Editing Bioinformatics (section)

==Structural bioinformatics==<!-- this is specific enough, compared to general knowledge in previous paragraph, that it would be nice to have a source or example -->
{{main|Structural bioinformatics|Protein structure prediction}}
{{See also|Structural motif|Structural domain}}
[[File:1kqf opm.png|thumbnail|left|3-dimensional protein structures such as this one are common subjects in bioinformatic analyses.]]

Finding the structure of proteins is an important application of bioinformatics. The Critical Assessment of Protein Structure Prediction (CASP) is an open competition where worldwide research groups submit protein models for evaluating unknown protein models.<ref>{{Cite journal|title=Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XIII|year=2019 |pmc=6927249 |last1=Kryshtafovych |first1=A. |last2=Schwede |first2=T. |last3=Topf |first3=M. |last4=Fidelis |first4=K. |last5=Moult |first5=J. |journal=Proteins |volume=87 |issue=12 |pages=1011–1020 |doi=10.1002/prot.25823 |pmid=31589781 }}</ref><ref>{{Cite web |title=Home - CASP14 |url=https://predictioncenter.org/casp14/ |access-date=2023-06-12 |website=predictioncenter.org |archive-date=30 January 2023 |archive-url=https://web.archive.org/web/20230130200222/https://predictioncenter.org/casp14/ |url-status=live }}</ref>

=== Amino acid sequence ===
The linear [[amino acid]] sequence of a protein is called the [[primary structure]]. The primary structure can be easily determined from the sequence of [[codons]] on the DNA gene that codes for it. In most proteins, the primary structure uniquely determines the 3-dimensional structure of a protein in its native environment. An exception is the misfolded [[prion]] protein involved in [[bovine spongiform encephalopathy]]. This structure is linked to the function of the protein. Additional structural information includes the ''[[secondary structure|secondary]]'', ''[[tertiary structure|tertiary]]'' and ''[[quaternary structure|quaternary]]'' structure. A viable general solution to the prediction of the function of a protein remains an open problem. Most efforts have so far been directed towards heuristics that work most of the time.{{citation needed|date=July 2015}}

=== Homology ===
In the genomic branch of bioinformatics, homology is used to predict the function of a gene: if the sequence of gene ''A'', whose function is known, is homologous to the sequence of gene ''B,'' whose function is unknown, one could infer that B may share A's function. In structural bioinformatics, homology is used to determine which parts of a protein are important in structure formation and interaction with other proteins. [[Homology modeling]] is used to predict the structure of an unknown protein from existing homologous proteins.

One example of this is hemoglobin in humans and the hemoglobin in legumes ([[leghemoglobin]]), which are distant relatives from the same [[protein superfamily]]. Both serve the same purpose of transporting oxygen in the organism. Although both of these proteins have very different amino acid sequences, their protein structures are very similar, reflecting their shared function and shared ancestor.<ref>{{cite journal |vauthors=Hoy JA, Robinson H, Trent JT, Kakar S, Smagghe BJ, Hargrove MS |title=Plant hemoglobins: a molecular fossil record for the evolution of oxygen transport |journal=Journal of Molecular Biology |volume=371 |issue=1 |pages=168–79 |date=August 2007 |pmid=17560601 |doi=10.1016/j.jmb.2007.05.029}}</ref>

Other techniques for predicting protein structure include protein threading and ''de novo'' (from scratch) physics-based modeling.

Another aspect of structural bioinformatics include the use of protein structures for [[Virtual screening|Virtual Screening]] models such as [[Quantitative Structure-Activity Relationship]] models and proteochemometric models (PCM). Furthermore, a protein's crystal structure can be used in simulation of for example ligand-binding studies and ''in silico'' mutagenesis studies.

A 2021 [[deep-learning]] algorithms-based software called [[AlphaFold]], developed by Google's [[DeepMind]], greatly outperforms all other prediction software methods,<ref>{{Cite journal |last1=Jumper |first1=John |last2=Evans |first2=Richard |last3=Pritzel |first3=Alexander |last4=Green |first4=Tim |last5=Figurnov |first5=Michael |last6=Ronneberger |first6=Olaf |last7=Tunyasuvunakool |first7=Kathryn |last8=Bates |first8=Russ |last9=Žídek |first9=Augustin |last10=Potapenko |first10=Anna |last11=Bridgland |first11=Alex |last12=Meyer |first12=Clemens |last13=Kohl |first13=Simon A. A. |last14=Ballard |first14=Andrew J. |last15=Cowie |first15=Andrew |date=August 2021 |title=Highly accurate protein structure prediction with AlphaFold |journal=Nature |language=en |volume=596 |issue=7873 |pages=583–589 |bibcode=2021Natur.596..583J |doi=10.1038/s41586-021-03819-2 |issn=1476-4687 |pmc=8371605 |pmid=34265844}}</ref>{{How|date=June 2023}} and has released predicted structures for hundreds of millions of proteins in the AlphaFold protein structure database.<ref>{{Cite web |title=AlphaFold Protein Structure Database |url=https://alphafold.ebi.ac.uk/ |access-date=2022-10-10 |website=alphafold.ebi.ac.uk |archive-date=24 July 2021 |archive-url=https://web.archive.org/web/20210724013505/https://alphafold.ebi.ac.uk/ |url-status=live}}</ref>