Sequence-similar, structure-dissimilar protein pairs in the PDB

It is often assumed that in the Protein Data Bank (PDB), two proteins with similar sequences will also have similar structures. Accordingly, it has proved useful to develop subsets of the PDB from which "redundant" structures have been removed, based on a sequence-based criterion for similarity. Similarly, when predicting protein structure using homology modeling, if a template structure for modeling a target sequence is selected by sequence alone, this implicitly assumes that all sequence-similar templates are equivalent. Here, we show that this assumption is often not correct and that standard approaches to create subsets of the PDB can lead to the loss of structurally and functionally important information. We have carried out sequence-based structural superpositions and geometry-based structural alignments of a large number of protein pairs to determine the extent to which sequence similarity ensures structural similarity. We find many examples where two proteins that are similar in sequence have structures that differ significantly from one another. The source of the structural differences usually has a functional basis. The number of such proteins pairs that are identified and the magnitude of the dissimilarity depend on the approach that is used to calculate the differences; in particular sequence-based structure superpositioning will identify a larger number of structurally dissimilar pairs than geometry-based structural alignments. When two sequences can be aligned in a statistically meaningful way, sequence-based structural superpositioning provides a meaningful measure of structural differences. This approach and geometry-based structure alignments reveal somewhat different information and one or the other might be preferable in a given application. Our results suggest that in some cases, notably homology modeling, the common use of nonredundant datasets, culled from the PDB based on sequence, may mask important structural and functional information. We have established a data base of sequence-similar, structurally dissimilar protein pairs that will help address this problem (http://luna.bioc.columbia.edu/rachel/seqsimstrdiff.htm).     

An effective sequence-alignment-free superpositioning of pairwise or multiple structures with missing data

Background

Superpositioning is an important problem in structural biology. Determining an optimal superposition requires a one-to-one correspondence between the atoms of two proteins structures. However, in practice, some atoms are missing from their original structures. Current superposition implementations address the missing data crudely by ignoring such atoms from their structures.

Results

In this paper, we propose an effective method for superpositioning pairwise and multiple structures without sequence alignment. It is a two-stage procedure including data reduction and data registration.

Conclusions

Numerical experiments demonstrated that our method is effective and efficient. The code package of protein structure superposition method for addressing the cases with missing data is implemented by MATLAB, and it is freely available from: http://sourceforge.net/projects/pssm123/files/?source=navbar

     

Conformational analysis of alternative protein structures

MOTIVATION: Alternative structural models determined experimentally are available for an increasing number of proteins. Structural and functional studies of these proteins need to take these models into consideration as they can present considerable structural differences. The characterization of the structural differences and similarities between these models is a fundamental task in structural biology requiring appropriate methods. RESULTS: We propose a method for characterizing sets of alternative structural models. Three types of analysis are performed: grouping according to structural similarity, visualization and detection of structural variation and comparison of subsets for identifying and locating distinct conformational states. The alpha carbon atoms are used in order to analyse the backbone conformations. Alternatively, side-chain atoms are used for detailed conformational analysis of specific sites. The method takes into account estimates of atom coordinate uncertainty. The invariant regions are used to generate optimal superpositions of these models. We present the results obtained for three proteins showing different degrees of conformational variability: relative motion of two structurally conserved subdomains, a disordered subdomain and flexibility in the functional site associated with ligand binding. The method has been applied in the analysis of the alternative models available in SCOP. Considerable structural variability can be observed for most proteins. AVAILABILITY: The results of the analysis of the SCOP alternative models, the estimates of coordinate uncertainty as well as the source code of the implementation are available in the STRuster web site: http://struster.bioinf.mpi-inf.mpg.de.     

