<Emphasis Type="Italic">ASAView</Emphasis>: Database and tool for solvent accessibility representation in proteins

Background  

Accessible surface area (ASA) or solvent accessibility of amino acids in a protein has important implications. Knowledge of surface residues helps in locating potential candidates of active sites. Therefore, a method to quickly see the surface residues in a two dimensional model would help to immediately understand the population of amino acid residues on the surface and in the inner core of the proteins.     

Maximum Allowed Solvent Accessibilites of Residues in Proteins

The relative solvent accessibility (RSA) of a residue in a protein measures the extent of burial or exposure of that residue in the 3D structure. RSA is frequently used to describe a protein''s biophysical or evolutionary properties. To calculate RSA, a residue''s solvent accessibility (ASA) needs to be normalized by a suitable reference value for the given amino acid; several normalization scales have previously been proposed. However, these scales do not provide tight upper bounds on ASA values frequently observed in empirical crystal structures. Instead, they underestimate the largest allowed ASA values, by up to 20%. As a result, many empirical crystal structures contain residues that seem to have RSA values in excess of one. Here, we derive a new normalization scale that does provide a tight upper bound on observed ASA values. We pursue two complementary strategies, one based on extensive analysis of empirical structures and one based on systematic enumeration of biophysically allowed tripeptides. Both approaches yield congruent results that consistently exceed published values. We conclude that previously published ASA normalization values were too small, primarily because the conformations that maximize ASA had not been correctly identified. As an application of our results, we show that empirically derived hydrophobicity scales are sensitive to accurate RSA calculation, and we derive new hydrophobicity scales that show increased correlation with experimentally measured scales.     

RVP-net: online prediction of real valued accessible surface area of proteins from single sequences

SUMMARY: RVP-net is an online program for the prediction of real valued solvent accessibility. All previous methods of accessible surface area (ASA) predictions classify amino acid residues into exposure states and named them buried or exposed based on different thresholds. Real values in some cases were generated by taking the mid points of these state thresholds. This is the first method, which provides a direct prediction of ASA without making exposure categories and achieves results better than 19% mean absolute error. To facilitate batch processing of several sequences, a standalone version of this tool is also provided. AVAILABILITY: Online predictions are available at http://www.netasa.org/rvp-net/. Standalone version of the program can be obtained from the corresponding author by E-mail request.     

SVM-Cabins: prediction of solvent accessibility using accumulation cutoff set and support vector machine

A number of methods for predicting levels of solvent accessibility or accessible surface area (ASA) of amino acid residues in proteins have been developed. These methods either predict regularly spaced states of relative solvent accessibility or an analogue real value indicating relative solvent accessibility. While discrete states of exposure can be easily obtained by post prediction assignment of thresholds to the predicted or computed real values of ASA, the reverse, that is, obtaining a real value from quantized states of predicted ASA, is not straightforward as a two-state prediction in such cases would give a large real valued errors. However, prediction of ASA into larger number of ASA states and then finding a corresponding scheme for real value prediction may be helpful in integrating the two approaches of ASA prediction. We report a novel method of obtaining numerical real values of solvent accessibility, using accumulation cutoff set and support vector machine. This so-called SVM-Cabins method first predicts discrete states of ASA of amino acid residues from their evolutionary profile and then maps the predicted states onto a real valued linear space by simple algebraic methods. Resulting performance of such a rigorous approach using 13-state ASA prediction is at least comparable with the best methods of ASA prediction reported so far. The mean absolute error in this method reaches the best performance of 15.1% on the tested data set of 502 proteins with a coefficient of correlation equal to 0.66. Since, the method starts with the prediction of discrete states of ASA and leads to real value predictions, performance of prediction in binary states and real values are simultaneously optimized.     

Solvent accessibility study on tRNAPhe

In order to assess the solvent–solute association in the tRNA^Phe molecule, solvent accessibility calculations were carried out for its crystalline and completely extended states following the method of Lee and Richards. To do this, results from the calculations on model trinucleotide systems pApXpA with different bases at position X were used. In the folded form of the molecule, it was found that the oxygen atoms O(I) and O(II) of almost all the phosphate groups and the O(2′) atoms of the sugar rings situated throughout the backbone were highly exposed to the solvent. The amount of reduction found in the solvent accessibilities of the various atoms in going from the extended state to the folded state of the molecule indicates the kind of compactness of the tertiary structure in tRNA^Phe. The results give quantitative support to many characteristics of the tRNA molecule, such as loop sections, buried/exposed residues, hydrophobic interactions, etc., which were thought to be due to other factors.     

