A probabilistic model for secondary structure prediction from protein chemical shifts

Protein chemical shifts encode detailed structural information that is difficult and computationally costly to describe at a fundamental level. Statistical and machine learning approaches have been used to infer correlations between chemical shifts and secondary structure from experimental chemical shifts. These methods range from simple statistics such as the chemical shift index to complex methods using neural networks. Notwithstanding their higher accuracy, more complex approaches tend to obscure the relationship between secondary structure and chemical shift and often involve many parameters that need to be trained. We present hidden Markov models (HMMs) with Gaussian emission probabilities to model the dependence between protein chemical shifts and secondary structure. The continuous emission probabilities are modeled as conditional probabilities for a given amino acid and secondary structure type. Using these distributions as outputs of first‐ and second‐order HMMs, we achieve a prediction accuracy of 82.3%, which is competitive with existing methods for predicting secondary structure from protein chemical shifts. Incorporation of sequence‐based secondary structure prediction into our HMM improves the prediction accuracy to 84.0%. Our findings suggest that an HMM with correlated Gaussian distributions conditioned on the secondary structure provides an adequate generative model of chemical shifts. Proteins 2013; © 2012 Wiley Periodicals, Inc.     

Conformational analysis of the full‐length M2 protein of the influenza A virus using solid‐state NMR

The influenza A M2 protein forms a proton channel for virus infection and mediates virus assembly and budding. While extensive structural information is known about the transmembrane helix and an adjacent amphipathic helix, the conformation of the N‐terminal ectodomain and the C‐terminal cytoplasmic tail remains largely unknown. Using two‐dimensional (2D) magic‐angle‐spinning solid‐state NMR, we have investigated the secondary structure and dynamics of full‐length M2 (M2FL) and found them to depend on the membrane composition. In 2D ¹³C DARR correlation spectra, 1,2‐dimyristoyl‐sn‐glycero‐3‐phosphocholine (DMPC)‐bound M2FL exhibits several peaks at β‐sheet chemical shifts, which result from water‐exposed extramembrane residues. In contrast, M2FL bound to cholesterol‐containing membranes gives predominantly α‐helical chemical shifts. Two‐dimensional J‐INADEQUATE spectra and variable‐temperature ¹³C spectra indicate that DMPC‐bound M2FL is highly dynamic while the cholesterol‐containing membranes significantly immobilize the protein at physiological temperature. Chemical‐shift prediction for various secondary‐structure models suggests that the β‐strand is located at the N‐terminus of the DMPC‐bound protein, while the cytoplasmic domain is unstructured. This prediction is confirmed by the 2D DARR spectrum of the ectodomain‐truncated M2(21–97), which no longer exhibits β‐sheet chemical shifts in the DMPC‐bound state. We propose that the M2 conformational change results from the influence of cholesterol, and the increased helicity of M2FL in cholesterol‐rich membranes may be relevant for M2 interaction with the matrix protein M1 during virus assembly and budding. The successful determination of the β‐strand location suggests that chemical‐shift prediction is a promising approach for obtaining structural information of disordered proteins before resonance assignment.     

Late‐glacial recolonization and phylogeography of European red deer (Cervus elaphus L.)

The Pleistocene was an epoch of extreme climatic and environmental changes. How individual species responded to the repeated cycles of warm and cold stages is a major topic of debate. For the European fauna and flora, an expansion–contraction model has been suggested, whereby temperate species were restricted to southern refugia during glacial times and expanded northwards during interglacials, including the present interglacial (Holocene). Here, we test this model on the red deer (Cervus elaphus) a large and highly mobile herbivore, using both modern and ancient mitochondrial DNA from the entire European range of the species over the last c. 40 000 years. Our results indicate that this species was sensitive to the effects of climate change. Prior to the Last Glacial Maximum (LGM) haplogroups restricted today to South‐East Europe and Western Asia reached as far west as the UK. During the LGM, red deer was mainly restricted to southern refugia, in Iberia, the Balkans and possibly in Italy and South‐Western Asia. At the end of the LGM, red deer expanded from the Iberian refugium, to Central and Northern Europe, including the UK, Belgium, Scandinavia, Germany, Poland and Belarus. Ancient DNA data cannot rule out refugial survival of red deer in North‐West Europe through the LGM. Had such deer survived, though, they were replaced by deer migrating from Iberia at the end of the glacial. The Balkans served as a separate LGM refugium and were probably connected to Western Asia with genetic exchange between the two areas.     

