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At least 217 records · Page 12Linked to original sources
PubMed1981

Secondary structure formation during RNA synthesis.

We observed the secondary structures that formed in an RNA molecule during its synthesis. Some of the secondary structures seen in nascent chains were observed to form, then to dissociate in favor of an alternative structure, and then to reform, as chain growth continued. The results show that secondary structures in an RNA molecule are in a state of dynamic equilibrium, and that the extension of a sequence by chain growth, or the reduction of a sequence by processing, may result in significant changes in the secondary structures that are present.

Base Sequence↗
PubMed2002

A critical assessment of the secondary structure alpha-helices and their termini in proteins.

Secondary structure prediction from amino acid sequence is a key component of protein structure prediction, with current accuracy at approximately 75%. We analysed two state-of-the-art secondary structure prediction methods, PHD and JPRED, comparing predictions with secondary structure assigned by the algorithms DSSP and STRIDE. The specific focus of our study was alpha-helix N-termini, as empirical free energy scales are available for residue preferences at N-terminal positions. Although these prediction methods perform well in general at predicting the alpha-helical locations and length distributions in proteins, they perform less well at predicting the correct helical termini. For example, although most predicted alpha-helices overlap a real alpha-helix (with relatively few completely missed or extra predicted helices), only one-third of JPRED and PHD predictions correctly identify the N-terminus. Analysis of neighbouring N-terminal sequences to predicted helical N-termini shows that the correct N-terminus is often within one or two residues. More importantly, the true N-terminal motif is, on average, more favourable as judged by our experimentally measured free energies. This suggests a simple, but powerful, strategy to improve secondary structure prediction using empirically derived energies to adjust the predicted output to a more favourable N-terminal sequence.

Algorithms↗
PubMed2006

Side-chain entropy effects on protein secondary structure formation.

Loss of conformational entropy is one of the primary factors opposing protein folding. Both the backbone and side-chain of each residue in a protein will have their freedom of motion restricted in the final folded structure. The type of secondary structure of which a residue is part will have a significant impact on how much side-chain entropy is lost. Side-chain conformational entropies have previously been determined for folded proteins, simple models of unfolded proteins, alpha-helices, and a dipeptide model for beta-strands, but not for polyproline II (PII) helices. In this work, we present side-chain conformational estimates for the three regular secondary structure types: alpha-helices, beta-strands, and PII helices. Entropies are estimated from Monte Carlo computer simulations. Beta-strands are modeled as two structures, parallel and antiparallel beta-strands. Our data indicate that restraining a residue to the PII helix or antiparallel beta-strand conformations results in side-chain entropies equal to or higher than those obtained by restraining residues to the parallel beta-strand conformation. Side-chains in the alpha-helix conformation have the lowest side-chain entropies. The observation that extended structures retain the most side-chain entropy suggests that such structures would be entropically favored in unfolded proteins under folding conditions. Our data indicate that the PII helix conformation would be somewhat favored over beta-strand conformations, with antiparallel beta-strand favored over parallel. Notably, our data imply that, under some circumstances, residues may gain side-chain entropy upon folding. Implications of our findings for protein folding and unfolded states are discussed.

Amino Acid Sequence↗
PubMed2001

Reverse transcription slippage over the mRNA secondary structure of the LIP1 gene.

The secondary structures in mRNA often cause early termination during the synthesis of cDNA. In an attempt to determine the 5'-untranslated region (UTR) of the gene LIP1 using the RNA ligase-mediated rapid amplification of cDNA ends (RLM-RACE), we found that reverse transcriptases skipped over the LIP1 RNA secondary structures and continued the DNA synthesis through RNA adapter sequences without early termination. A fragment of only three nucleotides upstream of the LIP1 translation initiation codon was obtained from the initial RACE-PCR, which was much shorter than the 57-nucleotide fragment obtained from the cDNA library screening. Analysis of the 5' end sequence indicates the presence of high G+C content and stem-loop secondary structures. Therefore, optimizations of the reaction with high temperature (70 degrees C) and a thermostable reverse transcriptase were performed to synthesize the first-strand cDNA, which was determined to have 73 nucleotides in the 5'-UTR. These results suggest that, under cDNA synthesis conditions at 42 degrees C and 60 degrees C, the reverse transcriptases skipped over the stem-loop structures of LIP1 mRNA and continued the cDNA synthesis until they reached the RNA adapter sequences. Thisfinding draws attention to adopting optimized conditions for cDNA synthesis on G+C-rich RNA templates.

