Assignment of PolyProline II conformation and analysis of sequence--structure relationship

Background

Secondary structures are elements of great importance in structural biology, biochemistry and bioinformatics. They are broadly composed of two repetitive structures namely α-helices and β-sheets, apart from turns, and the rest is associated to coil. These repetitive secondary structures have specific and conserved biophysical and geometric properties. PolyProline II (PPII) helix is yet another interesting repetitive structure which is less frequent and not usually associated with stabilizing interactions. Recent studies have shown that PPII frequency is higher than expected, and they could have an important role in protein – protein interactions.

Methodology/Principal Findings

A major factor that limits the study of PPII is that its assignment cannot be carried out with the most commonly used secondary structure assignment methods (SSAMs). The purpose of this work is to propose a PPII assignment methodology that can be defined in the frame of DSSP secondary structure assignment. Considering the ambiguity in PPII assignments by different methods, a consensus assignment strategy was utilized. To define the most consensual rule of PPII assignment, three SSAMs that can assign PPII, were compared and analyzed. The assignment rule was defined to have a maximum coverage of all assignments made by these SSAMs. Not many constraints were added to the assignment and only PPII helices of at least 2 residues length are defined.

Conclusions/Significance

The simple rules designed in this study for characterizing PPII conformation, lead to the assignment of 5% of all amino as PPII. Sequence – structure relationships associated with PPII, defined by the different SSAMs, underline few striking differences. A specific study of amino acid preferences in their N and C-cap regions was carried out as their solvent accessibility and contact patterns. Thus the assignment of PPII can be coupled with DSSP and thus opens a simple way for further analysis in this field.     

Analysis of loop boundaries using different local structure assignment methods

Loops connect regular secondary structures. In many instances, they are known to play important biological roles. Analysis and prediction of loop conformations depend directly on the definition of repetitive structures. Nonetheless, the secondary structure assignment methods (SSAMs) often lead to divergent assignments. In this study, we analyzed, both structure and sequence point of views, how the divergence between different SSAMs affect boundary definitions of loops connecting regular secondary structures. The analysis of SSAMs underlines that no clear consensus between the different SSAMs can be easily found. Because these latter greatly influence the loop boundary definitions, important variations are indeed observed, that is, capping positions are shifted between different SSAMs. On the other hand, our results show that the sequence information in these capping regions are more stable than expected, and, classical and equivalent sequence patterns were found for most of the SSAMs. This is, to our knowledge, the most exhaustive survey in this field as (i) various databank have been used leading to similar results without implication of protein redundancy and (ii) the first time various SSAMs have been used. This work hence gives new insights into the difficult question of assignment of repetitive structures and addresses the issue of loop boundaries definition. Although SSAMs give very different local structure assignments capping sequence patterns remain efficiently stable.     

A Hidden Markov Model method,capable of predicting and discriminating β-barrel outer membrane proteins

Background  

Integral membrane proteins constitute about 20–30% of all proteins in the fully sequenced genomes. They come in two structural classes, the α-helical and the β-barrel membrane proteins, demonstrating different physicochemical characteristics, structure and localization. While transmembrane segment prediction for the α-helical integral membrane proteins appears to be an easy task nowadays, the same is much more difficult for the β-barrel membrane proteins. We developed a method, based on a Hidden Markov Model, capable of predicting the transmembrane β-strands of the outer membrane proteins of gram-negative bacteria, and discriminating those from water-soluble proteins in large datasets. The model is trained in a discriminative manner, aiming at maximizing the probability of correct predictions rather than the likelihood of the sequences.     

Observation of glycine zipper and unanticipated occurrence of ambidextrous helices in the crystal structure of a chiral undecapeptide

Background  

The de novo design of peptides and proteins has recently surfaced as an approach for investigating protein structure and function. This approach vitally tests our knowledge of protein folding and function, while also laying the groundwork for the fabrication of proteins with properties not precedented in nature. The success of these studies relies heavily on the ability to design relatively short peptides that can espouse stable secondary structures. To this end, substitution with α, β-dehydroamino acids, especially α, β-dehydrophenylalanine (ΔPhe) comes in use for spawning well-defined structural motifs. Introduction of ΔPhe induces β-bends in small and 3₁₀-helices in longer peptide sequences.     

