RECOORD: a recalculated coordinate database of 500+ proteins from the PDB using restraints from the BioMagResBank

State-of-the-art methods based on CNS and CYANA were used to recalculate the nuclear magnetic resonance (NMR) solution structures of 500+ proteins for which coordinates and NMR restraints are available from the Protein Data Bank. Curated restraints were obtained from the BioMagResBank FRED database. Although the original NMR structures were determined by various methods, they all were recalculated by CNS and CYANA and refined subsequently by restrained molecular dynamics (CNS) in a hydrated environment. We present an extensive analysis of the results, in terms of various quality indicators generated by PROCHECK and WHAT_CHECK. On average, the quality indicators for packing and Ramachandran appearance moved one standard deviation closer to the mean of the reference database. The structural quality of the recalculated structures is discussed in relation to various parameters, including number of restraints per residue, NOE completeness and positional root mean square deviation (RMSD). Correlations between pairs of these quality indicators were generally low; for example, there is a weak correlation between the number of restraints per residue and the Ramachandran appearance according to WHAT_CHECK (r = 0.31). The set of recalculated coordinates constitutes a unified database of protein structures in which potential user- and software-dependent biases have been kept as small as possible. The database can be used by the structural biology community for further development of calculation protocols, validation tools, structure-based statistical approaches and modeling. The RECOORD database of recalculated structures is publicly available from http://www.ebi.ac.uk/msd/recoord.     

Association of common variation in the <Emphasis Type="Italic">PPARA</Emphasis>gene with incident myocardial infarction in individuals with type 2 diabetes: A Go-DARTS study

Background

Common variants of the PPARA gene have been found to associate with ischaemic heart disease in non diabetic men. The L162V variant was found to be protective while the C2528G variant increased risk. L162V has also been associated with altered lipid measures. We therefore sought to determine the effect of PPARA gene variation on susceptibility to myocardial infarction in patients with type 2 diabetes. 1810 subjects with type 2 diabetes from the prospective Go-DARTS study were genotyped for the L162V and C2528G variants in the PPARA gene and the association of the variants with incident non-fatal myocardial infarction was examined. Cox's proportional hazards was used to interrogate time to event from recruitment, and linear regression for analysing association of genotype with quantitative clinical traits.

Results

The V162 allele was associated with decreased risk of non-fatal myocardial infarction (HR = 0.31, 95%CI 0.10–0.93 p = 0.037) whereas the C2528 allele was associated with increased risk (HR = 2.77 95%CI 1.34–5.75 p = 0.006). Similarly V162 was associated with a later mean age of diagnosis with type 2 diabetes and C2582 an earlier age of diagnosis. C2528 was also associated with increased total cholesterol and LDL cholesterol, which did not account for the observed increased risk. Haplotype analysis demonstrated that when both rare variants occurred on the same haplotype the effect of each was abrogated.

Conclusion

Genetic variation at the PPARA locus is important in determining cardiovascular risk in both male and female patients with diabetes. This genotype associated risk appears to be independent of the effect of these genotypes on lipid profiles and age of diagnosis with diabetes.

     

Robust, integrated computational control of NMR experiments to achieve optimal assignment by ADAPT-NMR

ADAPT-NMR (Assignment-directed Data collection Algorithm utilizing a Probabilistic Toolkit in NMR) represents a groundbreaking prototype for automated protein structure determination by nuclear magnetic resonance (NMR) spectroscopy. With a [(13)C,(15)N]-labeled protein sample loaded into the NMR spectrometer, ADAPT-NMR delivers complete backbone resonance assignments and secondary structure in an optimal fashion without human intervention. ADAPT-NMR achieves this by implementing a strategy in which the goal of optimal assignment in each step determines the subsequent step by analyzing the current sum of available data. ADAPT-NMR is the first iterative and fully automated approach designed specifically for the optimal assignment of proteins with fast data collection as a byproduct of this goal. ADAPT-NMR evaluates the current spectral information, and uses a goal-directed objective function to select the optimal next data collection step(s) and then directs the NMR spectrometer to collect the selected data set. ADAPT-NMR extracts peak positions from the newly collected data and uses this information in updating the analysis resonance assignments and secondary structure. The goal-directed objective function then defines the next data collection step. The procedure continues until the collected data support comprehensive peak identification, resonance assignments at the desired level of completeness, and protein secondary structure. We present test cases in which ADAPT-NMR achieved results in two days or less that would have taken two months or more by manual approaches.     

Structural dependencies of protein backbone 2JNC' couplings

Protein folding can introduce strain in peptide covalent geometry, including deviations from planarity that are difficult to detect, especially for a protein in solution. We have found dependencies in protein backbone (2)J(NC') couplings on the planarity and the relative orientation of the sequential peptide planes. These dependences were observed in experimental (2)J(NC') couplings from seven proteins, and also were supported by DFT calculations for a model tripeptide. Findings indicate that elevated (2)J(NC') couplings may serve as reporters of structural strain in the protein backbone imposed by protein folds. Such information, supplemented with the H-bond strengths derived from (h3)J(NC') couplings, provides useful insight into the overall energy profile of the protein backbone in solution.     