Integrated prediction of one-dimensional structural features and their relationships with conformational flexibility in helical membrane proteins

Background  

Many structural properties such as solvent accessibility, dihedral angles and helix-helix contacts can be assigned to each residue in a membrane protein. Independent studies exist on the analysis and sequence-based prediction of some of these so-called one-dimensional features. However, there is little explanation of why certain residues are predicted in a wrong structural class or with large errors in the absolute values of these features. On the other hand, membrane proteins undergo conformational changes to allow transport as well as ligand binding. These conformational changes often occur via residues that are inherently flexible and hence, predicting fluctuations in residue positions is of great significance.     

Real value prediction of solvent accessibility from amino acid sequence

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.     

Prediction of protein accessible surface areas by support vector regression

A novel support vector regression (SVR) approach is proposed to predict protein accessible surface areas (ASAs) from their primary structures. In this work, we predict the real values of ASA in squared angstroms for residues instead of relative solvent accessibility. Based on protein residues, the mean and median absolute errors are 26.0 A(2) and 18.87 A(2), respectively. The correlation coefficient between the predicted and observed ASAs is 0.66. Cysteine is the best predicted amino acid (mean absolute error is 13.8 A(2) and median absolute error is 8.37 A(2)), while arginine is the least predicted amino acid (mean absolute error is 42.7 A(2) and median absolute error is 36.31 A(2)). Our work suggests that the SVR approach can be directly applied to the ASA prediction where data preclassification has been used.     

Atom-wise statistics and prediction of solvent accessibility in proteins

In this work, we explore a novel method to broaden the scope of sequence-based predictions of solvent accessibility or accessible surface area (ASA) to the atomic level. All 167 heavy atoms from the 20 types of amino acid residues in proteins have been studied. An analysis of ASA distribution of these atomic groups in different proteins has been performed and rotamer-style libraries have been developed. We observe that the ASA of some atomic groups (e.g., backbone C and N atoms) can be estimated from the sequence environment within a mean absolute error of 2-3 angstroms(2). However, some side chain atoms such as CG in Pro, NH1 in Arg and NE2 in Gln show a strong variability making it more difficult to estimate their ASA from sequence environment. In general, the prediction of ASA becomes more difficult for atomic positions at the side chain extremities of long amino acid residues (aromatic side chain terminals being the exception). Several atomic groups are frequently exposed to solvent. Some of them have a bimodal distribution, suggesting two stable conformations in terms of their solvent exposure. More detailed understanding and prediction of solvent accessibility, i.e., at an atomic level is expected to help in bioinformatics approaches to structure prediction, functional relevance of atomic solvent accessibilities and other interaction analyses.     

Experimentally Approaching the Solvent-Accessible Surface Area of a Protein: Insights into the Acid Molten Globule of Bovine α-Lactalbumin

Each conformational state of a protein is inextricably related to a defined extent of solvent exposure that plays a key role in protein folding and protein interactions. However, accurate measurement of the solvent-accessible surface area (ASA) is difficult for any state other than the native (N) state. We address this fundamental physicochemical parameter through a new experimental approach based on the reaction of the photochemical reagent diazirine (DZN) with the polypeptide chain. By virtue of its size, DZN is a reasonable molecular mimic of aqueous solvent. Here, we structurally characterize nonnative states of the paradigmatic protein α-lactalbumin. Covalent tagging resulting from unspecific methylene (:CH₂) reaction allows one to obtain a global estimate of ASA and to map out solvent accessibility along the amino acid sequence. By its mild apolar nature, DZN also reveals a hydrophobic phase in the acid-stabilized state of α-lactalbumin, in which there is clustering of core residues accessible to the solvent. In a fashion reminiscent of the N state, this acid-stabilized state also exhibits local regions where increased :CH₂ labeling indicates its nonhomogenous nature, likely pointing to the existence of packing defects. By contrast, the virtual absence of a defined long-range organization brings about a featureless labeling pattern for the unfolded state. Overall, :CH₂ labeling emerges as a fruitful technique that is able to quantify the ASA of the polypeptide chain, thus probing conformational features such as the outer exposed surface and inner cavities, as well as revealing the existence of noncompact apolar phases in nonnative states.     