5' Untranslated Regions↗
PubMed2001

["Target secondary structural motif" in the action of antisense oligodeoxynucleotides].

AIM: To optimize the antisense drug design based on the methods of secondary structure prediction of target mRNA by computer and the quantitative structure-activity relationship analysis. METHODS: The secondary structures of mRNA were predicted by the software RNAstructure, then the antisense phosphorothioate oligodeoxynucleotides (AS-ODN) were designed against the secondary structural elements. The in vitro anti-tumor bioactivity of AS-ODN was evaluated by A549 lung carcinoma cell line. The multiple regression was performed with the computer program SPSS. RESULTS: AS-ODN with high bioactivity concentrated on some local secondary structural motifs that were composed of several secondary structural elements, designated here as "target secondary structural motif" (target motif). The target motifs were relatively stable in the whole mRNA structures, but there were one or more unstable secondary structural elements (free energy > 0) such as internal loops, knots, and hairpins, especially the bulge loops in the motif. Bioactivities of AS-ODN targeting different "target motifs" were statistically different (P < 0.01) while AS-ODN against the same "target motif" were with similar effects. CONCLUSION: The concept of the "target motif" was helpful for optimizing the antisense drug design and might be useful for discovering the local function of mRNA and designing oligonucleotide probes and primers.

Adenocarcinoma↗
PubMed2005

Protein secondary structures (alpha-helix and beta-sheet) at a cellular level and protein fractions in relation to rumen degradation behaviours of protein: a new approach.

Studying the secondary structure of proteins leads to an understanding of the components that make up a whole protein, and such an understanding of the structure of the whole protein is often vital to understanding its digestive behaviour and nutritive value in animals. The main protein secondary structures are the alpha-helix and beta-sheet. The percentage of these two structures in protein secondary structures influences protein nutritive value, quality and digestive behaviour. A high percentage of beta-sheet structure may partly cause a low access to gastrointestinal digestive enzymes, which results in a low protein value. The objectives of the present study were to use advanced synchrotron-based Fourier transform IR (S-FTIR) microspectroscopy as a new approach to reveal the molecular chemistry of the protein secondary structures of feed tissues affected by heat-processing within intact tissue at a cellular level, and to quantify protein secondary structures using multicomponent peak modelling Gaussian and Lorentzian methods, in relation to protein digestive behaviours and nutritive value in the rumen, which was determined using the Cornell Net Carbohydrate Protein System. The synchrotron-based molecular chemistry research experiment was performed at the National Synchrotron Light Source at Brookhaven National Laboratory, US Department of Energy. The results showed that, with S-FTIR microspectroscopy, the molecular chemistry, ultrastructural chemical make-up and nutritive characteristics could be revealed at a high ultraspatial resolution ( approximately 10 microm). S-FTIR microspectroscopy revealed that the secondary structure of protein differed between raw and roasted golden flaxseeds in terms of the percentages and ratio of alpha-helixes and beta-sheets in the mid-IR range at the cellular level. By using multicomponent peak modelling, the results show that the roasting reduced (P<0.05) the percentage of alpha-helixes (from 47.1 % to 36.1 %: S-FTIR absorption intensity), increased the percentage of beta-sheets (from 37.2 % to 49.8 %: S-FTIR absorption intensity) and reduced the alpha-helix to beta-sheet ratio (from 0.3 to 0.7) in the golden flaxseeds, which indicated a negative effect of the roasting on protein values, utilisation and bioavailability. These results were proved by the Cornell Net Carbohydrate Protein System in situ animal trial, which also revealed that roasting increased the amount of protein bound to lignin, and well as of the Maillard reaction protein (both of which are poorly used by ruminants), and increased the level of indigestible and undegradable protein in ruminants. The present results demonstrate the potential of highly spatially resolved synchrotron-based infrared microspectroscopy to locate 'pure' protein in feed tissues, and reveal protein secondary structures and digestive behaviour, making a significant step forward in and an important contribution to protein nutritional research. Further study is needed to determine the sensitivities of protein secondary structures to various heat-processing conditions, and to quantify the relationship between protein secondary structures and the nutrient availability and digestive behaviour of various protein sources. Information from the present study arising from the synchrotron-based IR probing of the protein secondary structures of protein sources at the cellular level will be valuable as a guide to maintaining protein quality and predicting digestive behaviours.

