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Search Results: 1 - 10 of 1439 matches for " Charlotte Deane "
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Protein protein interactions, evolutionary rate, abundance and age
Ramazan Saeed, Charlotte M Deane
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-128
Abstract: Here we perform a large scale study over multiple data sets in order to demonstrate that the major reason for conflict between previous studies is the use of different but overlapping datasets. We show that lack of correlation, between evolutionary rate and number of interactions in a data set is related to the error rate. We also demonstrate that the correlation is not an artifact of the underlying distributions of evolutionary distance and interactions and is therefore likely to be biologically relevant. Further to this, we consider the claim that the dependence is due to gene expression levels and find some supporting evidence. A strong and positive correlation between the number of interactions and the age of a protein is also observed and we show this relationship is independent of expression levels.A correlation between number of interactions and evolutionary rate is observed but is dependent on the accuracy of the dataset being used. However it appears that the number of interactions a protein participates in depends more on the age of the protein than the rate at which it changes.It has been suggested many times that the rate at which a protein evolves decreases with the number of physical interactions it participates in [3-6]. The intuition behind this idea is that proteins with a greater fraction of amino acid residues playing an essential role will, on the whole, evolve slower then those with a small ratio of such crucial residues. Thus highly interacting proteins will evolve at a slower rate. A recent study by Fraser et al (2002) demonstrated the negative correlation, which this theory would suggest between protein-protein interactions and evolutionary rate. The negative correlation was determined by estimating the evolutionary distance between orthologous proteins from yeast Saccharomyces cerevisiae and the nematode worm Caenorhabditis elegans. Using interaction data from studies conducted by Uetz and "core data" from Ito it was shown that yeast protein
Structural Bridges through Fold Space
Hannah Edwards?,Charlotte M. Deane
PLOS Computational Biology , 2015, DOI: 10.1371/journal.pcbi.1004466
Abstract: Several protein structure classification schemes exist that partition the protein universe into structural units called folds. Yet these schemes do not discuss how these units sit relative to each other in a global structure space. In this paper we construct networks that describe such global relationships between folds in the form of structural bridges. We generate these networks using four different structural alignment methods across multiple score thresholds. The networks constructed using the different methods remain a similar distance apart regardless of the probability threshold defining a structural bridge. This suggests that at least some structural bridges are method specific and that any attempt to build a picture of structural space should not be reliant on a single structural superposition method. Despite these differences all representations agree on an organisation of fold space into five principal community structures: all-α, all-β sandwiches, all-β barrels, α/β and α + β. We project estimated fold ages onto the networks and find that not only are the pairings of unconnected folds associated with higher age differences than bridged folds, but this difference increases with the number of networks displaying an edge. We also examine different centrality measures for folds within the networks and how these relate to fold age. While these measures interpret the central core of fold space in varied ways they all identify the disposition of ancestral folds to fall within this core and that of the more recently evolved structures to provide the peripheral landscape. These findings suggest that evolutionary information is encoded along these structural bridges. Finally, we identify four highly central pivotal folds representing dominant topological features which act as key attractors within our landscapes.
Using Phylogeny to Improve Genome-Wide Distant Homology Recognition
Sanne Abeln,Carlo Teubner,Charlotte M Deane
PLOS Computational Biology , 2007, DOI: 10.1371/journal.pcbi.0030003
Abstract: The gap between the number of known protein sequences and structures continues to widen, particularly as a result of sequencing projects for entire genomes. Recently there have been many attempts to generate structural assignments to all genes on sets of completed genomes using fold-recognition methods. We developed a method that detects false positives made by these genome-wide structural assignment experiments by identifying isolated occurrences. The method was tested using two sets of assignments, generated by SUPERFAMILY and PSI-BLAST, on 150 completed genomes. A phylogeny of these genomes was built and a parsimony algorithm was used to identify isolated occurrences by detecting occurrences that cause a gain at leaf level. Isolated occurrences tend to have high e-values, and in both sets of assignments, a sudden increase in isolated occurrences is observed for e-values >10?8 for SUPERFAMILY and >10?4 for PSI-BLAST. Conditions to predict false positives are based on these results. Independent tests confirm that the predicted false positives are indeed more likely to be incorrectly assigned. Evaluation of the predicted false positives also showed that the accuracy of profile-based fold-recognition methods might depend on secondary structure content and sequence length. We show that false positives generated by fold-recognition methods can be identified by considering structural occurrence patterns on completed genomes; occurrences that are isolated within the phylogeny tend to be less reliable. The method provides a new independent way to examine the quality of fold assignments and may be used to improve the output of any genome-wide fold assignment method.
