dbSWEET: An Integrated Resource for SWEET Superfamily to Understand,Analyze and Predict the Function of Sugar Transporters in Prokaryotes and Eukaryotes

SWEET (Sweet Will Eventually be Exported Transporter) proteins have been recently discovered and form one of the three major families of sugar transporters. Homologs of SWEET are found in both prokaryotes and eukaryotes. Bacterial SWEET homologs have three transmembrane segments forming a triple-helical bundle and the functional form is dimers. Eukaryotic SWEETs have seven transmembrane helical segments forming two triple-helical bundles with a linker helix. Members of SWEET homologs have been shown to be involved in several important physiological processes in plants. However, not much is known regarding the biological significance of SWEET homologs in prokaryotes and in mammals. We have collected more than 2000 SWEET homologs from both prokaryotes and eukaryotes. For each homolog, we have modeled three different conformational states representing outward open, inward open and occluded states. We have provided details regarding substrate-interacting residues and residues forming the selectivity filter for each SWEET homolog. Several search and analysis options are available. The users can generate a phylogenetic tree and structure-based sequence alignment for selected set of sequences. With no metazoan SWEETs functionally characterized, the features observed in the selectivity filter residues can be used to predict the potential substrates that are likely to be transported across the metazoan SWEETs. We believe that this database will help the researchers to design mutational experiments and simulation studies that will aid to advance our understanding of the physiological role of SWEET homologs. This database is freely available to the scientific community at http://bioinfo.iitk.ac.in/bioinfo/dbSWEET/Home.     

DynBench3D,a Web-Resource to Dynamically Generate Benchmark Sets of Large Heteromeric Protein Complexes

Multi-protein machines are responsible for most cellular tasks, and many efforts have been invested in the systematic identification and characterization of thousands of these macromolecular assemblies. However, unfortunately, the (quasi) atomic details necessary to understand their function are available only for a tiny fraction of the known complexes. The computational biology community is developing strategies to integrate structural data of different nature, from electron microscopy to X-ray crystallography, to model large molecular machines, as it has been done for individual proteins and interactions with remarkable success. However, unlike for binary interactions, there is no reliable gold-standard set of three-dimensional (3D) complexes to benchmark the performance of these methodologies and detect their limitations. Here, we present a strategy to dynamically generate non-redundant sets of 3D heteromeric complexes with three or more components. By changing the values of sequence identity and component overlap between assemblies required to define complex redundancy, we can create sets of representative complexes with known 3D structure (i.e., target complexes). Using an identity threshold of 20% and imposing a fraction of component overlap of < 0.5, we identify 495 unique target complexes, which represent a real non-redundant set of heteromeric assemblies with known 3D structure. Moreover, for each target complex, we also identify a set of assemblies, of varying degrees of identity and component overlap, that can be readily used as input in a complex modeling exercise (i.e., template subcomplexes). We hope that resources like this will significantly help the development and progress assessment of novel methodologies, as docking benchmarks and blind prediction contests did. The interactive resource is accessible at https://DynBench3D.irbbarcelona.org.     

Kinetic modeling of [18F]VAT,a novel radioligand for positron emission tomography imaging vesicular acetylcholine transporter in non‐human primate brain

Molecular imaging of vesicular acetylcholine transporter (VACh T) in the brain provides an important cholinergic biomarker for the pathophysiology and treatment of dementias including Alzheimer's disease. In this study, kinetics modeling methods were applied and compared for quantifying regional brain uptake of the VACh T‐specific positron emission tomography radiotracer, ((?)‐(1‐(‐8‐(2‐fluoroethoxy)‐3‐hydroxy‐1,2,3,4‐tetrahydronaphthalen‐2‐yl)piperidin‐4‐yl)(4‐fluorophenyl)‐methanone) ([¹⁸F]VAT ) in macaques. Total volume distribution (V _T ) estimates were compared for one‐tissue compartment model (1TCM ), two‐tissue compartment model (2TCM ), Logan graphic analysis (LoganAIF ) and multiple linear analysis (MA 1) with arterial blood input function using data from three macaques. Using the cerebellum‐hemispheres as the reference region with data from seven macaques, three additional models were compared: reference tissue model (RTM ), simplified RTM (SRTM ), and Logan graphic analysis (LoganREF ). Model selection criterion indicated that a) 2TCM and SRTM were the most appropriate kinetics models for [¹⁸F]VAT ; and b) SRTM was strongly correlated with 2TCM (Pearson's coefficients r  > 0.93, p  < 0.05). Test–retest studies demonstrated that [¹⁸F]VAT has good reproducibility and reliability (TRV < 10%, ICC > 0.72). These studies demonstrate [¹⁸F]VAT is a promising VACh T positron emission tomography tracer for quantitative assessment of VACh T levels in the brain of living subjects.

     

Can hyperparasitoids cause large‐scale outbreaks of insect herbivores?

Synchronous population fluctuations occur in many species and have large economic impacts, but remain poorly understood. Dispersal, climate and natural enemies have been hypothesized to cause synchronous population fluctuations across large areas. For example, insect herbivores cause extensive forest defoliation and have many natural enemies, such as parasitoids, that may cause landscape‐scale changes in density. Between outbreaks, parasitoid‐caused mortality of hosts/herbivores is high, but it drops substantially during outbreak episodes. Because of their essential role in regulating herbivore populations, we need to include parasitoids in spatial modelling approaches to more effectively manage insect defoliation. However, classic host‐parasitoid population models predict parasitoid density, and parasitoid density is difficult to relate to host‐level observations of parasitoid‐caused mortality. We constructed a novel model to study how parasitoids affect insect outbreaks at the landscape scale. The model represents metacommunity dynamics, in which herbivore regulation, colonisation and extinction are driven by interactions with the forest, primary parasitoids and hyperparasitoids. The model suggests that parasitoid spatial dynamics can produce landscape‐scale outbreaks. Our results propose the testable prediction that hyperparasitoid prevalence should increase just before the onset of an outbreak because of hyperparasitoid overexploitation. If verified empirically, hyperparasitoid distribution could provide a biotic indicator that an outbreak will occur.     

