Quantification of protein–lipid selectivity using FRET

Membrane proteins exhibit different affinities for different lipid species, and protein–lipid selectivity regulates the membrane composition in close proximity to the protein, playing an important role in the formation of nanoscale membrane heterogeneities. The sensitivity of Förster resonance energy transfer (FRET) for distances of 10 Å up to 100 Å is particularly useful to retrieve information on the relative distribution of proteins and lipids in the range over which protein–lipid selectivity is expected to influence membrane composition. Several FRET-based methods applied to the quantification of protein–lipid selectivity are described herein, and different formalisms applied to the analysis of FRET data for particular geometries of donor–acceptor distribution are critically assessed.     

Foraging preferences of Canada geese among turfgrasses: Implications for reducing human–goose conflicts

Canada geese (Branta canadensis) can cause serious damage to turfgrass areas and create human health and safety concerns (e.g., collisions with aircraft, disease transmission). We conducted a study during 2005–2007 to determine if Canada geese exhibit a feeding preference among various commercially available turfgrasses. Behavioral responses of captive geese to 9 turfgrasses, bare ground, and litter were observed over 6 4-week trials during July–September following the installation of selected turfgrasses into experimental arenas. Captive geese preferred to forage on Kentucky bluegrass, creeping bentgrass, and fine fescue sods compared to centipedegrass, St. Augustinegrass, and zoysiagrass. Forage qualities and macronutrient levels varied among the turfgrasses and might explain the foraging preferences geese exhibited during this study. Canada goose feeding rate was positively correlated with crude protein, nitrogen content, and calcium, but negatively correlated with acid detergent fiber content, within various turfgrasses. Our findings suggest careful selection of turfgrasses could be an effective method for reducing Canada goose conflicts in urban and suburban areas. © 2011 The Wildlife Society.     

Implications of disease-related mutations at protein–protein interfaces

Protein–protein interfaces have been attracting great attention owing to their critical roles in protein–protein interactions and the fact that human disease-related mutations are generally enriched in them. Recently, substantial research progress has been made in this field, which has significantly promoted the understanding and treatment of various human diseases. For example, many studies have discovered the properties of disease-related mutations. Besides, as more large-scale experimental data become available, various computational approaches have been proposed to advance our understanding of disease mutations from the data. Here, we overview recent advances in characteristics of disease-related mutations at protein–protein interfaces, mutation effects on protein interactions, and investigation of mutations on specific diseases.     

NLDB: a database for 3D protein–ligand interactions in enzymatic reactions

NLDB (Natural Ligand DataBase; URL: http://nldb.hgc.jp) is a database of automatically collected and predicted 3D protein–ligand interactions for the enzymatic reactions of metabolic pathways registered in KEGG. Structural information about these reactions is important for studying the molecular functions of enzymes, however a large number of the 3D interactions are still unknown. Therefore, in order to complement such missing information, we predicted protein–ligand complex structures, and constructed a database of the 3D interactions in reactions. NLDB provides three different types of data resources; the natural complexes are experimentally determined protein–ligand complex structures in PDB, the analog complexes are predicted based on known protein structures in a complex with a similar ligand, and the ab initio complexes are predicted by docking simulations. In addition, NLDB shows the known polymorphisms found in human genome on protein structures. The database has a flexible search function based on various types of keywords, and an enrichment analysis function based on a set of KEGG compound IDs. NLDB will be a valuable resource for experimental biologists studying protein–ligand interactions in specific reactions, and for theoretical researchers wishing to undertake more precise simulations of interactions.     

Advances in the study of protein–DNA interaction

Protein-DNA interaction plays an important role in many biological processes. The classical methods and the novel technologies advanced have been developed for the interaction of protein-DNA. Recent developments of these methods and research achievements have been reviewed in this paper.     

212 Shifting interfaces: changes in protein–protein and protein–DNA interfaces probed via molecular dynamics

Over the past decade, there has been a growing interest in studying the binding of DNA to the MutSalpha protein complex. This heterodimeric protein complex, the Msh2/Msh6 complex in humans, is the initial complex that binds mismatched DNA and other DNA defects that occur during replication. This complex has also been shown to bind at least some types of damaged DNA, such as the cross-linked adducts due to the chemotherapeutics cisplatin and carboplatin, or the incorporation of the chemotherapeutic, FdU. As a result of this interest, multiple studies have contrasted the interactions of MutSalpha with its normal mismatched substrate and with the interactions of MutsSalpha with the DNA damaged by chemotherapeutic cisplatin. To complement these studies, we examine the interaction between MutSalpha and the DNA damaged by carboplatin via all-atom molecular dynamics simulations. These simulations provide evidence for subtle changes in the protein–DNA and protein–protein interfaces. The interfaces shifts found are broadly similar to those found in binding with adduct from cis-platin, but have distinct differences. These subtle differences may play a role in the way of the different damages and mismatched DNA are signaled by MutSalpha, and suggest a signaling mechanism for DNA damage that chiefly involves shifts in protein–protein interactions as opposed to changes in protein conformation.     