基于林业清查资料的桂西北植被碳空间分布及其变化特征

基于2005-2010年林业资源清查数据,采用材积源生物量法,运用地理信息系统技术,估算和分析了桂西北植被碳密度及其储量的空间分布及其变化。结果显示:(1) 研究区域从2005年到2010年呈现碳汇变化趋势,植被碳储量由4.19×10⁴t增加到4.27×10⁴t(增幅为1.84%),植被碳密度从29.04t/hm²增加到29.57 t/hm²。(2) 从治理措施、林种起源方式及林种类型来看,自然保护区的植被碳密度最大,超过40 t/hm²。2005-2010年,人工植苗、直播、飞播和萌生方式植被碳密度增加,退耕还林工程的植被碳密度均呈明显增长(增加3.00 t/hm²),所有林种碳密度都呈不同程度的增长。 (3)植被碳密度空间分布上,大致表现为西部高、中东部低,北部高、南部低。西部区植被碳密度均值超过40 t/hm²,中东部区植被碳密度均值低于25 t/hm²。植被碳密度变化在空间分布上表现为无论是非喀斯特区还是喀斯特区的植被碳密度都有增长趋势,其中有7个县市植被碳密度升级为更高等级。研究表明,随着退耕还林、生态移民等治理措施的实施,区域植被碳密度显著增加,生态环境好转。     

Prediction of protein domain boundaries from inverse covariances

It has been known even since relatively few structures had been solved that longer protein chains often contain multiple domains, which may fold separately and play the role of reusable functional modules found in many contexts. In many structural biology tasks, in particular structure prediction, it is of great use to be able to identify domains within the structure and analyze these regions separately. However, when using sequence data alone this task has proven exceptionally difficult, with relatively little improvement over the naive method of choosing boundaries based on size distributions of observed domains. The recent significant improvement in contact prediction provides a new source of information for domain prediction. We test several methods for using this information including a kernel smoothing‐based approach and methods based on building alpha‐carbon models and compare performance with a length‐based predictor, a homology search method and four published sequence‐based predictors: DOMCUT, DomPRO, DLP‐SVM, and SCOOBY‐DOmain. We show that the kernel‐smoothing method is significantly better than the other ab initio predictors when both single‐domain and multidomain targets are considered and is not significantly different to the homology‐based method. Considering only multidomain targets the kernel‐smoothing method outperforms all of the published methods except DLP‐SVM. The kernel smoothing method therefore represents a potentially useful improvement to ab initio domain prediction. Proteins 2013. © 2012 Wiley Periodicals, Inc.     

3Drefine: Consistent protein structure refinement by optimizing hydrogen bonding network and atomic‐level energy minimization

One of the major limitations of computational protein structure prediction is the deviation of predicted models from their experimentally derived true, native structures. The limitations often hinder the possibility of applying computational protein structure prediction methods in biochemical assignment and drug design that are very sensitive to structural details. Refinement of these low‐resolution predicted models to high‐resolution structures close to the native state, however, has proven to be extremely challenging. Thus, protein structure refinement remains a largely unsolved problem. Critical assessment of techniques for protein structure prediction (CASP) specifically indicated that most predictors participating in the refinement category still did not consistently improve model quality. Here, we propose a two‐step refinement protocol, called 3Drefine, to consistently bring the initial model closer to the native structure. The first step is based on optimization of hydrogen bonding (HB) network and the second step applies atomic‐level energy minimization on the optimized model using a composite physics and knowledge‐based force fields. The approach has been evaluated on the CASP benchmark data and it exhibits consistent improvement over the initial structure in both global and local structural quality measures. 3Drefine method is also computationally inexpensive, consuming only few minutes of CPU time to refine a protein of typical length (300 residues). 3Drefine web server is freely available at http://sysbio.rnet.missouri.edu/3Drefine/ . Proteins 2013. © 2012 Wiley Periodicals, Inc.     

