Yoichi Murakami

Applying the Naïve Bayes classifier with kernel density estimation to the prediction of protein–protein interaction sites

MOTIVATION The limited availability of protein structures often restricts the functional annotation of proteins and the identification of their protein-protein interaction sites. Computational methods to identify interaction sites from protein sequences alone are, therefore, required for unraveling the functions of many proteins. This article describes a new method (PSIVER) to predict interaction sites, i.e. residues binding to other proteins, in protein sequences. Only sequence features (position-specific scoring matrix and predicted accessibility) are used for training a Naïve Bayes classifier (NBC), and conditional probabilities of each sequence feature are estimated using a kernel density estimation method (KDE). RESULTS The leave-one out cross-validation of PSIVER achieved a Matthews correlation coefficient (MCC) of 0.151, an F-measure of 35.3%, a precision of 30.6% and a recall of 41.6% on a non-redundant set of 186 protein sequences extracted from 105 heterodimers in the Protein Data Bank (consisting of 36 219 residues, of which 15.2% were known interface residues). Even though the dataset used for training was highly imbalanced, a randomization test demonstrated that the proposed method managed to avoid overfitting. PSIVER was also tested on 72 sequences not used in training (consisting of 18 140 residues, of which 10.6% were known interface residues), and achieved an MCC of 0.135, an F-measure of 31.5%, a precision of 25.0% and a recall of 46.5%, outperforming other publicly available servers tested on the same dataset. PSIVER enables experimental biologists to identify potential interface residues in unknown proteins from sequence information alone, and to mutate those residues selectively in order to unravel protein functions. AVAILABILITY Freely available on the web at http://tardis.nibio.go.jp/PSIVER/

SHARP2: protein--protein interaction predictions using patch analysis

UNLABELLED SHARP2 is a flexible web-based bioinformatics tool for predicting potential protein-protein interaction sites on protein structures. It implements a predictive algorithm that calculates multiple parameters for overlapping patches of residues on the surface of a protein. Six parameters are calculated: solvation potential, hydrophobicity, accessible surface area, residue interface propensity, planarity and protrusion (SHARP2). Parameter scores for each patch are combined, and the patch with the highest combined score is predicted as a potential interaction site. SHARP2 enables users to upload 3D protein structure files in PDB format, to obtain information on potential interaction sites as downloadable HTML tables and to view the location of the sites on the 3D structure using Jmol. The server allows for the input of multiple structures and multiple combinations of parameters. Therefore predictions can be made for complete datasets, as well as individual structures. AVAILABILITY http://www.bioinformatics.sussex.ac.uk/SHARP2.

eF-seek: prediction of the functional sites of proteins by searching for similar electrostatic potential and molecular surface shape

We have developed a method to predict ligand-binding sites in a new protein structure by searching for similar binding sites in the Protein Data Bank (PDB). The similarities are measured according to the shapes of the molecular surfaces and their electrostatic potentials. A new web server, eF-seek, provides an interface to our search method. It simply requires a coordinate file in the PDB format, and generates a prediction result as a virtual complex structure, with the putative ligands in a PDB format file as the output. In addition, the predicted interacting interface is displayed to facilitate the examination of the virtual complex structure on our own applet viewer with the web browser (URL: http://eF-site.hgc.jp/eF-seek).

PiRaNhA: a server for the computational prediction of RNA-binding residues in protein sequences

The PiRaNhA web server is a publicly available online resource that automatically predicts the location of RNA-binding residues (RBRs) in protein sequences. The goal of functional annotation of sequences in the field of RNA binding is to provide predictions of high accuracy that require only small numbers of targeted mutations for verification. The PiRaNhA server uses a support vector machine (SVM), with position-specific scoring matrices, residue interface propensity, predicted residue accessibility and residue hydrophobicity as features. The server allows the submission of up to 10 protein sequences, and the predictions for each sequence are provided on a web page and via email. The prediction results are provided in sequence format with predicted RBRs highlighted, in text format with the SVM threshold score indicated and as a graph which enables users to quickly identify those residues above any specific SVM threshold. The graph effectively enables the increase or decrease of the false positive rate. When tested on a non-redundant data set of 42 protein sequences not used in training, the PiRaNhA server achieved an accuracy of 85%, specificity of 90% and a Matthews correlation coefficient of 0.41 and outperformed other publicly available servers. The PiRaNhA prediction server is freely available at http://www.bioinformatics.sussex.ac.uk/PIRANHA.

Protein function annotation from sequence

MOTIVATION All eukaryotic proteomes are characterized by a significant percentage of proteins of unknown function. Comp-utational function prediction methods are therefore essential as initial steps in the function annotation process. This article describes an annotation method (PiRaNhA) for the prediction of RNA-binding residues (RBRs) from protein sequence information. A series of sequence properties (position specific scoring matrices, interface propensities, predicted accessibility and hydrophobicity) are used to train a support vector machine. This method is then evaluated for its potential to be applied to RNA-binding function prediction at the level of the complete protein. RESULTS The 5-fold cross-validation of PiRaNhA on a dataset of 81 RNA-binding proteins achieves a Matthews Correlation Coefficient (MCC) of 0.50 and accuracy of 87.2%. When used to predict RBRs in 42 proteins not used in training, PiRaNhA achieves an MCC of 0.41 and accuracy of 84.5%. Decision values from the PiRaNhA predictions were used in a second SVM to make predictions of RNA-binding function at the protein level, achieving an MCC of 0.53 and accuracy of 76.1%. The PiRaNhA RBR predictions allow experimentalists to perform more targeted experiments for function annotation; and the prediction of RNA-binding function at the protein level shows promise for proteome-wide annotations. AVAILABILITY AND IMPLEMENTATION Freely available on the web at www.bioinformatics.sussex.ac.uk/PIRANHA or http://piranha.protein.osaka-u.ac.jp. SUPPLEMENTARY INFORMATION Supplementary data are available at the Bioinformatics online.