Animal Feed↗
PubMed1996

Classification of RNA secondary structures using the techniques of cluster analysis.

An RNA molecule has a lot of suboptimal secondary structures. Some of these suboptimal structures are very similar and some are entirely different. In some cases, the free energy of these suboptimal structures does not differ much from that of the optimal structure. In order to characterize this relationship more clearly, we defined a metric among the secondary structures by the unweighted pair group method, using the arithmetic average and extended this metric to the sets of secondary structures. We report two applications of this metric. First, we developed a method for the classification of these suboptimal secondary structures of a given RNA sequence. Results of classification are presented with the secondary structures of cadang-cadang coconut viroid, potato spindle tuber viroid and polio virus as examples. Second, we applied this metric to the classification of the secondary structures derived from a set of mutant RNA sequences. We discuss that the mutation of a given RNA sequence changes not only the optimal secondary structure, but also the population of the cluster of the secondary structures.

Animals↗
PubMed1999

Enhanced protein fold recognition using secondary structure information from NMR.

NMR offers the possibility of accurate secondary structure for proteins that would be too large for structure determination. In the absence of an X-ray crystal structure, this information should be useful as an adjunct to protein fold recognition methods based on low resolution force fields. The value of this information has been tested by adding varying amounts of artificial secondary structure data and threading a sequence through a library of candidate folds. Using a literature test set, the threading method alone has only a one-third chance of producing a correct answer among the top ten guesses. With realistic secondary structure information, one can expect a 60-80% chance of finding a homologous structure. The method has then been applied to examples with published estimates of secondary structure. This implementation is completely independent of sequence homology, and sequences are optimally aligned to candidate structures with gaps and insertions allowed. Unlike work using predicted secondary structure, we test the effect of differing amounts of relatively reliable data.

Algorithms↗
PubMed1993

Secondary structure prediction for modelling by homology.

An improved method of secondary structure prediction has been developed to aid the modelling of proteins by homology. Selected data from four published algorithms are scaled and combined as a weighted mean to produce consensus algorithms. Each consensus algorithm is used to predict the secondary structure of a protein homologous to the target protein and of known structure. By comparison of the predictions to the known structure, accuracy values are calculated and a consensus algorithm chosen as the optimum combination of the composite data for prediction of the homologous protein. This customized algorithm is then used to predict the secondary structure of the unknown protein. In this manner the secondary structure prediction is initially tuned to the required protein family before prediction of the target protein. The method improves statistical secondary structure prediction and can be incorporated into more comprehensive systems such as those involving consensus prediction from multiple sequence alignments. Thirty one proteins from five families were used to compare the new method to that of Garnier, Osguthorpe and Robson (GOR) and sequence alignment. The improvement over GOR is naturally dependent on the similarity of the homologous protein, varying from a mean of 3% to 7% with increasing alignment significance score.

Algorithms↗
PubMed2003

Amino acid code of protein secondary structure.

The calculation of protein three-dimensional structure from the amino acid sequence is a fundamental problem to be solved. This paper presents principles of the code theory of protein secondary structure, and their consequence--the amino acid code of protein secondary structure. The doublet code model of protein secondary structure, developed earlier by the author (Shestopalov, 1990), is part of this theory. The theory basis are: 1) the name secondary structure is assigned to the conformation, stabilized only by the nearest (intraresidual) and middle-range (at a distance no more than that between residues i and i + 5) interactions; 2) the secondary structure consists of regular (alpha-helical and beta-structural) and irregular (coil) segments; 3) the alpha-helices, beta-strands and coil segments are encoded, respectively, by residue pairs (i, i + 4), (i, i + 2), (i, i = 1), according to the numbers of residues per period, 3.6, 2, 1; 4) all such pairs in the amino acid sequence are codons for elementary structural elements, or structurons; 5) the codons are divided into 21 types depending on their strength, i.e. their encoding capability; 6) overlappings of structurons of one and the same structure generate the longer segments of this structure; 7) overlapping of structurons of different structures is forbidden, and therefore selection of codons is required, the codon selection is hierarchic; 8) the code theory of protein secondary structure generates six variants of the amino acid code of protein secondary structure. There are two possible kinds of model construction based on the theory: the physical one using physical properties of amino acid residues, and the statistical one using results of statistical analysis of a great body of structural data. Some evident consequences of the theory are: a) the theory can be used for calculating the secondary structure from the amino acid sequence as a partial solution of the problem of calculation of protein three-dimensional structure from the amino acid sequence, and the calculated secondary structure and codon strength distribution can be used for simulating the next step of protein folding; b) one can propose that the same secondary structures can be folded into different tertiary structures and, vice versa, different secondary structures can be folded into the same tertiary structures, provided codon distributions are considered also; c) codons can be considered as first elements of protein three-dimensional structure language.