LabCaS: Labeling calpain substrate cleavage sites from amino acid sequence using conditional random fields

The calpain family of Ca²⁺‐dependent cysteine proteases plays a vital role in many important biological processes which is closely related with a variety of pathological states. Activated calpains selectively cleave relevant substrates at specific cleavage sites, yielding multiple fragments that can have different functions from the intact substrate protein. Until now, our knowledge about the calpain functions and their substrate cleavage mechanisms are limited because the experimental determination and validation on calpain binding are usually laborious and expensive. In this work, we aim to develop a new computational approach (LabCaS) for accurate prediction of the calpain substrate cleavage sites from amino acid sequences. To overcome the imbalance of negative and positive samples in the machine‐learning training which have been suffered by most of the former approaches when splitting sequences into short peptides, we designed a conditional random field algorithm that can label the potential cleavage sites directly from the entire sequences. By integrating the multiple amino acid features and those derived from sequences, LabCaS achieves an accurate recognition of the cleave sites for most calpain proteins. In a jackknife test on a set of 129 benchmark proteins, LabCaS generates an AUC score 0.862. The LabCaS program is freely available at: http://www.csbio.sjtu.edu.cn/bioinf/LabCaS . Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Memory and prediction in natural gaze control

In addition to stimulus properties and task factors, memory is an important determinant of the allocation of attention and gaze in the natural world. One way that the role of memory is revealed is by predictive eye movements. Both smooth pursuit and saccadic eye movements demonstrate predictive effects based on previous experience. We have previously shown that unskilled subjects make highly accurate predictive saccades to the anticipated location of a ball prior to a bounce in a virtual racquetball setting. In this experiment, we examined this predictive behaviour. We asked whether the period after the bounce provides subjects with visual information about the ball trajectory that is used to programme the pursuit movement initiated when the ball passes through the fixation point. We occluded a 100 ms period of the ball''s trajectory immediately after the bounce, and found very little effect on the subsequent pursuit movement. Subjects did not appear to modify their strategy to prolong the fixation. Neither were we able to find an effect on interception performance. Thus, it is possible that the occluded trajectory information is not critical for subsequent pursuit, and subjects may use an estimate of the ball''s trajectory to programme pursuit. These results provide further support for the role of memory in eye movements.     

植物器官大小相关基因研究进展

器官大小是植物形态的一个重要特征,而且具有严格的种属特异性。植物器官大小虽然受到外在的环境因素（如光照、营养等）的影响,但它由内在特有的细胞数目和细胞大小决定。许多通过转录调节、蛋白合成、激素调节或松弛细胞壁等途径作用于植物细胞繁殖和/或细胞扩张的基因已经被鉴定,它们的过表达或缺失表达能促进植物器官大小和加快植物生长。尽管如此,这些基因通过相对独立的途径起作用,在植物中难以阐明一个相对整合的器官大小基因调控网络,这也是该研究领域的亟待需要解决的问题。目前,一些器官大小相关基因已经应用农作物育种,并培育出显著增大的农作物品种,这也证实了利用器官大小基因进行植物品种选育的可行性。因此,通过研究药用植物器官大小的基因,人为地在分子水平上有目的的调控器官的大小和形态,是缓解当前许多药用植物面临的资源紧缺、枯竭濒危困境的可考虑途径之一。