Everything about Secondary Structure totally explained
In
biochemistry and
structural biology,
secondary structure is the general three-dimensional form of
local segments of
biopolymers such as
proteins and
nucleic acids (DNA/RNA). It does not, however, describe specific atomic positions in three-dimensional space, which are considered to be
tertiary structure.
Secondary structure is formally defined by the
hydrogen bonds of the biopolymer, as observed in an atomic-resolution structure. In proteins, the secondary structure is defined by patterns of hydrogen bonds between backbone amide groups (sidechain-mainchain and sidechain-sidechain hydrogen bonds are irrelevant), where the
DSSP definition of a hydrogen bond is used. In nucleic acids, the secondary structure is defined by the hydrogen bonding between the nitrogenous bases.
The hydrogen bonding is correlated with other structural features, however, which has given rise to
less formal definitions of secondary structure. For example, residues in protein helices generally
adopt backbone
dihedral angles in a particular region of the
Ramachandran plot; thus, a segment
of residues with such dihedral angles is often called a "helix", regardless of whether it has the correct
hydrogen bonds. Many other less formal definitions have been proposed, often applying concepts from the
differential geometry of curves, such as
curvature and
torsion. Least formally, structural biologists
solving a new atomic-resolution structure will sometimes assign its secondary structure "by eye" and record
their assignments in the corresponding
PDB file.
The rough secondary-structure content of a biopolymer (for example, "this protein is 40% α-helix and 20% β-sheet.")
can often be estimated
spectroscopically. For proteins, a common method is far-ultraviolet
(far-UV, 170-250 nm)
circular dichroism. A pronounced double minimum at 208 and 222 nm indicate α-helical
structure, whereas a single minimum at 204 nm or 217 nm reflects random-coil or β-sheet structure, respectively.
A less common method is
infrared spectroscopy, which detects differences in the bond
oscillations of amide groups due to hydrogen-bonding. Finally, secondary-structure contents may be
estimated accurately using the
chemical shifts of an unassigned
NMR spectrum.
Secondary structure was introduced by
Kaj Ulrik Linderstrøm-Lang in the 1952 Lane medical lectures at
Stanford.
Proteins
Secondary structure in proteins consists of local inter-residue interactions mediated by hydrogen bonds. The most common secondary structures are
alpha helices and
beta sheets. Other helices, such as the
310 helix and
π helix, are calculated to have energetically favorable hydrogen-bonding patterns but are rarely if ever observed in natural proteins except at the ends of α helices due to unfavorable backbone packing in the center of the helix. Other extended structures such as the
polyproline helix and
alpha sheet are rare in
native state proteins but are often hypothesized as important
protein folding intermediates. Tight
turns and loose, flexible loops link the more "regular" secondary structure elements. The
random coil isn't a true secondary structure, but is the class of conformations that indicate an absence of regular secondary structure.
Amino acids vary in their ability to form the various secondary structure elements.
Proline and
glycine are sometimes known as "helix breakers" because they disrupt the regularity of the α helical backbone conformation; however, both have unusual conformational abilities and are commonly found in
turns. Amino acids that prefer to adopt
helical conformations in proteins include
methionine,
alanine,
leucine,
glutamate and
lysine ("MALEK" in
amino-acid 1-letter codes); by contrast, the large aromatic residues (
tryptophan,
tyrosine and
phenylalanine) and
According to
DSSP, an H-bond exists if and only if
is less than -0.5 kcal/mol. Although the DSSP formula is a relatively crude approximation of the
physical H-bond energy, it's generally accepted as a tool for defining secondary structure.