Distill: a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins

Background  

We describe Distill, a suite of servers for the prediction of protein structural features: secondary structure; relative solvent accessibility; contact density; backbone structural motifs; residue contact maps at 6, 8 and 12 Angstrom; coarse protein topology. The servers are based on large-scale ensembles of recursive neural networks and trained on large, up-to-date, non-redundant subsets of the Protein Data Bank. Together with structural feature predictions, Distill includes a server for prediction of C _α traces for short proteins (up to 200 amino acids).     

CSSI-PRO: a method for secondary structure type editing,assignment and estimation in proteins using linear combination of backbone chemical shifts

Estimation of secondary structure in polypeptides is important for studying their structure, folding and dynamics. In NMR spectroscopy, such information is generally obtained after sequence specific resonance assignments are completed. We present here a new methodology for assignment of secondary structure type to spin systems in proteins directly from NMR spectra, without prior knowledge of resonance assignments. The methodology, named Combination of Shifts for Secondary Structure Identification in Proteins (CSSI-PRO), involves detection of specific linear combination of backbone ¹H^α and ¹³C′ chemical shifts in a two-dimensional (2D) NMR experiment based on G-matrix Fourier transform (GFT) NMR spectroscopy. Such linear combinations of shifts facilitate editing of residues belonging to α-helical/β-strand regions into distinct spectral regions nearly independent of the amino acid type, thereby allowing the estimation of overall secondary structure content of the protein. Comparison of the predicted secondary structure content with those estimated based on their respective 3D structures and/or the method of Chemical Shift Index for 237 proteins gives a correlation of more than 90% and an overall rmsd of 7.0%, which is comparable to other biophysical techniques used for structural characterization of proteins. Taken together, this methodology has a wide range of applications in NMR spectroscopy such as rapid protein structure determination, monitoring conformational changes in protein-folding/ligand-binding studies and automated resonance assignment. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Energetics of superhelicity and of B-Z transitions in superhelical DNA

The linking difference, α, imposed upon a superhelically constrained DNA molecule must be partitioned between twisting and bending deformations. Transitions to alternative secondary structures can occur at susceptible sites, altering the local molecular twist by an amount ΔTw _trans. That part of the linking difference not accommodated in this way, the residual linking difference α_res, must be manifested as smooth torsional and flexural deformations of secondary structure. The competition among the alternative ways of accommodating the imposed linking difference α determines a stressed equilibrium state. The superhelical free energy,G(α), is the excess free energy of the equilibrium state at linking difference α above that of the relaxed state under identical conditions. In this paper a method is described by which the free energies associated both to linking,G(α), and to residual linking differences can be determined from data on superhelical conformational transitions. The application of this approach to previously published experimental data on the B-Z transition suggests that the free energy associated with α_res is about 30% larger at substantial superhelicities than it is near the relaxed state. At the onset of transition the functional form ofG(α) is shown to change in a manner dependent upon the length of the Z-susceptible site.     

RDC derived protein backbone resonance assignment using fragment assembly

Experimental residual dipolar couplings (RDCs) in combination with structural models have the potential for accelerating the protein backbone resonance assignment process because RDCs can be measured accurately and interpreted quantitatively. However, this application has been limited due to the need for very high-resolution structural templates. Here, we introduce a new approach to resonance assignment based on optimal agreement between the experimental and calculated RDCs from a structural template that contains all assignable residues. To overcome the inherent computational complexity of such a global search, we have adopted an efficient two-stage search algorithm and included connectivity data from conventional assignment experiments. In the first stage, a list of strings of resonances (CA-links) is generated via exhaustive searches for short segments of sequentially connected residues in a protein (local templates), and then ranked by the agreement of the experimental ¹³C_α chemical shifts and ¹⁵N-¹H RDCs to the predicted values for each local template. In the second stage, the top CA-links for different local templates in stage I are combinatorially connected to produce CA-links for all assignable residues. The resulting CA-links are ranked for resonance assignment according to their measured RDCs and predicted values from a tertiary structure. Since the final RDC ranking of CA-links includes all assignable residues and the assignment is derived from a “global minimum”, our approach is far less reliant on the quality of experimental data and structural templates. The present approach is validated with the assignments of several proteins, including a 42 kDa maltose binding protein (MBP) using RDCs and structural templates of varying quality. Since backbone resonance assignment is an essential first step for most of biomolecular NMR applications and is often a bottleneck for large systems, we expect that this new approach will improve the efficiency of the assignment process for small and medium size proteins and will extend the size limits assignable by current methods for proteins with structural models.     