Amino Acid Sequence↗
PubMed1990

Evidence for allosteric transitions in secondary structure induced by superhelical stress.

Previous studies suggest that the global secondary structures of native supercoiled and equilibrium linear DNAs may differ somewhat. Recent evidence also indicates that metastable secondary structure commonly persists following complete relaxation of the superhelical stress by intercalating dyes or by the action of topoisomerase I. In this work, the torsion constants (alpha) of pBR322, pUC8 and M13mp7 (replicative form) DNAs are determined by time-resolved fluorescence polarization anisotropy at various times subsequent to linearization. In all three cases, the torsion constants are relatively low immediately after linearization, and evolve for eight to ten weeks before reaching their apparent equilibrium values. It is shown in detail how the persistence of metastable secondary structure, subsequent to relaxation of superhelical stress, necessarily implies that one or more transitions in equilibrium secondary structure are induced as the superhelix density is varied from zero to native, or vice versa. Samples of pUC8 dimer (5434 base-pairs) with different superhelix densities are prepared by the action of topoisomerase I in the presence of various amounts of ethidium. Their median linking number differences are determined by standard band counting methods. The translational diffusion coefficient (Do) and the plateau diffusion coefficient (Dplat) characterizing internal motions over short distances (225 A) are determined by dynamic light-scattering. The torsion constant (alpha) between base-pairs and the circular dichroism spectrum are also measured for each sample. Curves of Dplat, Do, alpha and molar ellipticity ([theta]) (at the minimum near 250 nm) versus superhelix density (sigma) are constructed. The curve of Do versus sigma is very similar to that for sedimentation coefficient versus sigma for simian virus 40 (SV40) and polyoma DNAs. The curves of Dplat, Do, alpha and [theta] versus sigma show that, with increasing negative superhelix density, a structural transition occurs near sigma = -0.020 to an intermediate state with low torsion constant, and a second structural transition occurs near sigma = -0.035 to a state that exhibits more normal properties by sigma = -0.048. These data are consistent with the hypothesis that supercoiling induces two successive allosteric transitions to alternative global secondary structures. The data are much less consistent with the hypothesis that supercoiling induces some radical secondary structure at one or a few sites of small extent at sigma = -0.020, and at other sites at sigma = -0.035, or with hypotheses based on changes in tertiary structure alone.(ABSTRACT TRUNCATED AT 400 WORDS)

Circular Dichroism↗
PubMed1996

Prediction of secondary structural content of proteins from their amino acid composition alone. I. New analytic vector decomposition methods.

