Spectroscopic methods for analysis of protein secondary structure

Several methods for determination of the secondary structure of proteins by spectroscopic measurements are reviewed. Circular dichroism (CD) spectroscopy provides rapid determinations of protein secondary structure with dilute solutions and a way to rapidly assess conformational changes resulting from addition of ligands. Both CD and Raman spectroscopies are particularly useful for measurements over a range of temperatures. Infrared (IR) and Raman spectroscopy require only small volumes of protein solution. The frequencies of amide bands are analyzed to determine the distribution of secondary structures in proteins. NMR chemical shifts may also be used to determine the positions of secondary structure within the primary sequence of a protein. However, the chemical shifts must first be assigned to particular residues, making the technique considerably slower than the optical methods. These data, together with sophisticated molecular modeling techniques, allow for refinement of protein structural models as well as rapid assessment of conformational changes resulting from ligand binding or macromolecular interactions. A selected number of examples are given to illustrate the power of the techniques in applications of biological interest.     

Nucleic acid secondary structure prediction and display.

A set of programs has been developed for the prediction and display of nucleic acid secondary structures. Information from experimental data can be used to restrict or enforce secondary structural elements. The predictions can be displayed either on normal line printers or on graphic devices like plotters or graphic terminals.     

Integrated analysis of residue coevolution and protein structure in ABC transporters

Intraprotein side chain contacts can couple the evolutionary process of amino acid substitution at one position to that at another. This coupling, known as residue coevolution, may vary in strength. Conserved contacts thus not only define 3-dimensional protein structure, but also indicate which residue-residue interactions are crucial to a protein's function. Therefore, prediction of strongly coevolving residue-pairs helps clarify molecular mechanisms underlying function. Previously, various coevolution detectors have been employed separately to predict these pairs purely from multiple sequence alignments, while disregarding available structural information. This study introduces an integrative framework that improves the accuracy of such predictions, relative to previous approaches, by combining multiple coevolution detectors and incorporating structural contact information. This framework is applied to the ABC-B and ABC-C transporter families, which include the drug exporter P-glycoprotein involved in multidrug resistance of cancer cells, as well as the CFTR chloride channel linked to cystic fibrosis disease. The predicted coevolving pairs are further analyzed based on conformational changes inferred from outward- and inward-facing transporter structures. The analysis suggests that some pairs coevolved to directly regulate conformational changes of the alternating-access transport mechanism, while others to stabilize rigid-body-like components of the protein structure. Moreover, some identified pairs correspond to residues previously implicated in cystic fibrosis.     

FTIR protein secondary structure analysis of human ascending aortic tissues

The advent of moderate dilatations in ascending aortas is often accompanied by structural modifications of the main components of the aortic tissue, elastin and collagen. In this study, we have undertaken an approach based on FTIR microscopy coupled to a curve‐fitting procedure to analyze secondary structure modifications in these proteins in human normal and pathological aortic tissues. We found that the outcome of the aortic pathology is strongly influenced by these proteins, which are abundant in the media of the aortic wall, and that the advent of an aortic dilatation is generally accompanied by a decrease of parallel β‐sheet structures. Elastin, essentially composed of β‐sheet structures, seems to be directly related to these changes and therefore indicative of the elastic alteration of the aortic wall. Conventional microscopy and confocal fluorescence microscopy were used to compare FTIR microscopy results with the organization of the elastic fibers present in the tissues. This in‐vitro study on 6 patients (three normal and three pathologic), suggests that such a spectroscopic marker, specific to aneurismal tissue characterization, could be important information for surgeons who face the dilemma of moderate aortic tissue dilatation of the ascending aortas. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

An improved algorithm for nucleic acid secondary structure display

An improved algorithm for the display of nucleic acid secondarystructures is presented. It is particularly suitable for largesequence segments and it automatically generates an aestheticallypleasing display of the structure with very limited overlapof strands. Structural similarities in different structuresare conserved in the display thus greatly aiding structuralhomology comparisons. Using the algorithm, we illustrate theeffect of ribosome translocation on the secondary structureof a rat neuropeptide messenger RNA. Received on September 21, 1987; accepted on October 22, 1987     

