Structural interpretation of mutations and SNPs using STRAP-NT

Visualization of residue positions in protein alignments and mapping onto suitable structural models is an important first step in the interpretation of mutations or polymorphisms in terms of protein function, interaction, and thermodynamic stability. Selecting and highlighting large numbers of residue positions in a protein structure can be time-consuming and tedious with currently available software. Previously, a series of tasks and analyses had to be performed one-by-one to map mutations onto 3D protein structures; STRAP-NT is an extension of STRAP that automates these tasks so that users can quickly and conveniently map mutations onto 3D protein structures. When the structure of the protein of interest is not yet available, a related protein can frequently be found in the structure databases. In this case the alignment of both proteins becomes the crucial part of the analysis. Therefore we embedded these program modules into the Java-based multiple sequence alignment program STRAP-NT. STRAP-NT can simultaneously map an arbitrary number of mutations denoted using either the nucleotide or amino acid sequence. When the designations of the mutations refer to genomic sites, STRAP-NT translates them into the corresponding amino acid positions, taking intron-exon boundaries into account. STRAP-NT tightly integrates a number of current protein structure viewers (currently PYMOL, RASMOL, JMOL, and VMD) with which mutations and polymorphisms can be directly displayed on the 3D protein structure model. STRAP-NT is available at the PDB site and at http://www.charite.de/bioinf/strap/ or http://strapjava.de.     

On the relation between residue flexibility and residue interactions in proteins

B-factor from X-ray crystal structure can well measure protein structural flexibility, which plays an important role in different biological processes, such as catalysis, binding and molecular recognition. Understanding the essence of flexibility can be helpful for the further study of the protein function. In this study, we attempted to correlate the flexibility of a residue to its interactions with other residues by representing the protein structure as a residue contact network. Here, several well established network topological parameters were employed to feature such interactions. A prediction model was constructed for B-factor of a residue by using support vector regression (SVR). Pearson correlation coefficient (CC) was used as the performance measure. CC values were 0.63 and 0.62 for single amino acid and for the whole sequence, respectively. Our results revealed well correlations between B-factors and network topological parameters. This suggests that the protein structural flexibility could be well characterized by the inter-amino acid interactions in a protein.     

FlexPred: a web-server for predicting residue positions involved in conformational switches in proteins

Conformational switches observed in the protein backbone play a key role in a variety of fundamental biological activities. This paper describes a web-server that implements a pattern recognition algorithm trained on the examples from the Database of Macromolecular Movements to predict residue positions involved in conformational switches. Prediction can be performed at an adjustable false positive rate using a user-supplied protein sequence in FASTA format or a structure in a Protein Data Bank (PDB) file. If a protein sequence is submitted, then the web-server uses sequence-derived information only (such as evolutionary conservation of residue positions). If a PDB file is submitted, then the web-server uses sequence-derived information and residue solvent accessibility calculated from this file.     

Comparative analysis of sequence covariation methods to mine evolutionary hubs: Examples from selected GPCR families

Covariation between positions in a multiple sequence alignment may reflect structural, functional, and/or phylogenetic constraints and can be analyzed by a wide variety of methods. We explored several of these methods for their ability to identify covarying positions related to the divergence of a protein family at different hierarchical levels. Specifically, we compared seven methods on a model system composed of three nested sets of G‐protein‐coupled receptors (GPCRs) in which a divergence event occurred. The covariation methods analyzed were based on: χ² test, mutual information, substitution matrices, and perturbation methods. We first analyzed the dependence of the covariation scores on residue conservation (measured by sequence entropy), and then we analyzed the networking structure of the top pairs. Two methods out of seven—OMES (Observed minus Expected Squared) and ELSC (Explicit Likelihood of Subset Covariation)—favored pairs with intermediate entropy and a networking structure with a central residue involved in several high‐scoring pairs. This networking structure was observed for the three sequence sets. In each case, the central residue corresponded to a residue known to be crucial for the evolution of the GPCR family and the subfamily specificity. These central residues can be viewed as evolutionary hubs, in relation with an epistasis‐based mechanism of functional divergence within a protein family. Proteins 2014; 82:2141–2156. © 2014 Wiley Periodicals, Inc.     

