Protein Secondary Structure
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Protein secondary structure

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, 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.

Types

Geometry attributeα-helix310 helixπ-helix
Residues per turn3.63.04.4
Translation per residue1.5 Å (0.15 nm)2.0 Å (0.20 nm)1.1 Å (0.11 nm)
Radius of helix2.3 Å (0.23 nm)1.9 Å (0.19 nm)2.8 Å (0.28 nm)
Pitch5.4 Å (0.54 nm)6.0 Å (0.60 nm)4.8 Å (0.48 nm)
Structural features of the three major forms of protein helices
Steven Bottomley (2004). “Interactive Protein Structure Tutorial”. Archived from the original on March 1, 2011. Retrieved January 9, 2011. Schulz, G. E. (Georg E.), 1939- (1979). Principles of protein structure. Schirmer, R. Heiner, 1942-. New York: Springer-Verlag. ISBN 0-387-90386-0. OCLC 4498269. Schulz, G. E. (Georg E.), 1939- (1979). Principles of protein structure. Schirmer, R. Heiner, 1942-. New York: Springer-Verlag. ISBN 0-387-90386-0. OCLC 4498269.

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.

Interactive diagram of hydrogen bonds in protein secondary structure. Cartoon above, atoms below with nitrogen in blue, oxygen in red (PDB: 1AXC​​)

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., 

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.
  • DSSP,
    • 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. S2CID 29185760.
      • The DSSP algorithm is the standard method for assigning secondary structure to the amino acids of a protein, given the atomic-resolution coordinates of the protein. The abbreviation is only mentioned once in the 1983 paper describing this algorithm,
        • Kabsch W, Sander C (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. S2CID 29185760. 
      • where it is the name of the Pascal program that implements the algorithm Define Secondary Structure of Proteins.
      • DSSP begins by identifying the intra-backbone hydrogen bonds of the protein using a purely electrostatic definition, assuming partial charges of -0.42 e and +0.20 e to the carbonyl oxygen and amide hydrogen respectively, their opposites assigned to the carbonyl carbon and amide nitrogen. A hydrogen bond is identified if E in the following equation is less than -0.5 kcal/mol:{\displaystyle E=0.084\left\{{\frac {1}{r_{ON}}}+{\frac {1}{r_{CH}}}-{\frac {1}{r_{OH}}}-{\frac {1}{r_{CN}}}\right\}\cdot 332\,\mathrm {kcal/mol} }E=0.084\left\{{\frac {1}{r_{{ON}}}}+{\frac {1}{r_{{CH}}}}-{\frac {1}{r_{{OH}}}}-{\frac {1}{r_{{CN}}}}\right\}\cdot 332\,{\mathrm {kcal/mol}} where the {\displaystyle r_{AB}}r_{{AB}} terms indicate the distance between atoms A and B, taken from the carbon (C) and oxygen (O) atoms of the C=O group and the nitrogen (N) and hydrogen (H) atoms of the N-H group.
      • Based on this, eight types of secondary structure are assigned. The 310 helix, α helix and π helix have symbols G, H and I and are recognized by having a repetitive sequence of hydrogen bonds in which the residues are three, four, or five residues apart respectively. Two types of beta sheet structures exist; a beta bridge has symbol B while longer sets of hydrogen bonds and beta bulges have symbol E. T is used for turns, featuring hydrogen bonds typical of helices, S is used for regions of high curvature (where the angle between {\displaystyle {\overrightarrow {C_{i}^{\alpha }C_{i+2}^{\alpha }}}}\overrightarrow {C_{i}^{\alpha }C_{{i+2}}^{\alpha }} and {\displaystyle {\overrightarrow {C_{i-2}^{\alpha }C_{i}^{\alpha }}}}\overrightarrow {C_{{i-2}}^{\alpha }C_{i}^{\alpha }} is at least 70°), and a blank (or space) is used if no other rule applies, referring to loops.
      • These eight types are usually grouped into three larger classes: helix (G, H and I), strand (E and B) and loop (S, T, and C, where C sometimes is represented also as blank space).
      • π helices – In the original DSSP algorithm, residues were preferentially assigned to α helices, rather than π helices. In 2011, it was shown that DSSP failed to annotate many “cryptic” π helices, which are commonly flanked by α helices.
      • In 2012, DSSP was rewritten so that the assignment of π helices was given preference over α helices, resulting in better detection of π helices. 
      • Versions of DSSP from 2.1.0 onwards therefore produce slightly different output from older versions.
      • Variants – In 2002, a continuous DSSP assignment was developed by introducing multiple hydrogen bond thresholds, where the new assignment was found to correlate with protein motion.
  • DEFINE,
    • 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. S2CID 29126855. 
  • STRIDE,
    • Frishman D, Argos P (Dec 1995). “Knowledge-based protein secondary structure assignment” (PDF). Proteins. 23 (4): 566–79. CiteSeerX 10.1.1.132.9420. doi:10.1002/prot.340230412. PMID 8749853. S2CID 17487756. Archived from the original (PDF) on 2010-06-13. 
      • In protein structure, STRIDE (Structural identification) is an algorithm for the assignment of protein secondary structure elements given the atomic coordinates of the protein, as defined by X-ray crystallography, protein NMR, or another protein structure determination method. In addition to the hydrogen bond criteria used by the more common DSSP algorithm, the STRIDE assignment criteria also include dihedral angle potentials. As such, its criteria for defining individual secondary structures are more complex than those of DSSP. The STRIDE energy function contains a hydrogen-bond term containing a Lennard-Jones-like 8-6 distance-dependent potential and two angular dependence factors reflecting the planarity of the optimized hydrogen bond geometry. The criteria for individual secondary structural elements, which are divided into the same groups as those reported by DSSP, also contain statistical probability factors derived from empirical examinations of solved structures with visually assigned secondary structure elements extracted from the Protein Data Bank.
      • Although DSSP is the older method and continues to be the most commonly used, the original STRIDE definition reported it to give a more satisfactory structural assignment in at least 70% of cases. In particular, STRIDE was observed to correct for the propensity of DSSP to assign shorter secondary structures than would be assigned by an expert crystallographer, usually due to the minor local variations in structure that are most common near the termini of secondary structure elements.
      • Using a sliding-window method to smooth variations in assignment of single terminal residues, current implementations of STRIDE and DSSP are reported to agree in up to 95.4% of cases.
        • Martin J, * Letellier G, Marin A, Taly JF, de Brevern AG, Gibrat JF. (2005). Protein secondary structure assignment revisited: a detailed analysis of different assignment methods. BMC Struct Biol 5:17. PMID 16164759
      • Both STRIDE and DSSP, among other common secondary structure assignment methods, are believed to underpredict pi helices.
        •  Fodje MN, Al-Karadaghi S. (2002). Occurrence, conformational features and amino acid propensities for the pi-helix. Protein Eng 15(5):353-8. PMID 12034854