Prediction of protein structural classes using support vector machines

Summary. The support vector machine, a machine-learning method, is used to predict the four structural classes, i.e. mainly α, mainly β, α–β and fss, from the topology-level of CATH protein structure database. For the binary classification, any two structural classes which do not share any secondary structure such as α and β elements could be classified with as high as 90% accuracy. The accuracy, however, will decrease to less than 70% if the structural classes to be classified contain structure elements in common. Our study also shows that the dimensions of feature space 20² = 400 (for dipeptide) and 20³ = 8 000 (for tripeptide) give nearly the same prediction accuracy. Among these 4 structural classes, multi-class classification gives an overall accuracy of about 52%, indicating that the multi-class classification technique in support of vector machines may still need to be further improved in future investigation.     

Analysis of Chameleon Sequences by Energy Decomposition on a Pairwise Per-residue Basis

The conversion from α-helix to β-strand that has been widely observed in so-called chameleon sequences has received considerable attention since such a structural change may induce many amyloidogenic proteins to self-assemble into fibrils thus causing fatal diseases. Here we report a large scale-analysis of the energetics of secondary structural conversions in a collection of chameleon sequences retrieved from the Protein Data Bank. Major energetic contributions to the secondary structural conversion were analyzed by carrying out energy decomposition on a pairwise per-residue basis, i.e., (i,i), (i,i ± 1), (i,i ± 2), (i,i ± 3), (i,i ± 4) and > (i,i ± 4) intra-/inter-residual interactions. While the overall potential energy differences were subtle, individual residue-based interacting energy differences were observed to vary significantly depending on the specific type of secondary structural conversion. The average energy difference between α-helix and β-strand, <ΔE _α→β>, in the chameleon sequences varied significantly in (i,i), (i,i ± 1) and > (i,i ± 4) interactions. The major energetic factors in secondary structure conversions were electrostatic interactions and the polar term for solvation energy. In addition, residue-based average energy differences in α-helix → β-strand conversions were well-correlated to those in α-helix → random coil → β-strand conversions (R ² = 0.92). Assuming that three secondary structural elements can transform in either direction, this strong correlation indicates that the present energy decomposition method using database structures of chameleon sequences provides a reliable tool for the characterization of secondary structure fluctuations in amino acid sequences.     

Uncovering symmetry-breaking vector and reliability order for assigning secondary structures of proteins from atomic NMR chemical shifts in amino acids

