Residue depth: a novel parameter for the analysis of protein structure and stability.

BACKGROUND: Accessible surface area is a parameter that is widely used in analyses of protein structure and stability. Accessible surface area does not, however, distinguish between atoms just below the protein surface and those in the core of the protein. In order to differentiate between such buried residues we describe a computational procedure for calculating the depth of a residue from the protein surface. RESULTS: Residue depth correlates significantly better than accessibility with effects of mutations on protein stability and on protein-protein interactions. The deepest residues in the native state invariably undergo hydrogen exchange by global unfolding of the protein and are often significantly protected in the corresponding molten-globule states. CONCLUSIONS: Depth is often a more useful gage of residue burial than accessibility. This is probably related to the fact that the protein interior and surrounding solvent differ significantly in polarity and packing density. Hence, the strengths of van der Waals and electrostatic interactions between residues in a protein might be expected to depend on the distance of the residue(s) from the protein surface.     

Folding of beta/alpha-unit scrambled forms of S. cerevisiae triosephosphate isomerase: Evidence for autonomy of substructure formation and plasticity of hydrophobic and hydrogen bonding interactions in core of (beta/alpha)8-barrel

We show that residues at the interfaces of protein-protein complexes have higher side-chain energy than other surface residues. Eight different sets of protein complexes were analyzed. For each protein pair, the complex structure was used to identify the interface residues in the unbound monomer structures. Side-chain energy was calculated for each surface residue in the unbound monomer using our previously developed scoring function.1 The mean energy was calculated for the interface residues and the other surface residues. In 15 of the 16 monomers, the mean energy of the interface residues was higher than that of other surface residues. By decomposing the scoring function, we found that the energy term of the buried surface area of non-hydrogen-bonded hydrophilic atoms is the most important factor contributing to the high energy of the interface regions. In spite of lacking hydrophilic residues, the interface regions were found to be rich in buried non-hydrogen-bonded hydrophilic atoms. Although the calculation results could be affected by the inaccuracy of the scoring function, patch analysis of side-chain energy on the surface of an isolated protein may be helpful in identifying the possible protein-protein interface. A patch was defined as 20 residues surrounding the central residue on the protein surface, and patch energy was calculated as the mean value of the side-chain energy of all residues in the patch. In 12 of the studied monomers, the patch with the highest energy overlaps with the observed interface. The results are more remarkable when only three residues with the highest energy in a patch are averaged to derive the patch energy. All three highest-energy residues of the top energy patch belong to interfacial residues in four of the eight small protomers. We also found that the residue with the highest energy score on the surface of a small protomer is very possibly the key interaction residue.     

Assessing the role of tryptophan residues in the binding site

Instead of looking at the interfacial area as a measure of the extent of a protein--protein recognition site, a new procedure has been developed to identify the importance of a specific residue, namely tryptophan, in the binding process. Trp residues which contribute more towards the free energy of binding have their accessible surface area reduced, on complex formation, for both the main-chain and side-chain atoms, whereas for the less important residues the reduction is restricted only to the aromatic ring of the side chain. The two categories of residues are also distinguished by the presence or absence of hydrogen bonds involving the Trp residue in the complex. A comparison of the observed change in the accessible surface area with the value calculated using an analytical expression provides another way of characterizing the Trp residues critical for binding and this has been used to identify such residues involved in binding non-proteinaceous molecules in protein structures.     

The interdependence of protein surface topography and bound water molecules revealed by surface accessibility and fractal density measures.

To characterize water binding to proteins, which is fundamental to protein folding, stability and activity, the relationships of 10,837 bound water positions to protein surface shape and residue type were analyzed in 56 high-resolution crystallographic structures. Fractal atomic density and accessibility algorithms provided an objective characterization of deep grooves in solvent-accessible protein surfaces. These deep grooves consistently had approximately the diameter of one water molecule, suggesting that deep grooves are formed by the interactions between protein atoms and bound water molecules. Protein surface topography dominates the chemistry and extent of water binding. Protein surface area within grooves bound three times as many water molecules as non-groove surface; grooves accounted for one-quarter of the total surface area yet bound half the water molecules. Moreover, only within grooves did bound water molecules discriminate between different side-chains. In grooves, main-chain surface was as hydrated as that of the most hydrophilic side-chains, Asp and Glu, whereas outside grooves all main and side-chains bound water to a similar, and much decreased, extent. This identification of the interdependence of protein surface shape and hydration has general implications for modelling and prediction of protein surface shape, recognition, local folding and solvent binding.     

