用人工神经网络方法确定真核基因启动子

本文运用神经网络方法,并结合分子生物学的有关理论与实验统计事实,对真核基因启动子区域进行了识别．文中选择了人类、牛、猪、猫、山羊、兔、绵羊、大鼠、小鼠、小马、仓鼠、鸡、鸭、大豆等共13种真核生物360个基因组,作为研究对象．学习组选择了300个基因组,预测组选择了60基因组．结果表明,将神经网络模型与基因理论相结合,能够运用计算方法,从大量的可能启动子组合中排列出唯一的启动子区域．     

Molecular View of 43 S Complex Formation and Start Site Selection in Eukaryotic Translation Initiation

A central step to high fidelity protein synthesis is selection of the proper start codon. Recent structural, biochemical, and genetic analyses have provided molecular insights into the coordinated activities of the initiation factors in start codon selection. A molecular model is emerging in which start codon recognition is linked to dynamic reorganization of factors on the ribosome and structural changes in the ribosome itself.     

The Eukaryotic Initiation Factor (eIF) 4G HEAT Domain Promotes Translation Re-initiation in Yeast Both Dependent on and Independent of eIF4A mRNA Helicase

Translation re-initiation provides the molecular basis for translational control of mammalian ATF4 and yeast GCN4 mediated by short upstream open reading (uORFs) in response to eIF2 phosphorylation. eIF4G is the major adaptor subunit of eIF4F that binds the cap-binding subunit eIF4E and the mRNA helicase eIF4A and is also required for re-initiation in mammals. Here we show that the yeast eIF4G2 mutations altering eIF4E- and eIF4A-binding sites increase re-initiation at GCN4 and impair recognition of the start codons of uORF1 or uORF4 located after uORF1. The increase in re-initiation at GCN4 was partially suppressed by increasing the distance between uORF1 and GCN4, suggesting that the mutations decrease the migration rate of the scanning ribosome in the GCN4 leader. Interestingly, eIF4E overexpression suppressed both the phenotypes caused by the mutation altering eIF4E-binding site. Thus, eIF4F is required for accurate AUG selection and re-initiation also in yeast, and the eIF4G interaction with the mRNA-cap appears to promote eIF4F re-acquisition by the re-initiating 40 S subunit. However, eIF4A overexpression suppressed the impaired AUG recognition but not the increase in re-initiation caused by the mutations altering eIF4A-binding site. These results not only provide evidence that mRNA unwinding by eIF4A stimulates start codon recognition, but also suggest that the eIF4A-binding site on eIF4G made of the HEAT domain stimulates the ribosomal scanning independent of eIF4A. Based on the RNA-binding activities identified within the unstructured segments flanking the eIF4G2 HEAT domain, we discuss the role of the HEAT domain in scanning beyond loading eIF4A onto the pre-initiation complex.     

Bacterial and archaeal globins — A revised perspective

A bioinformatics survey of putative globins in over 2200 bacterial and some 140 archaeal genomes revealed that over half the bacterial and approximately one fifth of archaeal genomes contain genes encoding globins that were classified into three families: the M (myoglobin-like), and S (sensor) families all exhibiting the canonical 3/3 myoglobin fold, and the T family (truncated myoglobin fold). Although the M family comprises 2 subfamilies, flavohemoglobins (FHbs) and single domain globins (SDgbs), the S family encompasses chimeric globin-coupled sensors (GCSs), single domain Pgbs (protoglobins) and SSDgbs (sensor single domain globins). The T family comprises three classes TrHb1s, TrHb2s and TrHb3s, characterized by the abbreviated 2/2 myoglobin fold. The Archaea contain only Pgbs, GCSs and TrHb1s. The smallest globin-bearing genomes are the streamlined genomes (~ 1.3 Mbp) of the SAR11 clade of alphaproteobacteria and the slightly larger (ca. 1.7 Mbp) genomes of Aquificae. The smallest genome with members of all three families is the 2.3 Mbp genome of the extremophile Methylacidiphilum infernorum (Verrumicrobia). Of the 147 possible combinations of the eight globin subfamilies, only 83 are observed. Although binary combinations are infrequent and ternary combinations are rare, the FHb + TrHb2 combination is the most commonly observed. Of the possible functions of bacterial globins we discuss the two principal ones — nitric oxide detoxification via the NO dioxygenase or denitrosylase activities and the sensing of oxygen concentration in the environmental niche. In only few cases has a physiological role been demonstrated in vivo. This article is part of a Special Issue entitled: Oxygen Binding and Sensing Proteins.     

