SVM‐Cabins: Prediction of solvent accessibility using accumulation cutoff set and support vector machine

The solvent accessibility of amino acid residues has been predicted in the past by classifying them into exposure states with varying thresholds. This classification provides a wide range of values for the accessible surface area (ASA) within which a residue may fall. Thus far, no attempt has been made to predict real values of ASA from the sequence information without a priori classification into exposure states. Here, we present a new method with which to predict real value ASAs for residues, based on neighborhood information. Our real value prediction neural network could estimate the ASA for four different nonhomologous, nonredundant data sets of varying size, with 18.0-19.5% mean absolute error, defined as per residue absolute difference between the predicted and experimental values of relative ASA. Correlation between the predicted and experimental values ranged from 0.47 to 0.50. It was observed that the ASA of a residue could be predicted within a 23.7% mean absolute error, even when no information about its neighbors is included. Prediction of real values answers the issue of arbitrary choice of ASA state thresholds, and carries more information than category prediction. Prediction error for each residue type strongly correlates with the variability in its experimental ASA values.

Protein structure prediction is often assisted by predicting one-dimensional structural properties including relative solvent accessibility (RSA) surface and backbone torsion angles (BTA) of residues, and these two properties are continuously varying variables because proteins can move freely in a three-dimensional space. Instead of subdividing them into a few arbitrarily defined states that many popular approaches used, this paper proposes an integrated system for realvalue prediction of protein structural motifs’ two local properties, based on the modified Hidden Markov Model that we previously presented. The model was used to capture the relevance of RSA and the dependency of BTA between adjacent residues along the local protein chain in motifs with definite probabilities. These two properties were predicted according to their own probability distribution. The method was applied to a protein fragment library. For nine different classes of motifs, real values of RSA were predicted with mean absolute error (MAE) of 0.122-0.175 and Pearson’s correlation coefficient (PCC) of 0.623-0.714 between predicted and actual RSA. Meanwhile, real values of BTA were obtained with MAE of 8.50-29.40 for Φ angles, 11.20-38.50 for ψ angles and PCC of 0.601-0.716 for Φ, 0.597-0.713 for ψ. The results were compared with well-known Real-SPINE Server, and indicate the proposed method may at least serve as the foundation to obtain better local properties from structural motifs for protein structure prediction. Keywords: Protein structure predication, structural motifs, hidden Markov model, solvent accessibility surface, backbone torsion angles, directional statistics distribution

BackgroundProtein solvent accessibility prediction is a pivotal intermediate step towards modeling protein tertiary structures directly from one-dimensional sequences. It also plays an important part in identifying protein folds and domains. Although some methods have been presented to the protein solvent accessibility prediction in recent years, the performance is far from satisfactory. In this work, we propose PredRSA, a computational method that can accurately predict relative solvent accessible surface area (RSA) of residues by exploring various local and global sequence features which have been observed to be associated with solvent accessibility. Based on these features, a novel and efficient approach, Gradient Boosted Regression Trees (GBRT), is first adopted to predict RSA.ResultsExperimental results obtained from 5-fold cross-validation based on the Manesh-215 dataset show that the mean absolute error (MAE) and the Pearson correlation coefficient (PCC) of PredRSA are 9.0 % and 0.75, respectively, which are better than that of the existing methods. Moreover, we evaluate the performance of PredRSA using an independent test set of 68 proteins. Compared with the state-of-the-art approaches (SPINE-X and ASAquick), PredRSA achieves a significant improvement on the prediction quality.ConclusionsOur experimental results show that the Gradient Boosted Regression Trees algorithm and the novel feature combination are quite effective in relative solvent accessibility prediction. The proposed PredRSA method could be useful in assisting the prediction of protein structures by applying the predicted RSA as useful restraints.

Solvent accessible surface area (SASA) of proteins has always been considered as a decisive factor in protein folding and stability studies. It is defined as the surface characterized around a protein by a hypothetical centre of a solvent sphere with the van der Waals contact surface of the molecule. Based on SASA values, amino acid residues of a protein can be classified as buried or exposed. There are various types of SASAs starting from relative solvent accessibility to absolute surface areas. Direct estimation of accurate SASAs of folded proteins experimentally at the atomic level is not possible. However, the SASA of a native protein can be estimated computationally from the atomic coordinates. Similarly, various simulation methods are available to compute the SASA of a protein in its unfolded state. In efforts to estimate the changes in SASA related to the protein folding, a number of the unfolded state models have been proposed. In this review, we have summarized different algorithms and computational tools for SASA estimations. Furthermore, online resources for SASA calculations and representations have also been discussed in detail. This review will be useful for protein chemists and biologists for the accurate measurements of SASA and its subsequent applications for the calculation of various biophysical and thermodynamic properties of proteins.

