Revisiting the phosphotyrosine binding pocket of Fyn SH2 domain led to the identification of novel SH2 superbinders.

Protein engineering through directed evolution is an effective way to obtain proteins with novel functions with the potential applications as tools for diagnosis or therapeutics. Many natural proteins have undergone directed evolution in vitro in the test tubes in the laboratories worldwide, resulting in the numerous protein variants with novel or enhanced functions. we constructed here an SH2 variant library by randomizing 8 variable residues in its phosphotyrosine (pTyr) binding pocket. Selection of this library by a pTyr peptide led to the identification of SH2 variants with enhanced affinities measured by EC50. Fluorescent polarization was then applied to quantify the binding affinities of the newly identified SH2 variants. As a result, three SH2 variants, named V3, V13 and V24, have comparable binding affinities with the previously identified SH2 triple‐mutant superbinder. Biolayer Interferometry assay was employed to disclose the kinetics of the binding of these SH2 superbinders to the phosphotyrosine peptide. The results indicated that all the SH2 superbinders have two‐orders increase of the dissociation rate when binding the pTyr peptide while there was no significant change in their associate rates. Intriguingly, though binding the pTyr peptide with comparable affinity with other SH2 superbinders, the V3 does not bind to the sTyr peptide. However, variant V13 and V24 have cross‐reactivity with both pTyr and sTyr peptides. The newly identified superbinders could be utilized as tools for the identification of pTyr‐containing proteins from tissues under different physiological or pathophysiological conditions and may have the potential in the therapeutics.     

The use and misuse of regression models in landscape genetic analyses

The field of landscape genetics has been rapidly evolving, adopting and adapting analytical frameworks to address research questions. Current studies are increasingly using regression‐based frameworks to infer the individual contributions of landscape and habitat variables on genetic differentiation. This paper outlines appropriate and inappropriate uses of multiple regression for these purposes, and demonstrates through simulation the limitations of different analytical frameworks for making correct inference. Of particular concern are recent studies seeking to explain genetic differences by fitting regression models with effective distance variables calculated independently on separate landscape resistance surfaces. When moving across the landscape, organisms cannot respond independently and uniquely to habitat and landscape features. Analyses seeking to understand how landscape features affect gene flow should model a single conductance or resistance surface as a parameterized function of relevant spatial covariates, and estimate the values of these parameters by linking a single set of resistance distances to observed genetic dissimilarity via a loss function. While this loss function may involve a regression‐like step, the associated nuisance parameters are not interpretable in terms of organismal movement and should not be conflated with what is actually of interest: the mapping between spatial covariates and conductance/resistance. The growth and evolution of landscape genetics as a field has been rapid and exciting. It is the goal of this paper to highlight past missteps and demonstrate limitations of current approaches to ensure that future use of regression models will appropriately consider the process being modeled, which will provide clarity to model interpretation.     

Gene expression remodelling and immune response during adaptive divergence in an African cichlid fish

Variation in gene expression contributes to ecological speciation by facilitating population persistence in novel environments. Likewise, immune responses can be of relevance in speciation driven by adaptation to different environments. Previous studies examining gene expression differences between recently diverged ecotypes have often relied on only one pair of populations, targeted the expression of only a subset of genes or used wild‐caught individuals. Here, we investigated the contribution of habitat‐specific parasites and symbionts and the underlying immunological abilities of ecotype hosts to adaptive divergence in lake–river population pairs of the cichlid fish Astatotilapia burtoni. To shed light on the role of phenotypic plasticity in adaptive divergence, we compared parasite and microbiota communities, immune response, and gene expression patterns of fish from natural habitats and a lake‐like pond set‐up. In all investigated population pairs, lake fish were more heavily parasitized than river fish, in terms of both parasite taxon composition and infection abundance. The innate immune response in the wild was higher in lake than in river populations and was elevated in a river population exposed to lake parasites in the pond set‐up. Environmental differences between lake and river habitat and their distinct parasite communities have shaped differential gene expression, involving genes functioning in osmoregulation and immune response. Most changes in gene expression between lake and river samples in the wild and in the pond set‐up were based on a plastic response. Finally, gene expression and bacterial communities of wild‐caught individuals and individuals acclimatized to lake‐like pond conditions showed shifts underlying adaptive phenotypic plasticity.     