DBAli: a database of protein structure alignments

SUMMARY: The DBAli database includes approximately 35000 alignments of pairs of protein structures from SCOP (Lo Conte et al., Nucleic Acids Res., 28, 257-259, 2000) and CE (Shindyalov and Bourne, Protein Eng., 11, 739-747, 1998). DBAli is linked to several resources, including Compare3D (Shindyalov and Bourne, http://www.sdsc.edu/pb/software.htm, 1999) and ModView (Ilyin and Sali, http://guitar.rockefeller.edu/ModView/, 2001) for visualizing sequence alignments and structure superpositions. A flexible search of DBAli by protein sequence and structure properties allows construction of subsets of alignments suitable for a number of applications, such as benchmarking of sequence-sequence and sequence-structure alignment methods under a variety of conditions. AVAILABILITY: http://guitar.rockefeller.edu/DBAli/     

Habitat selection by adult Golden Eagles Aquila chrysaetos during the breeding season and implications for wind farm establishment

Capsule: Global Positioning System (GPS)-tagged adult Golden Eagles Aquila chrysaetos breeding in forests in northern Sweden selected clear-cuts, coniferous forests with lichens and steep slopes during the breeding season but avoided wetlands and mixed forest.

Aims: To investigate the habitat selection patterns of tree-nesting Golden Eagles, and identify how potential conflicts with wind farm development could be minimized.

Methods: The study is based on GPS tracking data from 22 adult eagles. We estimated home range sizes using a biased random bridge approach and habitat selection patterns using resource selection functions following a use-availability design.

Results: Core home range size among adults was variable during the breeding season (5–30?km²). Individual movement extents were variable, but sexes did not significantly differ in their scale of movement. At the landscape scale, individuals selected for clear-cuts and coniferous forest with ground lichens, whereas wetland, water bodies and mixed forest were avoided. Steeper and south facing slopes were selected for, whereas, north facing slopes were avoided.

Conclusions: Potential conflicts between eagles and wind energy establishment can be reduced if wind farms are placed away from steep slopes, minimizing areas that are clear-cut during construction, and locating turbines within dense, young and other less favoured forest habitats.     

The Structure Superposition Database

The need for new tools for investigating biological systems on a large scale is becoming acute, particularly with respect to computationally intensive analyses such as comparisons of many three-dimensional protein structures. Structure superposition is a valuable approach for understanding evolutionary relationships and for the prediction of function. But while available tools are adequate for generating and viewing superpositions of single pairs of protein structures, these tools are generally too cumbersome and time-consuming for examining multiple superpositions. To address this need, we have created the Structure Superposition Database (SSD) for accessing, viewing and understanding large sets of structure superposition data. The initial implementation of the SSD contains the results of pairwise, all-by-all superpositions of a representative set of 115 (beta/alpha)8 barrel structures (TIM barrels). Future plans call for extending the database to include representative structure superpositions for many additional folds. The SSD can be browsed with a user interface module developed as an extension to Chimera, an extensible molecular modeling program. Features of the user interface module facilitate viewing multiple superpositions together. The SSD interface module can be downloaded from http://ssd.rbvi.ucsf.edu.     

Stochastic simulation GUI for biochemical networks

MOTIVATION: This article describes the development of a useful graphical user interface for stochastic simulation of biochemical networks which allows model builders to run stochastic simulations of their models and perform statistical analysis on the results. These include the construction of correlations, power-spectral densities and transfer functions between selected inputs and outputs. AVAILABILITY: The software is licensed under the BSD open source license and is available at http://sourceforge.net/projects/jdesigner. In addition, a more detailed account of the algorithms employed in the tool can be found at the Wiki at http://www.sys-bio.org/sbwWiki. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Using multiple biomarkers and determinants to obtain a better measurement of oxidative stress: a latent variable structural equation model approach

Purpose: Since oxidative stress involves a variety of cellular changes, no single biomarker can serve as a complete measure of this complex biological process. The analytic technique of structural equation modeling (SEM) provides a possible solution to this problem by modelling a latent (unobserved) variable constructed from the covariance of multiple biomarkers.