C1, the complex that triggers the classic pathway of complement, is a 790-kDa assembly resulting from association of a recognition protein C1q with a Ca(2+)-dependent tetramer comprising two copies of the proteases C1r and C1s. Early structural investigations have shown that the extended C1s-C1r-C1r-C1s tetramer folds into a compact conformation in C1. Recent site-directed mutagenesis studies have identified the C1q-binding sites in C1r and C1s and led to a three-dimensional model of the C1 complex (Bally, I., Rossi, V., Lunardi, T., Thielens, N. M., Gaboriaud, C., and Arlaud, G. J. (2009) J. Biol. Chem. 284, 19340-19348). In this study, we have used a mass spectrometry-based strategy involving a label-free semi-quantitative analysis of protein samples to gain new structural insights into C1 assembly. Using a stable chemical modification, we have compared the accessibility of the lysine residues in the isolated tetramer and in C1. The labeling data account for 51 of the 73 lysine residues of C1r and C1s. They strongly support the hypothesis that both C1s CUB(1)-EGF-CUB(2) interaction domains, which are distant in the free tetramer, associate with each other in the C1 complex. This analysis also provides the first experimental evidence that, in the proenzyme form of C1, the C1s serine protease domain is partly positioned inside the C1q cone and yields precise information about its orientation in the complex. These results provide further structural insights into the architecture of the C1 complex, allowing significant improvement of our current C1 model.

Sequence-based prediction of one dimensional structural properties of proteins has been a long-standing subproblem of protein structure prediction. Recently, prediction accuracy has been significantly improved due to the rapid expansion of protein sequence and structure libraries and advances in deep learning techniques, such as residual convolutional networks (ResNets) and Long-Short-Term Memory Cells in Bidirectional Recurrent Neural Networks (LSTM-BRNNs). Here we leverage an ensemble of LSTM-BRNN and ResNet models, together with predicted residue-residue contact maps, to continue the push towards the attainable limit of prediction for 3- and 8-state secondary structure, backbone angles (θ, τ, ϕ and ψ), half-sphere exposure, contact numbers and solvent accessible surface area (ASA). The new method, named SPOT-1D, achieves similar, high performance on a large validation set and test set (≈1000 proteins in each set), suggesting robust performance for unseen data. For the large test set, it achieves 87% and 77% in 3- and 8-state secondary structure prediction and 0.82 and 0.86 in correlation coefficients between predicted and measured ASA and contact numbers, respectively. Comparison to current state-of-the-art techniques reveals substantial improvement in secondary structure and backbone angle prediction. In particular, 44% of 40-residue fragment structures constructed from predicted backbone Cα-based θ and τ angles are less than 6 Å root-mean-squared-distance from their native conformations, nearly 20% better than the next best. The method is expected to be useful for advancing protein structure and function prediction. SPOT-1D and its data is available at: http://sparks-lab.org/. Supplementary data are available at Bioinformatics online.

The accuracy of predicting protein local and global structural properties such as secondary structure and solvent accessible surface area has been stagnant for many years because of the challenge of accounting for non-local interactions between amino acid residues that are close in three-dimensional structural space but far from each other in their sequence positions. All existing machine-learning techniques relied on a sliding window of 10-20 amino acid residues to capture some 'short to intermediate' non-local interactions. Here, we employed Long Short-Term Memory (LSTM) Bidirectional Recurrent Neural Networks (BRNNs) which are capable of capturing long range interactions without using a window. We showed that the application of LSTM-BRNN to the prediction of protein structural properties makes the most significant improvement for residues with the most long-range contacts (|i-j| >19) over a previous window-based, deep-learning method SPIDER2. Capturing long-range interactions allows the accuracy of three-state secondary structure prediction to reach 84% and the correlation coefficient between predicted and actual solvent accessible surface areas to reach 0.80, plus a reduction of 5%, 10%, 5% and 10% in the mean absolute error for backbone ϕ , ψ , θ and τ angles, respectively, from SPIDER2. More significantly, 27% of 182724 40-residue models directly constructed from predicted C α atom-based θ and τ have similar structures to their corresponding native structures (6Å RMSD or less), which is 3% better than models built by ϕ and ψ angles. We expect the method to be useful for assisting protein structure and function prediction. The method is available as a SPIDER3 server and standalone package at http://sparks-lab.org . yaoqi.zhou@griffith.edu.au or yuedong.yang@griffith.edu.au. Supplementary data are available at Bioinformatics online.

