Prediction of β-Turns

Kohonen's self-organization model, a neural network model, is applied to predict the -turns in proteins. There are 455 -turn tetrapeptides and 3807 non--turn tetrapeptides in the training database. The rates of correct prediction for the 110 -turn tetrapeptides and 30,229 non--turn tetrapeptides in the testing database are 81.8% and 90.7%, respectively. The high quality of prediction of neural network model implies that the residue-coupled effect along a polypeptide chain is important for the formation of reversal turns, such as -turns, during the process of protein folding.     

Prediction of β-Turns

Kohonen's self-organization model, a neural network model, is applied to predict the β-turns in proteins. There are 455 β-turn tetrapeptides and 3807 non-β-turn tetrapeptides in the training database. The rates of correct prediction for the 110 β-turn tetrapeptides and 30,229 non-β-turn tetrapeptides in the testing database are 81.8% and 90.7%, respectively. The high quality of prediction of neural network model implies that the residue-coupled effect along a polypeptide chain is important for the formation of reversal turns, such as β-turns, during the process of protein folding.     

Classification and Prediction of β-Turn Types

Although a β-turn consists of only four amino acids, it assumes many different types in proteins. Is this basically dependent on the tetrapeptide sequence alone or is it due to a variety of interactions with the other part of a protein? To answer this question, a residue-coupled model is proposed that can reflect the sequence-coupling effect for a tetrapeptide in not only a β-turn or non-β-turn, but also different types of a β-turn. The predicted results by the model for 6022 tetrapeptides indicate that the rates of correct prediction for β-turn types I, I′, II, II′, VI, and VIII and non-β-turns are 68.54%, 93.60%, 85.19%, 97.75%, 100%, 88.75%, and 61.02%, respectively. Each of these seven rates is significantly higher than $\frac{1}{7}$ = 14.29%, the completely randomized rate, implying that the formation of different β-turn types or non-β-turns is considerably correlated with the sequences of a tetrapeptide.     

Computational Prediction of Protein–Protein Interactions

Recently a number of computational approaches have been developed for the prediction of protein–protein interactions. Complete genome sequencing projects have provided the vast amount of information needed for these analyses. These methods utilize the structural, genomic, and biological context of proteins and genes in complete genomes to predict protein interaction networks and functional linkages between proteins. Given that experimental techniques remain expensive, time-consuming, and labor-intensive, these methods represent an important advance in proteomics. Some of these approaches utilize sequence data alone to predict interactions, while others combine multiple computational and experimental datasets to accurately build protein interaction maps for complete genomes. These methods represent a complementary approach to current high-throughput projects whose aim is to delineate protein interaction maps in complete genomes. We will describe a number of computational protocols for protein interaction prediction based on the structural, genomic, and biological context of proteins in complete genomes, and detail methods for protein interaction network visualization and analysis.     

Oxypred： Prediction and Classification of Oxygen-Binding Proteins

This study describes a method for predicting and classifying oxygen-binding pro- teins. Firstly, support vector machine (SVM) modules were developed using amino acid composition and dipeptide composition for predicting oxygen-binding pro- teins, and achieved maximum accuracy of 85.5% and 87.8%, respectively. Sec- ondly, an SVM module was developed based on amino acid composition, classify- ing the predicted oxygen-binding proteins into six classes with accuracy of 95.8%, 97.5%, 97.5%, 96.9%, 99.4%, and 96.0% for erythrocruorin, hemerythrin, hemo- cyanin, hemoglobin, leghemoglobin, and myoglobin proteins, respectively. Finally, an SVM module was developed using dipeptide composition for classifying the oxygen-binding proteins, and achieved maximum accuracy of 96.1%, 98.7%, 98.7%, 85.6%, 99.6%, and 93.3% for the above six classes, respectively. All modules were trained and tested by five-fold cross validation. Based on the above approach, a web server Oxypred was developed for predicting and classifying oxygen-binding proteins(available from http://www.imtech.res.in/raghava/oxypred/).     

Prediction of subcellular locations of proteins: where to proceed?

Since the proposal of the signal hypothesis on protein subcellular sorting, a number of computational analyses have been performed in this field. A typical example is the development of prediction algorithms for the subcellular localization sites of input protein sequences. In this review, we mainly focus on the biological grounds of the prediction methods rather than the algorithmic issues because we believe the former will be more fruitful for future development. Recent advances on the study of protein sorting signals will hopefully be incorporated into future prediction methods. Unfortunately, many of the state-of-the-art methods are published without sufficient objective tests. In fact, a simple test employed in this article shows that the performance of specifically developed predictors is not significantly better than that of a homology search. We suspect that this is a general problem associated with the interpretation of genome sequences, which have evolved through gene duplication and speciation.     