Methods: Using three pooled datasets, we modelled a latent oxidative stress variable from five biomarkers related to oxidative stress: F₂-isoprostanes (FIP), fluorescent oxidation products, mitochondrial DNA copy number, γ-tocopherol (Gtoc) and C-reactive protein (CRP, an inflammation marker closely linked to oxidative stress). We validated the latent variable by assessing its relation to pro- and anti-oxidant exposures.

Results: FIP, Gtoc and CRP characterized the latent oxidative stress variable. Obesity, smoking, aspirin use and β-carotene were statistically significantly associated with oxidative stress in the theorized directions; the same exposures were weakly and inconsistently associated with the individual biomarkers.

Conclusions: Our results suggest that using SEM with latent variables decreases the biomarker-specific variability, and may produce a better measure of oxidative stress than do single variables. This methodology can be applied to similar areas of research in which a single biomarker is not sufficient to fully describe a complex biological phenomenon.     

Presynaptic Calmodulin targets: lessons from structural proteomics

Introduction: Calmodulin (CaM) is a highly conserved Ca²⁺-binding protein that is exceptionally abundant in the brain. In the presynaptic compartment of neurons, CaM transduces changes in Ca²⁺ concentration into the regulation of synaptic transmission dynamics.

Areas covered: We review selected literature including published CaM interactor screens and outline established and candidate presynaptic CaM targets. We present a workflow of biochemical and structural proteomic methods that were used to identify and characterize the interactions between CaM and Munc13 proteins. Finally, we outline the potential of ion mobility-mass spectrometry (IM-MS) for conformational screening and of protein-protein cross-linking for the structural characterization of CaM complexes.

Expert commentary: Cross-linking/MS and native MS can be applied with considerable throughput to protein mixtures under near-physiological conditions, and thus effectively complement high-resolution structural biology techniques. Experimental distance constraints are applicable best when obtained by combining different cross-linking strategies, i.e. by using cross-linkers with different spacer length and reactivity, and by using the incorporation of unnatural photo-reactive amino acids. Insights from structural proteomics can be used to generate CaM-insensitive mutants of CaM targets for functional studies in vitro or ideally in vivo.     

BAliBASE (Benchmark Alignment dataBASE): enhancements for repeats, transmembrane sequences and circular permutations

BAliBASE is specifically designed to serve as an evaluation resource to address all the problems encountered when aligning complete sequences. The database contains high quality, manually constructed multiple sequence alignments together with detailed annotations. The alignments are all based on three-dimensional structural superpositions, with the exception of the transmembrane sequences. The first release provided sets of reference alignments dealing with the problems of high variability, unequal repartition and large N/C-terminal extensions and internal insertions. Here we describe version 2.0 of the database, which incorporates three new reference sets of alignments containing structural repeats, trans-membrane sequences and circular permutations to evaluate the accuracy of detection/prediction and alignment of these complex sequences. BAliBASE can be viewed at the web site http://www-igbmc.u-strasbg. fr/BioInfo/BAliBASE2/index.html or can be downloaded from ftp://ftp-igbmc.u-strasbg.fr/pub/BAliBASE2 /.     

Obesity is an independent determinant of elevated C-reactive protein in healthy women but not men

Background and aims: High-sensitivity C-reactive protein (hs CRP) has emerged as an inflammatory biomarker to predict metabolic syndrome. Here, we investigate the association of hs CRP with metabolic variables and determine the risks for elevated hs CRP levels in healthy Singaporean adults.

Methods: We conducted a cross-sectional study of 225 participants (104 men). The levels of hs CRP and fasting lipid parameters were analyzed by COBAS. Body composition was determined with dual-energy X-ray absorptiometry.

Results: Twenty-one (9?%) participants had elevated hs CRP levels (>3?mg/mL). The levels of hs CRP had significant correlations (p?<0.05) with obesity and metabolic variables among women. Stepwise multivariate regression analysis identified FM (%) (accounted for 22.5% of the variability in hs CRP levels) as a major determinant of hs CRP levels. On multivariate regression, FM (%) was the independent determinant of intermediate and elevated hs CRP in women after adjustment for the potential confounders.