Unravelling the complex correlation between chemical shifts of ¹³ C ^α, ¹³ C ^β, ¹³ C′, ¹ H ^α, ¹⁵ N, ¹ H ^N atoms in amino acids of proteins from NMR experiment and local structural environments of amino acids facilitates the assignment of secondary structures of proteins. This is an important impetus for both determining the three-dimensional structure and understanding the biological function of proteins. The previous empirical correlation scores which relate chemical shifts of ¹³ C ^α, ¹³ C ^β, ¹³ C′, ¹ H ^α, ¹⁵ N, ¹ H ^N atoms to secondary structures resulted in progresses toward assigning secondary structures of proteins. However, the physical-mathematical framework for these was elusive partly due to both the limited and orthogonal exploration of higher-dimensional chemical shifts of hetero-nucleus and the lack of physical-mathematical understanding underlying those correlation scores. Here we present a simple multi-dimensional hetero-nuclear chemical shift score function (MDHN-CSSF) which captures systematically the salient feature of such complex correlations without any references to a random coil state of proteins. We uncover the symmetry-breaking vector and its reliability order not only for distinguishing different secondary structures of proteins but also for capturing the delicate sensitivity interplayed among chemical shifts of ¹³ C ^α, ¹³ C ^β, ¹³ C′, ¹ H ^α, ¹⁵ N, ¹ H ^N atoms simultaneously, which then provides a straightforward framework toward assigning secondary structures of proteins. MDHN-CSSF could correctly assign secondary structures of training (validating) proteins with the favourable (comparable) Q3 scores in comparison with those from the previous correlation scores. MDHN-CSSF provides a simple and robust strategy for the systematic assignment of secondary structures of proteins and would facilitate the de novo determination of three-dimensional structures of proteins.     

Direct methods and residue type specific isotope labeling in NMR structure determination and model-driven sequential assignment

Direct methods in NMR based structure determination start from an unassigned ensemble of unconnected gaseous hydrogen atoms. Under favorable conditions they can produce low resolution structures of proteins. Usually a prohibitively large number of NOEs is required, to solve a protein structure ab-initio, but even with a much smaller set of distance restraints low resolution models can be obtained which resemble a protein fold. One problem is that at such low resolution and in the absence of a force field it is impossible to distinguish the correct protein fold from its mirror image. In a hybrid approach these ambiguous models have the potential to aid in the process of sequential backbone chemical shift assignment when ¹³C^β and ¹³C′ shifts are not available for sensitivity reasons. Regardless of the overall fold they enhance the information content of the NOE spectra. These, combined with residue specific labeling and minimal triple-resonance data using ¹³C^α connectivity can provide almost complete sequential assignment. Strategies for residue type specific labeling with customized isotope labeling patterns are of great advantage in this context. Furthermore, this approach is to some extent error-tolerant with respect to data incompleteness, limited precision of the peak picking, and structural errors caused by misassignment of NOEs. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Studying the structural properties of polyalanine and polyglutamine peptides

Poly-(Ala) and poly-(Gln) peptides have important biological effects, and can cause various human illnesses and neurodegenerative diseases. Conformational analysis of these homo-oligopeptides (HOPs) was carried out by simulated annealing in order to identify their structural properties regarding secondary structures and intramolecular H-bonding patterns. Poly-(Ala) and poly-(Gln) peptides composed of 7, 10, 14 or 20 amino acids were modelled in both charged and terminally blocked forms. In the case of conformers derived from simulated annealing calculations, the presence of various secondary structural elements (different types of β-turns, α-helix, 3₁₀-helix, poly-proline II helix, parallel and antiparallel β-strands) was investigated. Moreover, the intramolecular H-bonding patterns formed either between the backbone atoms for both HOPs or between the backbone and side-chain atoms for the poly-(Gln) peptides were examined. Our results showed that different secondary structural elements (type I and type III β-turns, α-helix, 3₁₀-helix, antiparallel β-strand) could be observed in both poly-(Ala) and poly-(Gln) peptides and, according to their presence, characteristic H-bonding patterns formed mainly by i←i+3 and i←i+4 H-bonds could be found.     

High-Resolution NMR Reveals Secondary Structure and Folding of Amino Acid Transporter from Outer Chloroplast Membrane

Solving high-resolution structures for membrane proteins continues to be a daunting challenge in the structural biology community. In this study we report our high-resolution NMR results for a transmembrane protein, outer envelope protein of molar mass 16 kDa (OEP16), an amino acid transporter from the outer membrane of chloroplasts. Three-dimensional, high-resolution NMR experiments on the ¹³C, ¹⁵N, ²H-triply-labeled protein were used to assign protein backbone resonances and to obtain secondary structure information. The results yield over 95% assignment of N, H_N, CO, C_α, and C_β chemical shifts, which is essential for obtaining a high resolution structure from NMR data. Chemical shift analysis from the assignment data reveals experimental evidence for the first time on the location of the secondary structure elements on a per residue basis. In addition T _1Z and T₂ relaxation experiments were performed in order to better understand the protein dynamics. Arginine titration experiments yield an insight into the amino acid residues responsible for protein transporter function. The results provide the necessary basis for high-resolution structural determination of this important plant membrane protein.     