Estimation of protein secondary structure and error analysis from circular dichroism spectra

The estimation of protein secondary structure from circular dichroism spectra is described by a multivariate linear model with noise (Gauss-Markoff model). With this formalism the adequacy of the linear model is investigated, paying special attention to the estimation of the error in the secondary structure estimates. It is shown that the linear model is only adequate for the alpha-helix class. Since the failure of the linear model is most likely due to nonlinear effects, a locally linearized model is introduced. This model is combined with the selection of the estimate whose fractions of secondary structure summate to approximately one. Comparing the estimation from the CD spectra with the X-ray data (by using the data set of W.C. Johnson Jr., 1988, Annu. Rev. Biophys. Chem. 17, 145-166) the root mean square residuals are 0.09 (alpha-helix), 0.12 (anti-parallel beta-sheet), 0.08 (parallel beta-sheet), 0.07 (beta-turn), and 0.09 (other). These residuals are somewhat larger than the errors estimated from the locally linearized model. In addition to alpha-helix, in this model the beta-turn and "other" class are estimated adequately. But the estimation of the antiparallel and parallel beta-sheet class remains unsatisfactory. We compared the linear model and the locally linearized model with two other methods (S. W. Provencher and J. Gl?ckner, 1981, Biochemistry 20, 1085-1094; P. Manavalan and W. C. Johnson Jr., 1988, Anal. Biochem. 167, 76-85). The locally linearized model and the Provencher and Gl?ckner method provided the smallest residuals. However, an advantage of the locally linearized model is the estimation of the error in the secondary structure estimates.     

Combining protein evolution and secondary structure

An evolutionary model that combines protein secondary structure and amino acid replacement is introduced. It allows likelihood analysis of aligned protein sequences and does not require the underlying secondary (or tertiary) structures of these sequences to be known. One component of the model describes the organization of secondary structure along a protein sequence and another specifies the evolutionary process for each category of secondary structure. A database of proteins with known secondary structures is used to estimate model parameters representing these two components. Phylogeny, the third component of the model, can be estimated from the data set of interest. As an example, we employ our model to analyze a set of sucrose synthase sequences. For the evolution of sucrose synthase, a parametric bootstrap approach indicates that our model is statistically preferable to one that ignores secondary structure.      

Band-shape analysis and display of fine structure in protein spectra: a new approach to perturbation spectroscopy

The shape of light absorption bands of proteins to about 250 nm can be described as the sum of two overlapping lognormal distribution curves. A plot of the differences between the mathematically smooth fitted curve and the experimental points provides a vivid display of vibronic fine structure. Band parameters and difference plots are provided for the N-acetyl-ethyl esters of the aromatic amino acids and are compared with those of glucagon, ribonuclease, chymotrypsinogen, lysozyme and apoaspartate aminotransferase. Changes in band parameters and fine structure are observed upon denaturation and in conversion of glucagon to fibril form.     

Determination of protein secondary structure using factor analysis of infrared spectra

A method is presented for determining the secondary structural composition of a protein in aqueous solution from its infrared spectrum. A factor analysis approach is used to analyze the infrared spectra of 18 proteins whose crystal structures are known from X-ray studies. Factor analysis followed by multiple linear regression identifies those eigenspectra that correlate with the variation in properties described by the calibration set. The properties of interest in this study are % alpha-helix, % beta-sheet, and % turns. In the analysis of an unknown, the factor loadings required to reproduce its spectrum are substituted in the regression equation for each property to predict its secondary structural composition. The accuracy of the method was determined by removing each standard, in turn, from the calibration set and using a calibration set generated from the remainder to predict its composition. By this method we obtain standard errors of prediction of 3.9% for alpha-helix, 8.3% for beta-sheet, and 6.6% for turns. The method may also be applied to the spectra of proteins in 2H2O. The method has important advantages over those currently in use for the quantitative analysis of the infrared spectra of proteins. Manipulation of the spectrum is kept to a minimum, no curve-fitting is necessary, and the several amide I band components need not be assigned.     

