaling cascades. The present study first aims to identify a novel peptide ligand that binds to human CD3ε in a specific manner and to perform an initial evaluation of its biological efficacy on the human T cell line, Jurkat cells. We screened a phage-display peptide library against human CD3ε using a subtractive biopanning process, from which we identified 13 phage clones displaying unique peptide sequences. One dominant phage clone displaying the 7 amino acid sequence of WSLGYTG, which occupied 90% of tested plaques (18 out of 20) after the 5th round of biopanning, demonstrated a superior binding behavior to other clones in the binding assays against recombinant CD3ε on microbeads or Jurkat cells. The synthesized peptide also showed specific binding to Jurkat cells in a dose-dependent manner but not to B cell lymphoma line, 2PK3 cells. Molecular modeling and docking simulation confirmed that the selected peptide ligand in an energetically stable conformation binds to a pocket of CD3ε that is not hidden by either CD3γ or CD3δ. Lastly, magnetic microbeads conjugated with the synthesized peptide ligands showed a weak but specific association with Jurkat cells and induced the calcium flux, a hallmark indication of proximal T cell receptor signaling, which gave rise to an enhancement of IL-2 section and cell proliferation. The novel peptide ligand and its various multivalent forms have a great potential in applications related to T cell biology and T cell immunotherapy.

The carboxylation status of osteocalcin has important consequences for its structure and dynamics.

BACKGROUND: The carboxylation status of Osteocalcin (Ocn) not only influences formation and structure in bones but also has important endocrine functions affecting energy metabolism and expenditure. In this study, the role of γ-carboxylation of the glutamate residues in the structure-dynamics-function relationship in Ocn is investigated. METHODS: Three forms of Ocn, differentially carboxylated at the Glu-17, 21 and 24 residues, along with a mutated form of Ocn carrying Glu/Ala mutations, are modeled and simulated using molecular dynamics (MD) simulation in the presence of calcium ions. RESULTS: Characterization of the global conformational dynamics of Ocn, described in terms of the orientational variations within its 3-helical domain, highlights large structural variations in the non-carboxylated osteocalcin (nOcn). The bi-carboxylated Ocn (bOcn) and tri-carboxylated (tOcn) species, in contrast, display relatively rigid tertiary structures, with the dynamics of most regions strongly correlated. Radial distribution functions calculated for both bOcn and tOcn show long-range ordering of the calcium ion distribution around the carboxylated glutamate (γGlu) residues, likely playing an important role in promoting stability of these Ocns. Additionally, the same calcium ions are observed to coordinate with neighboring γGlu, better shielding their negative charges and in turn stabilizing these systems more than do the singly coordinating calcium ions observed in the case of nOcn. bOcn is also found to exhibit a more helical C-terminal structure, that has been shown to activate its cellular receptor GPRC6A, highlighting the allosteric role of Ocn carboxylation in modulating the stability and binding potential of the active C-terminal. CONCLUSIONS: The carboxylation status of Ocn as well and its calcium coordination appear to have a direct influence on Ocn structure and dynamics, possibly leading to the known differences in Ocn biological function. GENERAL SIGNIFICANCE: Modification of Ocn sequence or its carboxylation state may provide the blueprint for developing high-affinity peptides targeting its cellular receptor GPRC6A, with therapeutic potential for treatment of metabolic disorders.

Structure based virtual screening identifies small molecule effectors for the sialoglycan binding protein Hsa.

Infective endocarditis (IE) is a cardiovascular disease often caused by bacteria of the viridans group of streptococci, which includes Streptococcus gordonii and Streptococcus sanguinis. Previous research has found that serine-rich repeat (SRR) proteins on the S. gordonii bacterial surface play a critical role in pathogenesis by facilitating bacterial attachment to sialylated glycans displayed on human platelets. Despite their important role in disease progression, there are currently no anti-adhesive drugs available on the market. Here, we performed structure-based virtual screening using an ensemble docking approach followed by consensus scoring to identify novel small molecule effectors against the sialoglycan binding domain of the SRR adhesin protein Hsa from the S. gordonii strain DL1. The screening successfully predicted nine compounds which were able to displace the native ligand (sialyl-T antigen) in an in vitro assay and bind competitively to Hsa. Furthermore, hierarchical clustering based on the MACCS fingerprints showed that eight of these small molecules do not share a common scaffold with the native ligand. This study indicates that SRR family of adhesin proteins can be inhibited by diverse small molecules and thus prevent the interaction of the protein with the sialoglycans. This opens new avenues for discovering potential drugs against IE.