Attenuation of tick-borne encephalitis virus by structure-based site-specific mutagenesis of a putative flavivirus receptor binding site

The impact of a specific region of the envelope protein E of tick-borne encephalitis (TBE) virus on the biology of this virus was investigated by a site-directed mutagenesis approach. The four amino acid residues that were analyzed in detail (E308 to E311) are located on the upper-lateral surface of domain III according to the X-ray structure of the TBE virus protein E and are part of an area that is considered to be a potential receptor binding determinant of flaviviruses. Mutants containing single amino acid substitutions, as well as combinations of mutations, were constructed and analyzed for their virulence in mice, growth properties in cultured cells, and genetic stability. The most significant attenuation in mice was achieved by mutagenesis of threonine 310. Combining this mutation with deletion mutations in the 3'-noncoding region yielded mutants that were highly attenuated. The biological effects of mutation Thr 310 to Lys, however, could be reversed to a large degree by a mutation at a neighboring position (Lys 311 to Glu) that arose spontaneously during infection of a mouse. Mutagenesis of the other positions provided evidence for the functional importance of residue 308 (Asp) and its charge interaction with residue 311 (Lys), whereas residue 309 could be altered or even deleted without any notable consequences. Deletion of residue 309 was accompanied by a spontaneous second-site mutation (Phe to Tyr) at position 332, which in the three-dimensional structure of protein E is spatially close to residue 309. The information obtained in this study is relevant for the development of specific attenuated flavivirus strains that may serve as future live vaccines.     

Quantifying the accessible surface area of protein residues in their local environment

The quantification of the packing of residues in proteins and docking of ligands to macromolecules is important in understanding protein stability and drug design. The number of atoms in contact (within a distance of 4.5 A) can be used to describe the local environment of a residue. As this number increases, the accessible surface area (ASA) of the residue decreases exponentially and the variation can be described in terms of an exponential equation of the form y = a(1)exp(-x/a(2)), each residue having its own set of parameters a(1) and a(2), which also depend on whether the whole residue or just the side chain is considered. Hydrophobic and hydrophilic residues can be distinguished on the basis of both the average number of surrounding atoms and the variation of ASA. For a given number of partner atoms, a comparison of the observed ASA with the expected value obtained from the equation provides a method of assessing the goodness of packing of the residue in a protein structure or its importance in the binding of a ligand. The equation provides a method to estimate the ASA of a protein molecule and the average relative accessibilities of different residues, the latter being inversely correlated with hydrophobicity values.     

Predicting absolute contact numbers of native protein structure from amino acid sequence

The contact number of an amino acid residue in a protein structure is defined by the number of C(beta) atoms around the C(beta) atom of the given residue, a quantity similar to, but different from, solvent accessible surface area. We present a method to predict the contact numbers of a protein from its amino acid sequence. The method is based on a simple linear regression scheme and predicts the absolute values of contact numbers. When single sequences are used for both parameter estimation and cross-validation, the present method predicts the contact numbers with a correlation coefficient of 0.555 on average. When multiple sequence alignments are used, the correlation increases to 0.627, which is a significant improvement over previous methods. In terms of discrete states prediction, the accuracies for 2-, 3-, and 10-state predictions are, respectively, 71.4%, 54.1%, and 18.9% with residue type-dependent unbiased thresholds, and 76.3%, 59.2%, and 21.8% with residue type-independent unbiased thresholds. The difference between accessible surface area and contact number from a prediction viewpoint and the application of contact number prediction to three-dimensional structure prediction are discussed.     

Exploiting a reduced set of weighted average features to improve prediction of DNA-binding residues from 3D structures

Predicting DNA-binding residues from a protein three-dimensional structure is a key task of computational structural proteomics. In the present study, based on machine learning technology, we aim to explore a reduced set of weighted average features for improving prediction of DNA-binding residues on protein surfaces. Via constructing the spatial environment around a DNA-binding residue, a novel weighting factor is first proposed to quantify the distance-dependent contribution of each neighboring residue in determining the location of a binding residue. Then, a weighted average scheme is introduced to represent the surface patch of the considering residue. Finally, the classifier is trained on the reduced set of these weighted average features, consisting of evolutionary profile, interface propensity, betweenness centrality and solvent surface area of side chain. Experimental results on 5-fold cross validation and independent tests indicate that the new feature set are effective to describe DNA-binding residues and our approach has significantly better performance than two previous methods. Furthermore, a brief case study suggests that the weighted average features are powerful for identifying DNA-binding residues and are promising for further study of protein structure-function relationship. The source code and datasets are available upon request.     