Structural elements of the C-terminal domain of subunit E (E<Subscript>133-222</Subscript>) from the <Emphasis Type="Italic">Saccharomyces cerevisiae</Emphasis> V<Subscript>1</Subscript>V<Subscript>O</Subscript> ATPase determined by solution NMR spectroscopy

Subunit E of the vacuolar ATPase (V-ATPase) contains an N-terminal extended α helix (Rishikesan et al. J Bioenerg Biomembr 43:187–193, 2011) and a globular C-terminal part that is predicted to consist of a mixture of α-helices and β-sheets (Grüber et al. Biochem Biophys Res Comm 298:383–391, 2002). Here we describe the production, purification and 2D structure of the C-terminal segment E_133-222 of subunit E from Saccharamyces cerevisiae V-ATPase in solution based on the secondary structure calculation from NMR spectroscopy studies. E_133-222 consists of four β-strands, formed by the amino acids from K136-V139, E170-V173, G186-V189, D195-E198 and two α-helices, composed of the residues from R144-A164 and T202-I218. The sheets and helices are arranged as β1:α1:β2:β3:β4:α2, which are connected by flexible loop regions. These new structural details of subunit E are discussed in the light of the structural arrangements of this subunit inside the V₁- and V₁V_O ATPase.     

Cα-Methyl, Cα-phenylglycine peptides: A structural study

Summary In order to obtain further information on the role played by phenyl ring position in the C^α-methylated α-amino acid side chain on peptide preferred conformation, the crystal-state structural preferences of C^α-methyl, C^α-phenylglycine peptides have been determined by X-ray diffraction. This study shows that either the fully extended conformation or the β-bend/3₁₀-helical structures are adopted by peptides characterized by this C^α-methylated, β-branched, aromatic α-amino acid.     

Combining NMR ensembles and molecular dynamics simulations provides more realistic models of protein structures in solution and leads to better chemical shift prediction

While chemical shifts are invaluable for obtaining structural information from proteins, they also offer one of the rare ways to obtain information about protein dynamics. A necessary tool in transforming chemical shifts into structural and dynamic information is chemical shift prediction. In our previous work we developed a method for 4D prediction of protein ¹H chemical shifts in which molecular motions, the 4th dimension, were modeled using molecular dynamics (MD) simulations. Although the approach clearly improved the prediction, the X-ray structures and single NMR conformers used in the model cannot be considered fully realistic models of protein in solution. In this work, NMR ensembles (NMRE) were used to expand the conformational space of proteins (e.g. side chains, flexible loops, termini), followed by MD simulations for each conformer to map the local fluctuations. Compared with the non-dynamic model, the NMRE+MD model gave 6–17% lower root-mean-square (RMS) errors for different backbone nuclei. The improved prediction indicates that NMR ensembles with MD simulations can be used to obtain a more realistic picture of protein structures in solutions and moreover underlines the importance of short and long time-scale dynamics for the prediction. The RMS errors of the NMRE+MD model were 0.24, 0.43, 0.98, 1.03, 1.16 and 2.39 ppm for ¹Hα, ¹HN, ¹³Cα, ¹³Cβ, ¹³CO and backbone ¹⁵N chemical shifts, respectively. The model is implemented in the prediction program 4DSPOT, available at .     

