Protein secondary structure#Protein

{{Alpha beta structure}}

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 C^β-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.{{cite journal | vauthors = Perticaroli S, Nickels JD, Ehlers G, O'Neill H, Zhang Q, Sokolov AP | title = Secondary structure and rigidity in model proteins | journal = Soft Matter | volume = 9 | issue = 40 | pages = 9548–56 | date = October 2013 | pmid = 26029761 | doi = 10.1039/C3SM50807B | bibcode = 2013SMat....9.9548P }}{{cite journal | vauthors = Perticaroli S, Nickels JD, Ehlers G, Sokolov AP | title = Rigidity, secondary structure, and the universality of the boson peak in proteins | journal = Biophysical Journal | volume = 106 | issue = 12 | pages = 2667–74 | date = June 2014 | pmid = 24940784 | pmc = 4070067 | doi = 10.1016/j.bpj.2014.05.009 | bibcode = 2014BpJ...106.2667P }} Neutron scattering measurements have directly connected the spectral feature at ~1 THz to collective motions of the secondary structure of beta-barrel protein GFP.{{cite journal | vauthors = Nickels JD, Perticaroli S, O'Neill H, Zhang Q, Ehlers G, Sokolov AP | title = Coherent neutron scattering and collective dynamics in the protein, GFP | journal = Biophys. J. | volume = 105 | issue = 9 | pages = 2182–87 | year = 2013 | pmid = 24209864 | pmc = 3824694 | doi = 10.1016/j.bpj.2013.09.029 | bibcode = 2013BpJ...105.2182N }}

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,{{cite journal | vauthors = Kabsch W, Sander C | title = Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features | journal = Biopolymers | volume = 22 | issue = 12 | pages = 2577–637 | date = Dec 1983 | pmid = 6667333 | doi = 10.1002/bip.360221211 | s2cid = 29185760 }} DEFINE,{{cite journal | vauthors = Richards FM, Kundrot CE | title = Identification of structural motifs from protein coordinate data: secondary structure and first-level supersecondary structure | journal = Proteins | volume = 3 | issue = 2 | pages = 71–84 | year = 1988 | pmid = 3399495 | doi = 10.1002/prot.340030202 | s2cid = 29126855 }} STRIDE,{{cite journal | vauthors = Frishman D, Argos P | title = Knowledge-based protein secondary structure assignment | journal = Proteins | volume = 23 | issue = 4 | pages = 566–79 | date = Dec 1995 | pmid = 8749853 | doi = 10.1002/prot.340230412 | url = http://nook.cs.ucdavis.edu/~koehl/Classes/ECS289/reprints/Paper_Stride.pdf | url-status = dead | archive-url = https://web.archive.org/web/20100613184204/http://nook.cs.ucdavis.edu/~koehl/Classes/ECS289/reprints/Paper_Stride.pdf | archive-date = 2010-06-13 | citeseerx = 10.1.1.132.9420 | s2cid = 17487756 }} ScrewFit,{{cite journal | vauthors = Calligari PA, Kneller GR | title = ScrewFit: combining localization and description of protein secondary structure | journal = Acta Crystallographica Section D | volume = 68 | issue = Pt 12 | pages = 1690–3 | date = December 2012 | pmid = 23151634 | doi = 10.1107/s0907444912039029 }} [http://lcb.infotech.monash.edu.au/sst SST]{{cite journal | vauthors = Konagurthu AS, Lesk AM, Allison L | title = Minimum message length inference of secondary structure from protein coordinate data | journal = Bioinformatics | volume = 28 | issue = 12 | pages = i97–i105 | date = Jun 2012 | pmid = 22689785 | pmc = 3371855 | doi = 10.1093/bioinformatics/bts223 }}).

{{Main|DSSP (algorithm)}}

Image:SegmentLengths.dist.png

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

:$$

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 {{cvt|-0.5|kcal/mol|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{{cite web | url=http://lcb.infotech.monash.edu.au/sstweb2 | title=SST (Web server): Secondary STructure assignment to protein coordinates using MML inference -- Submission page }} is a Bayesian method to assign secondary structure to protein coordinate data using the Shannon information criterion of Minimum Message Length (MML) inference. [http://lcb.infotech.monash.edu.au/sstweb2 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:{{Cite web|url=http://lcb.infotech.monash.edu.au/sst|title=SST web server|access-date=17 April 2018}}

• E = (Extended) strand of a β-pleated sheet
• G = Right-handed 3₁₀ helix
• H = Right-handed α-helix
• I = Right-handed π-helix
• g = Left-handed 3₁₀ helix
• h = Left-handed α-helix
• i = Left-handed π-helix
• 3 = 3₁₀-like Turn
• 4 = α-like Turn
• 5 = π-like Turn
• T = Unspecified Turn
• C = Coil
• - = Unassigned residue

SST{{cite web | url=http://lcb.infotech.monash.edu.au/sstweb2 | title=SST (Web server): Secondary STructure assignment to protein coordinates using MML inference -- Submission page }} 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.