SHARP2: protein-protein interaction predictions using patch analysis

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.     

Classification of the Environment of Protein Residues

We have studied the classification of the environment of residues within protein structures. Eisenberg's original idea created environmental categories to discriminate between similar residues [Bowie et al., Science (1991), 253, 164–170]. These environments grouped residues based upon their buried surface area, polarity of the surrounding environment, and secondary structure element in which the residue is found. However, Eisenberg's original categories led to incomplete discrimination between residues that only partially substitute for each other. We have expanded on Eisenberg's original idea of environmental categories, by both considering additional contacts in the calculation of the solvent-accessible molecular surface area and by subdividing the environmental plot into regions based upon its theoretical features. Our alternative surface area calculations were used in conjunction with the polarity of the environment of the residue to define a new set of environmental categories. These new categories were able to discriminate between residues such as threonine, valine, and aspartic acid while reflecting the propensity of these residues to substitute for each other.     

Predicting residue solvent accessibility from protein sequence by considering the sequence environment

The solvent accessibility of each residue is predicted on the basis of the protein sequence. A set of 338 monomeric, non-homologous and high-resolution protein crystal structures is used as a learning set and a jackknife procedure is applied to each entry. The prediction is based on the comparison of the observed and the average values of the solvent-accessible area. It appears that the prediction accuracy is significantly improved by considering the residue types preceding and/or following the residue whose accessibility must be predicted. In contrast, the separate treatment of different secondary structural types does not improve the quality of the prediction. It is furthermore shown that the residue accessibility is much better predicted in small than in larger proteins. Such a discrepancy must be carefully considered in any algorithm for predicting residue accessibility.     

Systematic analysis of added-value in simple comparative models of protein structure

Added-value is the additional information that a model carries with respect to the template structure used for model building. Thousands of single-template models, corresponding to proteins of known structure, were analyzed. The accuracy of structure-derived properties, such as residue accessibility, surface area, electrostatic potential, and others, was determined as a function of template:target sequence identity by comparing the models with their corresponding experimental structures. Added-value was determined by comparing the accuracy in models with that from templates. Geometry-dependent properties such as neighborhood of buried residues and accessible surface area showed low added-value. Properties that also depend on the protein sequence, such as presence of polar areas and electrostatic potential, showed high added-value. In general added-value increases when template:target sequence identity decreases, but it is also affected by alignment errors. This study justifies the use of models instead of the use of templates to estimate structure-derived properties of a target protein.     

Fast accurate evaluation of protein solvent exposure

Protein solvation energies are often taken to be proportional to solvent-accessible surface areas. Computation of these areas is numerically demanding and may become a bottleneck for folding and design applications. Fast graph-based methods, such as dead-end elimination (DEE), become possible if all energies, including solvation energies, are expressed as single-residue and pair-residue terms. To this end, Street and Mayo originated a pair-residue approximation for solvent-accessible surface areas (Street AG, Mayo SL. Pairwise calculation of protein solvent accessible surface areas. Fold Des 1998;3:253-258). The dominant source of error in this method is the overlapping burial of side-chain surfaces in the protein core. Here we report a new pair-residue approximation, which greatly reduces this overlap error by the use of optimized generic side-chains. We have tested the generic-side-chain method for the ten proteins studied by Street and Mayo and for 377 single-domain proteins from the CATH database (Orengo CA, Michie AD, Jones S, Jones DT, Swindells MB, Thornton JM. CATH-A hierarchic classification of protein domain structures. Structure 1997;5:1093-1108). With little additional cost in computation, the new method consistently reduces error for total areas and residue-by-residue areas by more than a factor of two. For example, the residue-by-residue error (for buried area) is reduced from 7.42 A(2) to 3.70 A(2). This difference translates into a solvation energy difference of approximately 0.2 kcal/mol per residue, amounting to a reduction in root-mean-square energy error of 2 kcal/mol for a 100 residue chain, a potentially critical difference for both protein folding and design applications.     

