Sampling strategy optimization to increase statistical power in landscape genomics: A simulation‐based approach

An increasing number of studies are using landscape genomics to investigate local adaptation in wild and domestic populations. Implementation of this approach requires the sampling phase to consider the complexity of environmental settings and the burden of logistical constraints. These important aspects are often underestimated in the literature dedicated to sampling strategies. In this study, we computed simulated genomic data sets to run against actual environmental data in order to trial landscape genomics experiments under distinct sampling strategies. These strategies differed by design approach (to enhance environmental and/or geographical representativeness at study sites), number of sampling locations and sample sizes. We then evaluated how these elements affected statistical performances (power and false discoveries) under two antithetical demographic scenarios. Our results highlight the importance of selecting an appropriate sample size, which should be modified based on the demographic characteristics of the studied population. For species with limited dispersal, sample sizes above 200 units are generally sufficient to detect most adaptive signals, while in random mating populations this threshold should be increased to 400 units. Furthermore, we describe a design approach that maximizes both environmental and geographical representativeness of sampling sites and show how it systematically outperforms random or regular sampling schemes. Finally, we show that although having more sampling locations (between 40 and 50 sites) increase statistical power and reduce false discovery rate, similar results can be achieved with a moderate number of sites (20 sites). Overall, this study provides valuable guidelines for optimizing sampling strategies for landscape genomics experiments.     

RCSB Protein Data Bank: Enabling biomedical research and drug discovery

Analyses of publicly available structural data reveal interesting insights into the impact of the three‐dimensional (3D) structures of protein targets important for discovery of new drugs (e.g., G‐protein‐coupled receptors, voltage‐gated ion channels, ligand‐gated ion channels, transporters, and E3 ubiquitin ligases). The Protein Data Bank (PDB) archive currently holds > 155,000 atomic‐level 3D structures of biomolecules experimentally determined using crystallography, nuclear magnetic resonance spectroscopy, and electron microscopy. The PDB was established in 1971 as the first open‐access, digital‐data resource in biology, and is now managed by the Worldwide PDB partnership (wwPDB; wwPDB.org ). US PDB operations are the responsibility of the Research Collaboratory for Structural Bioinformatics PDB (RCSB PDB). The RCSB PDB serves millions of RCSB.org users worldwide by delivering PDB data integrated with ～40 external biodata resources, providing rich structural views of fundamental biology, biomedicine, and energy sciences. Recently published work showed that the PDB archival holdings facilitated discovery of ～90% of the 210 new drugs approved by the US Food and Drug Administration 2010–2016. We review user‐driven development of RCSB PDB services, examine growth of the PDB archive in terms of size and complexity, and present examples and opportunities for structure‐guided drug discovery for challenging targets (e.g., integral membrane proteins).     

Glutaminyl‐tRNA Synthetase from Pseudomonas aeruginosa: Characterization,structure, and development as a screening platform

Pseudomonas aeruginosa has a high potential for developing resistance to multiple antibiotics. The gene (glnS) encoding glutaminyl‐tRNA synthetase (GlnRS) from P. aeruginosa was cloned and the resulting protein characterized. GlnRS was kinetically evaluated and the K_M and k_cat^obs, governing interactions with tRNA, were 1.0 μM and 0.15 s^?1, respectively. The crystal structure of the α₂ form of P. aeruginosa GlnRS was solved to 1.9 Å resolution. The amino acid sequence and structure of P. aeruginosa GlnRS were analyzed and compared to that of GlnRS from Escherichia coli. Amino acids that interact with ATP, glutamine, and tRNA are well conserved and structure overlays indicate that both GlnRS proteins conform to a similar three‐dimensional structure. GlnRS was developed into a screening platform using scintillation proximity assay technology and used to screen ~2,000 chemical compounds. Three inhibitory compounds were identified and analyzed for enzymatic inhibition as well as minimum inhibitory concentrations against clinically relevant bacterial strains. Two of the compounds, BM02E04 and BM04H03, were selected for further studies. These compounds displayed broad‐spectrum antibacterial activity and exhibited moderate inhibitory activity against mutant efflux deficient strains of P. aeruginosa and E. coli. Growth of wild‐type strains was unaffected, indicating that efflux was likely responsible for the lack of sensitivity. The global mode of action was determined using time‐kill kinetics. BM04H03 did not inhibit the growth of human cell cultures at any concentration and BM02E04 only inhibit cultures at the highest concentration tested (400 μg/ml). In conclusion, GlnRS from P. aeruginosa is shown to have a structure similar to that of E. coli GlnRS and two natural product compounds were identified as inhibitors of P. aeruginosa GlnRS with the potential for utility as lead candidates in antibacterial drug development in a time of increased antibiotic resistance.     