Blind prediction of interfacial water positions in CAPRI.

We report the first assessment of blind predictions of water positions at protein–protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community‐wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions—20 groups submitted a total of 195 models—were assessed by measuring the recall fraction of water‐mediated protein contacts. Of the 176 high‐ or medium‐quality docking models—a very good docking performance per se—only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 Å, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high‐quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein–water interactions and their role in stabilizing protein complexes. Proteins 2014; 82:620–632.

Targeting BIG3–PHB2 interaction to overcome tamoxifen resistance in breast cancer cells

The acquisition of endocrine resistance is a common obstacle in endocrine therapy of patients with oestrogen receptor-α (ERα)-positive breast tumours. We previously demonstrated that the BIG3–PHB2 complex has a crucial role in the modulation of oestrogen/ERα signalling in breast cancer cells. Here we report a cell-permeable peptide inhibitor, called ERAP, that regulates multiple ERα-signalling pathways associated with tamoxifen resistance in breast cancer cells by inhibiting the interaction between BIG3 and PHB2. Intrinsic PHB2 released from BIG3 by ERAP directly binds to both nuclear- and membrane-associated ERα, which leads to the inhibition of multiple ERα-signalling pathways, including genomic and non-genomic ERα activation and ERα phosphorylation, and the growth of ERα-positive breast cancer cells both in vitro and in vivo. More importantly, ERAP treatment suppresses tamoxifen resistance and enhances tamoxifen responsiveness in ERα-positive breast cancer cells. These findings suggest inhibiting the interaction between BIG3 and PHB2 may be a new therapeutic strategy for the treatment of luminal-type breast cancer.

Homology-based prediction of interactions between proteins using Averaged One-Dependence Estimators

BackgroundIdentification of protein-protein interactions (PPIs) is essential for a better understanding of biological processes, pathways and functions. However, experimental identification of the complete set of PPIs in a cell/organism (“an interactome”) is still a difficult task. To circumvent limitations of current high-throughput experimental techniques, it is necessary to develop high-performance computational methods for predicting PPIs.ResultsIn this article, we propose a new computational method to predict interaction between a given pair of protein sequences using features derived from known homologous PPIs. The proposed method is capable of predicting interaction between two proteins (of unknown structure) using Averaged One-Dependence Estimators (AODE) and three features calculated for the protein pair: (a) sequence similarities to a known interacting protein pair (FSeq), (b) statistical propensities of domain pairs observed in interacting proteins (FDom) and (c) a sum of edge weights along the shortest path between homologous proteins in a PPI network (FNet). Feature vectors were defined to lie in a half-space of the symmetrical high-dimensional feature space to make them independent of the protein order. The predictability of the method was assessed by a 10-fold cross validation on a recently created human PPI dataset with randomly sampled negative data, and the best model achieved an Area Under the Curve of 0.79 (pAUC0.5% = 0.16). In addition, the AODE trained on all three features (named PSOPIA) showed better prediction performance on a separate independent data set than a recently reported homology-based method.ConclusionsOur results suggest that FNet, a feature representing proximity in a known PPI network between two proteins that are homologous to a target protein pair, contributes to the prediction of whether the target proteins interact or not. PSOPIA will help identify novel PPIs and estimate complete PPI networks. The method proposed in this article is freely available on the web at http://mizuguchilab.org/PSOPIA.

Docking of protein molecular surfaces with evolutionary trace analysis

We have developed a new method to predict protein– protein complexes based on the shape complementarity of the molecular surfaces, along with sequence conservation obtained by evolutionary trace (ET) analysis. The docking is achieved by optimization of an object function that evaluates the degree of shape complementarity weighted by the conservation of the interacting residues. The optimization is carried out using a genetic algorithm in combination with Monte Carlo sampling. We applied this method to CAPRI targets and evaluated the performance systematically. Consequently, our method could achieve native‐like predictions in several cases. In addition, we have analyzed the feasibility of the ET method for docking simulations, and found that the conservation information was useful only in a limited category of proteins (signal related proteins and enzymes). Proteins 2007.

Experimental elucidation: Microscopic mechanism of resonant X-ray scattering in manganite films

Resonant X-ray scattering experiments have been performed on perovskite manganite La 0.5 Sr 0.5 MnO 3 thin films, which are grown on three distinct perovskite substrates with a coherent epitaxial strain and have a forced ferro -type orbital ordering of Mn 3 d orbitals. Using an interference technique, we have successfully observed the resonant X-ray scattering signal from the system having the ferro -type orbital ordering and also revealed the energy scheme of Mn 4 p bands. For the forced ferro -type orbital ordering system, the present results evidence that the resonant X-ray scattering signal originates from the band structure effect due to the Jahn–Teller distortion of a MnO 6 octahedron, and not from the Coulomb interaction between 3 d and 4 p electrons.