Вторичная структура белков: формы и характеристики геликсных элементов.

General three dimensional form of local segments of proteins
secondary structure in protein

Protein secondary structure is the local spatial conformation of the polypeptide backbone excluding the side chains. The two most common secondary structural elements are alpha helices and beta sheets, though beta turns and omega loops occur as well. Secondary structure elements typically spontaneously form as an intermediate before the protein folds into its three dimensional tertiary structure. Secondary structure is formally defined by the pattern of hydrogen bonds between the amino hydrogen and carboxyl oxygen atoms in the peptide backbone. Secondary structure may alternatively be defined based on the regular pattern of backbone dihedral angles in a particular region of the Ramachandran plot regardless of whether it has the correct hydrogen bonds. The concept of secondary structure was first introduced by Kaj Ulrik Linderstrøm Lang at Stanford in 1952. Other types of biopolymers such as nucleic acids also possess characteristic secondary structures.

+ Structural features of the three major forms of protein helicesGeometry attributeα helix310 helixπ helixResidues per turn 3.6 3.0 4.4Translation per residue Radius of helix Pitch

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 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 is not 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 Cβ branched amino acids (isoleucine, valine, and threonine) prefer to adopt β strand conformations. However, these preferences are not strong enough to produce a reliable method of predicting secondary structure from sequence alone. Low frequency collective vibrations are thought to be sensitive to local rigidity within proteins, revealing beta structures to be generically more rigid than alpha or disordered proteins. Neutron scattering measurements have directly connected the spectral feature at ~1 THz to collective motions of the secondary structure of beta barrel protein GFP. Hydrogen bonding patterns in secondary structures may be significantly distorted, which makes automatic determination of secondary structure difficult. There are several methods for formally defining protein secondary structure (e. g., DSSP, DEFINE, STRIDE, ScrewFit, SST).

The rough secondary structure content of a biopolymer (e. g., "this protein is 40% α helix and 20% β sheet.") can 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 initially unassigned NMR spectrum.

Predicting protein tertiary structure from only its amino sequence is a very challenging problem (see protein structure prediction), but using the simpler secondary structure definitions is more tractable. Early methods of secondary structure prediction were restricted to predicting the three predominate states: helix, sheet, or random coil. These methods 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. The first widely used techniques to predict protein secondary structure from the amino acid sequence were the Chou–Fasman method and the GOR method. Although such methods claimed to achieve ~60% accurate in predicting which of the three states (helix/sheet/coil) a residue adopts, blind computing assessments later showed that the actual accuracy was much lower. 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 were evaluated by the Critical Assessment of protein Structure Prediction (CASP) experiments and continuously benchmarked, e. g. by EVA (benchmark). Based on these tests, the most accurate methods were Psipred, SAM, PORTER, PROF, and SABLE. 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.

Both protein and nucleic acid 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. thus designing functional all α proteins is likely to be easier that designing proteins with both helices and strands; this has been recently confirmed experimentally.