Prediction of the packing arrangement of strands in β-sheets of globular proteins

A method is proposed for predicting the adjacency order in which strands pack in a -sheet in a protein, on the basis of its amino acid sequence alone. The method is based on the construction of a predicted contact map for the protein, in which the probability that various residue pairs are close to each other is computed from statistically determined average distances of residue pairs in globular proteins of known structure. Compact regions, i.e., portions of the sequence with many interresidue contacts, are determined on the map by using an objective search procedure. The proximity of strands in a -sheet is predicted from the density of contacts in compact regions associated with each pair of strands. The most probable -sheet structures are those with the highest density of contacts. The method has been tested by computing the probable strand arrangements in a five-strand -sheet in five proteins or protein domains, containing 62–138 residues. Of the theoretically possible 60 strand arrangements, the method selects two to eight arrangements as most probable; i.e., it leads to a large reduction in the number of possibilities. The native strand arrangement is among those predicted for three of the five proteins. For the other two, it would be included in the prediction by a slight relaxation of the cutoff criteria used to analyze the density of contacts.     

Efficiency of the miRNA–mRNA Interaction Prediction Programs

miRNAs play a key role in regulation of gene expression. Nowadays it is known more than 2500 human miRNAs, while a majority of miRNA–mRNA interactions remains unidentified. The recent development of a high-throughput CLASH (crosslinking, ligation and sequencing of hybrids) technique for discerning miRNA–mRNA interactions allowed an experimental analysis of the human miRNA–mRNA interactome. Therefore, it allowed us, for the first time, make an experimental analysis of the human miRNA–mRNA interactome as a whole and an evaluation of the quality of most commonly used miRNA prediction tools (TargetScan, PicTar, PITA, RNA22 and miRanda). To estimate efficiency of the miRNA–mRNA prediction tools, we used next parameters: sensitivity, positive predicted value, predictions in different mRNA regions (3' UTR, CDS, 5' UTR), predictions for different types of interactions (5 classes), predictions of “canonical” and “nocanonical” interactions, similarity with the random generated data. The analysis revealed low efficiency of all prediction programs in comparison with the CLASH data in terms of the all examined parameters.     

VGIchan： Prediction and Classification of Voltage-Gated Ion Channels

This study describes methods for predicting and classifying voltage-gated ion channels. Firstly, a standard support vector machine （SVM） method was developed for predicting ion channels by using amino acid composition and dipeptide composition, with an accuracy of 82.89% and 85.56%, respectively. The accuracy of this SVM method was improved from 85.56% to 89.11% when combined with PSIBLAST similarity search. Then we developed an SVM method for classifying ion channels （potassium, sodium, calcium, and chloride） by using dipeptide composition and achieved an overall accuracy of 96.89%. We further achieved a classification accuracy of 97.78% by using a hybrid method that combines dipeptidebased SVM and hidden Markov model methods. A web server VGIchan has been developed for predicting and classifying voltage-gated ion channels using the above approaches. VGIchan is freely available at www.imtech.res.in/raghava/vgichan/.     

Prediction of leachate concentrations in petroleum‐contaminated soils

The efficacy of cleanup methods in reducing gasoline contamination at spill sites is typically determined by measuring benzene, toluene, xylene (BTX), and total petroleum hydrocarbon (TPH) concentrations in soil samples. Although these values may provide a direct measurement of soil contamination, they may not be indicative of what is transferred to percolating water. This study addresses this issue by measuring TPH, toluene, m‐ and p‐xylene, and naphthalene levels in gasoline‐contaminated soil columns before and after forced‐air venting and relating these values to the aqueous‐phase concentrations measured when water is percolated through the same columns.

Sandy soils with and without organic matter were packed into glass columns. The soils were brought to residual water and residual gasoline saturations by applying a vacuum to a ceramic pressure plate at the column bottom. Venting was performed by passing clean, moist air through the columns. The columns were subsequently leached under unsaturated conditions.

Soil samples were taken from the bottom of the columns upon completion of the venting or leaching phases of the experiments. Toluene, m‐ and p‐xylene, naphthalene, and TPH values were measured in soil samples extracted with either freon or methanol. Aqueous phase concentrations of these compounds were predicted using measured soil concentrations and either Raoult's law or organic matter‐water and fuel‐water partitioning theory (Boyd and Sun, 1990). The predicted results were compared with measured leachate concentrations from the same columns.

Mole fractions estimated from soil concentrations and TPH values used in Raoult's law gave good predictions of aqueous phase concentrations for compounds that had a high mole fraction in the residual nonaqueous phase liquid (NAPL). For compounds at low concentrations in the residual NAPL, an approach using a distribution coefficient that accounted for both the organic matter and residual NAPL in the soil provided better estimates than those based on Raoult's law.     

Prediction of peptide structure: How far are we?

