Editing Antibody (section)

==Structure prediction and computational antibody design==
The importance of antibodies in health care and the [[biotechnology]] industry demands knowledge of their structures at [[Image resolution|high resolution]]. This information is used for [[protein engineering]], modifying the antigen binding affinity, and identifying an epitope, of a given antibody. [[X-ray crystallography]] is one commonly used method for determining antibody structures. However, crystallizing an antibody is often laborious and time-consuming. Computational approaches provide a cheaper and faster alternative to crystallography, but their results are more equivocal, since they do not produce empirical structures. Online web servers such as ''Web Antibody Modeling'' (WAM)<ref>{{webarchive |url=https://web.archive.org/web/20110717212251/http://antibody.bath.ac.uk/abmod.html |date=17 July 2011 }}<br /> [http://antibody.bath.ac.uk/abmod.html WAM]</ref> and ''Prediction of Immunoglobulin Structure'' (PIGS)<ref>{{cite journal | vauthors = Marcatili P, Rosi A, Tramontano A | title = PIGS: automatic prediction of antibody structures | journal = Bioinformatics | volume = 24 | issue = 17 | pages = 1953–4 | date = September 2008 | pmid = 18641403 | doi = 10.1093/bioinformatics/btn341 | url = http://biocomputing.it/pigs/ | archive-url = https://web.archive.org/web/20101126235204/http://arianna.bio.uniroma1.it/pigs/ | url-status = live | archive-date = 26 November 2010 | doi-access = free }}<br /> [http://biocomputing.it/pigs/ Prediction of Immunoglobulin Structure (PIGS)] {{Webarchive|url=https://web.archive.org/web/20101126235204/http://arianna.bio.uniroma1.it/pigs/ |date=26 November 2010 }}</ref> enable computational modeling of antibody variable regions. Rosetta Antibody is a novel antibody F<sub>V</sub> region structure prediction [[Server (computing)|server]], which incorporates sophisticated techniques to minimize CDR loops and optimize the relative orientation of the light and heavy chains, as well as [[homology (biology)|homology]] models that predict successful docking of antibodies with their unique antigen.<ref>{{webarchive|url=https://web.archive.org/web/20110719215959/http://antibody.graylab.jhu.edu/ |date=19 July 2011 }}<br />[http://antibody.graylab.jhu.edu RosettaAntibody]</ref> However, describing an antibody's binding site using only one single static structure limits the understanding and characterization of the antibody's function and properties. To improve antibody structure prediction and to take the strongly correlated CDR loop and interface movements into account, antibody paratopes should be described as interconverting states in solution with varying probabilities.<ref name="Fernandez-Quintero2021" />

The ability to describe the antibody through binding affinity to the antigen is supplemented by information on antibody structure and amino acid sequences for the purpose of patent claims.<ref>{{cite web|title= Written Description Problems of the Monoclonal Antibody Patents after Centocor v. Abbott| vauthors = Park H |url= http://jolt.law.harvard.edu/digest/patent/written-description-problems-of-the-monoclonal-antibody-patents-after-centocor-v-abbott|website= jolt.law.harvard.edu|access-date= 12 December 2014|archive-url= https://web.archive.org/web/20141213031525/http://jolt.law.harvard.edu/digest/patent/written-description-problems-of-the-monoclonal-antibody-patents-after-centocor-v-abbott|archive-date= 13 December 2014|url-status= dead}}</ref> Several methods have been presented for computational design of antibodies based on the structural [[bioinformatics]] studies of antibody CDRs.<ref>{{cite journal | vauthors = Adolf-Bryfogle J, Kalyuzhniy O, Kubitz M, Weitzner BD, Hu X, Adachi Y, Schief WR, Dunbrack RL | title = RosettaAntibodyDesign (RAbD): A general framework for computational antibody design | journal = PLOS Computational Biology | volume = 14 | issue = 4 | pages = e1006112 | date = April 2018 | pmid = 29702641 | pmc = 5942852 | doi = 10.1371/journal.pcbi.1006112 | bibcode = 2018PLSCB..14E6112A | doi-access = free }}</ref><ref>{{cite journal | vauthors = Lapidoth GD, Baran D, Pszolla GM, Norn C, Alon A, Tyka MD, Fleishman SJ | title = AbDesign: An algorithm for combinatorial backbone design guided by natural conformations and sequences | journal = Proteins | volume = 83 | issue = 8 | pages = 1385–406 | date = August 2015 | pmid = 25670500 | pmc = 4881815 | doi = 10.1002/prot.24779 }}</ref><ref>{{cite journal | vauthors = Li T, Pantazes RJ, Maranas CD | title = OptMAVEn--a new framework for the de novo design of antibody variable region models targeting specific antigen epitopes | journal = PLOS ONE | volume = 9 | issue = 8 | pages = e105954 | date = 2014 | pmid = 25153121 | pmc = 4143332 | doi = 10.1371/journal.pone.0105954 | bibcode = 2014PLoSO...9j5954L | doi-access = free }}</ref>