Improving prediction of burial state of residues by exploiting correlation among residues

Background

Residues in a protein might be buried inside or exposed to the solvent surrounding the protein. The buried residues usually form hydrophobic cores to maintain the structural integrity of proteins while the exposed residues are tightly related to protein functions. Thus, the accurate prediction of solvent accessibility of residues will greatly facilitate our understanding of both structure and functionalities of proteins. Most of the state-of-the-art prediction approaches consider the burial state of each residue independently, thus neglecting the correlations among residues.

Results

In this study, we present a high-order conditional random field model that considers burial states of all residues in a protein simultaneously. Our approach exploits not only the correlation among adjacent residues but also the correlation among long-range residues. Experimental results showed that by exploiting the correlation among residues, our approach outperformed the state-of-the-art approaches in prediction accuracy. In-depth case studies also showed that by using the high-order statistical model, the errors committed by the bidirectional recurrent neural network and chain conditional random field models were successfully corrected.

Conclusions

Our methods enable the accurate prediction of residue burial states, which should greatly facilitate protein structure prediction and evaluation.

     

Sequence based residue depth prediction using evolutionary information and predicted secondary structure

Background  

Residue depth allows determining how deeply a given residue is buried, in contrast to the solvent accessibility that differentiates between buried and solvent-exposed residues. When compared with the solvent accessibility, the depth allows studying deep-level structures and functional sites, and formation of the protein folding nucleus. Accurate prediction of residue depth would provide valuable information for fold recognition, prediction of functional sites, and protein design.     

Analysis of accessible surface of residues in proteins

We analyzed the total, hydrophobic, and hydrophilic accessible surfaces (ASAs) of residues from a nonredundant bank of 587 3D structure proteins. In an extended fold, residues are classified into three families with respect to their hydrophobicity balance. As expected, residues lose part of their solvent-accessible surface with folding but the three groups remain. The decrease of accessibility is more pronounced for hydrophobic than hydrophilic residues. Amazingly, Lysine is the residue with the largest hydrophobic accessible surface in folded structures. Our analysis points out a clear difference between the mean (other studies) and median (this study) ASA values of hydrophobic residues, which should be taken into consideration for future investigations on a protein-accessible surface, in order to improve predictions requiring ASA values. The different secondary structures correspond to different accessibility of residues. Random coils, turns, and beta-structures (outside beta-sheets) are the most accessible folds, with an average of 30% accessibility. The helical residues are about 20% accessible, and the difference between the hydrophobic and the hydrophilic residues illustrates the amphipathy of many helices. Residues from beta-sheets are the most inaccessible to solvent (10% accessible). Hence, beta-sheets are the most appropriate structures to shield the hydrophobic parts of residues from water. We also show that there is an equal balance between the hydrophobic and the hydrophilic accessible surfaces of the 3D protein surfaces irrespective of the protein size. This results in a patchwork surface of hydrophobic and hydrophilic areas, which could be important for protein interactions and/or activity.     

Solvent accessibility studies on polysaccharides

The solvent accessible surface area (ASA) of the polysaccharides, namely (i) carrageenan (1CAR); (ii) agarose (1AGA); (iii) guaran (GUR); (iv) capsular polysaccharide (1CAP); and (v) hyaluronan (1HUA), have been computed using the solvent accessibility technique of Lee and Richards. The results show that the average variation of ASA for the various atoms in the molecules lie in the range 1-30 A(2). Irrespective of position of sulfation, either at two or four in the sugar residues in 1CAR, the charged groups interact almost equally with the solvent. The ASA values for the chains A and B in 1AGA and 1CAR indicate that there are not much interchain interactions and the chains in both the molecules interact equally with the solvent. Residue-wise analysis indicates that the ASAs of residues vary alternately, high-low-high value pattern that is similar to that of the hydrophobic behaviour of beta-strands in proteins. The results also suggest that in these polysaccharides D-configuration residues have higher ASA than L-configuration residues.     

Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information

Background  

Structural properties of proteins such as secondary structure and solvent accessibility contribute to three-dimensional structure prediction, not only in the ab initio case but also when homology information to known structures is available. Structural properties are also routinely used in protein analysis even when homology is available, largely because homology modelling is lower throughput than, say, secondary structure prediction. Nonetheless, predictors of secondary structure and solvent accessibility are virtually always ab initio.     

A hydrophobic spine stabilizes a surface-exposed α-helix according to analysis of the solvent-accessible surface area

Background

Most of hydrophilic and hydrophobic residues are thought to be exposed and buried in proteins, respectively. In contrast to the majority of the existing studies on protein folding characteristics using protein structures, in this study, our aim was to design predictors for estimating relative solvent accessibility (RSA) of amino acid residues to discover protein folding characteristics from sequences.

