77 research outputs found
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In silico identification and characterization of protein-ligand binding sites
Protein–ligand binding site prediction methods aim to predict, from amino acid sequence, protein–ligand interactions, putative ligands, and ligand binding site residues using either sequence information, structural information, or a combination of both. In silico characterization of protein–ligand interactions has become extremely important to help determine a protein’s functionality, as in vivo-based functional elucidation is unable to keep pace with the current growth of sequence databases. Additionally, in vitro biochemical functional elucidation is time-consuming, costly, and may not be feasible for large-scale analysis, such as drug discovery. Thus, in silico prediction of protein–ligand interactions must be utilized to aid in functional elucidation. Here, we briefly discuss protein function prediction, prediction of protein–ligand interactions, the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated EvaluatiOn (CAMEO) competitions, along with their role in shaping the field. We also discuss, in detail, our cutting-edge web-server method, FunFOLD for the structurally informed prediction of protein–ligand interactions. Furthermore, we provide a step-by-step guide on using the FunFOLD web server and FunFOLD3 downloadable application, along with some real world examples, where the FunFOLD methods have been used to aid functional elucidation
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ModFOLD6: an accurate web server for the global and local quality estimation of 3D protein models
Methods that reliably estimate the likely similarity between the predicted and native structures of proteins have become essential for driving the acceptance and adoption of three-dimensional protein models by life scientists. ModFOLD6 is the latest version of our leading resource for Estimates of Model Accuracy (EMA), which uses a pioneering hybrid quasi-single model approach. The ModFOLD6 server integrates scores from three pure-single model methods and three quasi-single model methods using a neural network to estimate local quality scores. Additionally, the server provides three options for producing global score estimates, depending on the requirements of the user: (i) ModFOLD6_rank, which is optimized for ranking/selection, (ii) ModFOLD6_cor, which is optimized for correlations of predicted and observed scores and (iii) ModFOLD6 global for balanced performance. The ModFOLD6 methods rank among the top few for EMA, according to independent blind testing by the CASP12 assessors. The ModFOLD6 server is also continuously automatically evaluated as part of the CAMEO project, where significant performance gains have been observed compared to our previous server and other publicly available servers. The ModFOLD6 server is freely available at: http://www.reading.ac.uk/bioinf/ModFOLD/
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ReFOLD: a server for the refinement of 3D protein models guided by accurate quality estimates
ReFOLD is a novel hybrid refinement server with integrated high performance global and local Accuracy Self Estimates (ASEs). The server attempts to identify and to fix likely errors in user supplied 3D models of proteins via successive rounds of refinement. The server is unique in providing output for multiple alternative refined models in a way that allows users to quickly visualize the key residue locations, which are likely to have been improved. This is important, as global refinement of a full chain model may not always be possible, whereas local regions, or individual domains, can often be much improved. Thus, users may easily compare the specific regions of the alternative refined models in which they are most interested e.g. key interaction sites or domains. ReFOLD was used to generate hundreds of alternative refined models for the CASP12 experiment, boosting our group's performance in the main tertiary structure prediction category. Our successful refinement of initial server models combined with our built-in ASEs were instrumental to our second place ranking on Template Based Modeling (TBM) and Free Modeling (FM)/TBM targets. The ReFOLD server is freely available at: http://www.reading.ac.uk/bioinf/ReFOLD/
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Characterisation of HvVIP1 and expression profile analysis of stress response regulators in barley under Agrobacterium and Fusarium infections
Arabidopsis thaliana’s VirE2-Interacting Protein 1 (VIP1) interacts with Agrobacterium tumefaciens VirE2 protein and regulates stress responses and plant immunity signaling occurring downstream of the Mitogen-Activated Protein Kinase (MPK3) signal transduction pathway. In this study, a full-length cDNA of 972bp encoding HvVIP1 was obtained from barley (Hordeum vulgare L.) leaves. A corresponding 323 amino acid poly-peptide was shown to carry the conserved bZIP (Basic Leucine Zipper) domain within its 157th and 223rd amino acid residue. 13 non-synonymous SNPs were spotted within the HvVIP1 bZIP domain sequence when compared with AtVIP1. Moreover, minor differences in the bZIP domain locations and lengths were noted when comparing Arabidopsis thaliana and Hordeum vulgare VIP1 proteins through the 3D models, structural domain predictions and disorder prediction profiling. The expression of HvVIP1 was stable in barley tissues infected by pathogen (whether Agrobacterium tumefaciens or Fusarium culmorum), but was induced at specific time points. We found a strong correlation between the transcript accumulation of HvVIP1 and barley PR- genes HvPR1, HvPR4 and HvPR10, but not with HvPR3 and HvPR5, probably due to low induction of those particular genes. In addition, a gene encoding for a member of the barley MAPK family, HvMPK1, showed significantly higher expression after pathogenic infection of barley cells. Collectively, our results might suggest that early expression of PR genes upon infection in barley cells play a pivotal role in the Agrobacterium-resistance of this plant
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The ModFOLD4 server for the quality assessment of 3D protein models
Once you have generated a 3D model of a protein,
how do you know whether it bears any resemblance
to the actual structure? To determine the usefulness
of 3D models of proteins, they must be assessed in
terms of their quality by methods that predict their
similarity to the native structure. The ModFOLD4
server is the latest version of our leading independent
server for the estimation of both the global and
local (per-residue) quality of 3D protein models. The
server produces both machine readable and graphical
output, providing users with intuitive visual
reports on the quality of predicted protein tertiary
structures. The ModFOLD4 server is freely available
to all at: http://www.reading.ac.uk/bioinf/ModFOLD/
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GRID and docking analyses reveal a molecular basis for flavonoid inhibition of src-family kinase activity
Flavonoids reduce cardiovascular disease risk through anti-inflammatory, anti-coagulant and anti-platelet actions. One key flavonoid inhibitory mechanism is blocking kinase activity that drives these processes. Flavonoids attenuate activities of kinases including phosphoinositide-3-kinase (PI3K), Fyn, Lyn, Src, Syk, PKC, PIM1/2, ERK, JNK, and PKA. X-ray crystallographic analyses of kinase-flavonoid complexes show that flavonoid ring systems and their hydroxyl substitutions are important structural features for their binding to kinases. A clearer understanding of structural interactions of flavonoids with kinases is necessary to allow construction of more potent and selective counterparts.