The predictive limits of the amino acid composition for the secondary structural content (percentage of residues in the secondary structural states helix, sheet, and coil) in proteins are assessed quantitatively. For the first time, techniques for prediction of secondary structural content are presented which rely on the amino acid composition as the only information on the query protein. In our first method, the amino acid composition of an unknown protein is represented by the best (in a least square sense) linear combination of the characteristic amino acid compositions of the three secondary structural types computed from a learning set of tertiary structures. The second technique is a generalization of the first one and takes into account also possible compositional couplings between any two sorts of amino acids. Its mathematical formulation results in an eigenvalue/eigenvector problem of the second moment matrix describing the amino acid compositional fluctuations of secondary structural types in various proteins of a learning set. Possible correlations of the principal directions of the eigenspaces with physical properties of the amino acids were also checked. For example, the first two eigenvectors of the helical eigenspace correlate with the size and hydrophobicity of the residue types respectively. As learning and test sets of tertiary structures, we utilized representative, automatically generated subsets of Protein Data Bank (PDB) consisting of non-homologous protein structures at the resolution thresholds < or = 1.8A, < or = 2.0A, < or = 2.5A, and < or = 3.0 A. We show that the consideration of compositional couplings improves prediction accuracy, albeit not dramatically. Whereas in the self-consistency test (learning with the protein to be predicted), a clear decrease of prediction accuracy with worsening resolution is observed, the jackknife test (leave the predicted protein out) yielded best results for the largest dataset (< or = 3.0A, almost no difference to the self-consistency test!), i.e., only this set, with more than 400 proteins, is sufficient for stable computation of the parameters in the prediction function of the second method. The average absolute error in predicting the fraction of helix, sheet, and coil from amino acid composition of the query protein are 13.7, 12.6, and 11.4%, respectively with r.m.s. deviations in the range of 8.6 divided by 11.8% for the 3.0 A dataset in a jackknife test. The absolute precision of the average absolute errors is in the range of 1 divided by 3% as measured for other representative subsets of the PDB. Secondary structural content prediction methods found in the literature have been clustered in accordance with their prediction accuracies. To our surprise, much more complex secondary structure prediction methods utilized for the same purpose of secondary structural content prediction achieve prediction accuracies very similar to those of the present analytic techniques, implying that all the information beyond the amino acid composition is, in fact, mainly utilized for positioning the secondary structural state in the sequence but not for determination of the overall number of residues in a secondary structural type. This result implies that higher prediction accuracies cannot be achieved relying solely on the amino acid composition of an unknown query protein as prediction input. Our prediction program SSCP has been made available as a World Wide Web and E-mail service.

Amino Acids↗
PubMed2003

Statistical model of amino acid code of protein secondary structure.

In the previous paper (Shestopalov, 2003) we presented the amino acid code of protein secondary structure as a partial solution of the fundamental problem of the protein three-dimensional structure calculation from the amino acid sequence. Here a statistical model of the code is described. The model is based on the structural data from 2258 protein chains (417,112 amino acid residues used). 60 and 61% of the secondary structure, calculated using the model, coincide, respectively, with the observed secondary structure in the training subset and test subset (104 protein chains and 21,166 residues used). This is equal to the threshold value for all the secondary structure calculations, based on the models, where, similarly as here, only the nearest and middle-range interactions are considered. Therefore the constructed model can be applied for the protein structure prediction from the amino acid sequence, especially when additional information is used along with expert analysis, as in the most successful prediction methods. The model can be used for analysis of the secondary structure changes during protein folding by comparison of the calculated and observed secondary structures. The information about the conformationally invariant segments can serve for the simulation of the supersecondary structure formation. One can try to obtain and examine the protein subset, in which the calculated and observed secondary structures are very similar.

Amino Acid Sequence↗
PubMed1999

A bayesian statistical algorithm for RNA secondary structure prediction.

A Bayesian approach for predicting RNA secondary structure that addresses the following three open issues is described: (1) the need for a representation of the full ensemble of probable structures; (2) the need to specify a fixed set of energy parameters; (3) the desire to make statistical inferences on all variables in the problem. It has recently been shown that Bayesian inference can be employed to relax or eliminate the need to specify the parameters of bioinformatics recursive algorithms and to give a statistical representation of the full ensemble of probable solutions with the incorporation of uncertainty in parameter values. In this paper, we make an initial exploration of these potential advantages of the Bayesian approach. We present a Bayesian algorithm that is based on stacking energy rules but relaxes the need to specify the parameters. The algorithm returns the exact posterior distribution of the number of destabilizing loops, stacking energy matrices, and secondary structures. The algorithm generates statistically representative structures from the full ensemble of probable secondary structures in exact proportion to the posterior probabilities. Once the forward recursions for the algorithm are completed, the backward recursive sampling executes in O(n) time, providing a very efficient approach for generating representative structures. We demonstrate the utility of the Bayesian approach with several tRNA sequences. The potential of the approach for predicting RNA secondary structures and presenting alternative structures is illustrated with applications to the Escherichia coli tRNA(Ala) sequence and the Xenopus laevis oocyte 5S rRNA sequence.

Algorithms↗
PubMed2003

[Secondary structure of insulin encapsulated within liposomes].

AIM: To determine the secondary structure of insulin encapsulated within liposomes. METHODS: The secondary structure of insulin, mixture of insulin with liposomes (I) and insulin liposomes (II) were determined by Fourier transform infrared (FTIR) spectroscopy. RESULTS: The figures of secondary structure of insulin, sample I and II were similar. The results showed that the amount of alpha-helix and beta-sheet of insulin, sample I and II had little difference, from 36.09% (insulin) to 31.68% (sample I) and 31.45% (sample II), and from 47.83% (insulin) to 53.29% (I) and 51.36% (II), respectively. The results showed that the insulin of sample I and II did not insert in liposomes membrane, only adsorbed or extendedon the surface of the liposomes. CONCLUSION: The secondary structure of insulin encapsulated within liposomes has not been destroyed and still remain the original state.

