Starch-binding domains in the post-genome era

Starch belongs to the most abundant biopolymers on Earth. As a source of energy, starch is degraded by a large number of various amylolytic enzymes. However, only about 10% of them are capable of binding and degrading raw starch. These enzymes usually possess a distinct sequence-structural module, the so-called starchbinding domain (SBD). In general, all carbohydrate-binding modules (CBMs) have been classified into the CBM families. In this sequence-based classification the individual types of SBDs have been placed into seven CBM families: CBM20, CBM21, CBM25, CBM26, CBM34, CBM41 and CBM45. The family CBM20, known also as a classical C-terminal SBD of microbial amylases, is the most thoroughly studied. The three-dimensional structures have already been determined by X-ray crystallography or nuclear magnetic resonance for SBDs from five CBM families (20, 25, 26, 34 and 41), and the structure of the CBM21 has been modelled. Despite differences among the amino acid sequences, the fold of a distorted β-barrel seems to be conserved together with a similar way of substrate binding (mainly stacking interactions between aromatic residues and glucose rings). SBDs have recently been discovered in many non-amylolytic proteins. These may, for example, have regulatory functions in starch metabolism in plants or glycogen metabolism in mammals. SBDs have also found practical uses. Received 25 May 2006; received after revision 26 June 2006; accepted 3 August 2006     

Applications of Transition Metals in Organic Synthesis and Polymerization

1 Results Classic oxidants require rigorous control of the experimental conditions added with the problem of lack of selectivity. Catalysis by transition metals with environmentally safe oxidants provides synthetic routes to minimize pollution by giving environmental benign by-products. Fe (Ⅵ) is a powerful and a selective oxidant with Fe(Ⅲ) as a by-product, while hydrogen peroxide is clean with water as the only by-product. Separation of sodium or potassium ferrates requires tedious processes. Associat...     

Variation in complement factor 3 is associated with risk of age-related macular degeneration

The association of variants in complement factors H and B with age-related macular degeneration has led to more intense genetic and functional analysis of the complement pathway. We identify a nonsynonymous coding change in complement factor 3 that is strongly associated with risk of age-related macular degeneration in a large case-control sample.     

Structural Changes of Water Molecules Upon the Reduction of Quinones in The Reaction Center from Rhodobactery Sphaeroides

1 Results The photosynthetic bacterial reaction center (RC) is a membrane protein complex.The RC is composed of three protein subunits and redox components such as bacteriochlorophylls, bacteriopheophytins,and quinones.The RC performs the photochemical electron transfer from the bacteriochlorophyll dimer through a series of electron donor and acceptor molecules to a secondary quinone,QB.QB accepts electrons from a primary quinone,QA,in two sequential electron transfer reactions.The second electron trans...     

Computational protein function prediction: Are we making progress?

The computational prediction of gene and protein function is rapidly gaining ground as a central undertaking in computational biology. Making sense of the flood of genomic data requires fast and reliable annotation. Many ingenious algorithms have been devised to infer a protein's function from its amino acid sequence, 3D structure and chromosomal location of the encoding genes. However, there are significant challenges in assessing how well these programs perform. In this article we explore those challenges and review our own attempt at assessing the performance of those programs. We conclude that the task is far from complete and that a critical assessment of the performance of function prediction programs is necessary to make true progress in computational function prediction.     

Mutations in the gene encoding the basal body protein RPGRIP1L, a nephrocystin-4 interactor, cause Joubert syndrome

Protein-protein interaction analyses have uncovered a ciliary and basal body protein network that, when disrupted, can result in nephronophthisis (NPHP), Leber congenital amaurosis, Senior-L?ken syndrome (SLSN) or Joubert syndrome (JBTS). However, details of the molecular mechanisms underlying these disorders remain poorly understood. RPGRIP1-like protein (RPGRIP1L) is a homolog of RPGRIP1 (RPGR-interacting protein 1), a ciliary protein defective in Leber congenital amaurosis. We show that RPGRIP1L interacts with nephrocystin-4 and that mutations in the gene encoding nephrocystin-4 (NPHP4) that are known to cause SLSN disrupt this interaction. RPGRIP1L is ubiquitously expressed, and its protein product localizes to basal bodies. Therefore, we analyzed RPGRIP1L as a candidate gene for JBTS and identified loss-of-function mutations in three families with typical JBTS, including the characteristic mid-hindbrain malformation. This work identifies RPGRIP1L as a gene responsible for JBTS and establishes a central role for cilia and basal bodies in the pathophysiology of this disorder.     

Forecasting volatility by means of threshold models

The aim of this paper is to compare the forecasting performance of competing threshold models, in order to capture the asymmetric effect in the volatility. We focus on examining the relative out‐of‐sample forecasting ability of the SETAR‐Threshold GARCH (SETAR‐TGARCH) and the SETAR‐Threshold Stochastic Volatility (SETAR‐THSV) models compared to the GARCH model and Stochastic Volatility (SV) model. However, the main problem in evaluating the predictive ability of volatility models is that the ‘true’ underlying volatility process is not observable and thus a proxy must be defined for the unobservable volatility. For the class of nonlinear state space models (SETAR‐THSV and SV), a modified version of the SIR algorithm has been used to estimate the unknown parameters. The forecasting performance of competing models has been compared for two return time series: IBEX 35 and S&P 500. We explore whether the increase in the complexity of the model implies that its forecasting ability improves. Copyright © 2007 John Wiley & Sons, Ltd.