How long is a piece of loop?
Yoonjoo Choi,Sumeet Agarwal,Charlotte M. Deane
PeerJ , 2013, DOI: 10.7717/peerj.1
Abstract: Loops are irregular structures which connect two secondary structure elements in proteins. They often play important roles in function, including enzyme reactions and ligand binding. Despite their importance, their structure remains difficult to predict. Most protein loop structure prediction methods sample local loop segments and score them. In particular protein loop classifications and database search methods depend heavily on local properties of loops. Here we examine the distance between a loop’s end points (span). We find that the distribution of loop span appears to be independent of the number of residues in the loop, in other words the separation between the anchors of a loop does not increase with an increase in the number of loop residues. Loop span is also unaffected by the secondary structures at the end points, unless the two anchors are part of an anti-parallel beta sheet. As loop span appears to be independent of global properties of the protein we suggest that its distribution can be described by a random fluctuation model based on the Maxwell–Boltzmann distribution. It is believed that the primary difficulty in protein loop structure prediction comes from the number of residues in the loop. Following the idea that loop span is an independent local property, we investigate its effect on protein loop structure prediction and show how normalised span (loop stretch) is related to the structural complexity of loops. Highly contracted loops are more difficult to predict than stretched loops.
Exploring Fold Space Preferences of New-born and Ancient Protein Superfamilies
Hannah Edwards,Sanne Abeln,Charlotte M. Deane
PLOS Computational Biology , 2013, DOI: 10.1371/journal.pcbi.1003325
Abstract: The evolution of proteins is one of the fundamental processes that has delivered the diversity and complexity of life we see around ourselves today. While we tend to define protein evolution in terms of sequence level mutations, insertions and deletions, it is hard to translate these processes to a more complete picture incorporating a polypeptide's structure and function. By considering how protein structures change over time we can gain an entirely new appreciation of their long-term evolutionary dynamics. In this work we seek to identify how populations of proteins at different stages of evolution explore their possible structure space. We use an annotation of superfamily age to this space and explore the relationship between these ages and a diverse set of properties pertaining to a superfamily's sequence, structure and function. We note several marked differences between the populations of newly evolved and ancient structures, such as in their length distributions, secondary structure content and tertiary packing arrangements. In particular, many of these differences suggest a less elaborate structure for newly evolved superfamilies when compared with their ancient counterparts. We show that the structural preferences we report are not a residual effect of a more fundamental relationship with function. Furthermore, we demonstrate the robustness of our results, using significant variation in the algorithm used to estimate the ages. We present these age estimates as a useful tool to analyse protein populations. In particularly, we apply this in a comparison of domains containing greek key or jelly roll motifs.
Large Scale Characterization of the LC13 TCR and HLA-B8 Structural Landscape in Reaction to 172 Altered Peptide Ligands: A Molecular Dynamics Simulation Study
Bernhard Knapp ,James Dunbar,Charlotte M. Deane
PLOS Computational Biology , 2014, DOI: doi/10.1371/journal.pcbi.1003748
Abstract: The interplay between T cell receptors (TCRs) and peptides bound by major histocompatibility complexes (MHCs) is one of the most important interactions in the adaptive immune system. Several previous studies have computationally investigated their structural dynamics. On the basis of these simulations several structural and dynamical properties have been proposed as effectors of the immunogenicity. Here we present the results of a large scale Molecular Dynamics simulation study consisting of 100 ns simulations of 172 different complexes. These complexes consisted of all possible point mutations of the Epstein Barr Virus peptide FLRGRAYGL bound by HLA-B*08:01 and presented to the LC13 TCR. We compare the results of these 172 structural simulations with experimental immunogenicity data. We found that simulations with more immunogenic peptides and those with less immunogenic peptides are in fact highly similar and on average only minor differences in the hydrogen binding footprints, interface distances, and the relative orientation between the TCR chains are present. Thus our large scale data analysis shows that many previously suggested dynamical and structural properties of the TCR/peptide/MHC interface are unlikely to be conserved causal factors for peptide immunogenicity.
