Lipid analysis and lipidomics by structurally selective ion mobility-mass spectrometry

Recent advances in mass spectrometry approaches to the analysis of lipids include the ability to incorporate both lipid class identification with lipid structural information for increased characterization capabilities. The detailed examination of lipids and their biosynthetic and biochemical pathways made possible by novel instrumental and bioinformatics approaches is advancing research in fundamental cellular and medical studies. Recently, high-throughput structural analysis has been demonstrated through the use of rapid gas-phase separation on the basis of the ion mobility (IM) analytical technique combined with mass spectrometry (IM-MS). While IM-MS has been extensively utilized in biochemical research for peptide, protein and small molecule analysis, the role of IM-MS in lipid research is still an active area of development. In this review of lipid-based IM-MS research, we begin with an overview of three contemporary IM techniques which show great promise in being applied towards the analysis of lipids. Fundamental concepts regarding the integration of IM-MS are reviewed with emphasis on the applications of IM-MS towards simplifying and enhancing complex biological sample analysis. Finally, several recent IM-MS lipid studies are highlighted and the future prospects of IM-MS for integrated omics studies and enhanced spatial profiling through imaging IM-MS are briefly described.     

STRALCP--structure alignment-based clustering of proteins

Protein structural annotation and classification is an important and challenging problem in bioinformatics. Research towards analysis of sequence-structure correspondences is critical for better understanding of a protein's structure, function, and its interaction with other molecules. Clustering of protein domains based on their structural similarities provides valuable information for protein classification schemes. In this article, we attempt to determine whether structure information alone is sufficient to adequately classify protein structures. We present an algorithm that identifies regions of structural similarity within a given set of protein structures, and uses those regions for clustering. In our approach, called STRALCP (STRucture ALignment-based Clustering of Proteins), we generate detailed information about global and local similarities between pairs of protein structures, identify fragments (spans) that are structurally conserved among proteins, and use these spans to group the structures accordingly. We also provide a web server at http://as2ts.llnl.gov/AS2TS/STRALCP/ for selecting protein structures, calculating structurally conserved regions and performing automated clustering.     

Challenges at the frontiers of structural biology

Knowledge of the three-dimensional structures of proteins is the key to unlocking the full potential of genomic information. There are two distinct directions along which cutting-edge research in structural biology is currently moving towards this goal. On the one hand, tightly focused long-term research in individual laboratories is leading to the determination of the structures of macromolecular assemblies of ever-increasing size and complexity. On the other hand, large consortia of structural biologists, inspired by the pace of genome sequencing, are developing strategies to determine new protein structures rapidly, so that it will soon be possible to predict reasonably accurate structures for most protein domains. We anticipate that a small number of complex systems, studied in depth, will provide insights across the field of biology with the aid of genome-based comparative structural analysis.     

Challenges at the frontiers of structural biology

Knowledge of the three-dimensional structures of proteins is the key to unlocking the full potential of genomic information. There are two distinct directions along which cutting-edge research in structural biology is currently moving towards this goal. On the one hand, tightly focused long-term research in individual laboratories is leading to the determination of the structures of macromolecular assemblies of ever-increasing size and complexity. On the other hand, large consortia of structural biologists, inspired by the pace of genome sequencing, are developing strategies to determine new protein structures rapidly, so that it will soon be possible to predict reasonably accurate structures for most protein domains. We anticipate that a small number of complex systems, studied in depth, will provide insights across the field of biology with the aid of genome-based comparative structural analysis.     

Challenges at the frontiers of structural biology

Knowledge of the three-dimensional structures of proteins is the key to unlocking the full potential of genomic information. There are two distinct directions along which cutting-edge research in structural biology is currently moving towards this goal. On the one hand, tightly focused long-term research in individual laboratories is leading to the determination of the structures of macromolecular assemblies of ever-increasing size and complexity. On the other hand, large consortia of structural biologists, inspired by the pace of genome sequencing, are developing strategies to determine new protein structures rapidly, so that it will soon be possible to predict reasonably accurate structures for most protein domains. We anticipate that a small number of complex systems, studied in depth, will provide insights across the field of biology with the aid of genome-based comparative structural analysis.     