Probabilistic Identification of Spin Systems and their Assignments including Coil–Helix Inference as Output (PISTACHIO)

We present a novel automated strategy (PISTACHIO) for the probabilistic assignment of backbone and sidechain chemical shifts in proteins. The algorithm uses peak lists derived from various NMR experiments as input and provides as output ranked lists of assignments for all signals recognized in the input data as constituting spin systems. PISTACHIO was evaluate00000000d by comparing its performance with raw peak-picked data from 15 proteins ranging from 54 to 300 residues; the results were compared with those achieved by experts analyzing the same datasets by hand. As scored against the best available independent assignments for these proteins, the first-ranked PISTACHIO assignments were 80–100% correct for backbone signals and 75–95% correct for sidechain signals. The independent assignments benefited, in a number of cases, from structural data (e.g. from NOESY spectra) that were unavailable to PISTACHIO. Any number of datasets in any combination can serve as input. Thus PISTACHIO can be used as datasets are collected to ascertain the current extent of secure assignments, to identify residues with low assignment probability, and to suggest the types of additional data needed to remove ambiguities. The current implementation of PISTACHIO, which is available from a server on the Internet, supports input data from 15 standard double- and triple-resonance experiments. The software can readily accommodate additional types of experiments, including data from selectively labeled samples. The assignment probabilities can be carried forward and refined in subsequent steps leading to a structure. The performance of PISTACHIO showed no direct dependence on protein size, but correlated instead with data quality (completeness and signal-to-noise). PISTACHIO represents one component of a comprehensive probabilistic approach we are developing for the collection and analysis of protein NMR data.Electronic Supplementary Material Electronic Supplementary material is available for this article at and accessible for authorised users.     

民族植物学：学科发展动态与展望

对民族植物学的学科发展进行了简要综述。民族植物学自1896年在美国诞生以来,经历了百年漫长的发展过程。民族植物学由早期的描述编目有用植物,已经发展到了实验性、技术性和定量性研究的新阶段;民族植物学的原理与方法已被广泛应用于植物资源的可持续利用、社区发展和生物多样性保护。近代民族植物学的学科发展可归纳为3个特征：(1)研究方法的进展主要表现在由记载编目和描述到实验性、技术性的定量研究和从基础研究到实际应用的发展;(2)研究途径的发展表现在由调查记录、访谈式的被动研究到参与式研究和取证分析;(3)研究地区已由局部区域性研究扩大到全球范围的民族植物学研究,从对原住民和少数民族的民族植物学研究扩展到所有不同文化背景民族的民族植物学研究。特别是发展中国家的民族植物学研究在近半个世纪以来得到迅速发展,并广泛应用于植物资源的管理和生物多样性保护的实际工作中。民族植物学在中国起步较晚,但在过去20年里已从无到有,建立起了我国民族植物学研究的理论框架、内容、方法和途径,将民族植物学从基础描述性研究(调查、记载、编目),推进到应用研究,在植物资源的可持续利用、生物多样性保护、农村发展和山区扶贫等方面取得了若干进展。当前,我国民族植物学研究面临着发展的新机遇和新的挑战,必须紧跟国民经济发展的新形势,投入西部大开发,加强学科建设,推进研究与产业发展的结合,把民族植物学由地区性研究推进到全国性研究,进一步完善中国特色的民族植物学研究体系,积极参与国际合作与交流,对国民经济的持续发展、民族地区的团结稳定和生物多样性保护作出更大的贡献。     

4种桦树幼苗耐盐性分析与评价

耐盐植物引种和培育是开发利用盐碱地的主要方式,具有重要的研究价值。本试验以哈萨克斯坦引进的吉尔吉斯白桦（Betula kirghisorum）、欧洲白桦（B.pendula）、毛枝桦（B.pubescens）和本地的白桦（B.platyphylla Suk.）1年生幼苗为试验材料,于2014年7月在东北林业大学进行中性盐（NaCl）和碱性盐（NaHCO₃）的胁迫试验,测定生长量、光合参数和叶绿素含量,并通过因子分析法,对比评价4种桦树幼苗的耐盐碱能力,筛选出综合性状优良的桦树树种,为耐盐植物引种和培育提供有价值的数据。结果表明：随着盐浓度的升高,桦树幼苗的高生长和光合效率受到显著抑制,而当浓度≥0.5%时,大部分幼苗枯死。株高增长量、基径增长量、净光合速率（P_n）、光能利用效率（SUE）、羧化效率（CUE）、表观量子效率（AQY）及叶绿素含量之间的相关性多数达到了显著水平;最后利用因子分析法分别构建了0.1% NaCl、0.3% NaCl、0.1% NaHCO3和0.3% NaHCO₃胁迫处理的综合评价公式,并分别筛选出了综合性状相对优良的单株,其中NaCl胁迫下较优单株为32、33、34、35;NaHCO₃胁迫下较优单株为262、263、264、35。综合比较认为,吉尔吉斯白桦对低中浓度的中性盐的抗性最强,本地对照白桦对低中浓度碱性盐的抗性最强,而吉尔吉斯白桦和毛枝桦对高浓度碱性盐抗性较强。