Rational design of peptides is a challenge, which would benefit from a better knowledge of the rules of sequence-structure-function relationships. Peptide structures can be approached by spectroscopy and NMR techniques but data from these approaches too frequently diverge. Structures can also be calculated in silico from primary sequence information using three algorithms: Pepstr, Robetta, and PepLook. The most recent algorithm, PepLook introduces indexes for evaluating structural polymorphism and stability. For peptides with converging experimental data, calculated structures from PepLook and, to a lesser extent from Pepstr, are close to NMR models. The PepLook index for polymorphism is low and the index for stability points out possible binding sites. For peptides with divergent experimental data, calculated and NMR structures can be similar or, can be different. These differences are apparently due to polymorphism and to different conditions of structure assays and calculations. The PepLook index for polymorphism maps the fragments encoding disorder. This should provide new means for the rational design of peptides.     

Prediction of predator–prey populations modelled by perturbed ODEs

In this paper we explore a stochastic model in continuous time for predator-prey interactions, which accounts for the periodical behaviour observed in many animal populations. More precisely, we consider a solution to the classical Lotka-Volterra system of equations, but we view the actual population sizes as random perturbations of the solutions to this ODE system. Namely, we assume that the perturbations follow correlated Ornstein-Uhlenbeck processes; this approach generalizes the one of Froda and Colavita [Aust N Z J Stat 2:235-254, 2005] who considered only i.i.d. errors. This type of perturbed deterministic model allows to perform parameter estimation and to predict population sizes at future times. On the other hand, the present model refines the previous one since it takes into account the variability due to external factors and the time dependence in the random component. Moreover, this more flexible model improves the predictions of population sizes at future times. In order to illustrate this last point, we analyse two data sets.     

Prediction of the β-Hairpins in Proteins Using Support Vector Machine

By using of the composite vector with increment of diversity and scoring function to express the information of sequence, a support vector machine (SVM) algorithm for predicting β-hairpin motifs is proposed. The prediction is done on a dataset of 3,088 non homologous proteins containing 6,027 β-hairpins. The overall accuracy of prediction and Matthew’s correlation coefficient are 79.9% and 0.59 for the independent testing dataset. In addition, a higher accuracy of 83.3% and Matthew’s correlation coefficient of 0.67 in the independent testing dataset are obtained on a dataset previously used by Kumar et al. (Nuclic Acid Res 33:154–159). The performance of the method is also evaluated by predicting the β-hairpins of in the CASP6 proteins, and the better results are obtained. Moreover, this method is used to predict four kinds of supersecondary structures. The overall accuracy of prediction is 64.5% for the independent testing dataset.     

Prediction of β-barrel membrane proteins by searching for restricted domains

Background  

The identification of β-barrel membrane proteins out of a genomic/proteomic background is one of the rapidly developing fields in bioinformatics. Our main goal is the prediction of such proteins in genome/proteome wide analyses.     

Prediction of complex super-secondary structure βαβ motifs based on combined features

Prediction of a complex super-secondary structure is a key step in the study of tertiary structures of proteins. The strand-loop-helix-loop-strand (βαβ) motif is an important complex super-secondary structure in proteins. Many functional sites and active sites often occur in polypeptides of βαβ motifs. Therefore, the accurate prediction of βαβ motifs is very important to recognizing protein tertiary structure and the study of protein function. In this study, the βαβ motif dataset was first constructed using the DSSP package. A statistical analysis was then performed on βαβ motifs and non-βαβ motifs. The target motif was selected, and the length of the loop-α-loop varies from 10 to 26 amino acids. The ideal fixed-length pattern comprised 32 amino acids. A Support Vector Machine algorithm was developed for predicting βαβ motifs by using the sequence information, the predicted structure and function information to express the sequence feature. The overall predictive accuracy of 5-fold cross-validation and independent test was 81.7% and 76.7%, respectively. The Matthew’s correlation coefficient of the 5-fold cross-validation and independent test are 0.63 and 0.53, respectively. Results demonstrate that the proposed method is an effective approach for predicting βαβ motifs and can be used for structure and function studies of proteins.     

Prediction of adsorption equilibria of water–methanol mixtures in zeolite NaA by molecular simulation

Predictions for the adsorption of mixtures of water and methanol in zeolite NaA are reported. The pressure dependence of the adsorption properties such as equilibrium amounts of adsorption and isosteric heats of adsorption are calculated at 378 K by molecular simulations using effective pair potential models. These data are also determined for the adsorption from liquid mixtures. The models predict selectivity inversion in the investigated range of pressure. The change in adsorption ratios can partly be explained by the structural characteristics of the system.     

Prediction of mutant activity and its application in molecular design of tumor necrosis factor-α

Two models for prediction of the activity and stability of site-directed mutagenesis on tumor necrosis factor-α are established. The models are based on straightforward structural considerations, which do not require the elaboration of sitedirected mutagenesis on the protein core and the hydrophobic surface area by analyzing the pmperties of the mutated amino acid residues. The reliabilities of the models have been tested by analyzing the mutants of tumor necrosis factor-α (TNF-α) whose two leucine residues (L29, L157) were mutated. Based on these models, a TNFα mutant with high activity was created by molecular design.