Periodicity of polar and nonpolar amino acids is the major determinant of secondary structure in self-assembling oligomeric peptides.

The tendency of a polypeptide chain to form alpha-helical or beta-strand secondary structure depends upon local and nonlocal effects. Local effects reflect the intrinsic propensities of the amino acid residues for particular secondary structures, while nonlocal effects reflect the positioning of the individual residues in the context of the entire amino acid sequence. In particular, the periodicity of polar and nonpolar residues specifies whether a given sequence is consistent with amphiphilic alpha-helices or beta-strands. The importance of intrinsic propensities was compared to that of polar/nonpolar periodicity by a direct competition. Synthetic peptides were designed using residues with intrinsic propensities that favored one or the other type of secondary structure. The polar/nonpolar periodicities of the peptides were designed either to be consistent with the secondary structure favored by the intrinsic propensities of the component residues or in other cases to oppose these intrinsic propensities. Characterization of the synthetic peptides demonstrated that in all cases the observed secondary structure correlates with the periodicity of the peptide sequence--even when this secondary structure differs from that predicted from the intrinsic propensities of the component amino acids. The observed secondary structures are concentration dependent, indicating that oligomerization of the amphiphilic peptides is responsible for the observed secondary structures. Thus, for self-assembling oligomeric peptides, the polar/nonpolar periodicity can overwhelm the intrinsic propensities of the amino acid residues and serves as the major determinant of peptide secondary structure.

Influence of N-Terminus Modifications on the Biological Activity, Membrane Interaction, and Secondary Structure of the Antimicrobial Peptide Hylin-a1

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information

Background The majority of peptide bonds in proteins are found to occur in the trans conformation. However, for proline residues, a considerable fraction of Prolyl peptide bonds adopt the cis form. Proline cis/trans isomerization is known to play a critical role in protein folding, splicing, cell signaling and transmembrane active transport. Accurate prediction of proline cis/trans isomerization in proteins would have many important applications towards the understanding of protein structure and function. Results In this paper, we propose a new approach to predict the proline cis/trans isomerization in proteins using support vector machine (SVM). The preliminary results indicated that using Radial Basis Function (RBF) kernels could lead to better prediction performance than that of polynomial and linear kernel functions. We used single sequence information of different local window sizes, amino acid compositions of different local sequences, multiple sequence alignment obtained from PSI-BLAST and the secondary structure information predicted by PSIPRED. We explored these different sequence encoding schemes in order to investigate their effects on the prediction performance. The training and testing of this approach was performed on a newly enlarged dataset of 2424 non-homologous proteins determined by X-Ray diffraction method using 5-fold cross-validation. Selecting the window size 11 provided the best performance for determining the proline cis/trans isomerization based on the single amino acid sequence. It was found that using multiple sequence alignments in the form of PSI-BLAST profiles could significantly improve the prediction performance, the prediction accuracy increased from 62.8% with single sequence to 69.8% and Matthews Correlation Coefficient (MCC) improved from 0.26 with single local sequence to 0.40. Furthermore, if coupled with the predicted secondary structure information by PSIPRED, our method yielded a prediction accuracy of 71.5% and MCC of 0.43, 9% and 0.17 higher than the accuracy achieved based on the singe sequence information, respectively. Conclusion A new method has been developed to predict the proline cis/trans isomerization in proteins based on support vector machine, which used the single amino acid sequence with different local window sizes, the amino acid compositions of local sequence flanking centered proline residues, the position-specific scoring matrices (PSSMs) extracted by PSI-BLAST and the predicted secondary structures generated by PSIPRED. The successful application of SVM approach in this study reinforced that SVM is a powerful tool in predicting proline cis/trans isomerization in proteins and biological sequence analysis.