UBC9 autosumoylation negatively regulates sumoylation of septins in Saccharomyces cerevisiae

Sumoylation regulates a wide range of cellular processes. However, little is known about the regulation of the SUMO machinery. In this study, we demonstrate that two lysine residues (Lys-153 and Lys-157) in the C-terminal region of the yeast E2-conjugating enzyme Ubc9 are the major and minor autosumoylation sites, respectively. Surprisingly, mutation of Lys-157 (ubc9(K157R)) significantly stimulates the level of Ubc9 autosumoylation at Lys-153. The functional role of Ubc9 autosumoylation is exemplified in our findings that cell cycle-dependent sumoylation of cytoskeletal septin proteins is inversely correlated with the Ubc9 autosumoylation level and that mutation of the Ubc9 autosumoylation sites results in aberrant cell morphology. Our study elucidates a regulatory mechanism that utilizes automodification of the E2 enzyme of the sumoylation machinery to control substrate sumoylation.     

Complete nucleotide sequence of mouse 18 S rRNA gene: comparison with other available homologs

We present the complete sequence of mouse 18 S rRNA. As indicated by comparison with yeast, Xenopus and rat, the conservation of eukaryotic 18 S rRNA sequences is extensive. However, this conservation is far from being uniform along the molecule: most of the base changes and the size differences between species are concentrated at specific locations. Two distinct classes of divergent traces can be detected which differ markedly in their rates of nucleotide substitution during evolution, and should prove valuable in additional comparative analyses, both for eukaryotic taxonomy and for rRNA higher order organization. Mouse and rat 18 S rRNA sequences differ by only 14 point changes over the 1869 nucleotides of the molecule.     

Cellular preservation of excysting developmental stages of new eukaryotes from the early Ediacaran Weng’an Biota

The Ediacaran Weng’an Biota provides a unique window on marine diversity during the interval in which the fundamental animal body plans were being established. Here we describe a previously unreported component of the assemblage, millimeter-scale encysted spheres that exhibit a characteristic but simple slit-shaped excystment mechanism (Sporosphaera guizhouensis n. gen. n. sp.), reminiscent of acritarchs. The cysts contain a large inner body or numerous small discrete membrane-bounded bodies. It is possible that the inner bodies represent disaggregated cells of a multicellular body, like an embryo, but there is no evidence to support this interpretation and the occurrence of the excystment structure is not readily compatible with an embryo interpretation. Rather, we interpret the encysted organisms as multicellular stages within the lifecycle of otherwise probably unicellular eukaryotes. The developmental mode exhibited by Sporosphaera, incorporating a resting stage, implies an adaptation to adverse environmental conditions. This parallels the appearance of Large Ornamented Ediacaran Microfossils (LOEMs) which have been interpreted as diapause stages in the embryology of early animals. Sporosphaera is distinct from LOEMs by ornamentation instead of size, which may implicate that not all LOEMs are animal embryos, if any.     

FragAnchor： A Large-Scale Predictor of Glycosylphosphatidylinositol Anchors in Eukaryote Protein Sequences by Qualitative Scoring

A glycosylphosphatidylinositol （GPI） anchor is a common but complex C-terminal post-translational modification of extracellular proteins in eukaryotes. Here we investigate the problem of correctly annotating GPI-anchored proteins for the growing number of sequences in public databases. We developed a computational system, called FragAnchor, based on the tandem use of a neural network （NN） and a hidden Markov model （HMM）. Firstly, NN selects potential GPI-anchored proteins in a dataset, then HMM parses these potential GPI signals and refines the prediction by qualitative scoring. FragAnchor correctly predicted 91% of all the GPI-anchored proteins annotated in the Swiss-Prot database. In a large-scale analysis of 29 eukaryote proteomes, FragAnchor predicted that the percentage of highly probable GPI-anchored proteins is between 0.21% and 2.01%. The distinctive feature of FragAnchor, compared with other systems, is that it targets only the C-terminus of a protein, making it less sensitive to the background noise found in databases and possible incomplete protein sequences. Moreover, FragAnchor can be used to predict GPI-anchored proteins in all eukaryotes. Finally, by using qualitative scoring, the predictions combine both sensitivity and information content. The predictor is publicly available at http：//navet .ics.hawaii.edu/- fraganchor/NNHMM/NNHMM.ht ml.