Protein secondary structure

Protein primary structure Beta sheet Alpha helix Protein tertiary structure Protein quaternary structure
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Interactive diagram of protein structure, using PCNA as an example. (PDB: 1AXC​)

Protein secondary structure is the three dimensional form of local segments of proteins. The two most common secondary structural elements are alpha helices and beta sheets. 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 amine hydrogen and carbonyl 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.[1][2] Other types of biopolymers such as nucleic acids also possess characteristic secondary structures.

Types

Structural features of the three major forms of protein helices[3]
Geometry attribute α-helix 310 helix π-helix
Residues per turn 3.6 3.0 4.4
Translation per residue 1.5 Å (0.15 nm) 2.0 Å (0.20 nm) 1.1 Å (0.11 nm)
Radius of helix 2.3 Å (0.23 nm) 1.9 Å (0.19 nm) 2.8 Å (0.28 nm)
Pitch 5.4 Å (0.54 nm) 6.0 Å (0.60 nm) 4.8 Å (0.48 nm)
Hydrogen bonds (yellow dots) stabilizing an alpha-helix

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.[4][5] Neutron scattering measurements have directly connected the spectral feature at ~1 THz to collective motions of the secondary structure of beta-barrel protein GFP.[6]

Hydrogen bonding patterns in secondary structures may be significantly distorted, which makes an automatic determination of secondary structure difficult. There are several methods for formally defining protein secondary structure (e.g., DEFINE,[7] DSSP,[8] STRIDE,[9] SST[10]).

DSSP classification

Main article: DSSP (protein)
Distribution obtained from non-redundant pdb_select dataset (March 2006); Secondary structure assigned by DSSP; 8 conformational states reduced to 3 states: H=HGI, E=EB, C=STC. Visible are mixtures of (gaussian) distributions, resulting also from the reduction of DSSP states.

The Dictionary of Protein Secondary Structure, in short DSSP, is commonly used to describe the protein secondary structure with single letter codes. The secondary structure is assigned based on hydrogen bonding patterns as those initially proposed by Pauling et al. in 1951 (before any protein structure had ever been experimentally determined). There are eight types of secondary structure that DSSP defines:

'Coil' is often codified as ' ' (space), C (coil) or '-' (dash). The helices (G,H and I) and sheet conformations are all required to have a reasonable length. This means that 2 adjacent residues in the primary structure must form the same hydrogen bonding pattern. If the helix or sheet hydrogen bonding pattern is too short they are designated as T or B, respectively. Other protein secondary structure assignment categories exist (sharp turns, Omega loops, etc.), but they are less frequently used.

Secondary structure is defined by hydrogen bonding, so the exact definition of a hydrogen bond is critical. The standard H-bond definition for secondary structure is that of DSSP, which is a purely electrostatic model. It assigns charges of to the carbonyl carbon and oxygen, respectively, and charges of to the amide hydrogen and nitrogen, respectively. The electrostatic energy is

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 is generally accepted as a tool for defining secondary structure.

Experimental determination

The rough secondary-structure content of a biopolymer (e.g., "this protein is 40% α-helix and 20% β-sheet.") can be estimated spectroscopically.[11] 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.[12]

Prediction

Predicting protein tertiary structure from only its amino acid 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. Such methods were typically ~60% accurate in predicting which of the three states (helix/sheet/coil) a residue adopts. The first widely used technique to predict protein secondary structure from the amino acid sequence was the Chou–Fasman method.[13][14][15]

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.[16][17] 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 was continuously benchmarked, e.g., EVA (benchmark). Based on these tests, the most accurate methods were Psipred, SAM,[18] PORTER,[19] PROF,[20] and SABLE.[21] 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.[22]

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.[23]

Applications

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.[16]

