Lectin Receptor Kinases in Plants

Referee: Dr. Philip Becraft, Zoology and Genetics/Agronomy Depts., 2116 Molecular Building, lowa State University, Ames, IA 50011 Forty-two lectin receptor kinase (lecRK)-related sequences and nine related soluble legume lectin sequences were identified in the Arabidopsis thaliana genome. The genes are scattered as a single or gathered copies at different loci throughout the five chromosomes, and four predicted lecRK probably correspond to pseudogenes. Both structural alignments and molecular modeling revealed striking similarities between the lectinlike domain of lecRK, and related A. thaliana soluble lectins and legume lectins. The hydrophobic cavity is extremely conserved, whereas most of the residues forming the monosaccharide-binding site and the bivalent cation-binding site of legume lectins are poorly conserved. LecRK should be unable to bind the simple sugars usually recognized by genuine legume lectins. Molecular modeling of the kinase domain suggests that, except for two apparently inactive receptors, all other lecRK contain a putative functional Ser/Thr kinase catalytic domain. Both the juxtamembrane and C-terminal domains, which are considered important regions for regulating the kinase activity, exhibit a few specific stretches of amino acid residues. Some phylogenetic relationships are inferred from the phylogenetic trees built up from the different lecRK domain sequences. LecRK cluster in three distinct classes (A,B,C), one of them (B) being more closely related to soluble lectins of A. thaliana and legume lectins.     

Functional Clustering of Immunoglobulin Superfamily Proteins with Protein–Protein Interaction Information Calibrated Hidden Markov Model Sequence Profiles

Secreted and cell-surface-localized members of the immunoglobulin superfamily (IgSF) play central roles in regulating adaptive and innate immune responses and are prime targets for the development of protein-based therapeutics. An essential activity of the ectodomains of these proteins is the specific recognition of cognate ligands, which are often other members of the IgSF. In this work, we provide functional insight for this important class of proteins through the development of a clustering algorithm that groups together extracellular domains of the IgSF with similar binding preferences. Information from hidden Markov model-based sequence profiles and domain architecture is calibrated against manually curated protein interaction data to define functional families of IgSF proteins. The method is able to assign 82% of the 477 extracellular IgSF protein to a functional family, while the rest are either single proteins with unique function or proteins that could not be assigned with the current technology. The functional clustering of IgSF proteins generates hypotheses regarding the identification of new cognate receptor–ligand pairs and reduces the pool of possible interacting partners to a manageable level for experimental validation.     

基于知识的蛋白质结构预测

介绍了近几年基于知识的蛋白质三维结构预测方法及其进展．目前,基于知识的结构预测方法主要有两类,一类是同源蛋白模建,这种技术比较成熟,模建的结果可靠性比较高,但只适用于同源性比较高的目标序列的模建;另一类方法即蛋白质逆折叠技术,主要包括3D profile方法和基于势函数的方法,给出的是目标蛋白质的空间走向,它主要可用于序列同源性比较低的蛋白质的结构预测．     

ProDom: automated clustering of homologous domains

The ProDom database is a comprehensive set of protein domain families automatically generated from the SWISS-PROT and TrEMBL sequence databases. An associated database, ProDom-CG, has been derived as a restriction of ProDom to completely sequenced genomes. The ProDom construction method is based on iterative PSI-BLAST searches and multiple alignments are generated for each domain family. The ProDom web server provides the user with a set of tools to visualise multiple alignments, phylogenetic trees and domain architectures of proteins, as well as a BLAST-based server to analyse new sequences for homologous domains. The comprehensive nature of ProDom makes it particularly useful to help sustain the growth of InterPro.     

Construction of a sequence motif characteristic of aminergic G protein-coupled receptors

An approach to discover sequence patterns characteristic of ligand classes is described and applied to aminergic G protein-coupled receptors (GPCRs). Putative ligand-binding residue positions were inferred from considering three lines of evidence: conservation in the subfamily absent or underrepresented in the superfamily, any available mutation data, and the physicochemical properties of the ligand. For aminergic GPCRs, the motif is composed of a conserved aspartic acid in the third transmembrane (TM) domain (rhodopsin position 117) and a conserved tryptophan in the seventh TM domain (rhodopsin position 293); the roles of each are readily justified by molecular modeling of ligand-receptor interactions. This minimally defined motif is an appropriate computational tool for identifying additional, potentially novel aminergic GPCRs from a set of experimentally uncharacterized "orphan" GPCRs, complementing existing sequence matching, clustering, and machine-learning techniques. Motif sensitivity stems from the stepwise addition of residues characteristic of an entire class of ligand (and not tailored for any particular biogenic amine). This sensitivity is balanced by careful consideration of residues (evidence drawn from mutation data, correlation of ligand properties to residue properties, and location with respect to the extracellular face), thereby maintaining specificity for the aminergic class. A number of orphan GPCRs assigned to the aminergic class by this motif were later discovered to be a novel subfamily of trace amine GPCRs, as well as the successful classification of the histamine H4 receptor.     