Relationship between aquifer biofilms and unattached microbial indicators of urban groundwater contamination

Aquifers, springs and other groundwater‐dependent ecosystems are threatened by urban land use, which causes water quality deterioration through nutrient loading, sewage infiltration, groundwater extraction and, along coasts, seawater intrusion. The presence of certain microbes in groundwater can indicate that an aquifer is anthropogenically contaminated. Interpretations made from observations of indicator microbes in groundwater are limited because the relationship between the presumably allochthonous indicator microbes and relevant autochthonous microbial communities has not been characterized. This study addressed whether autochthonous aquifer biofilms can influence the presence of presumed microbial indicators in groundwater, and simultaneously used microbial indicators to trace sources of urban contamination at a karst spring of conservation concern. These questions were approached using a 17‐month time series analysis of attached biofilm and adjacent unattached bacteria in the submerged karst aquifer conduit associated with this spring. Environmental 16S rRNA gene sequencing was performed to characterize these communities, and community structure data were contextualized with groundwater geochemical and hydrogeological measurements. Linear regression models were developed to explain the relative abundance patterns of indicator microbes and other unattached microbes at this site. The results of this study suggest that dominant aquifer biofilms do not influence the presence of unattached microbial taxa that are presumed to be indicators of groundwater contamination, and generated new information about the origin of coliform bacteria at the study site. These results build confidence in the use of microbial indicators in groundwater‐dependent ecosystem conservation strategies and inform future management plans for urban aquifers and springs worldwide.     

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.     

The AutoDock suite at 30

The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third‐party developers.     

Using Integrative Modeling Platform to compute,validate, and archive a model of a protein complex structure

Biology is advanced by producing structural models of biological systems, such as protein complexes. Some systems are recalcitrant to traditional structure determination methods. In such cases, it may still be possible to produce useful models by integrative structure determination that depends on simultaneous use of multiple types of data. An ensemble of models that are sufficiently consistent with the data is produced by a structural sampling method guided by a data‐dependent scoring function. The variation in the ensemble of models quantified the uncertainty of the structure, generally resulting from the uncertainty in the input information and actual structural heterogeneity in the samples used to produce the data. Here, we describe how to generate, assess, and interpret ensembles of integrative structural models using our open source Integrative Modeling Platform program ( https://integrativemodeling.org ).     

北京城市蝴蝶蜜源植物网络特征及重要蜜源植物识别

物种间相互作用网络研究能为物种多样性的保护、城市生态系统稳定性的维持提供指导。基于群落水平的城市蝴蝶蜜源植物互作网络的研究较少,对城市蝴蝶蜜源网络结构缺乏深入认识。研究在国内城市生态系统中构建蝴蝶蜜源网络,并探讨不同类型植物对网络特征的影响。2020年6-9月,在北京26个城市公园中记录访花蝴蝶和蜜源植物物种及互作频次,采用交互多样性（ID）、交互均匀性（IE）、专业化程度（H2''）定量化生态网络结构特征,采用Kruskal-Wallis秩和检验和变差分解分析不同生长型、起源、栽培方式的植物类型对网络结构的影响差异。采用物种的伙伴多样性（PD）和专业化程度（d''）识别重要蜜源植物。研究结果表明：（1）北京城市公园中22种蝴蝶与81种开花植物的交互作用,形成趋于泛化的生态网络结构;（2）不同生长型及不同起源的植物-蝴蝶网络的交互多样性及专业化程度有显著差异,草本及乡土植物对丰富网络中交互多样性和支持专业性更高的蝴蝶物种具重要作用,而植物的栽培方式对蜜源网络结构影响较小;（3）伙伴多样性高且专业化程度高的植物可被视为重要蜜源植物。基于蝴蝶多样性保护的目标,在城市生态系统中,绿色空间应注重构建乡土草本植物群落,优先选择重要蜜源植物。我们的发现印证了蝴蝶-蜜源植物生态网络方法作为联结生态研究和城市绿地实践管理的有效工具,能为城市生态系统中生物多样性保护提供科学策略,具有重要意义。