An information-theoretic classification of amino acids for the assessment of interfaces in protein–protein docking

Docking represents a versatile and powerful method to predict the geometry of protein–protein complexes. However, despite significant methodical advances, the identification of good docking solutions among a large number of false solutions still remains a difficult task. We have previously demonstrated that the formalism of mutual information (MI) from information theory can be adapted to protein docking, and we have now extended this approach to enhance its robustness and applicability. A large dataset consisting of 22,934 docking decoys derived from 203 different protein–protein complexes was used for an MI-based optimization of reduced amino acid alphabets representing the protein–protein interfaces. This optimization relied on a clustering analysis that allows one to estimate the mutual information of whole amino acid alphabets by considering all structural features simultaneously, rather than by treating them individually. This clustering approach is fast and can be applied in a similar fashion to the generation of reduced alphabets for other biological problems like fold recognition, sequence data mining, or secondary structure prediction. The reduced alphabets derived from the present work were converted into a scoring function for the evaluation of docking solutions, which is available for public use via the web service score-MI: http://score-MI.biochem.uni-erlangen.de     

Role for protein–protein interaction databases in human genetics

Proteomics and the study of protein–protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein–protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein–protein interactions in human genetics and genetic epidemiology. Since protein–protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies.     

A simple method for monitoring protein–DNA interactions

A simple, efficient and cheap method is reported for monitoring interactions between single stranded desoxyribonucleic acids and proteins, using fluorescence spectroscopy and complexes of 5′-dye–DNA conjugates with bovine serum albumin as probes. In the presence of a single stranded DNA-binding protein the complexes with bovine serum albumin are disrupted, which results in a reduction of fluorescence intensity.     

High-throughput single-molecule studies of protein–DNA interactions

Fluorescence and force-based single-molecule studies of protein–nucleic acid interactions continue to shed critical insights into many aspects of DNA and RNA processing. As single-molecule assays are inherently low-throughput, obtaining statistically relevant datasets remains a major challenge. Additionally, most fluorescence-based single-molecule particle-tracking assays are limited to observing fluorescent proteins that are in the low-nanomolar range, as spurious background signals predominate at higher fluorophore concentrations. These technical limitations have traditionally limited the types of questions that could be addressed via single-molecule methods. In this review, we describe new approaches for high-throughput and high-concentration single-molecule biochemical studies. We conclude with a discussion of outstanding challenges for the single-molecule biologist and how these challenges can be tackled to further approach the biochemical complexity of the cell.     

Landscape of π–π and sugar–π contacts in DNA–protein interactions

There were 1765 contacts identified between DNA nucleobases or deoxyribose and cyclic (W, H, F, Y) or acyclic (R, E, D) amino acids in 672 X-ray structures of DNA–protein complexes. In this first study to compare π-interactions between the cyclic and acyclic amino acids, visual inspection was used to categorize amino acid interactions as nucleobase π–π (according to biological edge) or deoxyribose sugar–π (according to sugar edge). Overall, 54% of contacts are nucleobase π–π interactions, which involve all amino acids, but are more common for Y, F, and R, and involve all DNA nucleobases with similar frequencies. Among binding arrangements, cyclic amino acids prefer more planar (stacked) π-systems than the acyclic counterparts. Although sugar–π interactions were only previously identified with the cyclic amino acids and were found to be less common (38%) than nucleobase–cyclic amino acid contacts, sugar–π interactions are more common than nucleobase π–π contacts for the acyclic series (61% of contacts). Similar to DNA–protein π–π interactions, sugar–π contacts most frequently involve Y and R, although all amino acids adopt many binding orientations relative to deoxyribose. These DNA–protein π-interactions stabilize biological systems, by up to approximately ?40 kJ mol^?1 for neutral nucleobase or sugar–amino acid interactions, but up to approximately ?95 kJ mol^?1 for positively or negatively charged contacts. The high frequency and strength, despite variation in structure and composition, of these π-interactions point to an important function in biological systems.