RSARF: prediction of residue solvent accessibility from protein sequence using random forest method

Prediction of protein structure from its amino acid sequence is still a challenging problem. The complete physicochemical understanding of protein folding is essential for the accurate structure prediction. Knowledge of residue solvent accessibility gives useful insights into protein structure prediction and function prediction. In this work, we propose a random forest method, RSARF, to predict residue accessible surface area from protein sequence information. The training and testing was performed using 120 proteins containing 22006 residues. For each residue, buried and exposed state was computed using five thresholds (0%, 5%, 10%, 25%, and 50%). The prediction accuracy for 0%, 5%, 10%, 25%, and 50% thresholds are 72.9%, 78.25%, 78.12%, 77.57% and 72.07% respectively. Further, comparison of RSARF with other methods using a benchmark dataset containing 20 proteins shows that our approach is useful for prediction of residue solvent accessibility from protein sequence without using structural information. The RSARF program, datasets and supplementary data are available at http://caps.ncbs.res.in/download/pugal/RSARF/.     

A hidden Markov model for predicting protein interfaces

Protein-protein interactions play a defining role in protein function. Identifying the sites of interaction in a protein is a critical problem for understanding its functional mechanisms, as well as for drug design. To predict sites within a protein chain that participate in protein complexes, we have developed a novel method based on the Hidden Markov Model, which combines several biological characteristics of the sequences neighboring a target residue: structural information, accessible surface area, and transition probability among amino acids. We have evaluated the method using 5-fold cross-validation on 139 unique proteins and demonstrated precision of 66% and recall of 61% in identifying interfaces. These results are better than those achieved by other methods used for identification of interfaces.     

Z-score biological significance of binding hot spots of protein interfaces by using crystal packing as the reference state

Characterization of binding hot spots of protein interfaces is a fundamental study in molecular biology. Many computational methods have been proposed to identify binding hot spots. However, there are few studies to assess the biological significance of binding hot spots. We introduce the notion of biological significance of a contact residue for capturing the probability of the residue occurring in or contributing to protein binding interfaces. We take a statistical Z-score approach to the assessment of the biological significance. The method has three main steps. First, the potential score of a residue is defined by using a knowledge-based potential function with relative accessible surface area calculations. A null distribution of this potential score is then generated from artifact crystal packing contacts. Finally, the Z-score significance of a contact residue with a specific potential score is determined according to this null distribution. We hypothesize that residues at binding hot spots have big absolute values of Z-score as they contribute greatly to binding free energy. Thus, we propose to use Z-score to predict whether a contact residue is a hot spot residue. Comparison with previously reported methods on two benchmark datasets shows that this Z-score method is mostly superior to earlier methods. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.     

Prodepth: Predict Residue Depth by Support Vector Regression Approach from Protein Sequences Only

Residue depth (RD) is a solvent exposure measure that complements the information provided by conventional accessible surface area (ASA) and describes to what extent a residue is buried in the protein structure space. Previous studies have established that RD is correlated with several protein properties, such as protein stability, residue conservation and amino acid types. Accurate prediction of RD has many potentially important applications in the field of structural bioinformatics, for example, facilitating the identification of functionally important residues, or residues in the folding nucleus, or enzyme active sites from sequence information. In this work, we introduce an efficient approach that uses support vector regression to quantify the relationship between RD and protein sequence. We systematically investigated eight different sequence encoding schemes including both local and global sequence characteristics and examined their respective prediction performances. For the objective evaluation of our approach, we used 5-fold cross-validation to assess the prediction accuracies and showed that the overall best performance could be achieved with a correlation coefficient (CC) of 0.71 between the observed and predicted RD values and a root mean square error (RMSE) of 1.74, after incorporating the relevant multiple sequence features. The results suggest that residue depth could be reliably predicted solely from protein primary sequences: local sequence environments are the major determinants, while global sequence features could influence the prediction performance marginally. We highlight two examples as a comparison in order to illustrate the applicability of this approach. We also discuss the potential implications of this new structural parameter in the field of protein structure prediction and homology modeling. This method might prove to be a powerful tool for sequence analysis.