Phosphorylation of the carboxy-terminal domain of histone H1: effects on secondary structure and DNA condensation

Linker histone H1 plays an important role in chromatin folding. Phosphorylation by cyclin-dependent kinases is the main post-translational modification of histone H1. We studied the effects of phosphorylation on the secondary structure of the DNA-bound H1 carboxy-terminal domain (CTD), which contains most of the phosphorylation sites of the molecule. The effects of phosphorylation on the secondary structure of the DNA-bound CTD were site-specific and depended on the number of phosphate groups. Full phosphorylation significantly increased the proportion of -structure and decreased that of -helix. Partial phosphorylation increased the amount of undefined structure and decreased that of -helix without a significant increase in -structure. Phosphorylation had a moderate effect on the affinity of the CTD for the DNA, which was proportional to the number of phosphate groups. Partial phosphorylation drastically reduced the aggregation of DNA fragments by the CTD, but full phosphorylation restored to a large extent the aggregation capacity of the unphosphorylated domain. These results support the involvement of H1 hyperphosphorylation in metaphase chromatin condensation and of H1 partial phosphorylation in interphase chromatin relaxation. More generally, our results suggest that the effects of phosphorylation are mediated by specific structural changes and are not simply a consequence of the net charge.

A conjugate of the lytic peptide Hecate and gallic acid: structure, activity against cervical cancer, and toxicity

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Structure-activity relationship of mastoparan analogs: effects of the number and positioning of Lys residues on secondary structure, interaction with membrane-mimetic systems and biological activity

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Sequence-specific polypeptoids: A diverse family of heteropolymers with stable secondary structure

We have synthesized and characterized a family of structured oligo-N-substituted-glycines (peptoids) up to 36 residues in length by using an efficient solid-phase protocol to incorporate chemically diverse side chains in a sequence-specific fashion. We investigated polypeptoids containing side chains with a chiral center adjacent to the main chain nitrogen. Some of these sequences have stable secondary structure, despite the achirality of the polymer backbone and its lack of hydrogen bond donors. In both aqueous and organic solvents, peptoid oligomers as short as five residues give rise to CD spectra that strongly resemble those of peptide α-helices. Differential scanning calorimetry and CD measurements show that polypeptoid secondary structure is highly stable and that unfolding is reversible and cooperative. Thermodynamic parameters obtained for unfolding are similar to those obtained for the α-helix to coil transitions of peptides. This class of biomimetic polymers may enable the design of self-assembling macromolecules with novel structures and functions.

Secondary structure element alignment kernel method for prediction of protein structural classes

In this paper, we aim at predicting protein structural classes for low-homology data sets based on predicted secondary structures. We propose a new and simple kernel method, named as SSEAKSVM, to predict protein structural classes. The secondary structures of all protein sequences are obtained by using the tool PSIPRED and then a linear kernel on the basis of secondary structure element alignment scores is constructed for training a support vector machine classifier without parameter adjusting. Our method SSEAKSVM was evaluated on two low-homology datasets 25PDB and 1189 with sequence homology being 25% and 40%, respectively. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies on these two data sets are 86.3% and 84.5%, respectively, which are higher than those obtained by other existing methods. Especially, our method achieves higher accuracies (88.1% and 88.5%) for differentiating the α + β class and the α/β class compared to other methods. This suggests that our method is valuable to predict protein structural classes particularly for low-homology protein sequences. The source code of the method in this paper can be downloaded at http://math.xtu.edu.cn/myphp/math/research/source/SSEAK_source_code.rar.

CSSP (Consensus Secondary Structure Prediction): a web-based server for structural biologists

Sequence-structure correlation studies are important in deciphering the relationships between various structural aspects, which may shed light on the protein-folding problem. The first step of this process is the prediction of secondary structure for a protein sequence of unknown three-dimensional structure. To this end, a web server has been created to predict the consensus secondary structure using well known algorithms from the literature. Furthermore, the server allows users to see the occurrence of predicted secondary structural elements in other structure and sequence databases and to visualize predicted helices as a helical wheel plot. The web server is accessible at http://bioserver1.physics.iisc.ernet.in/cssp/.

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 H-1(alpha) and C-13' 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 alpha-helical/beta-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.