Methods

The proposed 20 real-value RSA predictors were designed on the basis of the support vector regression method with a set of informative physicochemical properties (PCPs) obtained by means of an optimal feature selection algorithm. Then, molecular dynamics simulations were performed for validating the knowledge discovered by analysis of the selected PCPs.

Results

The RSA predictors had the mean absolute error of 14.11% and a correlation coefficient of 0.69, better than the existing predictors. The hydrophilic-residue predictors preferred PCPs of buried amino acid residues to PCPs of exposed ones as prediction features. A hydrophobic spine composed of exposed hydrophobic residues of an α-helix was discovered by analyzing the PCPs of RSA predictors corresponding to hydrophobic residues. For example, the results of a molecular dynamics simulation of wild-type sequences and their mutants showed that proteins 1MOF and 2WRP_H16I (Protein Data Bank IDs), which have a perfectly hydrophobic spine, have more stable structures than 1MOF_I54D and 2WRP do (which do not have a perfectly hydrophobic spine).

Conclusions

We identified informative PCPs to design high-performance RSA predictors and to analyze these PCPs for identification of novel protein folding characteristics. A hydrophobic spine in a protein can help to stabilize exposed α-helices.

     

Heterologous expression and characterization of a proxidomal ascorbate peroxidase from <Emphasis Type="Italic">Populus tomentosa</Emphasis>

The present study reported for the first time, cloning, expression and characteristics of a Proxidomal APX gene (PpAPX) from Populus tomentosa. The PpAPX gene encodes a protein of 287 amino acid residues with a calculated molecular mass of 31.58 kDa. The over-expressed recombinant PpAPX protein showed high activity towards the substrates ascorbate aicd (ASA) and H₂O₂. At fixed ASA concentrations, the K _m and V _max values were 0.12 ± 0.01 mM and 23.4 ± 4.2 mmol/min mg for H₂O₂. And at fixed H₂O₂ concentrations, the K _m and V _max values were 0.53 ± 0.04 mM and 20.0 ± 2.3 mmol/min mg for ASA.     

In silico analysis of Myoglobin in Channa striata

Myoglobin is a cytoplasmic hemoprotein, expressed solely in cardiac myocytes and oxidative skeletal muscle fibers, that reversibly binds O₂ by its heme residue. Myoglobin is an essential oxygen-storage hemoprotein capable of facilitating oxygen transport and modulating nitric oxide homeostasis within cardiac and skeletal myocytes. Functionally, myoglobin is well accepted as an O₂- storage protein in muscle, capable of releasing O₂ during periods of hypoxia or anoxia. There is no evidence available regarding active sites, ligand binding sites, antigenic determinants and the ASA value of myoglobin in Channa striata. We further document the predicted active sites in the structural model with solvent exposed ASA residues. During this study, the model was built by CPH program and validated through PROCHECK, Verify 3D, ERRAT and ProSA for reliability. The active sites were predicted in the model with further ASA analysis of active site residues. The discussed information thus provides the predicted active sites, ligand binding sites, antigenic determinants and ASA values of myoglobin model in Channa striata.     

Prediction and evolutionary information analysis of protein solvent accessibility using multiple linear regression

A multiple linear regression method was applied to predict real values of solvent accessibility from the sequence and evolutionary information. This method allowed us to obtain coefficients of regression and correlation between the occurrence of an amino-acid residue at a specific target and its sequence neighbor positions on the one hand, and the solvent accessibility of that residue on the other. Our linear regression model based on sequence information and evolutionary models was found to predict residue accessibility with 18.9% and 16.2% mean absolute error respectively, which is better than or comparable to the best available methods. A correlation matrix for several neighbor positions to examine the role of evolutionary information at these positions has been developed and analyzed. As expected, the effective frequency of hydrophobic residues at target positions shows a strong negative correlation with solvent accessibility, whereas the reverse is true for charged and polar residues. The correlation of solvent accessibility with effective frequencies at neighboring positions falls abruptly with distance from target residues. Longer protein chains have been found to be more accurately predicted than their smaller counterparts.     

Amino acid empirical contact energy definitions for fold recognition in the space of contact maps

Background  

Contradicting evidence has been presented in the literature concerning the effectiveness of empirical contact energies for fold recognition. Empirical contact energies are calculated on the basis of information available from selected protein structures, with respect to a defined reference state, according to the quasi-chemical approximation. Protein-solvent interactions are estimated from residue solvent accessibility.