Drug Carriers↗
PubMed1996

Conserved sequence motifs, alignment, and secondary structure for the third domain of animal 12S rRNA.

Secondary structure models are an important step for aligning sequences, understanding probabilities of nucleotide substitutions, and evaluating the reliability of phylogenetic reconstructions. A set of conserved sequence motifs is derived from comparative sequence analysis of 184 invertebrate and vertebrate taxa (including many taxa from the same genera, families, and orders) with reference to a secondary structure model for domain III of animal mitochondrial small subunit (12S) ribosomal RNA. A template is presented to assist with secondary structure drawing. Our model is similar to previous models but is more specific to mitochondrial DNA, fitting both invertebrate and vertebrate groups, including taxa with markedly different nucleotide compositions. The second half of the domain III sequence can be difficult to align precisely, even when secondary structure information is considered. This is especially true for comparisons of anciently diverged taxa, but well-conserved motifs assist in determining biologically meaningful alignments. Patterns of conservation and variability in both paired and unpaired regions make differential phylogenetic weighting in terms of "stems" and "loops" unsatisfactory. We emphasize looking carefully at the sequence data before and during analyses, and advocate the use of conserved motifs and other secondary structure information for assessing sequencing fidelity.

Animals↗
PubMed1996

Improving protein secondary structure prediction with aligned homologous sequences.

Most recent protein secondary structure prediction methods use sequence alignments to improve the prediction quality. We investigate the relationship between the location of secondary structural elements, gaps, and variable residue positions in multiple sequence alignments. We further investigate how these relationships compare with those found in structurally aligned protein families. We show how such associations may be used to improve the quality of prediction of the secondary structure elements, using the Quadratic-Logistic method with profiles. Furthermore, we analyze the extent to which the number of homologous sequences influences the quality of prediction. The analysis of variable residue positions shows that surprisingly, helical regions exhibit greater variability than do coil regions, which are generally thought to be the most common secondary structure elements in loops. However, the correlation between variability and the presence of helices does not significantly improve prediction quality. Gaps are a distinct signal for coil regions. Increasing the coil propensity for those residues occurring in gap regions enhances the overall prediction quality. Prediction accuracy increases initially with the number of homologues, but changes negligibly as the number of homologues exceeds about 14. The alignment quality affects the prediction more than other factors, hence a careful selection and alignment of even a small number of homologues can lead to significant improvements in prediction accuracy.

Amino Acid Sequence↗
PubMed1991

Analysis of protein main-chain solvation as a function of secondary structure.

We have analysed the hydration of main-chain carbonyl and amide groups in 24 high-resolution well-refined protein structures as a function of the secondary structure in which these polar groups occur. We find that main-chain atoms in beta-sheets are as hydrated as those in alpha-helices, with most interactions involving "free" amide and carbonyl groups that do not participate in secondary structure hydrogen bonds. The distributions of water molecules around these non-bonded carbonyl groups reflect specific steric interactions due to the local secondary structure. Approximately 20% and 4%, respectively of bonded carbonyl and amide groups interact with solvent. These include interactions with carbonyl groups on the exposed faces of alpha-helices that have been correlated previously with bending of the helix. Water molecules interacting with alpha-helices occur mainly at the amino and carbonyl termini of the helices, in which case the solvent sites maintain the hydrogen bonding by bridging between residues i and i-3 or i-4 at the amino terminus and between i and i+3 or i+4 at the carbonyl terminus. We also see a number of solvent-mediated Ncap and Ccap interactions. The water molecules interacting with beta-sheets occur mainly at the edges, in which case they extend the sheet structure, or at the ends of strands, in which case they extend the beta-ladder. In summary, the solvent networks appear to extend the hydrogen-bonding structure of the secondary structures. In beta-turns, which usually occur at the surface of a protein, exposed amide and carbonyl groups are often hydrated, especially close to glycine residues. Occasionally water molecules form a bridge between residues i and i+3 in the turn and this may provide extra stabilization.

Amides↗