Protein secondary-structure prediction
Early methods of secondary-structure prediction were based on the helix- or sheet-forming propensities of individual amino acids, sometimes coupled with rules for estimating the free energy of forming secondary structure elements. Such methods are typically ~60% accurate in predicting which of the three states (helix/sheet/coil) a residue adopts. A significant increase in accuracy (to nearly ~80%) was made by exploiting
multiple sequence alignment; knowing the full distribution of amino acids that occur at a position (and in its vicinity, typically ~7 residues on either side) throughout
evolution provides a much better picture of the structural tendencies near that position. For illustration, a given protein might have a
glycine at a given position, which by itself might suggest a random coil there. However, multiple sequence alignment might reveal that helix-favoring amino acids occur at that position (and nearby positions) in 95% of homologous proteins spanning nearly a billion years of evolution. Moreover, by examining the average
hydrophobicity at that and nearby positions, the same alignment might also suggest a pattern of residue
solvent accessibility consistent with an α-helix. Taken together, these factors would suggest that the glycine of the original protein adopts α-helical structure, rather than random coil. Several types of methods are used to combine all the available data to form a 3-state prediction, including
neural networks,
hidden Markov models and
support vector machines. Modern prediction methods also provide a confidence score for their predictions at every position.
Secondary-structure prediction methods are continuously benchmarked, for example, in the
EVA
experiment. Based on ~270 weeks of testing, the most accurate methods at present are
PsiPRED,
SAM,
PORTER,
PROF and
SABLE. Interestingly, it doesn't seem to be possible to improve upon these methods by taking a consensus of them
[citationneeded]. The chief area for improvement appears to be the prediction of β-strands; residues confidently predicted as β-strand are likely to be so, but the methods are apt to overlook some β-strand segments (false negatives). There is likely an upper limit of ~90% prediction accuracy overall, due to the idiosyncrasies of the standard method (
DSSP) for assigning secondary-structure classes (helix/strand/coil) to PDB structures, against which the predictions are benchmarked.
Accurate secondary-structure prediction is a key element in the prediction of
tertiary structure, in all but the simplest (
homology modeling) cases. For example, a confidently predicted pattern of six secondary structure elements βαββαβ is the signature of a
ferredoxin fold.
Nucleic acids
Nucleic acids also have secondary structure, most notably single-stranded
RNA molecules. RNA secondary structure is generally divided into helices (contiguous base pairs), and various kinds of loops (unpaired nucleotides surrounded by helices). The
stem-loop structure in which a base-paired helix ends in a short unpaired loop is extremely common and is a building block for larger structural motifs such as cloverleaf structures, which are four-helix junctions such as those found in
transfer RNA. Internal loops (a short series of unpaired bases in a longer paired helix) and bulges (regions in which one strand of a helix has "extra" inserted bases with no counterparts in the opposite strand) are also frequent. Finally, both
pseudoknots and base triples are present in RNA (though not DNA).
Since it's almost entirely base pair-mediated, RNA secondary structure can be said to define which bases are paired in a molecule or complex. However, the traditional
Watson-Crick base pair isn't the only type of pairing that's permissible in RNA;
Hoogsteen base pairing is also common.
RNA secondary structure prediction
See also
RNA structure
One application of
bioinformatics uses predicted RNA secondary structures in searching a
genome for noncoding but functional forms of RNA. For example,
microRNAs have canonical long stem-loop structures interrupted by small internal loops. A general method of calculating probable RNA secondary structure is
dynamic programming, although this has the disadvantage that it can't detect
pseudoknots or other cases in which base pairs are not fully nested. More general methods are based on
stochastic context-free grammars. A web server that implements a type of dynamic programming is
Mfold.
For many RNA molecules, the secondary structure is highly important to the correct function of the RNA — often more so than the actual sequence. This fact aids in the analysis of
non-coding RNA sometimes termed "RNA genes". RNA secondary structure can be predicted with some accuracy by computer and many
bioinformatics applications use some notion of secondary structure in analysis of RNA.
Alignment
Both protein and RNA secondary structures can be used to aid in multiple
sequence alignment. These alignments can be made more accurate by the inclusion of secondary structure information in addition to simple sequence information. This is sometimes less useful in RNA because base pairing is much more highly conserved than sequence. Distant relationships between proteins whose primary structures are unalignable can sometimes be found by secondary structure.
Further Information
Get more info on 'Secondary Structure'.
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