Conclusions: Obesity may play a direct role in the elevated hs CRP levels in women, but not men living in Singapore. This is probably due to different body composition or different effects of sex hormones on adipose tissue between men and women.     

phangorn: phylogenetic analysis in R

SUMMARY: phangorn is a package for phylogenetic reconstruction and analysis in the R language. Previously it was only possible to estimate phylogenetic trees with distance methods in R. phangorn, now offers the possibility of reconstructing phylogenies with distance based methods, maximum parsimony or maximum likelihood (ML) and performing Hadamard conjugation. Extending the general ML framework, this package provides the possibility of estimating mixture and partition models. Furthermore, phangorn offers several functions for comparing trees, phylogenetic models or splits, simulating character data and performing congruence analyses. AVAILABILITY: phangorn can be obtained through the CRAN homepage http://cran.r-project.org/web/packages/phangorn/index.html. phangorn is licensed under GPL 2.     

Effects of transcranial direct current stimulation (tDCS) on balance improvement: a systematic review and meta-analysis

Background: Transcranial direct current stimulation (tDCS) has emerged as a promising therapeutic tool to improve balance and optimize rehabilitation strategies. However, current literature shows the methodological heterogeneity of tDCS protocols and results, hindering any clear conclusions about the effects of tDCS on postural control.

Objective: Evaluate the effectiveness of tDCS on postural control, and identify the most beneficial target brain areas and the effect on different populations.

Methods: Two independent reviewers selected randomized tDCS clinical-trials studies from PubMed, Scopus, Web of Science, and reference lists of retrieved articles published between 1998 and 2017. Most frequently reported centre of pressure (COP) variables were selected for meta-analysis. Other postural control outcomes were discussed in the review.

Results: Thirty studies were included in the systematic review, and 11 were submitted to a meta-analysis. A reduction of COP displacement area has been significantly achieved by tDCS, evidencing an improvement in balance control. Individuals with cerebral palsy (CP) and healthy young adults are mostly affected by stimulation. The analysis of the impact of tDCS over different brain areas revealed a significant effect after primary motor cortex (M1) stimulation, however, with no clear results after cerebellar stimulation due to divergent results among studies.

Conclusions: tDCS appears to improve balance control, more evident in healthy and CP subjects. Effects are observed when primary MI is stimulated. Cerebellar stimulation should be better investigated.     

A tridentate bis(pyrazolyl) ligand binds to Cu(II), without using the pyrazole group: a very unusual coordination mode of the ligand Hbdmpb, 1,3-bis(3,5-dimethylpyrazol-1-yl)-2-butanoic acid

A new pyrazole-based ligand, namely 1,3-bis(3,5-dimethylpyrazol-1-yl)-2-butanoic acid (Hbdmpb) was synthesised together with its copper complex Na[Cu(bdmpb)₂(OOCCH₃)H₂O] · 4H₂O. Both the free ligand and its Cu compound were fully characterised and their crystal structures were determined by X-ray analysis. The free-ligand molecular structure is uneventful. The Cu compound is highly unusual, as the pyrazole nitrogen atoms do not bind to the Cu ion. The copper(II) ion is coordinated by four nearly coplanar oxygen atoms from two dehydronated ligands bdmpb (CuO(1a) 1.942(4), CuO(1b) 1.933(4) Å), a monodentate acetate group (CuO(1) 1.927(3) Å) and a water molecule (CuO(1w) 1.937(4) Å). The nitrogen atoms of the pyrazole rings do not coordinate to the metal center, but instead are involved in strong intramolecular hydrogen bonds. The coordinated water molecule is strongly H-bonded to two pyrazole N atoms from two bdmpb ligands (N(12a) ? HO(1w) 2.762(7), N(12b) ? HO(1w) 2.774(7) Å). The other two pyrazole N atoms with a lone pair are hydrogen-bonded to water molecules in the lattice (N(22a) ? HO(2w) 2.763(7), N(22b) ? HO(6w) 2.892(7) Å). The sodium ion is six-coordinated by the oxygen atom O(2) of the acetato ligand and by five water molecules. The EPR spectrum recorded in the solid state shows a characteristic signal for an axial anisotropic S = 1/2 species. The spectrum recorded in methanol glass confirms the absence of the coordination of pyrazole nitrogen atoms to the copper centers.     