Sequence variations within protein families are linearly related to structural variations

It is commonly believed that similarities between the sequences of two proteins infer similarities between their structures. Sequence alignments reliably recognize pairs of protein of similar structures provided that the percentage sequence identity between their two sequences is sufficiently high. This distinction, however, is statistically less reliable when the percentage sequence identity is lower than 30% and little is known then about the detailed relationship between the two measures of similarity. Here, we investigate the inverse correlation between structural similarity and sequence similarity on 12 protein structure families. We define the structure similarity between two proteins as the cRMS distance between their structures. The sequence similarity for a pair of proteins is measured as the mean distance between the sequences in the subsets of sequence space compatible with their structures. We obtain an approximation of the sequence space compatible with a protein by designing a collection of protein sequences both stable and specific to the structure of that protein. Using these measures of sequence and structure similarities, we find that structural changes within a protein family are linearly related to changes in sequence similarity.     

Consensus Protein Design without Phylogenetic Bias

Consensus design is an appealing strategy for the stabilization of proteins. It exploits amino acid conservation in sets of homologous proteins to identify likely beneficial mutations. Nevertheless, its success depends on the phylogenetic diversity of the sequence set available. Here, we show that randomization of a single protein represents a reliable alternative source of sequence diversity that is essentially free of phylogenetic bias. A small number of functional protein sequences selected from binary-patterned libraries suffice as input for the consensus design of active enzymes that are easier to produce and substantially more stable than individual members of the starting data set. Although catalytic activity correlates less consistently with sequence conservation in these extensively randomized proteins, less extreme mutagenesis strategies might be adopted in practice to augment stability while maintaining function.     

Computational and Experimental Evidence for the Evolution of a (βα)8-Barrel Protein from an Ancestral Quarter-Barrel Stabilised by Disulfide Bonds

The evolution of the prototypical (βα)₈-barrel protein imidazole glycerol phosphate synthase (HisF) was studied by complementary computational and experimental approaches. The 4-fold symmetry of HisF suggested that its constituting (βα)₂ quarter-barrels have a common evolutionary origin. This conclusion was supported by the computational reconstruction of the HisF sequence of the last common ancestor, which showed that its quarter-barrels were more similar to each other than are those of extant HisF proteins. A comprehensive sequence analysis identified HisF-N1 [corresponding to (βα)_1-2] as the slowest evolving quarter-barrel. This finding indicated that it is the closest relative of the common (βα)₂ predecessor, which must have been a stable and presumably tetrameric protein. In accordance with this prediction, a recombinantly produced HisF-N1 protein was properly folded and formed a tetramer being stabilised by disulfide bonds. The introduction of a disulfide bond in HisF-C1 [corresponding to (βα)_5-6] also resulted in the formation of a stable tetramer. The fusion of two identical HisF-N1 quarter-barrels yielded the stable dimeric half-barrel HisF-N1N1. Our findings suggest a two-step evolutionary pathway in which a HisF-N1-like predecessor was duplicated and fused twice to yield HisF. Most likely, the (βα)₂ quarter-barrel and (βα)₄ half-barrel intermediates on this pathway were stabilised by disulfide bonds that became dispensable upon consolidation of the (βα)₈-barrel.     

Topological characterization of neuronal arbor morphology via sequence representation: II - global alignment

Background

The increasing abundance of neuromorphological data provides both the opportunity and the challenge to compare massive numbers of neurons from a wide diversity of sources efficiently and effectively. We implemented a modified global alignment algorithm representing axonal and dendritic bifurcations as strings of characters. Sequence alignment quantifies neuronal similarity by identifying branch-level correspondences between trees.

Results

The space generated from pairwise similarities is capable of classifying neuronal arbor types as well as, or better than, traditional topological metrics. Unsupervised cluster analysis produces groups that significantly correspond with known cell classes for axons, dendrites, and pyramidal apical dendrites. Furthermore, the distinguishing consensus topology generated by multiple sequence alignment of a group of neurons reveals their shared branching blueprint. Interestingly, the axons of dendritic-targeting interneurons in the rodent cortex associates with pyramidal axons but apart from the (more topologically symmetric) axons of perisomatic-targeting interneurons.

Conclusions

Global pairwise and multiple sequence alignment of neurite topologies enables detailed comparison of neurites and identification of conserved topological features in alignment-defined clusters. The methods presented also provide a framework for incorporation of additional branch-level morphological features. Moreover, comparison of multiple alignment with motif analysis shows that the two techniques provide complementary information respectively revealing global and local features.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0605-1) contains supplementary material, which is available to authorized users.     

Clustal W—蛋白质与核酸序列分析软件

蛋白质与核酸的序列分析在现代生物学和生物信息学中发挥着重要作用,新的算法和软件层出不穷,本文介绍一个可运行在ＰＣ机上的完全免费的多序列比较软件－ＣｌｕｓｔａｌＷ,它不但可以进行蛋白质与核酸的多序列比较,分析不同序列之间的相似性关系,还可以绘制进化树。由于其灵活的输入输出格式、方便的参数设定和选择、详尽的在线帮助以及良好的可移植性,使得ＣｌｕｓｔａｌＷ在蛋白质与核酸的序列分析中得到了广泛应用。