Structure-based group A streptococcal vaccine design: Helical wheel homology predicts antibody cross-reactivity among streptococcal M protein-derived peptides.

Group A streptococcus (Strep A) surface M protein, an α-helical coiled-coil dimer, is a vaccine target and a major determinant of streptococcal virulence. The sequence-variable N-terminal region of the M protein defines the M type and also contains epitopes that promote opsonophagocytic killing of streptococci. Recent reports have reported considerable cross-reactivity among different M types, suggesting the prospect of identifying cross-protective epitopes that would constitute a broadly protective multivalent vaccine against Strep A isolates. Here, we have used a combination of immunological assays, structural biology, and cheminformatics to construct a recombinant M protein-based vaccine that included six Strep A M peptides that were predicted to elicit antisera that would cross-react with an additional 15 nonvaccine M types of Strep A. Rabbit antisera against this recombinant vaccine cross-reacted with 10 of the 15 nonvaccine M peptides. Two of the five nonvaccine M peptides that did not cross-react shared high sequence identity (≥50%) with the vaccine peptides, implying that high sequence identity alone was insufficient for cross-reactivity among the M peptides. Additional structural analyses revealed that the sequence identity at corresponding polar helical-wheel heptad sites between vaccine and nonvaccine peptides accurately distinguishes cross-reactive from non-cross-reactive peptides. On the basis of these observations, we developed a scoring algorithm based on the sequence identity at polar heptad sites. When applied to all epidemiologically important M types, this algorithm should enable the selection of a minimal number of M peptide-based vaccine candidates that elicit broadly protective immunity against Strep A.

A Politics of Intellectual Property: Creating a Patent System in Revolutionary France.

Economic interpretations, in particular the lens of the Industrial Revolution, have strongly influenced our understanding of the rise of intellectual property. This article examines the political origins of the 1791 patent law in France, which is usually seen as the birth of the modern patent system in that country. Although calls to reform the Old Regime's privileges of invention were increasingly frequent as the eighteenth century wore on, only the French Revolution provided the ideological resources necessary for such a transformation. The revolutionaries did more than just adopt the procedures of English legislation, such as replacing prior examination with a registration system. I argue that the new patents (brevets d'invention) reflected the Revolution's image of the ideal society-a society built on natural rights, property, and the social contract, and made of rational inventors and an enlightened public. In France, more so than in other countries, intellectual property was the child of a political revolution rather than industrial capitalism.

Ligand-Dependent Sodium Ion Dynamics within the A2A Adenosine Receptor: A Molecular Dynamics Study.

Sodium ions have long been known to reduce the binding of agonists in many class-A GPCRs while having little effect on antagonist binding. Here, using long-time scale classical all-atom molecular dynamics simulations, we explore, in atomic detail, the motion of sodium ions within the ligand-binding pocket of the A2A adenosine receptor (A2A-AR) both in the presence and absence of ligands and in the active and inactive state. We identify novel secondary ion binding sites within the pocket and find that the types of ion motions within the pocket are highly dependent on the presence and type of ligand within the pocket. Our results provide a first step toward developing a molecular understanding of the impact of sodium ions on class-A GPCRs.

Ensemble Docking in Drug Discovery: How Many Protein Configurations from Molecular Dynamics Simulations are Needed To Reproduce Known Ligand Binding?

Ensemble docking in drug discovery or chemical biology uses dynamical simulations of target proteins to generate binding site conformations for docking campaigns. We show that 600 ns molecular dynamics simulations of four G-protein-coupled receptors in their membrane environments generate ensembles of protein configurations that, collectively, are selected by 70?99% of the known ligands of these proteins. Therefore, the process of ligand recognition by conformational selection can be reproduced by combining molecular dynamics and docking calculations. Clustering of the molecular dynamics trajectories, however, does not necessarily identify the protein conformations that are most often selected by the ligands.

Examining inventions, shaping property: The savants and the French patent system.

In 1791, the Loi relative aux découvertes utiles instituted a new patent system in France. Because patents were seen as the expression of the natural right of inventors, prior examination was abolished. However, only a few years after the law was passed, an unofficial examination was reinstated, and it was entrusted to the Comité Consultatif des Arts et Manufactures - a consultative body composed of prominent scientists. I analyze the political significance of the involvement of the savants in the patent system, and based on the archives of the Comité, I study the scope and practicalities of the examination process, paying close attention to the ways through which the savants of the Comité directly intervened in the writing and drawing of specifications. I show how a distinct regime of intellectual property emerged in France and how it was constructed by the interests and norms of scientists, eager as they were to distinguish 'science' from 'industry' and establish the superiority of the former over the latter.