imported>Sammi Brie: Adding local short description: "Method to identify covarying pairs of amino acids in proteins", overriding Wikidata description "method to identify covarying pairs of amino acids in protein multiple sequence alignments"

2025-06-08T18:55:59Z

Adding local short description: "Method to identify covarying pairs of amino acids in proteins", overriding Wikidata description "method to identify covarying pairs of amino acids in protein multiple sequence alignments"

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{{Short description|Method to identify covarying pairs of amino acids in proteins}}
'''Statistical Coupling Analysis (SCA)''' is a method used in [[bioinformatics]] to study how pairs of [[amino acids]] in a protein sequence [[Covariation|evolve together]]. It analyzes a [[multiple sequence alignment]] (MSA), which is a display of the sequences of many related proteins arranged to highlight similarities and differences. SCA measures how much the amino acid makeup at one position in the protein changes when the amino acid makeup at another position is altered. This relationship is quantified as '''statistical coupling energy'''. A higher coupling energy indicates that the amino acids at both positions are more likely to have co-evolved and are therefore functionally or structurally linked. In simpler terms, it helps scientists understand which parts of a protein are working together and how they have changed over evolutionary time.<ref>{{cite web |title=Supplementary Material for 'Evolutionarily conserved networks of residues mediate allosteric communication in proteins.' |url=http://www.hhmi.swmed.edu/Labs/rr/SCA.html}}</ref>

==Definition of statistical coupling energy==
Statistical coupling energy measures how a perturbation of amino acid distribution at one site in an MSA affects the amino acid distribution at another site. For example, consider a multiple sequence alignment with sites (or columns) ''a'' through ''z'', where each site has some distribution of amino acids. At position ''i'', 60% of the sequences have a [[valine]] and the remaining 40% of sequences have a [[leucine]], at position ''j'' the distribution is 40% [[isoleucine]], 40% [[histidine]] and 20% [[methionine]], ''k'' has an average distribution (the 20 amino acids are present at roughly the same frequencies seen in all proteins), and ''l'' has 80% histidine, 20% valine. Since positions ''i'', ''j'' and ''l'' have an amino acid distribution different from the mean distribution observed in all proteins, they are said to have some degree of '''conservation'''.

In statistical coupling analysis, the conservation (ΔGstat) at each site (''i'') is defined as: <math>\Delta G_i^{stat} = \sqrt{\sum_x (\ln P_i^x)^2}</math>.<ref>{{cite journal | title=A perturbation-based method for calculating explicit likelihood of evolutionary co-variance in multiple sequence alignments | author=Dekker | journal=Bioinformatics | volume=20 | issue=10 | pages=1565–1572 | year=2004 | doi=10.1093/bioinformatics/bth128 | pmid=14962924 | last2=Fodor | first2=A | last3=Aldrich | first3=RW | last4=Yellen | first4=G|display-authors=etal| doi-access=free }}</ref>

Here, Pix describes the probability of finding amino acid ''x'' at position ''i'', and is defined by a function in [[Binomial_distribution#Mean|binomial form]] as follows:
<div align="center"><math>P_i^x = \frac{N!}{n_x!(N - n_x)!}p_x^{n_x}(1 - p_x)^{N - n_x}</math>,</div>
where N is 100, nx is the percentage of sequences with residue ''x'' (e.g. methionine) at position ''i'', and px corresponds to the approximate distribution of amino acid ''x'' in all positions among all sequenced proteins. The summation runs over all 20 amino acids. After ΔGistat is computed, the conservation for position ''i'' in a subalignment produced after a perturbation of amino acid distribution at ''j'' (ΔGi | δjstat) is taken. Statistical coupling energy, denoted ΔΔGi, jstat, is simply the difference between these two values. That is:

<div align="center"><math>\Delta\Delta G_{i, j}^{stat} = \Delta G_{i | \delta j}^{stat} - \Delta G_i^{stat}</math>, or, more commonly, <math>\Delta\Delta G_{i, j}^{stat} = \sqrt{\sum_x (\ln P_{i|\delta j}^x - \ln P_i^x)^2}</math></div>

Statistical coupling energy is often systematically calculated between a fixed, perturbated position, and all other positions in an MSA. Continuing with the example MSA from the beginning of the section, consider a perturbation at position ''j'' where the amino distribution changes from 40% I, 40% H, 20% M to 100% I. If, in a subsequent subalignment, this changes the distribution at ''i'' from 60% V, 40% L to 90% V, 10% L, but does not change the distribution at position ''l'', then there would be some amount of statistical coupling energy between ''i'' and ''j'' but none between ''l'' and ''j''.