Alteration in Ion Channel Function of Mouse Nicotinic Acetylcholine Receptor by Mutations in the M4 Transmembrane Domain

The effect of structural alterations of the M4 transmembrane segment in the Torpedo californica AChR has shown that substitution of specific residues can be critical to the channel gating (Lasalde et al., 1996). In a previous study we found that phenylalanine and tryptophan substitutions at the αC418 residue in the M4 transmembrane segment of the Torpedo californica AChR significantly altered ion channel function (Lee et al., 1994; Ortiz-Miranda et al., 1997). Cassette mutagenesis was used to mutate the Cys residue at the corresponding C418 position in the α subunit of mouse AChR. A total of nine mutations on the mouse αC418 position were tested, including the αC418A, αC418V, αC418L, αC418S, αC418M, αC418W, αC418H, αC418E and αC418G mutants. All the mutants tested were functional except the αC418G which was not expressed on the surface of the oocyte. The data obtained from macroscopic and single channel currents demonstrate that different types of amino acids can be accommodated at this presumably lipid-exposed position without loss of ion-channel function. As with the Torpedo AChR, the mutation of Cys to Trp dramatically decreased the EC₅₀ for acetylcholine and increased channel open time. The lack of expression of the mouse αC418G suggest that there are some differences in folding, oligomerization and perhaps transport to the surface membrane for this mutant between the Torpedo and the mammalian AChR. Received: 30 December 1998/Revised: 13 April 1999     

Top-down approach in protein RDC data analysis: <Emphasis Type="Italic">de novo</Emphasis> estimation of the alignment tensor

In solution NMR spectroscopy the residual dipolar coupling (RDC) is invaluable in improving both the precision and accuracy of NMR structures during their structural refinement. The RDC also provides a potential to determine protein structure de novo. These procedures are only effective when an accurate estimate of the alignment tensor has already been made. Here we present a top–down approach, starting from the secondary structure elements and finishing at the residue level, for RDC data analysis in order to obtain a better estimate of the alignment tensor. Using only the RDCs from N–H bonds of residues in α-helices and CA–CO bonds in β-strands, we are able to determine the offset and the approximate amplitude of the RDC modulation-curve for each secondary structure element, which are subsequently used as targets for global minimization. The alignment order parameters and the orientation of the major principal axis of individual helix or strand, with respect to the alignment frame, can be determined in each of the eight quadrants of a sphere. The following minimization against RDC of all residues within the helix or strand segment can be carried out with fixed alignment order parameters to improve the accuracy of the orientation. For a helical protein Bax, the three components A _xx , A _yy and A _zz , of the alignment order can be determined with this method in average to within 2.3% deviation from the values calculated with the available atomic coordinates. Similarly for β-sheet protein Ubiquitin they agree in average to within 8.5%. The larger discrepancy in β-strand parameters comes from both the diversity of the β-sheet structure and the lower precision of CA–CO RDCs. This top-down approach is a robust method for alignment tensor estimation and also holds a promise for providing a protein topological fold using limited sets of RDCs.     

Domain Evolution in the α-Amylase Family

The available amino acid sequences of the α-amylase family (glycosyl hydrolase family 13) were searched to identify their domain B, a distinct domain that protrudes from the regular catalytic (β/α)₈-barrel between the strand β3 and the helix α3. The isolated domain B sequences were inspected visually and also analyzed by Hydrophobic Cluster Analysis (HCA) to find common features. Sequence analyses and inspection of the few available three-dimensional structures suggest that the secondary structure of domain B varies with the enzyme specificity. Domain B in these different forms, however, may still have evolved from a common ancestor. The largest number of different specificities was found in the group with structural similarity to domain B from Bacillus cereus oligo-1,6-glucosidase that contains an α-helix succeeded by a three-stranded antiparallel β-sheet. These enzymes are α-glucosidase, cyclomaltodextrinase, dextran glucosidase, trehalose-6-phosphate hydrolase, neopullulanase, and a few α-amylases. Domain B of this type was observed also in some mammalian proteins involved in the transport of amino acids. These proteins show remarkable similarity with (β/α)₈-barrel elements throughout the entire sequence of enzymes from the oligo-1,6-glucosidase group. The transport proteins, in turn, resemble the animal 4F2 heavy-chain cell surface antigens, for which the sequences either lack domain B or contain only parts thereof. The similarities are compiled to indicate a possible route of domain evolution in the α-amylase family. Received: 4 December 1996 / Accepted: 13 March 1997