Differentiation between transmembrane helices and peripheral helices by the deconvolution of circular dichroism spectra of membrane proteins

Park, Kyusung; Perczel, András; Fasman, Gerald D.

doi:10.1002/pro.5560010809

Cited by 114 publications

(119 citation statements)

References 99 publications

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“…Most prediction methods designed for globular water-soluble proteins are typically based on more than 100 proteins (Rost & Sander, 1994 of known 3D structure as stored in the Protein Data Bank (PDB) (Bernstein et al, 1977). To obtain sufficiently large data sets, prediction methods for membrane proteins use data from experimental sources other than crystallography or spectroscopy (Manoil & Beckwith, 1986;Park et al, 1992;Hennessey & Broome-Smith, 1993). There are numerous examples for proteins for which "reliable experimental information" obtained from different groups is contradictory.…”

Section: Accuracy Of Prediction Methodsmentioning

confidence: 99%

Topology prediction for helical transmembrane proteins at 86% accuracy–Topology prediction at 86% accuracy

1996

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Previously, we introduced a neural network system predicting locations of transmembrane helices (HTMs) based on evolutionary profiles (PHDhtm, Rost B, Casadio R, Fariselli P, Sander C, 1995, Protein Sci 4:521-533). Here, we describe an improvement and an extension of that system. The improvement is achieved by a dynamic programming-like algorithm that optimizes helices compatible with the neural network output. The extension is the prediction of topology (orientation of first loop region with respect to membrane) by applying to the refined prediction the observation that positively charged residues are more abundant in extra-cytoplasmic regions. Furthermore, we introduce a method to reduce the number of false positives, i.e., proteins falsely predicted with membrane helices. The evaluation of prediction accuracy is based on a cross-validation and a double-blind test set (in total 131 proteins). The final method appears to be more accurate than other methods published: (1) For almost 89% (+.3%) of the test proteins, all HTMs are predicted correctly. (2) For more than 86% (*3%) of the proteins, topology is predicted correctly. (3) We define reliability indices that correlate with prediction accuracy: for one half of the proteins, segment accuracy raises to 98%; and for two-thirds, accuracy of topology prediction is 95%. (4) The rate of proteins for which HTMs are predicted falsely is below 2% (k 1070). Finally, the method is applied to 1,616 sequences of Haemophilus influenzae. We predict 19% of the genome sequences to contain one or moreHTMs. This appears to be lower than what we predicted previously for the yeast VI11 chromosome (about 25%).Keywords: .dynamic programming; genome analysis; Haemophilus influenzae; postprocessing neural network output; secondary structure prediction; structure prediction for integral membrane proteins; topology prediction for helical transmembrane proteins Integral membrane proteins comprise an important class of proteins for which experimental techniques for 3D structure determination are often not applicable. Fortunately, theoretical prediction of structural aspects is simpler for membrane proteins than for globular proteins because the lipid bilayer imposes strong constraints on the degrees of freedom for the 3D struc-

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Section: Accuracy Of Prediction Methodsmentioning

confidence: 99%

Topology prediction for helical transmembrane proteins at 86% accuracy–Topology prediction at 86% accuracy

1996

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“…In order to overcome the bias effect of the database composition, the CCA algorithm operates to extract the common components based on the number of components ( P ) as an input parameter (Perczel et al, 1991 b). The natural deconvolution by CCA algorithm has already been applied to measure conformational weights and extract pure components from the CD spectra sets of globular (Perczel et al, 1991b;Park et al, 1993) or membrane proteins (Park et al, 1992); to analyze the conformational transitions of linear polypeptides during ion titration (Hollosi et al, 1992); and to investigate the CD spectrum and conformational interconversion of &turn model peptides (Perczel et al, 1991a;Perczel & Fasman, 1 992).…”

Section: J Safar Et Aimentioning

confidence: 99%

“…The statistically satisfactory (Perczel et ai., 1991b;Park et al, 1992) deconvolution of the set of PrP27-30 CD spectra (Figs. 4, 5) was obtained with five ( P = 5) components.…”

Section: J Safar Et Aimentioning

confidence: 99%

Thermal stability and conformational transitions of scrapie amyloid (prion) protein correlate with infectivity

et al. 1993

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The scrapie amyloid (prion) protein (PrP27-30) is the protease-resistant core of a larger precursor (PrPsC) and a component of the infectious scrapie agent; the potential to form amyloid is a result of a posttranslational event or conformational abnormality. The conformation, heat stability, and solvent-induced conformational transitions of PrP27-30 were studied in the solid state in films by CD spectroscopy and correlated with the infectivity of rehydrated and equilibrated films. The exposure of PrP27-30 in films to 60 "C, 100 "C, and 132 "C for 30 min did not change the @-sheet secondary structure; the infectivity slightly diminished at 132 "C and correlated with a decreased solubility of PrP27-30 in sodium dodecyl sulfate (SDS), probably due to cross-linking. Exposing PrP27-30 films to formic acid (FA), trifluoroacetic acid (TFA), trifluoroethanol (TFE), hexafluoro-2-propanol (HFIP), and SDS transformed the amide CD band, diminished the mean residue ellipticity of aromatic bands, and inactivated scrapie infectivity. The convex constraint algorithm (CAA) deconvolution of the CD spectra of the solventexposed and rehydrated solid state PrP27-30 identified five common spectral components. The loss of infectivity quantitatively correlated with a decreasing proportion of native, @-pleated sheet-like secondary structure component, an increasing amount of a-helical component, and an increasingly disordered tertiary structure. The results demonstrate the unusual thermal stability of the @-sheet secondary structure of PrP27-30 protein in the solid state. The conformational perturbations of PrP27-30 parallel the changes in infectivity and suggest that the @-sheet structure plays a key role in the physical stability of scrapie amyloid and in the ability to propagate and replicate scrapie.

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“…It remains possible that these differences reflect the internal consistency of the spectra in each respective basis set rather than the expected general accuracy of the methods. In a recent paper, Sreerama & Woody [6] investigated the effect of the number of reference proteins (29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48) on the accuracy of the prediction obtained by three publicly available CD analysis software programs.…”

mentioning

confidence: 99%

The optimization of protein secondary structure determination with infrared and circular dichroism spectra

Oberg

Ruysschaert

Goormaghtigh

2004

European Journal of Biochemistry

167

116

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We have used the circular dichroism and infrared spectra of a specially designed 50 protein database [Oberg, K.A., Ruysschaert, J.M. & Goormaghtigh, E. (2003) Protein Sci. 12, 2015-2031 in order to optimize the accuracy of spectroscopic protein secondary structure determination using multivariate statistical analysis methods. The results demonstrate that when the proteins are carefully selected for the diversity in their structure, no smaller subset of the database contains the necessary information to describe the entire set. One conclusion of the paper is therefore that large protein databases, observing stringent selection criteria, are necessary for the prediction of unknown proteins. A second important conclusion is that only the comparison of analyses run on circular dichroism and infrared spectra independently is able to identify failed solutions in the absence of known structure. Interestingly, it was also found in the course of this study that the amide II band has high information content and could be used alone for secondary structure prediction in place of amide I.

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Differentiation between transmembrane helices and peripheral helices by the deconvolution of circular dichroism spectra of membrane proteins

Cited by 114 publications

References 99 publications

Topology prediction for helical transmembrane proteins at 86% accuracy–Topology prediction at 86% accuracy

Topology prediction for helical transmembrane proteins at 86% accuracy–Topology prediction at 86% accuracy

Thermal stability and conformational transitions of scrapie amyloid (prion) protein correlate with infectivity

The optimization of protein secondary structure determination with infrared and circular dichroism spectra

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