Editing Structural bioinformatics (section)

== Structure comparison ==

=== Structural alignment ===
[[Structural alignment]] is a method for comparison between 3D structures based on their shape and conformation.<ref>{{Cite web|url=https://www.sciencedaily.com/terms/structural_alignment.htm|title=Structural alignment (genomics)|website=ScienceDaily|language=en|access-date=2020-02-26}}</ref> It could be used to infer the evolutionary relationship among a set of proteins even with low sequence similarity. Structural alignment implies superimposing a 3D structure over a second one, rotating and translating atoms in corresponding positions (in general, using the ''C<sub>α</sub>'' atoms or even the backbone heavy atoms ''C'', ''N'', ''O'', and ''C<sub>α</sub>''). Usually, the alignment quality is evaluated based on the [[Root-mean-square deviation|root-mean-square deviation (RMSD)]] of atomic positions, ''i.e.'', the average distance between atoms after superimposition:

: <math>\mathrm{RMSD}=\sqrt{\frac{1}{N}\sum_{i=1}^N\delta_i^2}</math>

where ''δ<sub>i</sub>'' is the distance between atom ''i'' and either a reference atom corresponding in the other structure or the mean coordinate of the ''N'' equivalent atoms. In general, the RMSD outcome is measured in [[Ångström]] (Å) unit, which is equivalent to 10<sup>−10</sup> m. The nearer to zero the RMSD value, the more similar are the structures.

=== Graph-based structural signatures ===
Structural signatures, also called fingerprints, are [[macromolecule]] pattern representations that can be used to infer similarities and differences. Comparisons among a large set of proteins using [[Root-mean-square deviation|RMSD]] still is a challenge due to the high computational cost of structural alignments. Structural signatures based on graph distance patterns among atom pairs have been used to determine protein identifying vectors and to detect non-trivial information.<ref>{{cite journal | vauthors = Pires DE, de Melo-Minardi RC, dos Santos MA, da Silveira CH, Santoro MM, Meira W | title = Cutoff Scanning Matrix (CSM): structural classification and function prediction by protein inter-residue distance patterns | journal = BMC Genomics | volume = 12 Suppl 4 | issue = S4 | pages = S12 | date = December 2011 | pmid = 22369665 | pmc = 3287581 | doi = 10.1186/1471-2164-12-S4-S12 | doi-access = free }}</ref> Furthermore, linear algebra and [[machine learning]] can be used for clustering protein signatures, detecting protein-ligand interactions, predicting [[Gibbs free energy|ΔΔG]], and proposing mutations based on [[Euclidean distance]].<ref>{{cite journal | vauthors = Mariano DC, Santos LH, Machado KD, Werhli AV, de Lima LH, de Melo-Minardi RC | title = A Computational Method to Propose Mutations in Enzymes Based on Structural Signature Variation (SSV) | journal = International Journal of Molecular Sciences | volume = 20 | issue = 2 | pages = 333 | date = January 2019 | pmid = 30650542 | pmc = 6359350 | doi = 10.3390/ijms20020333 | doi-access = free }}</ref>