Predicting and Validating Protein Interactions Using Network Structure
Pao-Yang Chen ,Charlotte M. Deane,Gesine Reinert
PLOS Computational Biology , 2008, DOI: 10.1371/journal.pcbi.1000118
Abstract: Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resour?ces/PredictingInteractions.
Mutual information and variants for protein domain-domain contact prediction
Mireille Gomes, Rebecca Hamer, Gesine Reinert, Charlotte M Deane
BMC Research Notes , 2012, DOI: 10.1186/1756-0500-5-472
Abstract: Here we assess the predictive power of MI and variants for domain-domain contact prediction. We test original MI and these variants, which are called MIp, MIc and ZNMI, on 40 domain-domain test cases containing 10,753 sequences. We also propose and evaluate two new versions of MI that consider triangles of residues and the physiochemical properties of the amino acids, respectively.We found that all versions of MI are skewed towards predicting surface residues. Since domain-domain contacts are on the surface of each domain, we considered only surface residues when attempting to predict contacts. Our analysis shows that MIc is the best current MI domain-domain contact predictor. At 20% recall MIc achieved a precision of 44.9% when only surface residues were considered. Our triangle and reduced alphabet variants of MI highlight the delicate trade-off between signal and noise in the use of MI for domain-domain contact prediction. We also examine a specific “successful” case study and demonstrate that here, when considering surface residues, even the most accurate domain-domain contact predictor, MIc, performs no better than random.All tested variants of MI are skewed towards predicting surface residues. When considering surface residues only, we find MIc to be the best current MI domain-domain contact predictor. Its performance, however, is not as good as a non-MI based contact predictor, i-Patch. Additionally, the intra-protein contact prediction capabilities of MIc outperform its domain-domain contact prediction abilities.Proteins are actors in a complex system, with their functions to a large extent defined by their interactions with other proteins. It is the size, shape and chemical properties of the residues on the surface of the protein that dictate the capacity of a protein to interact with other proteins. The ability to predict the residues involved in these interactions would help to identify specific functionality, structural constraints and even disease-causi
Electrostatic and Functional Analysis of the Seven-Bladed WD β-Propellers
Najl V. Valeyev,A. Kristina Downing,John Sondek,Charlotte Deane
Evolutionary Bioinformatics , 2008,
Abstract: β-propeller domains composed of WD repeats are highly ubiquitous and typically used as multi-site docking platforms to coordinate and integrate the activities of groups of proteins. Here, we have used extensive homology modelling of the WD40-repeat family of seven-bladed β-propellers coupled with subsequent structural classification and clustering of these models to define subfamilies of β-propellers with common structural, and probable, functional characteristics. We show that it is possible to assign seven-bladed WD β-propeller proteins into functionally different groups based on the information gained from homology modelling. We examine general structural diversity within the WD40-repeat family of seven-bladed β-propellers and demonstrate that seven-bladed β-propellers composed of WD-repeats are structurally distinct from other seven-bladed β-propellers. We further provide some insights into the multifunctional diversity of the seven-bladed WD β-propeller surfaces. This report once again reinforces the importance of structural data and the usefulness of homology models in functional classification.
Local Network Patterns in Protein-Protein Interfaces
Qiang Luo, Rebecca Hamer, Gesine Reinert, Charlotte M. Deane
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0057031
Abstract: Protein-protein interfaces hold the key to understanding protein-protein interactions. In this paper we investigated local interaction network patterns beyond pair-wise contact sites by considering interfaces as contact networks among residues. A contact site was defined as any residue on the surface of one protein which was in contact with a residue on the surface of another protein. We labeled the sub-graphs of these contact networks by their amino acid types. The observed distributions of these labeled sub-graphs were compared with the corresponding background distributions and the results suggested that there were preferred chemical patterns of closely packed residues at the interface. These preferred patterns point to biological constraints on physical proximity between those residues on one protein which were involved in binding to residues which were close on the interacting partner. Interaction interfaces were far from random and contain information beyond pairs and triangles. To illustrate the possible application of the local network patterns observed, we introduced a signature method, called iScore, based on these local patterns to assess interface predictions. On our data sets iScore achieved 83.6% specificity with 82% sensitivity.
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