Predicting protein structures and structural annotation of proteomes

Protein structure prediction methods aim to predict the structures of proteins from their amino acid sequences, utilizing various computational algorithms. Structural genome annotation is the process of attaching biological information to every protein encoded within a genome via the production of three-dimensional protein models

The FunFOLD2 server for the prediction of protein-ligand interactions

The FunFOLD2 server is a new independent server that integrates our novel protein–ligand binding site and quality assessment protocols for the prediction of protein function (FN) from sequence via structure. Our guiding principles were, first, to provide a simple unified resource to make our function prediction software easily accessible to all via a simple web interface and, second, to produce integrated output for predictions that can be easily interpreted. The server provides a clean web interface so that results can be viewed on a single page and interpreted by non-experts at a glance. The output for the prediction is an image of the top predicted tertiary structure annotated to indicate putative ligand-binding site residues. The results page also includes a list of the most likely binding site residues and the types of predicted ligands and their frequencies in similar structures. The protein–ligand interactions can also be interactively visualized in 3D using the Jmol plug-in. The raw machine readable data are provided for developers, which comply with the Critical Assessment of Techniques for Protein Structure Prediction data standards for FN predictions. The FunFOLD2 webserver is freely available to all at the following web site: http://www.reading.ac.uk/bioinf/FunFOLD/FunFOLD_form_2_0.html

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/

FunFOLDQA: a quality assessment tool for protein-ligand binding site residue predictions

The estimation of prediction quality is important because without quality measures, it is difficult to determine the usefulness of a prediction. Currently, methods for ligand binding site residue predictions are assessed in the function prediction category of the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment, utilizing the Matthews Correlation Coefficient (MCC) and Binding-site Distance Test (BDT) metrics. However, the assessment of ligand binding site predictions using such metrics requires the availability of solved structures with bound ligands. Thus, we have developed a ligand binding site quality assessment tool, FunFOLDQA, which utilizes protein feature analysis to predict ligand binding site quality prior to the experimental solution of the protein structures and their ligand interactions. The FunFOLDQA feature scores were combined using: simple linear combinations, multiple linear regression and a neural network. The neural network produced significantly better results for correlations to both the MCC and BDT scores, according to Kendall’s τ, Spearman’s ρ and Pearson’s r correlation coefficients, when tested on both the CASP8 and CASP9 datasets. The neural network also produced the largest Area Under the Curve score (AUC) when Receiver Operator Characteristic (ROC) analysis was undertaken for the CASP8 dataset. Furthermore, the FunFOLDQA algorithm incorporating the neural network, is shown to add value to FunFOLD, when both methods are employed in combination. This results in a statistically significant improvement over all of the best server methods, the FunFOLD method (6.43%), and one of the top manual groups (FN293) tested on the CASP8 dataset. The FunFOLDQA method was also found to be competitive with the top server methods when tested on the CASP9 dataset. To the best of our knowledge, FunFOLDQA is the first attempt to develop a method that can be used to assess ligand binding site prediction quality, in the absence of experimental data

Dominant β-catenin mutations cause intellectual disability with recognizable syndromic features

The recent identification of multiple dominant mutations in the gene encoding β-catenin in both humans and mice has enabled exploration of the molecular and cellular basis of β-catenin function in cognitive impairment. In humans, β-catenin mutations that cause a spectrum of neurodevelopmental disorders have been identified. We identified de novo β-catenin mutations in patients with intellectual disability, carefully characterized their phenotypes, and were able to define a recognizable intellectual disability syndrome. In parallel, characterization of a chemically mutagenized mouse line that displays features similar to those of human patients with β-catenin mutations enabled us to investigate the consequences of β-catenin dysfunction through development and into adulthood. The mouse mutant, designated batface (Bfc), carries a Thr653Lys substitution in the C-terminal armadillo repeat of β-catenin and displayed a reduced affinity for membrane-associated cadherins. In association with this decreased cadherin interaction, we found that the mutation results in decreased intrahemispheric connections, with deficits in dendritic branching, long-term potentiation, and cognitive function. Our study provides in vivo evidence that dominant mutations in β-catenin underlie losses in its adhesion-related functions, which leads to severe consequences, including intellectual disability, childhood hypotonia, progressive spasticity of lower limbs, and abnormal craniofacial features in adult

Genetic analysis reveals a hierarchy of interactions between polycystin-encoding genes and genes controlling cilia function during left-right determination

During mammalian development, left-right (L-R) asymmetry is established by a cilia-driven leftward fluid flow within a midline embryonic cavity called the node. This ‘nodal flow’ is detected by peripherally-located crown cells that each assemble a primary cilium which contain the putative Ca2+ channel PKD2. The interaction of flow and crown cell cilia promotes left side-specific expression of Nodal in the lateral plate mesoderm (LPM). Whilst the PKD2-interacting protein PKD1L1 has also been implicated in L-R patterning, the underlying mechanism by which flow is detected and the genetic relationship between Polycystin function and asymmetric gene expression remains unknown. Here, we characterize a Pkd1l1 mutant line in which Nodal is activated bilaterally, suggesting that PKD1L1 is not required for LPM Nodal pathway activation per se, but rather to restrict Nodal to the left side downstream of nodal flow. Epistasis analysis shows that Pkd1l1 acts as an upstream genetic repressor of Pkd2. This study therefore provides a genetic pathway for the early stages of L-R determination. Moreover, using a system in which cultured cells are supplied artificial flow, we demonstrate that PKD1L1 is sufficient to mediate a Ca2+ signaling response after flow stimulation. Finally, we show that an extracellular PKD domain within PKD1L1 is crucial for PKD1L1 function; as such, destabilizing the domain causes L-R defects in the mouse. Our demonstration that PKD1L1 protein can mediate a response to flow coheres with a mechanosensation model of flow sensation in which the force of fluid flow drives asymmetric gene expression in the embryo

ROC analysis.

<p>SE standard error of AUC <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038219#pone.0038219-Hanley1" target="_blank">[41]</a>; AUC_0–0.1, AUC for false positive rate between 0 and 10% (false positives were defined as the top function prediction according to each score having an MCC or BDT score > = 0.5). The AUC and AUC_0–0.1 scores were calculated using ROCR <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038219#pone.0038219-Sing1" target="_blank">[39]</a>. The highest AUC and AUC_0–0.1 scores for each CASP prediction session and each performance measure (MCC and BDT) are indicated in bold.</p

All versus all analysis for the top methods in CASP8 along with the FunFOLD and FunFOLDQA methods.

<p>The analysis is based on common subsets of all CASP8 function prediction targets, with a minimum of 10 predictions in common. Predictions are scored using the BDT metric. Ho  =  No difference between the methods in the rows and the columns. H1  =  the methods in the row has a higher correlation. Bold values indicate significant p-values (P<0.05).</p

All versus all analysis for the top server methods in CASP9 along with the FunFOLD and FunFOLDQA methods.

<p>The analysis is based on common subsets of all CASP9 function prediction targets, with a minimum of 10 predictions in common. Predictions are scored using the BDT metric. Ho  =  No difference between the methods in the rows and the columns. H1  =  the methods in the row has a higher correlation. Bold values indicate significant p-values (p<0.05).</p