==Applications==
Ranganathan and Lockless originally developed SCA to examine thermodynamic (energetic) coupling of residue pairs in proteins.<ref>{{cite journal | title=Evolutionarily conserved pathways of energetic connectivity in protein families | author=Lockless SW, Ranaganathan R | journal=Science | volume=286 | pages=295–299 | year=1999 | doi=10.1126/science.286.5438.295 | pmid=10514373 | issue=5438}}</ref> Using the [[PDZ domain]] family, they were able to identify a small network of residues that were energetically coupled to a binding site residue. The network consisted of both residues spatially close to the binding site in the tertiary fold, called contact pairs, and more distant residues that participate in longer-range energetic interactions. Later applications of SCA by the [http://ranganathanlab.org/ Ranganathan group] on the [[GPCR]], [[serine protease]] and [[hemoglobin]] families also showed energetic coupling in sparse networks of residues that cooperate in [[Allosteric enzyme|allosteric communication]].<ref>{{cite journal | title=Evolutionarily conserved networks of residues mediate allosteric communication in proteins. | author=Suel | journal=Nature Structural Biology | volume=10 | issue=1 | pages=59–69 | year=2003 | doi=10.1038/nsb881 | pmid=12483203 | last2=Lockless | first2=SW | last3=Wall | first3=MA | last4=Ranganathan | first4=R| s2cid=67749580 |display-authors=etal}}</ref>

Statistical coupling analysis has also been used as a basis for computational protein design. In 2005, Socolich et al.<ref>{{cite journal | title=Evolutionary information for specifying a protein fold | author=Socolich | journal=Nature | volume=437 | pages=512–518 | year=2005 | doi=10.1038/nature03991 | pmid=16177782 | last2=Lockless | first2=SW | last3=Russ | first3=WP | last4=Lee | first4=H | last5=Gardner | first5=KH | last6=Ranganathan | first6=R | issue=7058| bibcode=2005Natur.437..512S | s2cid=4363255 |display-authors=etal}}</ref> used an SCA for the [[WW domain]] to create artificial proteins with similar [[Equilibrium_unfolding#Thermal_denaturation|thermodynamic stability]] and [[Protein structure|structure]] to natural WW domains. The fact that 12 out of the 43 designed proteins with the same SCA profile as natural WW domains properly folded provided strong evidence that little information—only coupling information—was required for specifying the protein fold. This support for the SCA hypothesis was made more compelling considering that a) the successfully folded proteins had only 36% average [[Sequence alignment|sequence identity]] to natural WW folds, and b) none of the artificial proteins designed without coupling information folded properly. An accompanying study showed that the artificial WW domains were functionally similar to natural WW domains in [[Ligand_(biochemistry)#Receptor/ligand_binding_affinity|ligand binding affinity and specificity]].<ref>{{cite journal | title=Natural-like function in artificial WW domains | author=Russ | journal=Nature | volume=437 | pages=579–583 | year=2005 | doi = 10.1038/nature03990 | pmid=16177795 | last2=Lowery | first2=DM | last3=Mishra | first3=P | last4=Yaffe | first4=MB | last5=Ranganathan | first5=R | issue=7058| bibcode=2005Natur.437..579R | s2cid=4424336 |display-authors=etal}}</ref>

In [[Protein_structure_prediction#Ab_initio_protein_modelling|''de novo'' protein structure prediction]], it has been shown that, when combined with a simple residue-residue distance metric, SCA-based scoring can fairly accurately distinguish native from non-native protein folds.<ref>{{cite journal | title=Using scores derived from statistical coupling analysis to distinguish correct and incorrect folds in de-novo protein structure prediction. | author=Bartlett GJ, Taylor WR | journal=Proteins | volume=71 | issue=1 | pages=950–959 | year=2008 | url=http://www3.interscience.wiley.com/cgi-bin/fulltext/116842426/HTMLSTART | archive-url=https://archive.today/20121217204114/http://www3.interscience.wiley.com/cgi-bin/fulltext/116842426/HTMLSTART | url-status=dead | archive-date=2012-12-17 | doi=10.1002/prot.21779 | pmid=18004776| s2cid=33836866 | url-access=subscription }}</ref>

==See also==
[[Mutual information]]

==External links==
* [http://www.bork.embl-heidelberg.de/Modules/ww_summary.html What is a WW domain?]
* [https://web.archive.org/web/20120213161040/http://esmane.physics.lsa.umich.edu/wl/external/ICSB/2005/20051021-umwlap001-02-ranganathan-movies/realaudio/f001.htm Ranganathan lecture on statistical coupling analysis (audio included)]
* [http://www.pandasthumb.org/archives/2005/10/protein-folding.html Protein folding — a step closer?] - A summary of the Ranganathan lab's SCA-based design of artificial yet functional WW domains.

==References==
{{reflist}}

[[Category:Bioinformatics]]

Statistical coupling analysis - Revision history

imported>Sammi Brie: Adding local short description: "Method to identify covarying pairs of amino acids in proteins", overriding Wikidata description "method to identify covarying pairs of amino acids in protein multiple sequence alignments"