竞争和景观格局相互作用对外来入侵物种传播影响的动态模拟

通过中性景观模型和元胞自动机模型模拟了不同景观格局、不同竞争力和关键种群特征的外来入侵物种的传播动态,模拟结果表明:(1)竞争力强的外来入侵物种,可利用生境面积越大,集聚度越高,越有利于其入侵和传播,而对竞争力弱的外来入侵物种来说,可利用生境面积越小,越分散,越有利于其生存。竞争力强可以有效利用整块的高集聚度的资源,而弱竞争力的外来入侵物种为了逃避土著种的竞争往往趋向于分布在分散的小生境中;(2)可利用生境面积大于50%时,景观格局集聚度越小,竞争力弱的外来入侵物种适应环境的弛豫时间越长;(3)外来入侵物种的传播与种子产量呈正相关关系,与繁殖年龄呈负相关关系,拟合关系因景观格局和竞争而异;(4)平均迁移距离对外来入侵物种传播的影响最大,在管理过程中应根据不同的目标,不同的景观格局、竞争力和种群特征选择合适的管理措施。     

Identification of line‐specific strategies for improving carotenoid production in synthetic maize through data‐driven mathematical modeling

Plant synthetic biology is still in its infancy. However, synthetic biology approaches have been used to manipulate and improve the nutritional and health value of staple food crops such as rice, potato and maize. With current technologies, production yields of the synthetic nutrients are a result of trial and error, and systematic rational strategies to optimize those yields are still lacking. Here, we present a workflow that combines gene expression and quantitative metabolomics with mathematical modeling to identify strategies for increasing production yields of nutritionally important carotenoids in the seed endosperm synthesized through alternative biosynthetic pathways in synthetic lines of white maize, which is normally devoid of carotenoids. Quantitative metabolomics and gene expression data are used to create and fit parameters of mathematical models that are specific to four independent maize lines. Sensitivity analysis and simulation of each model is used to predict which gene activities should be further engineered in order to increase production yields for carotenoid accumulation in each line. Some of these predictions (e.g. increasing Zmlycb/Gllycb will increase accumulated β‐carotenes) are valid across the four maize lines and consistent with experimental observations in other systems. Other predictions are line specific. The workflow is adaptable to any other biological system for which appropriate quantitative information is available. Furthermore, we validate some of the predictions using experimental data from additional synthetic maize lines for which no models were developed.     

Importance of anisotropy in detachment rates for force production and cargo transport by a team of motor proteins

Many cellular processes are driven by collective forces generated by a team consisting of multiple molecular motor proteins. One aspect that has received less attention is the detachment rate of molecular motors under mechanical force/load. While detachment rate of kinesin motors measured under backward force increases rapidly for forces beyond stall‐force; this scenario is just reversed for non‐yeast dynein motors where detachment rate from microtubule decreases, exhibiting a catch‐bond type behavior. It has been shown recently that yeast dynein responds anisotropically to applied load, i.e. detachment rates are different under forward and backward pulling. Here, we use computational modeling to show that these anisotropic detachment rates might help yeast dynein motors to improve their collective force generation in the absence of catch‐bond behavior. We further show that the travel distance of cargos would be longer if detachment rates are anisotropic. Our results suggest that anisotropic detachment rates could be an alternative strategy for motors to improve the transport properties and force production by the team.     

Boosting protein stability with the computational design of β‐sheet surfaces

β‐sheets often have one face packed against the core of the protein and the other facing solvent. Mutational studies have indicated that the solvent‐facing residues can contribute significantly to protein stability, and that the preferred amino acid at each sequence position is dependent on the precise structure of the protein backbone and the identity of the neighboring amino acids. This suggests that the most advantageous methods for designing β‐sheet surfaces will be approaches that take into account the multiple energetic factors at play including side chain rotamer preferences, van der Waals forces, electrostatics, and desolvation effects. Here, we show that the protein design software Rosetta, which models these energetic factors, can be used to dramatically increase protein stability by optimizing interactions on the surfaces of small β‐sheet proteins. Two design variants of the β‐sandwich protein from tenascin were made with 7 and 14 mutations respectively on its β‐sheet surfaces. These changes raised the thermal midpoint for unfolding from 45°C to 64°C and 74°C. Additionally, we tested an empirical approach based on increasing the number of potential salt bridges on the surfaces of the β‐sheets. This was not a robust strategy for increasing stability, as three of the four variants tested were unfolded.     

Modeling repetitive,non‐globular proteins

While ab initio modeling of protein structures is not routine, certain types of proteins are more straightforward to model than others. Proteins with short repetitive sequences typically exhibit repetitive structures. These repetitive sequences can be more amenable to modeling if some information is known about the predominant secondary structure or other key features of the protein sequence. We have successfully built models of a number of repetitive structures with novel folds using knowledge of the consensus sequence within the sequence repeat and an understanding of the likely secondary structures that these may adopt. Our methods for achieving this success are reviewed here.