We examined flavonoid (quercetin, apigenin and catechin) interactions with Src-family kinases (Lyn, Fyn and Hck) applying the Sybyl docking algorithm and GRID. A homology model (Lyn) was used in our analyses to demonstrate that high quality predicted kinase structures are suitable for flavonoid computational studies. Our docking results revealed potential hydrogen bond contacts between flavonoid hydroxyls and kinase catalytic site residues. Identification of plausible contacts indicated that quercetin formed the most energetically stable interactions, apigenin lacked hydroxyl groups necessary for important contacts, and the non-planar structure of catechin could not support predicted hydrogen bonding patterns. GRID analysis using a hydroxyl functional group supported docking results. Based on these findings, we predicted that quercetin would inhibit activities of Src-family kinases with greater potency than apigenin and catechin. We validated this prediction using in vitro kinase assays.
We conclude that our study can be used as a basis to construct virtual flavonoid interaction libraries to guide drug discovery using these compounds as molecular templates
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High throughput profile-profile based fold recognition for the entire human proteome
BACKGROUND: In order to maintain the most comprehensive structural annotation databases we must carry out regular updates for each proteome using the latest profile-profile fold recognition methods. The ability to carry out these updates on demand is necessary to keep pace with the regular updates of sequence and structure databases. Providing the highest quality structural models requires the most intensive profile-profile fold recognition methods running with the very latest available sequence databases and fold libraries. However, running these methods on such a regular basis for every sequenced proteome requires large amounts of processing power. In this paper we describe and benchmark the JYDE (Job Yield Distribution Environment) system, which is a meta-scheduler designed to work above cluster schedulers, such as Sun Grid Engine (SGE) or Condor. We demonstrate the ability of JYDE to distribute the load of genomic-scale fold recognition across multiple independent Grid domains. We use the most recent profile-profile version of our mGenTHREADER software in order to annotate the latest version of the Human proteome against the latest sequence and structure databases in as short a time as possible. RESULTS: We show that our JYDE system is able to scale to large numbers of intensive fold recognition jobs running across several independent computer clusters. Using our JYDE system we have been able to annotate 99.9% of the protein sequences within the Human proteome in less than 24 hours, by harnessing over 500 CPUs from 3 independent Grid domains. CONCLUSION: This study clearly demonstrates the feasibility of carrying out on demand high quality structural annotations for the proteomes of major eukaryotic organisms. Specifically, we have shown that it is now possible to provide complete regular updates of profile-profile based fold recognition models for entire eukaryotic proteomes, through the use of Grid middleware such as JYDE
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The binding site distance test score: a robust method for the assessment of predicted protein binding sites
We propose a novel method for scoring the accuracy of
protein binding site predictions – the Binding-site Distance Test
(BDT) score. Recently, the Matthews Correlation Coefficient (MCC)
has been used to evaluate binding site predictions, both by developers
of new methods and by the assessors for the community wide
prediction experiment – CASP8. Whilst being a rigorous scoring
method, the MCC does not take into account the actual 3D location
of the predicted residues from the observed binding site. Thus, an
incorrectly predicted site that is nevertheless close to the observed
binding site will obtain an identical score to the same number of nonbinding
residues predicted at random. The MCC is somewhat affected
by the subjectivity of determining observed binding residues
and the ambiguity of choosing distance cutoffs. By contrast the BDT
method produces continuous scores ranging between 0 and 1, relating
to the distance between the predicted and observed residues.
Residues predicted close to the binding site will score higher than
those more distant, providing a better reflection of the true accuracy
of predictions. The CASP8 function predictions were evaluated using
both the MCC and BDT methods and the scores were compared.
The BDT was found to strongly correlate with the MCC
scores whilst also being less susceptible to the subjectivity of defining
binding residues. We therefore suggest that this new simple
score is a potentially more robust method for future evaluations of
protein-ligand binding site predictions
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Protein structure prediction servers at University College London
A number of state-of-the-art protein structure prediction servers have been developed by researchers working in the Bioinformatics Unit at University College London. The popular PSIPRED server allows users to perform secondary structure prediction, transmembrane topology prediction and protein fold recognition. More recent servers include DISOPRED for the prediction of protein dynamic disorder and DomPred for domain boundary prediction. These servers are available from our software home page at
IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences
IntFOLD is an independent web server that integrates our leading methods for structure and function prediction. The server provides a simple unified interface that aims to make complex protein modelling data more accessible to life scientists. The server web interface is designed to be intuitive and integrates a complex set of quantitative data, so that 3D modelling results can be viewed on a single page and interpreted by non-expert modellers at a glance. The only required input to the server is an amino acid sequence for the target protein. Here we describe major performance and user interface updates to the server, which comprises an integrated pipeline of methods for: tertiary structure prediction, global and local 3D model quality assessment, disorder prediction, structural domain prediction, function prediction and modelling of protein-ligand interactions. The server has been independently validated during numerous CASP (Critical Assessment of Techniques for Protein Structure Prediction) experiments, as well as being continuously evaluated by the CAMEO (Continuous Automated Model Evaluation) project. The IntFOLD server is available at: http://www.reading.ac.uk/bioinf/IntFOLD
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