See also

Notes

    References

    1. ↑ Linderstrøm-Lang KU (1952). Lane Medical Lectures: Proteins and Enzymes. Stanford University Press. p. 115. ASIN B0007J31SC.
    2. ↑ Schellman JA, Schellman CG (1997). "Kaj Ulrik Linderstrøm-Lang (1896-1959)". Protein Sci. 6 (5): 1092–100. doi:10.1002/pro.5560060516. PMC 2143695Freely accessible. PMID 9144781. He had already introduced the concepts of the primary, secondary, and tertiary structure of proteins in the third Lane Lecture (Linderstram-Lang, 1952)
    3. ↑ Steven Bottomley (2004). "Interactive Protein Structure Tutorial". Retrieved January 9, 2011.
    4. ↑ Perticaroli S, Nickels JD, Ehlers G, O'Neill H, Zhang Q, Sokolov AP (October 2013). "Secondary structure and rigidity in model proteins". Soft Matter. 9 (40): 9548–56. doi:10.1039/C3SM50807B. PMID 26029761.
    5. ↑ Perticaroli S, Nickels JD, Ehlers G, Sokolov AP (June 2014). "Rigidity, secondary structure, and the universality of the boson peak in proteins". Biophysical Journal. 106 (12): 2667–74. doi:10.1016/j.bpj.2014.05.009. PMC 4070067Freely accessible. PMID 24940784.
    6. ↑ Nickels JD, Perticaroli S, O'Neill H, Zhang Q, Ehlers G, Sokolov AP (2013). "Coherent neutron scattering and collective dynamics in the protein, GFP". Biophys. J. 105 (9): 2182–87. doi:10.1016/j.bpj.2013.09.029. PMC 3824694Freely accessible. PMID 24209864.
    7. ↑ Richards FM, Kundrot CE (1988). "Identification of structural motifs from protein coordinate data: secondary structure and first-level supersecondary structure". Proteins. 3 (2): 71–84. doi:10.1002/prot.340030202. PMID 3399495.
    8. ↑ Kabsch W, Sander C (Dec 1983). "Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features". Biopolymers. 22 (12): 2577–637. doi:10.1002/bip.360221211. PMID 6667333.
    9. ↑ Frishman D, Argos P (Dec 1995). "Knowledge-based protein secondary structure assignment". Proteins. 23 (4): 566–79. doi:10.1002/prot.340230412. PMID 8749853.
    10. ↑ Konagurthu AS, Lesk AM, Allison L (Jun 2012). "Minimum message length inference of secondary structure from protein coordinate data". Bioinformatics (Oxford, England). 28 (12): i97–i105. doi:10.1093/bioinformatics/bts223. PMC 3371855Freely accessible. PMID 22689785.
    11. ↑ Pelton JT, McLean LR (2000). "Spectroscopic methods for analysis of protein secondary structure". Anal. Biochem. 277 (2): 167–76. doi:10.1006/abio.1999.4320. PMID 10625503.
    12. ↑ Meiler J, Baker D (2003). "Rapid protein fold determination using unassigned NMR data". Proc. Natl. Acad. Sci. U.S.A. 100 (26): 15404–09. doi:10.1073/pnas.2434121100. PMC 307580Freely accessible. PMID 14668443.
    13. ↑ Chou PY, Fasman GD (Jan 1974). "Prediction of protein conformation". Biochemistry. 13 (2): 222–45. doi:10.1021/bi00699a002. PMID 4358940.
    14. ↑ Chou PY, Fasman GD (1978). "Empirical predictions of protein conformation". Annual Review of Biochemistry. 47: 251–76. doi:10.1146/annurev.bi.47.070178.001343. PMID 354496.
    15. ↑ Chou PY, Fasman GD (1978). "Prediction of the secondary structure of proteins from their amino acid sequence". Advances in Enzymology and Related Areas of Molecular Biology. 47: 45–148. doi:10.1002/9780470122921.ch2. PMID 364941.
    16. 1 2 Simossis VA, Heringa J (Aug 2004). "Integrating protein secondary structure prediction and multiple sequence alignment". Current Protein & Peptide Science. 5 (4): 249–66. doi:10.2174/1389203043379675. PMID 15320732.
    17. ↑ Pirovano W, Heringa J (2010). "Protein secondary structure prediction". Methods Mol. Biol. 609: 327–48. doi:10.1007/978-1-60327-241-4_19. PMID 20221928.
    18. ↑ Karplus K (2009). "SAM-T08, HMM-based protein structure prediction". Nucleic Acids Res. 37 (Web Server issue): W492–97. doi:10.1093/nar/gkp403. PMC 2703928Freely accessible. PMID 19483096.
    19. ↑ Pollastri G, McLysaght A (2005). "Porter: a new, accurate server for protein secondary structure prediction". Bioinformatics. 21 (8): 1719–20. doi:10.1093/bioinformatics/bti203. PMID 15585524.
    20. ↑ Yachdav G, Kloppmann E, Kajan L, Hecht M, Goldberg T, Hamp T, Hönigschmid P, Schafferhans A, Roos M, Bernhofer M, Richter L, Ashkenazy H, Punta M, Schlessinger A, Bromberg Y, Schneider R, Vriend G, Sander C, Ben-Tal N, Rost B (2014). "PredictProtein--an open resource for online prediction of protein structural and functional features". Nucleic Acids Res. 42 (Web Server issue): W337–43. doi:10.1093/nar/gku366. PMC 4086098Freely accessible. PMID 24799431.
    21. ↑ Adamczak R, Porollo A, Meller J (2005). "Combining prediction of secondary structure and solvent accessibility in proteins". Proteins. 59 (3): 467–75. doi:10.1002/prot.20441. PMID 15768403.
    22. ↑ Kihara D (Aug 2005). "The effect of long-range interactions on the secondary structure formation of proteins". Protein Science : A Publication of the Protein Society. 14 (8): 1955–963. doi:10.1110/ps.051479505. PMC 2279307Freely accessible. PMID 15987894.
    23. ↑ Qi Y, Grishin NV (2005). "Structural classification of thioredoxin-like fold proteins" (PDF). Proteins. 58 (2): 376–88. doi:10.1002/prot.20329. PMID 15558583. Since the fold definition should include only the core secondary structural elements that are present in the majority of homologs, we define the thioredoxin-like fold as a two-layer ’/’ sandwich with the ’βαββαβ secondary-structure pattern. C1 control character in |quote= at position 183 (help)

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