Accurate structural correlations from maximum likelihood superpositions

The cores of globular proteins are densely packed, resulting in complicated networks of structural interactions. These interactions in turn give rise to dynamic structural correlations over a wide range of time scales. Accurate analysis of these complex correlations is crucial for understanding biomolecular mechanisms and for relating structure to function. Here we report a highly accurate technique for inferring the major modes of structural correlation in macromolecules using likelihood-based statistical analysis of sets of structures. This method is generally applicable to any ensemble of related molecules, including families of nuclear magnetic resonance (NMR) models, different crystal forms of a protein, and structural alignments of homologous proteins, as well as molecular dynamics trajectories. Dominant modes of structural correlation are determined using principal components analysis (PCA) of the maximum likelihood estimate of the correlation matrix. The correlations we identify are inherently independent of the statistical uncertainty and dynamic heterogeneity associated with the structural coordinates. We additionally present an easily interpretable method (“PCA plots”) for displaying these positional correlations by color-coding them onto a macromolecular structure. Maximum likelihood PCA of structural superpositions, and the structural PCA plots that illustrate the results, will facilitate the accurate determination of dynamic structural correlations analyzed in diverse fields of structural biology.     

Automated analysis of interatomic contacts in proteins.

MOTIVATION: New software has been designed to assist the molecular biologist in understanding the structural consequences of modifying a ligand and/or protein. RESULTS: Tools are described for the analysis of ligand-protein contacts (LPC software) and contacts of structural units (CSU software) such as helices, sheets, strands and residues. Our approach is based on a detailed analysis of interatomic contacts and interface complementarity. For any ligand or structural unit, these software automatically: (i) calculate the solvent-accessible surface of every atom; (ii) determine the contacting residues and type of interaction they undergo (hydrophobic-hydrophobic, aromatic-aromatic, etc.); (iii) indicate all putative hydrogen bonds. LPC software further predicts changes in binding strength following chemical modification of the ligand. AVAILABILITY: Both LPC and CSU can be accessed through the PDB and are integrated in the 3DB Atlas page of all PDB files. For any given file, the tools can also be accessed at http://www.pdb.bnl. gov/pdb-bin/lpc?PDB_ID= and http://www.pdb.bnl. gov/pdb-bin/csu?PDB_ID= with the four-letter PDB code added at the end in each case. Finally, LPC and CSU can be accessed at: http://sgedg.weizmann.ac.il/lpc and http://sgedg.weizmann.ac.il/csu.     

LFM-Pro: a tool for detecting significant local structural sites in proteins

MOTIVATION: The rapidly growing protein structure repositories have opened up new opportunities for discovery and analysis of functional and evolutionary relationships among proteins. Detecting conserved structural sites that are unique to a protein family is of great value in identification of functionally important atoms and residues. Currently available methods are computationally expensive and fail to detect biologically significant local features. RESULTS: We propose Local Feature Mining in Proteins (LFM-Pro) as a framework for automatically discovering family-specific local sites and the features associated with these sites. Our method uses the distance field to backbone atoms to detect geometrically significant structural centers of the protein. A feature vector is generated from the geometrical and biochemical environment around these centers. These features are then scored using a statistical measure, for their ability to distinguish a family of proteins from a background set of unrelated proteins, and successful features are combined into a representative set for the protein family. The utility and success of LFM-Pro are demonstrated on trypsin-like serine proteases family of proteins and on a challenging classification dataset via comparison with DALI. The results verify that our method is successful both in identifying the distinctive sites of a given family of proteins, and in classifying proteins using the extracted features. AVAILABILITY: The software and the datasets are freely available for academic research use at http://bioinfo.ceng.metu.edu.tr/Pub/LFMPro.