DSSP classification

Main article: DSSP (protein)

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:

  • G = 3-turn helix (310 helix). Min length 3 residues.
  • H = 4-turn helix (α helix). Minimum length 4 residues.
  • I = 5-turn helix (π helix). Minimum length 5 residues.
  • T = hydrogen bonded turn (3, 4 or 5 turn)
  • E = extended strand in parallel and/or anti-parallel β-sheet conformation. Min length 2 residues.
  • B = residue in isolated β-bridge (single pair β-sheet hydrogen bond formation)
  • S = bend (the only non-hydrogen-bond based assignment).
  • C = coil (residues which are not in any of the above conformations).

‘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 hydrogen-bond definition for secondary structure is that of DSSP, which is a purely electrostatic model. It assigns charges of ±q1 ≈ 0.42e to the carbonyl carbon and oxygen, respectively, and charges of ±q2 ≈ 0.20e to the amide hydrogen and nitrogen, respectively. The electrostatic energy is{\displaystyle E=q_{1}q_{2}\left({\frac {1}{r_{\mathrm {ON} }}}+{\frac {1}{r_{\mathrm {CH} }}}-{\frac {1}{r_{\mathrm {OH} }}}-{\frac {1}{r_{\mathrm {CN} }}}\right)\cdot 332{\text{ kcal/mol}}.}{\displaystyle E=q_{1}q_{2}\left({\frac {1}{r_{\mathrm {ON} }}}+{\frac {1}{r_{\mathrm {CH} }}}-{\frac {1}{r_{\mathrm {OH} }}}-{\frac {1}{r_{\mathrm {CN} }}}\right)\cdot 332{\text{ kcal/mol}}.}

According to DSSP, a hydrogen-bond exists if and only if E is less than −0.5 kcal/mol (−2.1 kJ/mol). Although the DSSP formula is a relatively crude approximation of the physical hydrogen-bond energy, it is generally accepted as a tool for defining secondary structure.