gated volatile and non-volatile components

BackgroundProtein structure mediates site-specific patterns of sequence divergence. In particular, residues in the core of a protein (solvent-inaccessible residues) tend to be more evolutionarily conserved than residues on the surface (solvent-accessible residues).ResultsHere, we present a model of sequence evolution that explicitly accounts for the relative solvent accessibility of each residue in a protein. Our model is a variant of the Goldman-Yang 1994 (GY94) model in which all model parameters can be functions of the relative solvent accessibility (RSA) of a residue. We apply this model to a data set comprised of nearly 600 yeast genes, and find that an evolutionary-rate ratio ω that varies linearly with RSA provides a better model fit than an RSA-independent ω or an ω that is estimated separately in individual RSA bins. We further show that the branch length t and the transition-transverion ratio κ also vary with RSA. The RSA-dependent GY94 model performs better than an RSA-dependent Muse-Gaut 1994 (MG94) model in which the synonymous and non-synonymous rates individually are linear functions of RSA. Finally, protein core size affects the slope of the linear relationship between ω and RSA, and gene expression level affects both the intercept and the slope.ConclusionsStructure-aware models of sequence evolution provide a significantly better fit than traditional models that neglect structure. The linear relationship between ω and RSA implies that genes are better characterized by their ω slope and intercept than by just their mean ω.

Solvent accessibility studies on polysaccharides

Article history: Received January 20, 2014 Accepted 5 July 2014 Available online August 18 2014 Financial statements as well as financial standards are always considered as primary sources for getting rich information of firms. The standards are normally divided in two categories of economic and accounting and each of these standards shows one of the specifications of the company and has its own advantageous and disadvantageous. There are different standards, which are used for firm assessment. Therefore, a company with more gained prominences has more change to succeed in attracting credits and financing from the capital market. Under such circumstances, such a company can attract more finance and it can be sold sooner on the stock exchange. This paper tries to investigate on the free cash flow as an effective factor in specifying the real value of the business enterprises on 56 selected firms from Tehran Stock Exchange. The results of this study have disclosed that, there was a direct and meaningful relationship between free cash flow of the business enterprises and their real values. Second, the real values of the business enterprises are more than their predicted values. Finally, the predicted value of the business enterprises on the basis of the free cash flow is more than their market value. © 2014 Growing Science Ltd. All rights reserved. Free Cash Flow Real Firm Value Predicted Firm Value Capital Cost

In this study, we are intends to enhance our method before in solving determination of distribution location center by using bio-soft approach. Before, we have proposed to employ length based approach in order to represent distances. However, by using this approach, we cannot present real distance value due to length of sequences constraint. Thus, in this paper, we proposes to employ melting temperature to represent a distances in real values. A numerical example is presented to show how this approach has work. Biochemical approach has been uses to obtain the optimal solution.

Numerical methods for interval and fuzzy number comparison based on the probabilistic approach and Dempster–Shafer theory

Knowledge of protein-protein interaction sites is vital to determine proteins' function and involvement in different pathways. Though a wide variety of methods has been proposed over the recent years in order to predict protein-protein interface residues, mainly based on single amino acid sequence inputs, each has its own drawbacks and limitations. We propose to use Support Vector Machines (SVM) to predict protein-protein interface residues by using the information derived from the position-specific scoring matrices (PSSMs) generated from PSI-BLAST profiles and accessible surface areas. The present approach achieved overall prediction accuracy of 73.8% for 77 individulal proteins collected from the Protein Data Bank, which is better than the previously reported accuracies.

Protein–RNA recognition often induces conformational changes in binding partners. Consequently, the solvent accessible surface area (SASA) buried in contact estimated from the co-crystal structures may differ from that calculated using their unbound forms. To evaluate the change in accessibility upon binding, we compare SASA of 126 protein-RNA complexes between bound and unbound forms. We observe, in majority of cases the interface of both the binding partners gain accessibility upon binding, which is often associated with either large domain movements or secondary structural transitions in RNA-binding proteins (RBPs), and binding-induced conformational changes in RNAs. At the non-interface region, majority of RNAs lose accessibility upon binding, however, no such preference is observed for RBPs. Side chains of RBPs have major contribution in change in accessibility. In case of flexible binding, we find a moderate correlation between the binding free energy and change in accessibility at the interface. Finally, we introduce a parameter, the ratio of gain to loss of accessibility upon binding, which can be used to identify the native solution among the flexible docking models. Our findings provide fundamental insights into the relationship between flexibility and solvent accessibility, and advance our understanding on binding induced folding in protein-RNA recognition.