The rough secondary-structure content of a biopolymer (e.g., "this protein is 40% α-helix and 20% β-sheet.") can be estimated spectroscopically.{{cite journal | vauthors = Pelton JT, McLean LR | title = Spectroscopic methods for analysis of protein secondary structure | journal = Anal. Biochem. | volume = 277 | issue = 2 | pages = 167–76 | year = 2000 | pmid = 10625503 | doi = 10.1006/abio.1999.4320 }} 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.{{cite journal | vauthors = Meiler J, Baker D | title = Rapid protein fold determination using unassigned NMR data | journal = Proc. Natl. Acad. Sci. U.S.A. | volume = 100 | issue = 26 | pages = 15404–09 | year = 2003 | pmid = 14668443 | pmc = 307580 | doi = 10.1073/pnas.2434121100 | bibcode = 2003PNAS..10015404M | doi-access = free }}

{{See also|Protein structure prediction|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{{cite journal | vauthors = Chou PY, Fasman GD | title = Prediction of protein conformation | journal = Biochemistry | volume = 13 | issue = 2 | pages = 222–45 | date = Jan 1974 | pmid = 4358940 | doi = 10.1021/bi00699a002 }}{{cite journal | vauthors = Chou PY, Fasman GD | title = Empirical predictions of protein conformation | journal = Annual Review of Biochemistry | volume = 47 | pages = 251–76 | year = 1978 | issue = 1 | pmid = 354496 | doi = 10.1146/annurev.bi.47.070178.001343 }}{{cite book | vauthors = Chou PY, Fasman GD | chapter = Prediction of the secondary structure of proteins from their amino acid sequence | volume = 47 | pages = [https://archive.org/details/advancesinenzymo0047unse/page/45 45–148] | year = 1978 | pmid = 364941 | doi = 10.1002/9780470122921.ch2 | series = Advances in Enzymology - and Related Areas of Molecular Biology | isbn = 9780470122921 | title = Advances in Enzymology and Related Areas of Molecular Biology | publisher = Wiley | chapter-url = https://archive.org/details/advancesinenzymo0047unse/page/45 }} and the GOR method.{{cite journal | vauthors = Garnier J, Osguthorpe DJ, Robson B | title = Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins | journal = Journal of Molecular Biology | volume = 120 | issue = 1 | pages = 97–120 | date = March 1978 | pmid = 642007 | doi = 10.1016/0022-2836(78)90297-8 }} 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.{{cite journal | vauthors = Kabsch W, Sander C | title = How good are predictions of protein secondary structure? | journal = FEBS Letters | volume = 155 | issue = 2 | pages = 179–82 | date = May 1983 | pmid = 6852232 | doi = 10.1016/0014-5793(82)80597-8 | bibcode = 1983FEBSL.155..179K | s2cid = 41477827 }}

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.{{cite journal | vauthors = Simossis VA, Heringa J | title = Integrating protein secondary structure prediction and multiple sequence alignment | journal = Current Protein & Peptide Science | volume = 5 | issue = 4 | pages = 249–66 | date = Aug 2004 | pmid = 15320732 | doi = 10.2174/1389203043379675 }}{{cite book | vauthors = Pirovano W, Heringa J | chapter = Protein Secondary Structure Prediction | title = Data Mining Techniques for the Life Sciences | volume = 609 | pages = 327–48 | year = 2010 | pmid = 20221928 | doi = 10.1007/978-1-60327-241-4_19 | series = Methods in Molecular Biology | publisher = Humana Press | location = Totowa, NJ | isbn = 978-1-60327-240-7 }} 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.{{cite journal|title=Fourier–based classification of protein secondary structures|journal=Biochemical and Biophysical Research Communications|date=15 April 2017|first1=Jian-Jun|last1=Shu|first2=K.-Y.|last2=Yong|volume=485|issue=4|pages=731–735|doi=10.1016/j.bbrc.2017.02.117|pmid=28246013|s2cid=1240804|arxiv=1704.08994}} 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 [http://predictioncenter.org/ 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,{{cite journal | vauthors = Karplus K | title = SAM-T08, HMM-based protein structure prediction | journal = Nucleic Acids Res. | volume = 37 | issue = Web Server issue | pages = W492–97 | year = 2009 | pmid = 19483096 | pmc = 2703928 | doi = 10.1093/nar/gkp403 }} PORTER,{{cite journal | vauthors = Pollastri G, McLysaght A | title = Porter: a new, accurate server for protein secondary structure prediction | journal = Bioinformatics | volume = 21 | issue = 8 | pages = 1719–20 | year = 2005 | pmid = 15585524 | doi = 10.1093/bioinformatics/bti203 | doi-access = free | hdl = 2262/39594 | hdl-access = free }} PROF,{{cite journal | vauthors = 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 | title = PredictProtein—an open resource for online prediction of protein structural and functional features | journal = Nucleic Acids Res. | volume = 42 | issue = Web Server issue | pages = W337–43 | year = 2014 | pmid = 24799431 | pmc = 4086098 | doi = 10.1093/nar/gku366 }} and SABLE.{{cite journal | vauthors = Adamczak R, Porollo A, Meller J | title = Combining prediction of secondary structure and solvent accessibility in proteins | journal = Proteins | volume = 59 | issue = 3 | pages = 467–75 | year = 2005 | pmid = 15768403 | doi = 10.1002/prot.20441 | s2cid = 13267624 }} 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.{{cite journal | vauthors = Kihara D | title = The effect of long-range interactions on the secondary structure formation of proteins | journal = Protein Science | volume = 14 | issue = 8 | pages = 1955–963 | date = Aug 2005 | pmid = 15987894 | pmc = 2279307 | doi = 10.1110/ps.051479505 }}