SST classification

SST is a Bayesian method to assign secondary structure to protein coordinate data using the Shannon information criterion of Minimum Message Length (MML) inference. 

SST treats any assignment of secondary structure as a potential hypothesis that attempts to explain (compress) given protein coordinate data. The core idea is that the best secondary structural assignment is the one that can explain (compress) the coordinates of a given protein coordinates in the most economical way, thus linking the inference of secondary structure to lossless data compression. SST accurately delineates any protein chain into regions associated with the following assignment types:

SST detects π and 310 helical caps to standard α-helices, and automatically assembles the various extended strands into consistent β-pleated sheets. It provides a readable output of dissected secondary structural elements, and a corresponding PyMol-loadable script to visualize the assigned secondary structural elements individually.

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.

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.

Prediction

See also: Protein structure prediction and List of protein secondary structure prediction programs

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.

  • Garnier J, Osguthorpe DJ, Robson B (March 1978). “Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins”. Journal of Molecular Biology. 120 (1): 97–120. doi:10.1016/0022-2836(78)90297-8. PMID 642007.
    • The GOR method (short for Garnier–Osguthorpe–Robson) is an information theory-based method for the prediction of secondary structures in proteins. It was developed in the late 1970s shortly after the simpler Chou–Fasman method. Like Chou–Fasman, the GOR method is based on probability parameters derived from empirical studies of known protein tertiary structures solved by X-ray crystallography. However, unlike Chou–Fasman, the GOR method takes into account not only the propensities of individual amino acids to form particular secondary structures, but also the conditional probability of the amino acid to form a secondary structure given that its immediate neighbors have already formed that structure. The method is therefore essentially Bayesian in its analysis.
      • Garnier, J.; Gibrat, J. F.; Robson, B. (1996). “GOR method for predicting protein secondary structure from amino acid sequence”. Methods Enzymol. 266: 540–53. doi:10.1016/S0076-6879(96)66034-0. PMID 8743705.
      • METHOD: The GOR method analyzes sequences to predict alpha helix, beta sheet, turn, or random coil secondary structure at each position based on 17-amino-acid sequence windows. The original description of the method included four scoring matrices of size 17×20, where the columns correspond to the log-odds score, which reflects the probability of finding a given amino acid at each position in the 17-residue sequence. The four matrices reflect the probabilities of the central, ninth amino acid being in a helical, sheet, turn, or coil conformation. In subsequent revisions to the method, the turn matrix was eliminated due to the high variability of sequences in turn regions (particularly over such a large window). The method was considered as best requiring at least four contiguous residues to score as alpha helices to classify the region as helical, and at least two contiguous residues for a beta sheet.
        • Mount, D. M. (2004). Bioinformatics: Sequence and Genome Analysis. Vol. 2. Cold Spring Harbor Laboratory Press. ISBN 0-87969-712-1.
      • ALGORITHM: The mathematics and algorithm of the GOR method were based on an earlier series of studies by Robson and colleagues reported mainly in the Journal of Molecular Biology and The Biochemical Journal.
      • The latter describes the information theoretic expansions in terms of conditional information measures. The use of the word “simple” in the title of the GOR paper reflected the fact that the above earlier methods provided proofs and techniques somewhat daunting by being rather unfamiliar in protein science in the early 1970s; even Bayes methods were then unfamiliar and controversial. An important feature of these early studies, which survived in the GOR method, was the treatment of the sparse protein sequence data of the early 1970s by expected information measures. That is, expectations on a Bayesian basis considering the distribution of plausible information measure values given the actual frequencies (numbers of observations).
      •  

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 

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.

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.

  • 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.

It has been shown that α-helices are more stable, robust to mutations and designable than β-strands in natural proteins,

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.

See also

Further reading

External links

Protein secondary structure
Biomolecular structure

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From Wikipedia where the main page was last updated 20 August 2022

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