Knowledge-based protein secondary structure assignment

We have developed an automatic algorithm STRIDE for protein secondary structure assignment from atomic coordinates based on the combined use of hydrogen bond energy and statistically derived backbone torsional angle information. Parameters of the pattern recognition procedure were optimized using designations provided by the crystallographers as a standard-of-truth. Comparison to the currently most widely used technique DSSP by Kabsch and Sander (Biopolymers 22:2577-2637, 1983) shows that STRIDE and DSSP assign secondary structural states in 58 and 31% of 226 protein chains in our data sample, respectively, in greater agreement with the specific residue-by-residue definitions provided by the discoverers of the structures while in 11% of the chains, the assignments are the same. STRIDE delineates every 11th helix and every 32nd strand more in accord with published assignments. © 1995 Wiley-Liss, Inc.     

Prediction of protein secondary structure content

All existing algorithms for predicting the content of protein secondary structure elements have been based on the conventional amino-acid-composition, where no sequence coupling effects are taken into account. In this article, an algorithm was developed for predicting the content of protein secondary structure elements that was based on a new amino-acid-composition, in which the sequence coupling effects are explicitly included through a series of conditional probability elements. The prediction was examined by a self-consistency test and an independent dataset test. Both indicated a remarkable improvement obtained when using the current algorithm to predict the contents of alpha-helix, beta-sheet, beta-bridge, 3(10)-helix, pi-helix, H-bonded turn, bend and random coil. Examples of the improved accuracy by introducing the new amino-acid-composition, as well as its impact on the study of protein structural class and biologically function, are discussed.     

Circular dichroism spectroscopy has intrinsic limitations for protein secondary structure analysis

Secondary structure content (SSC) cannot be calculated accurately from circular dichroism (CD) spectra for the majority of proteins whose three-dimensional structures have been solved. “Reliable” SSC that is significantly different from random SSC can be calculated from CD spectra only for all-α proteins and all-β proteins with canonical β-strand geometry.     

An algorithm for the display of nucleic acid secondary structure.

A simple algorithm is presented for the graphic display of nucleic acid secondary structure. Examples of secondary structure displays are given for tRNA, 5S RNA and part of the 16S RNA. Due to its speed, this algorithm could easily be used in conjunction with secondary structure programs which calculate various alternate structures.     

Implications of protein structure instability: from physiological to pathological secondary structure

Proteins are folded during their synthesis; this process may be spontaneous or assisted. Both phenomena are carefully regulated by the "housekeeping" mechanism and molecular chaperones to avoid the appearance of misfolded proteins. Unfolding process generally occurs during physiological degradation of protein, but in some specific cases it results from genetic or environmental changes and does not correspond to metabolic needs. The main outcome of these phenomena is the appearance of nonfunctional pathologically unfolded proteins with a strong tendency to aggregation. Moreover, for some of these unfolded proteins, the agglomeration that follows initial proteins association may give rise to highly structured soluble aggregates. These aggregates have been identified as the main cause of the so-called amyloidosis or amyloid diseases, such as Alzheimer's, Parkinson's, and Creutzfeldt-Jakob diseases, and type II diabetes mellitus. Although some common mechanisms of amyloid protein aggregation have been identified, the roles of the environmental conditions inducing amyloidosis remain to be clarified. In this review, we will summarize recent studies identifying the origin of amyloid nucleation and will try to predict the therapeutic prospects that may be opened by elucidation of the amyloidosis mechanisms.     

Applications of display technology in protein analysis

Display technology refers to a collection of methods for creating libraries of modularly coded biomolecules that can be screened for desired properties. It has become a routine tool for enriching molecular diversity and producing novel types of proteins. The combination of an ever-increasing variety of libraries of modularly coded protein complexxes with the development of innovative approaches to select a wide array of desired properties has facilitated large-scale analyses of protein-protein/protein-substrate interactions, rapid isolation of antibodies (or antibody mimetics) without immunization, and function-based protein analysis. Several practical and theoretical challenges remain to be addressed before display technology can be readily applied to proteomic studies.     

Review: protein secondary structure prediction continues to rise

Methods predicting protein secondary structure improved substantially in the 1990s through the use of evolutionary information taken from the divergence of proteins in the same structural family. Recently, the evolutionary information resulting from improved searches and larger databases has again boosted prediction accuracy by more than four percentage points to its current height of around 76% of all residues predicted correctly in one of the three states, helix, strand, and other. The past year also brought successful new concepts to the field. These new methods may be particularly interesting in light of the improvements achieved through simple combining of existing methods. Divergent evolutionary profiles contain enough information not only to substantially improve prediction accuracy, but also to correctly predict long stretches of identical residues observed in alternative secondary structure states depending on nonlocal conditions. An example is a method automatically identifying structural switches and thus finding a remarkable connection between predicted secondary structure and aspects of function. Secondary structure predictions are increasingly becoming the work horse for numerous methods aimed at predicting protein structure and function. Is the recent increase in accuracy significant enough to make predictions even more useful? Because the recent improvement yields a better prediction of segments, and in particular of beta strands, I believe the answer is affirmative. What is the limit of prediction accuracy? We shall see.     

PCI-SS: MISO dynamic nonlinear protein secondary structure prediction

Background  

Since the function of a protein is largely dictated by its three dimensional configuration, determining a protein's structure is of fundamental importance to biology. Here we report on a novel approach to determining the one dimensional secondary structure of proteins (distinguishing α-helices, β-strands, and non-regular structures) from primary sequence data which makes use of Parallel Cascade Identification (PCI), a powerful technique from the field of nonlinear system identification.     

Hydrogen-bond cooperativity in protein secondary structure

Hydrogen bonding in the α-helix and β-sheet has been studied by ab initio molecular orbital calculations carried out on complexes of formamide. Hydrogen-bond geometries were taken from x-ray crystallography of polypeptides. Positive cooperativity is found in all cases. The limiting value for infinite chains is obtained by use of a double-reciprocal plot and indicates an increase in the effective bond strength of 25% over that of a single isolated bond. Parallel calculations based on a classical electrostatic model yield qualitatively similar trends but underestimate the cooperativity by half. Charge redistribution accompanying cooperativity is characterized by a new type of charge-density difference plot, the cooperativity map. The magnitude and distance over which cooperativity acts suggest several significant biological consequences. Thus the average of α-helices and the number of β-sheet strands found in protein may be influenced by cooperativity. Cooperativity in the interpeptide hydrogen bond may also be partly responsible for the rapid formation of secondary structure in renaturing proteins and help stabilize secondary structure relative to the random-coil conformation.     

Continuum secondary structure captures protein flexibility

The DSSP program assigns protein secondary structure to one of eight states. This discrete assignment cannot describe the continuum of thermal fluctuations. Hence, a continuous assignment is proposed. Technically, the continuum results from averaging over ten discrete DSSP assignments with different hydrogen bond thresholds. The final continuous assignment for a single NMR model successfully reflected the structural variations observed between all NMR models in the ensemble. The structural variations between NMR models were verified to correlate with thermal motion; these variations were captured by the continuous assignments. Because the continuous assignment reproduces the structural variation between many NMR models from one single model, functionally important variation can be extracted from a single X-ray structure. Thus, continuous assignments of secondary structure may affect future protein structure analysis, comparison, and prediction.     

RNAmute: RNA secondary structure mutation analysis tool

Background  

RNAMute is an interactive Java application that calculates the secondary structure of all single point mutations, given an RNA sequence, and organizes them into categories according to their similarity with respect to the wild type predicted structure. The secondary structure predictions are performed using the Vienna RNA package. Several alternatives are used for the categorization of single point mutations: Vienna's RNAdistance based on dot-bracket representation, as well as tree edit distance and second eigenvalue of the Laplacian matrix based on Shapiro's coarse grain tree graph representation.