Focus: Zoonotic Disease: New Series of Imidazoles Showed Promising Growth Inhibitory and Curative Potential Against Trypanosoma Infection

The Trypanosoma spp. cause animal and human trypanosomiasis characterized with appreciable health and economic burden mostly in developing nations. There is currently no effective therapy for this parasitic disease, due to poor drug efficacy, drug resistance, and unwanted toxicity, etc. Therefore, new anti-Trypanosoma agents are urgently needed. This study explored new series of imidazoles for anti-Trypanosoma properties in vitro and in vivo. The imidazoles showed moderate to strong and specific action against growth of T. congolense. For example, the efficacy of the imidazole compounds to restrict Trypanosoma growth in vitro was ≥ 12-fold specific towards T. congolense relative to the mammalian cells. Additionally, the in vivo study revealed that the imidazoles exhibited promising anti-Trypanosoma efficacy corroborating the in vitro anti-parasite capacity. In particular, three imidazole compounds (C1, C6, and C8) not only cleared the systemic parasite burden but cured infected rats after no death was recorded. On the other hand, the remaining five imidazole compounds (C2, C3, C4, C5, and C7) drastically reduced the systemic parasite load while extending survival time of the infected rats by 14 days as compared with control. Untreated control died 3 days post-infection, while the rats treated with diminazene aceturate were cured comparable to the results obtained for C1, C6, and C8. In conclusion, this is the first study demonstrating the potential of these new series of imidazoles to clear the systemic parasite burden in infected rats. Furthermore, a high selectivity index of imidazoles towards T. congolense in vitro and the oral LD₅₀ in rats support anti-parasite specific action. Together, findings support the anti-parasitic prospects of the new series of imidazole derivatives.     

Covid‐19.bioreproducibility.org: A web resource for SARS‐CoV‐2‐related structural models

The COVID‐19 pandemic has triggered numerous scientific activities aimed at understanding the SARS‐CoV‐2 virus and ultimately developing treatments. Structural biologists have already determined hundreds of experimental X‐ray, cryo‐EM, and NMR structures of proteins and nucleic acids related to this coronavirus, and this number is still growing. To help biomedical researchers, who may not necessarily be experts in structural biology, navigate through the flood of structural models, we have created an online resource, covid19.bioreproducibility.org, that aggregates expert‐verified information about SARS‐CoV‐2‐related macromolecular models. In this article, we describe this web resource along with the suite of tools and methodologies used for assessing the structures presented therein.     

Efficient screening of predictive biomarkers for individual treatment selection

The development of molecular diagnostic tools to achieve individualized medicine requires identifying predictive biomarkers associated with subgroups of individuals who might receive beneficial or harmful effects from different available treatments. However, due to the large number of candidate biomarkers in the large‐scale genetic and molecular studies, and complex relationships among clinical outcome, biomarkers, and treatments, the ordinary statistical tests for the interactions between treatments and covariates have difficulties from their limited statistical powers. In this paper, we propose an efficient method for detecting predictive biomarkers. We employ weighted loss functions of Chen et al. to directly estimate individual treatment scores and propose synthetic posterior inference for effect sizes of biomarkers. We develop an empirical Bayes approach, namely, we estimate unknown hyperparameters in the prior distribution based on data. We then provide efficient screening methods for the candidate biomarkers via optimal discovery procedure with adequate control of false discovery rate. The proposed method is demonstrated in simulation studies and an application to a breast cancer clinical study in which the proposed method was shown to detect the much larger numbers of significant biomarkers than existing standard methods.     

Structural Pharmacology of Voltage-Gated Sodium Channels

Voltage-gated sodium (Na_V) channels initiate and propagate action potentials in excitable tissues to mediate key physiological processes including heart contraction and nervous system function. Accordingly, Na_V channels are major targets for drugs, toxins and disease-causing mutations. Recent breakthroughs in cryo-electron microscopy have led to the visualization of human Na_V1.1, Na_V1.2, Na_V1.4, Na_V1.5 and Na_V1.7 channel subtypes at high-resolution. These landmark studies have greatly advanced our structural understanding of channel architecture, ion selectivity, voltage-sensing, electromechanical coupling, fast inactivation, and the molecular basis underlying Na_V channelopathies. Na_V channel structures have also been increasingly determined in complex with toxin and small molecule modulators that target either the pore module or voltage sensor domains. These structural studies have provided new insights into the mechanisms of pharmacological action and opportunities for subtype-selective Na_V channel drug design. This review will highlight the structural pharmacology of human Na_V channels as well as the potential use of engineered and chimeric channels in future drug discovery efforts.     

Interrogating 7TM receptors: Does texture in the question yield greater texture in the answer?

The key to detecting and classifying drug effect at seven transmembrane (7TM) receptors is the pharmacological assay. Drug discovery had been rooted in testing of molecules on intact animal tissue until technology provided high-throughput binding assays for screening. While this allowed for the testing of large numbers of molecules, it also limited detection to molecules that interfere with the interaction of the receptor with a defined probe (i.e., radioligand). The ability to monitor functional changes in cells (recombinant or natural) provided a huge leap forward. Earlier functional assays were tied to specific signaling pathways (i.e., cyclic AMP and calcium) but now label-free assays in live cells provide the opportunity to detect more ligands and more fully characterize their efficacy. These ideas will be discussed in terms of harnessing the phenomenon of “functional selectivity” for therapeutic advantage.