Editing Bioinformatics (section)

=== 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>