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.{{cite journal | vauthors = Qi Y, Grishin NV | title = Structural classification of thioredoxin-like fold proteins | journal = Proteins | volume = 58 | issue = 2 | pages = 376–88 | year = 2005 | pmid = 15558583 | doi = 10.1002/prot.20329 | url = http://prodata.swmed.edu/Lab/Thiored_Proteins04.pdf | quote = 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 α/β sandwich with the βαβββα secondary-structure pattern. | citeseerx = 10.1.1.644.8150 | s2cid = 823339 }}

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.

It has been shown that α-helices are more stable, robust to mutations, and designable than β-strands in natural proteins,{{cite journal | vauthors = Abrusán G, Marsh JA | title = Alpha Helices Are More Robust to Mutations than Beta Strands | journal = PLOS Computational Biology | volume = 12 | issue = 12 | pages = e1005242 | date = December 2016 | pmid = 27935949 | pmc = 5147804 | doi = 10.1371/journal.pcbi.1005242 | bibcode = 2016PLSCB..12E5242A | doi-access = free }} 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.{{cite journal | vauthors = Rocklin GJ, Chidyausiku TM, Goreshnik I, Ford A, Houliston S, Lemak A, Carter L, Ravichandran R, Mulligan VK, Chevalier A, Arrowsmith CH, Baker D | display-authors = 6 | title = Global analysis of protein folding using massively parallel design, synthesis, and testing | journal = Science | volume = 357 | issue = 6347 | pages = 168–175 | date = July 2017 | pmid = 28706065 | pmc = 5568797 | doi = 10.1126/science.aan0693 | bibcode = 2017Sci...357..168R }}

{{Portal|Biology}}

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• Folding (chemistry)
• Nucleic acid secondary structure
• Translation
• Structural motif
• Protein circular dichroism data bank
• WHAT IF software
• List of protein secondary structure prediction programs

{{colend}}

{{Reflist|33em}}

{{refbegin|33em}}

• {{cite book | vauthors = Branden C, Tooze J | title = Introduction to protein structure | year = 1999 | publisher = Garland Science | location = New York | isbn = 978-0815323051 | edition = 2nd }}
• {{cite journal | vauthors = Pauling L, Corey RB | author-link1 = Linus Pauling | author-link2 =Robert Corey | title = Configurations of Polypeptide Chains With Favored Orientations Around Single Bonds: Two New Pleated Sheets | journal = Proc. Natl. Acad. Sci. U.S.A. | volume = 37 | issue = 11 | pages = 729–40 | year = 1951 | pmid = 16578412 | pmc = 1063460 | doi = 10.1073/pnas.37.11.729| bibcode = 1951PNAS...37..729P | doi-access = free }} (The original beta-sheet conformation article.)
• {{cite journal | vauthors = Pauling L, Corey RB, Branson HR | author-link1 = Linus Pauling | author-link2 = Robert Corey | author-link3 = Herman Branson | title = The structure of proteins; two hydrogen-bonded helical configurations of the polypeptide chain | journal = Proc. Natl. Acad. Sci. U.S.A. | volume = 37 | issue = 4 | pages = 205–11 | year = 1951 | pmid = 14816373 | pmc = 1063337 | doi = 10.1073/pnas.37.4.205| bibcode = 1951PNAS...37..205P | doi-access = free }} (alpha- and pi-helix conformations, since they predicted that $3\_\{10\}$ helices would not be possible.)

{{refend}}

• [http://www.cbs.dtu.dk/services/NetSurfP/ NetSurfP – Secondary Structure and Surface Accessibility predictor]
• [http://www.predictprotein.org PROF]
• [https://web.archive.org/web/20130702021354/http://dirac.cnrs-orleans.fr/ScrewFit/ ScrewFit]
• [http://zhanglab.ccmb.med.umich.edu/PSSpred/ PSSpred] A multiple neural network training program for protein secondary structure prediction
• [https://web.archive.org/web/20131220124154/https://genesilico.pl/meta2/ Genesilico metaserver] Metaserver which allows to run over 20 different secondary structure predictors by one click
• [http://lcb.infotech.monash.edu.au/sstweb SST] webserver: An information-theoretic (compression-based) secondary structural assignment.

{{Protein secondary structure}}

{{Biomolecular structure}}

Protein structure 2

Category:Stereochemistry