Application of information theory to feature selection in protein docking

In the era of structural genomics, the prediction of protein interactions using docking algorithms is an important goal. The success of this method critically relies on the identification of good docking solutions among a vast excess of false solutions. We have adapted the concept of mutual information (MI) from information theory to achieve a fast and quantitative screening of different structural features with respect to their ability to discriminate between physiological and nonphysiological protein interfaces. The strategy includes the discretization of each structural feature into distinct value ranges to optimize its mutual information. We have selected 11 structural features and two datasets to demonstrate that the MI is dimensionless and can be directly compared for diverse structural features and between datasets of different sizes. Conversion of the MI values into a simple scoring function revealed that those features with a higher MI are actually more powerful for the identification of good docking solutions. Thus, an MI-based approach allows the rapid screening of structural features with respect to their information content and should therefore be helpful for the design of improved scoring functions in future. In addition, the concept presented here may also be adapted to related areas that require feature selection for biomolecules or organic ligands.     

NMR screening in drug discovery

NMR methods in drug discovery have traditionally been used to obtain structural information for drug targets or target-ligand complexes. Recently, it has been shown that NMR may be used as an alternative approach for identification of ligands that bind to protein drug targets, shifting the emphasis of many NMR laboratories towards screening and design of potential drug molecules, rather than structural characterization.     

Shaping dots and lines: adding modularity into protein interaction networks using structural information

Determining protein interaction networks and generating models to simulate network changes in time and space are crucial for understanding a biological system and for predicting the effect of mutants found in diseases. In this review we discuss the great potential of using structural information together with computational tools towards reaching this goal: the prediction of new protein interactions, the estimation of affinities and kinetic rate constants between protein complexes, and finally the determination of which interactions are compatible with each other and which interactions are exclusive. The latter one will be important to reorganize large scale networks into functional modular networks.     

Optimized in vitro and in vivo expression of proteorhodopsin: a seven-transmembrane proton pump

Proteorhodopsin is an integral membrane light-harvesting proton pump that is found in bacteria distributed throughout global surface waters. Here, we present a protocol for functional in vitro production of pR using a commercial cell-free synthesis system yielding 1.0mg purified protein per milliliter of cell lysate. We also present an optimized protocol for in vivo over-expression of pR in Escherichia coli, and a two-step purification yielding 5mg of essentially pure functional protein per liter of culture. Both approaches are straightforward, rapid, and easily scalable. Thus either may facilitate the exploitation of pR for commercial biotechnological applications. Finally, the implications of some observations of the in vitro synthesis behavior, as well as preliminary results towards a structural determination of pR are discussed.     

Effects of solubilization on the structure and function of the sensory rhodopsin II/transducer complex

Lipid-protein interactions are known to play a crucial role in structure and physiological activity of integral membrane proteins. However, current technology for membrane protein purification necessitates extraction from the membrane into detergent micelles. Also, due to experimental protocols, most of the data available for membrane proteins is obtained using detergent-solubilized samples. Stable solubilization of membrane proteins is therefore an important issue in biotechnology as well as in biochemistry and structural biology. An understanding of solubilization effects on structural and functional properties of specific proteins is of utmost relevance for the evaluation and interpretation of experimental results. In this study, a comparison of structural and kinetic data obtained for the archaebacterial photoreceptor/transducer complex from Natronomonas pharaonis (NpSRII/NpHtrII) in detergent-solubilized and lipid-reconstituted states is presented. Laser flash photolysis, fluorescence spectroscopy, and electron paramagnetic resonance spectroscopy data reveal considerable influence of solubilization on the photocycle kinetics of the receptor protein and on the structure of the transducer protein. Especially the protein-membrane proximal region and the protein-protein interfacial domains are sensitive towards non-native conditions. These data demonstrate that relevance of biochemical and structural information obtained from solubilized membrane proteins or membrane protein complexes has to be evaluated carefully.     

Java GUI for InterProScan (JIPS): A tool to help process multiple InterProScans and perform ortholog analysis

Background  

Recent, rapid growth in the quantity of available genomic data has generated many protein sequences that are not yet biochemically classified. Thus, the prediction of biochemical function based on structural motifs is an important task in post-genomic analysis. The InterPro databases are a major resource for protein function information. For optimal results, these databases should be searched at regular intervals, since they are frequently updated.     

From fold to function

A number of recent advances have been made in deriving function information from protein structure. A fold relationship to an already characterized protein will often allow general information about function to be deduced. More detailed information can be obtained using sequence relationships to already studied proteins. Methods of deducing function directly from structure, without the use of evolutionary relationships, are developing rapidly. All such methods may be used with models of protein structure, rather than with experimentally determined ones, but model accuracy imposes limitations. The rapid expansion of the structural genomics field has created a new urgency for improved methods of structure-based annotation of function.     

Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network

It is a critical challenge to develop automated methods for fast and accurately determining the structures of proteins because of the increasingly widening gap between the number of sequence-known proteins and that of structure-known proteins in the post-genomic age. The knowledge of protein structural class can provide useful information towards the determination of protein structure. Thus, it is highly desirable to develop computational methods for identifying the structural classes of newly found proteins based on their primary sequence. In this study, according to the concept of Chou's pseudo amino acid composition (PseAA), eight PseAA vectors are used to represent protein samples. Each of the PseAA vectors is a 40-D (dimensional) vector, which is constructed by the conventional amino acid composition (AA) and a series of sequence-order correlation factors as original introduced by Chou. The difference among the eight PseAA representations is that different physicochemical properties are used to incorporate the sequence-order effects for the protein samples. Based on such a framework, a dual-layer fuzzy support vector machine (FSVM) network is proposed to predict protein structural classes. In the first layer of the FSVM network, eight FSVM classifiers trained by different PseAA vectors are established. The 2nd layer FSVM classifier is applied to reclassify the outputs of the first layer. The results thus obtained are quite promising, indicating that the new method may become a useful tool for predicting not only the structural classification of proteins but also their other attributes.     

Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network

Because a priori knowledge of a protein structural class can provide useful information about its overall structure, the determination of protein structural class is a quite meaningful topic in protein science. However, with the rapid increase in newly found protein sequences entering into databanks, it is both time-consuming and expensive to do so based solely on experimental techniques. Therefore, it is vitally important to develop a computational method for predicting the protein structural class quickly and accurately. To deal with the challenge, this article presents a dual-layer support vector machine (SVM) fusion network that is featured by using a different pseudo-amino acid composition (PseAA). The PseAA here contains much information that is related to the sequence order of a protein and the distribution of the hydrophobic amino acids along its chain. As a showcase, the rigorous jackknife cross-validation test was performed on the two benchmark data sets constructed by Zhou. A significant enhancement in success rates was observed, indicating that the current approach may serve as a powerful complementary tool to other existing methods in this area.     

Crystal structure of the type III effector AvrB from Pseudomonas syringae

AvrB is a Pseudomonas syringae type III effector protein that is translocated into host plant cells during attempted pathogenesis. Arabidopsis harboring the corresponding resistance protein RPM1 can detect AvrB and mount a rapid host defense response, thus avoiding active infection. In the plant cell, AvrB induces phosphorylation of RIN4, a key component in AvrB/RPM1 recognition. Although the AvrB/RPM1 system is among the best characterized of the numerous bacterial effector/plant resistance protein systems involved in plant disease resistance and pathogenesis, the details of the molecular recognition mechanism are still unclear. To gain further insights, the crystal structure of AvrB was determined. The 2.2 A structure exhibits a novel mixed alpha/beta bilobal fold. Aided by the structural information, we demonstrate that one lobe is the determinant of AvrB/RPM1 recognition specificity. This structural information and preliminary structure-function studies provide a framework for the future understanding of AvrB function on the molecular level.     

A scaleable and integrated crystallization pipeline applied to mining the Thermotoga maritima proteome

The wealth of genomic data available for many organisms has set the stage for the next phase of structure-function analysis. High-throughput structural genomics is currently the method of choice for rapid analysis of protein structure-function relationships on a proteome-wide basis. The Joint Center for Structural Genomics (JCSG), established in 2000 under the NIH/NIGMS Protein Structure Initiative, has developed and implemented an integrated high-throughput structure pipeline and applied it in a 2-tiered approach to mining the proteome of the thermophilic bacterium Thermotoga maritima. In the first tier, the successful application of this integrated pipeline has resulted in the cloning and expression of 73% of the T. maritima proteome (1376 out of 1877 predicted genes), and has identified 465 proteins which produced crystal hits. These 465 proteins were compared with existing structural information and a subset of 269 targets were selected to process towards structure determination in a second tier effort. To date, the JCSG pipeline applied to the Thermotoga maritima proteome has resulted in 55 new structures and has identified 6 novel folds and continues to identify structures with novel features.     

Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction

MOTIVATION: Compensating alterations during the evolution of protein families give rise to coevolving positions that contain important structural and functional information. However, a high background composed of random noise and phylogenetic components interferes with the identification of coevolving positions. RESULTS: We have developed a rapid, simple and general method based on information theory that accurately estimates the level of background mutual information for each pair of positions in a given protein family. Removal of this background results in a metric, MIp, that correctly identifies substantially more coevolving positions in protein families than any existing method. A significant fraction of these positions coevolve strongly with one or only a few positions. The vast majority of such position pairs are in contact in representative structures. The identification of strongly coevolving position pairs can be used to impose significant structural limitations and should be an important additional constraint for ab initio protein folding. AVAILABILITY: Alignments and program files can be found in the Supplementary Information.     

Protein structure similarity from principle component correlation analysis

Background  

Owing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD) in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities.