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Site-specific detection of protein secondary structure using 2D IR dihedral indexing: a proposed assembly mechanism of oligomeric hIAPP.

ABSTRACT: Human islet amyloid polypeptide (hIAPP) aggregates into fibrils through oligomers that have been postulated to contain ?-helices as well as ?-sheets. We employ a site-specific isotope labeling strategy that is capable of detecting changes in dihedral angles when used in conjunction with 2D IR spectroscopy. The method is analogous to the chemical shift index used in NMR spectroscopy for assigning protein secondary structure. We introduce isotope labels at two neighbouring residues, which results in an increased intensity and positive frequency shift if those residues are ?-helical versus a negative frequency shift in ?-sheets and turns. The 2D IR dihedral index approach is demonstrated for hIAPP in micelles for which the polypeptide structure is known, using pairs of 13C18O isotope labels L12A13 and L16V17, along with single labeled control experiments. Applying the approach to aggregation experiments performed in buffer, we show that about 27-38% of hIAPP peptides adopt an ?-helix secondary structure in the monomeric state at L12A13, prior to aggregation, but not at L16V17 residues. At L16V17, the kinetics are described solely by the monomer and fiber conformations, but at L12A13 the kinetics exhibit a third state that is created by an oligomeric intermediate. Control experiments performed with a single isotope label at A13 exhibit two-state kinetics, indicating that a previously unknown change in dihedral angle occurs at L12A13 as hIAPP transitions from the intermediate to fiber structures. We propose a mechanism for aggregation, in which helices seed oligomer formation via structures analogous to leucine rich repeat proteins.

SUBMITTER: Maj M

PROVIDER: S-EPMC5868010 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Site-specific detection of protein secondary structure using 2D IR dihedral indexing: a proposed assembly mechanism of oligomeric hIAPP.

Maj Michał M Lomont Justin P JP Rich Kacie L KL Alperstein Ariel M AM Zanni Martin T MT

Chemical science 20171103 2

Human islet amyloid polypeptide (hIAPP) aggregates into fibrils through oligomers that have been postulated to contain α-helices as well as β-sheets. We employ a site-specific isotope labeling strategy that is capable of detecting changes in dihedral angles when used in conjunction with 2D IR spectroscopy. The method is analogous to the chemical shift index used in NMR spectroscopy for assigning protein secondary structure. We introduce isotope labels at two neighbouring residues, which results ...[more]

PMID: 29619202

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Project description:BackgroundIdentification of subcellular localization in proteins is crucial to elucidate cellular processes and molecular functions in a cell. However, given a tremendous amount of sequence data generated in the post-genomic era, determining protein localization based on biological experiments can be expensive and time-consuming. Therefore, developing prediction systems to analyze uncharacterised proteins efficiently has played an important role in high-throughput protein analyses. In a eukaryotic cell, many essential biological processes take place in the nucleus. Nuclear proteins shuttle between nucleus and cytoplasm based on recognition of nuclear translocation signals, including nuclear localization signals (NLSs) and nuclear export signals (NESs). Currently, only a few approaches have been developed specifically to predict nuclear localization using sequence features, such as putative NLSs. However, it has been shown that prediction coverage based on the NLSs is very low. In addition, most existing approaches only attained prediction accuracy and Matthew's correlation coefficient (MCC) around 54%~70% and 0.250~0.380 on independent test set, respectively. Moreover, no predictor can generate sequence motifs to characterize features of potential NESs, in which biological properties are not well understood from existing experimental studies.ResultsIn this study, first we propose PSLNuc (Protein Subcellular Localization prediction for Nucleus) for predicting nuclear localization in proteins. First, for feature representation, a protein is represented by gapped-dipeptides and the feature values are weighted by homology information from a smoothed position-specific scoring matrix. After that, we incorporate probabilistic latent semantic indexing (PLSI) for feature reduction. Finally, the reduced features are used as input for a support vector machine (SVM) classifier. In addition to PSLNuc, we further identify gapped-dipeptide signatures for putative NLSs and NESs to develop a prediction method, PSLNTS (Protein Subcellular Localization prediction using Nuclear Translocation Signals). We apply PLSI to generate gapped-dipeptide signatures from both nuclear and non-nuclear proteins, and propose candidate sequence motifs for putative NLSs and NESs. Then, we incorporate only the proposed gapped-dipeptide signatures in an SVM classifier to mimic biological properties of NLSs and NESs for predicting nuclear localization in PSLNTS.ConclusionsExperiment results demonstrate that the proposed method shows a significant improvement for nuclear localization prediction. To compare our predictive performance with other approaches, we incorporate two non-redundant benchmark data sets, a training set and an independent test set. Evaluated by five-fold cross-validation on the training set, PSLNuc attains an overall accuracy of 79.7%, which is 4.8% improvement over the state-of-the-art system. In addition, our method also enhances the MCC from 0.497 to 0.595. Compared on the independent test set, PSLNuc outperforms other predictors by 3.9%~19.9% on accuracy and 0.077~0.207 on MCC. This suggests that, in addition to NLSs, which have been shown important for nuclear proteins, NESs can also be an effective indicator to detect non-nuclear proteins. Most notably, using only a few proposed gapped-dipeptide signatures as input features for the SVM classifier, PSLNTS further enhances the accuracy and MCC to 80.9% and 0.618, respectively. Our results demonstrate that gapped-dipeptide signatures can better discriminate nuclear and non-nuclear proteins. Moreover, the proposed gapped-dipeptide signatures can be biologically interpreted and used in further experiment analyses of nuclear translocation signals, including NLSs and NESs.

Dataset Information

Site-specific detection of protein secondary structure using 2D IR dihedral indexing: a proposed assembly mechanism of oligomeric hIAPP.

Publications

Site-specific detection of protein secondary structure using 2D IR dihedral indexing: a proposed assembly mechanism of oligomeric hIAPP.

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