A conservative amino acid substitution, arginine for lysine, abolishes export of a hybrid protein in Escherichia coli. Implications for the mechanism of protein secretion

A hybrid protein that comprises the beta-lactamase signal peptide fused precisely to chicken muscle triosephosphate isomerase is not secreted into the periplasm of Escherichia coli. The protein can be secreted, however, if an arginine residue at position 3 of the isomerase is replaced by either a serine or a proline residue. In contrast, replacement of a neighboring lysine residue has no effect on secretion of the protein. Furthermore, if the arginine is removed from position 3 to generate a secreted protein, but is then reintroduced in place of the neighboring lysine, the blockade to secretion is re-established. The singular effect of the arginine residue on secretion does not result from the role this residue plays in the formation or stabilization of the native isomerase structure: mutational alterations remote from the N terminus of the isomerase that prevent the proper folding of the protein do not relieve the block to secretion. The finding that an arginine residue prevents secretion while a lysine residue does not, suggests that basic residues near the mature N terminus of a secreted protein must be deprotonated if orderly export is to occur. This implies that the signal peptide along with the N-terminal portion of the mature protein partitions directly into the lipid bilayer in the course of the secretory process.     

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.     

Characterizing conserved structural contacts by pair-wise relative contacts and relative packing groups

To adequately deal with the inherent complexity of interactions between protein side-chains, we develop and describe here a novel method for characterizing protein packing within a fold family. Instead of approaching side-chain interactions absolutely from one residue to another, we instead consider the relative interactions of contacting residue pairs. The basic element, the pair-wise relative contact, is constructed from a sequence alignment and contact analysis of a set of structures and consists of a cluster of similarly oriented, interacting, side-chain pairs. To demonstrate this construct's usefulness in analyzing protein structure, we used the pair-wise relative contacts to analyze two sets of protein structures as defined by SCOP: the diverse globin-like superfamily (126 structures) and the more uniform heme binding globin family (a 94 structure subset of the globin-like superfamily). The superfamily structure set produced 1266 unique pair-wise relative contacts, whereas the family structure subset gave 1001 unique pair-wise relative contacts. For both sets, we show that these constructs can be used to accurately and automatically differentiate between fold classes. Furthermore, these pair-wise relative contacts correlate well with sequence identity and thus provide a direct relationship between changes in sequence and changes in structure. To capture the complexity of protein packing, these pair-wise relative contacts can be superimposed around a single residue to create a multi-body construct called a relative packing group. Construction of convex hulls around the individual packing groups provides a measure of the variation in packing around a residue and defines an approximate volume of space occupied by the groups interacting with a residue. We find that these relative packing groups are useful in understanding the structural quality of sequence or structure alignments. Moreover, they provide context to calculate a value for structural randomness, which is important in properly assessing the quality of a structural alignment. The results of this study provide the framework for future analysis for correlating sequence changes to specific structure changes.     

Quantitative Characterization of Local Protein Solvation To Predict Solvent Effects on Protein Structure

Characterization of solvent preferences of proteins is essential to the understanding of solvent effects on protein structure and stability. Although it is generally believed that solvent preferences at distinct loci of a protein surface may differ, quantitative characterization of local protein solvation has remained elusive. In this study, we show that local solvation preferences can be quantified over the entire protein surface from extended molecular dynamics simulations. By subjecting microsecond trajectories of two proteins (lysozyme and antibody fragment D1.3) in 4 M glycerol to rigorous statistical analyses, solvent preferences of individual protein residues are quantified by local preferential interaction coefficients. Local solvent preferences for glycerol vary widely from residue to residue and may change as a result of protein side-chain motions that are slower than the longest intrinsic solvation timescale of ～10 ns. Differences of local solvent preferences between distinct protein side-chain conformations predict solvent effects on local protein structure in good agreement with experiment. This study extends the application scope of preferential interaction theory and enables molecular understanding of solvent effects on protein structure through comprehensive characterization of local protein solvation.     

Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns

Given sufficient large protein families, and using a global statistical inference approach, it is possible to obtain sufficient accuracy in protein residue contact predictions to predict the structure of many proteins. However, these approaches do not consider the fact that the contacts in a protein are neither randomly, nor independently distributed, but actually follow precise rules governed by the structure of the protein and thus are interdependent. Here, we present PconsC2, a novel method that uses a deep learning approach to identify protein-like contact patterns to improve contact predictions. A substantial enhancement can be seen for all contacts independently on the number of aligned sequences, residue separation or secondary structure type, but is largest for β-sheet containing proteins. In addition to being superior to earlier methods based on statistical inferences, in comparison to state of the art methods using machine learning, PconsC2 is superior for families with more than 100 effective sequence homologs. The improved contact prediction enables improved structure prediction.