Calculation of accurate small angle X-ray scattering curves from coarse-grained protein models

Background  

Genome sequencing projects have expanded the gap between the amount of known protein sequences and structures. The limitations of current high resolution structure determination methods make it unlikely that this gap will disappear in the near future. Small angle X-ray scattering (SAXS) is an established low resolution method for routinely determining the structure of proteins in solution. The purpose of this study is to develop a method for the efficient calculation of accurate SAXS curves from coarse-grained protein models. Such a method can for example be used to construct a likelihood function, which is paramount for structure determination based on statistical inference.     

Prediction of transmembrane helix orientation in polytopic membrane proteins

Background  

Membrane proteins compose up to 30% of coding sequences within genomes. However, their structure determination is lagging behind compared with soluble proteins due to the experimental difficulties. Therefore, it is important to develop reliable computational methods to predict structures of membrane proteins.     

PASS2: an automated database of protein alignments organised as structural superfamilies

Background  

The functional selection and three-dimensional structural constraints of proteins in nature often relates to the retention of significant sequence similarity between proteins of similar fold and function despite poor sequence identity. Organization of structure-based sequence alignments for distantly related proteins, provides a map of the conserved and critical regions of the protein universe that is useful for the analysis of folding principles, for the evolutionary unification of protein families and for maximizing the information return from experimental structure determination. The Protein Alignment organised as Structural Superfamily (PASS2) database represents continuously updated, structural alignments for evolutionary related, sequentially distant proteins.     

Large-scale prediction of long disordered regions in proteins using random forests

Background  

Many proteins contain disordered regions that lack fixed three-dimensional (3D) structure under physiological conditions but have important biological functions. Prediction of disordered regions in protein sequences is important for understanding protein function and in high-throughput determination of protein structures. Machine learning techniques, including neural networks and support vector machines have been widely used in such predictions. Predictors designed for long disordered regions are usually less successful in predicting short disordered regions. Combining prediction of short and long disordered regions will dramatically increase the complexity of the prediction algorithm and make the predictor unsuitable for large-scale applications. Efficient batch prediction of long disordered regions alone is of greater interest in large-scale proteome studies.     

Evolutionary conservation of plant gibberellin signalling pathway components

Background:  

Gibberellins (GA) are plant hormones that can regulate germination, elongation growth, and sex determination. They ubiquitously occur in seed plants. The discovery of gibberellin receptors, together with advances in understanding the function of key components of GA signalling in Arabidopsis and rice, reveal a fairly short GA signal transduction route. The pathway essentially consists of GID1 gibberellin receptors that interact with F-box proteins, which in turn regulate degradation of downstream DELLA proteins, suppressors of GA-controlled responses.     

Regression applied to protein binding site prediction and comparison with classification

Background  

The structural genomics centers provide hundreds of protein structures of unknown function. Therefore, developing methods enabling the determination of a protein function automatically is imperative. The determination of a protein function can be achieved by studying the network of its physical interactions. In this context, identifying a potential binding site between proteins is of primary interest. In the literature, methods for predicting a potential binding site location generally are based on classification tools. The aim of this paper is to show that regression tools are more efficient than classification tools for patches based binding site predictors. For this purpose, we developed a patches based binding site localization method usable with either regression or classification tools.     

Molecular cloning and tissue expression of the fatty acid-binding protein (<Emphasis Type="Italic">Es-FABP</Emphasis>) gene in female Chinese mitten crab (<Emphasis Type="Italic">Eriocheir sinensis</Emphasis>)

Background  

Fatty acid-binding proteins (FABPs), small cytosolic proteins that function in the uptake and utilization of fatty acids, have been extensively studied in higher vertebrates while invertebrates have received little attention despite similar nutritional requirements during periods of reproductive activity.     

Immobilized pH gradient-driven paper-based IEF: a new method for fractionating complex peptide mixtures before MS analysis

Introduction  

The vast difference in the abundance of different proteins in biological samples limits the determination of the complete proteome of a cell type, requiring fractionation of proteins and peptides before MS analysis.     

SUPERFICIAL – Surface mapping of proteins via structure-based peptide library design

Background  

The determination of protein surfaces and the detection of binding sites are essential to our understanding of protein-protein interactions. Such binding sites can be characterised as linear and non-linear, the non-linear sites being prevailant. Conventional mapping techniques with arrays of synthetic peptides have limitations with regard to the location of discontinuous or non-linear binding sites of proteins.     

DBMLoc: a Database of proteins with multiple subcellular localizations

Background  

Subcellular localization information is one of the key features to protein function research. Locating to a specific subcellular compartment is essential for a protein to function efficiently. Proteins which have multiple localizations will provide more clues. This kind of proteins may take a high proportion, even more than 35%.     

Structural analysis of heme proteins: implications for design and prediction

Background  

Heme is an essential molecule and plays vital roles in many biological processes. The structural determination of a large number of heme proteins has made it possible to study the detailed chemical and structural properties of heme binding environment. Knowledge of these characteristics can provide valuable guidelines in the design of novel heme proteins and help us predict unknown heme binding proteins.     

Establishment of a Cre/loxP recombination system for N-terminal epitope tagging of genes in <Emphasis Type="Italic">Tetrahymena</Emphasis>

Background  

Epitope tagging is a powerful strategy to study the function of proteins. Although tools for C-terminal protein tagging in the ciliated protozoan Tetrahymena thermophila have been developed, N-terminal protein tagging in this organism is still technically demanding.     

PDB-UF: database of predicted enzymatic functions for unannotated protein structures from structural genomics

Background  

The number of protein structures from structural genomics centers dramatically increases in the Protein Data Bank (PDB). Many of these structures are functionally unannotated because they have no sequence similarity to proteins of known function. However, it is possible to successfully infer function using only structural similarity.     

Non-classical protein secretion in bacteria

Background  

We present an overview of bacterial non-classical secretion and a prediction method for identification of proteins following signal peptide independent secretion pathways. We have compiled a list of proteins found extracellularly despite the absence of a signal peptide. Some of these proteins also have known roles in the cytoplasm, which means they could be so-called "moon-lightning" proteins having more than one function.     

An automated stochastic approach to the identification of the protein specificity determinants and functional subfamilies

Background  

Recent progress in sequencing and 3 D structure determination techniques stimulated development of approaches aimed at more precise annotation of proteins, that is, prediction of exact specificity to a ligand or, more broadly, to a binding partner of any kind.     

PreDisorder: ab initio sequence-based prediction of protein disordered regions

Background  

Disordered regions are segments of the protein chain which do not adopt stable structures. Such segments are often of interest because they have a close relationship with protein expression and functionality. As such, protein disorder prediction is important for protein structure prediction, structure determination and function annotation.     

Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information

Background  

Protein-protein interactions play essential roles in protein function determination and drug design. Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost. Therefore, it is important to improve the performance for predicting protein interaction sites based on primary sequence alone.     

Fast identification of folded human protein domains expressed in <Emphasis Type="Italic">E. coli</Emphasis> suitable for structural analysis

Background  

High-throughput protein structure analysis of individual protein domains requires analysis of large numbers of expression clones to identify suitable constructs for structure determination. For this purpose, methods need to be implemented for fast and reliable screening of the expressed proteins as early as possible in the overall process from cloning to structure determination.     

Predicting protein folding pathways at the mesoscopic level based on native interactions between secondary structure elements

Background  

Since experimental determination of protein folding pathways remains difficult, computational techniques are often used to simulate protein folding. Most current techniques to predict protein folding pathways are computationally intensive and are suitable only for small proteins.