There are a variety of methods used to sequence an antibody including [[Edman degradation]], [[cDNA]], etc.; albeit one of the most common modern uses for peptide/protein identification is liquid [[chromatography]] coupled with [[tandem mass spectrometry]] (LC-MS/MS).<ref>{{cite journal | vauthors = Pham V, Henzel WJ, Arnott D, Hymowitz S, Sandoval WN, Truong BT, Lowman H, Lill JR | title = De novo proteomic sequencing of a monoclonal antibody raised against OX40 ligand | journal = Analytical Biochemistry | volume = 352 | issue = 1 | pages = 77–86 | date = May 2006 | pmid = 16545334 | doi = 10.1016/j.ab.2006.02.001 }}</ref> High volume antibody sequencing methods require computational approaches for the data analysis, including [[de novo sequencing]] directly from tandem mass spectra<ref>{{cite journal | vauthors = Ma B, Zhang K, Hendrie C, Liang C, Li M, Doherty-Kirby A, Lajoie G | title = PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry | journal = Rapid Communications in Mass Spectrometry | volume = 17 | issue = 20 | pages = 2337–42 | year = 2003 | pmid = 14558135 | doi = 10.1002/rcm.1196 | bibcode = 2003RCMS...17.2337M }}</ref> and database search methods that use existing [[protein sequence]] databases.<ref>{{cite journal | vauthors = Zhang J, Xin L, Shan B, Chen W, Xie M, Yuen D, Zhang W, Zhang Z, Lajoie GA, Ma B | title = PEAKS DB: de novo sequencing assisted database search for sensitive and accurate peptide identification | journal = Molecular & Cellular Proteomics | volume = 11 | issue = 4 | pages = M111.010587 | date = April 2012 | pmid = 22186715 | pmc = 3322562 | doi = 10.1074/mcp.M111.010587  |doi-access=free }}</ref><ref>{{cite journal | vauthors = Perkins DN, Pappin DJ, Creasy DM, Cottrell JS | title = Probability-based protein identification by searching sequence databases using mass spectrometry data | journal = Electrophoresis | volume = 20 | issue = 18 | pages = 3551–67 | date = December 1999 | pmid = 10612281 | doi = 10.1002/(SICI)1522-2683(19991201)20:18<3551::AID-ELPS3551>3.0.CO;2-2 | s2cid = 42423655 }}</ref> Many versions of shotgun protein sequencing are able to increase the coverage by utilizing CID/HCD/ETD<ref>{{cite journal | vauthors = Bandeira N, Tang H, Bafna V, Pevzner P | title = Shotgun protein sequencing by tandem mass spectra assembly | journal = Analytical Chemistry | volume = 76 | issue = 24 | pages = 7221–33 | date = December 2004 | pmid = 15595863 | doi = 10.1021/ac0489162 | bibcode = 2004AnaCh..76.7221B }}</ref> fragmentation methods and other techniques, and they have achieved substantial progress in attempt to fully sequence [[proteins]], especially antibodies. Other methods have assumed the existence of similar proteins,<ref>{{cite journal | vauthors = Liu X, Han Y, Yuen D, Ma B | title = Automated protein (re)sequencing with MS/MS and a homologous database yields almost full coverage and accuracy | journal = Bioinformatics | volume = 25 | issue = 17 | pages = 2174–80 | date = September 2009 | pmid = 19535534 | doi = 10.1093/bioinformatics/btp366 | doi-access = free }}</ref> a known [[genome sequence]],<ref>{{cite journal | vauthors = Castellana NE, Pham V, Arnott D, Lill JR, Bafna V | title = Template proteogenomics: sequencing whole proteins using an imperfect database | journal = Molecular & Cellular Proteomics | volume = 9 | issue = 6 | pages = 1260–70 | date = June 2010 | pmid = 20164058 | pmc = 2877985 | doi = 10.1074/mcp.M900504-MCP200  |doi-access=free }}</ref> or combined top-down and bottom up approaches.<ref>{{cite journal | vauthors = Liu X, Dekker LJ, Wu S, Vanduijn MM, Luider TM, Tolić N, Kou Q, Dvorkin M, Alexandrova S, Vyatkina K, Paša-Tolić L, Pevzner PA | title = De novo protein sequencing by combining top-down and bottom-up tandem mass spectra | journal = Journal of Proteome Research | volume = 13 | issue = 7 | pages = 3241–8 | date = July 2014 | pmid = 24874765 | doi = 10.1021/pr401300m }}</ref> Current technologies have the ability to assemble [[protein sequences]] with high accuracy by integrating [[de novo sequencing]] [[peptides]], intensity, and positional confidence scores from database and [[Homology (biology)|homology]] searches.<ref>{{cite journal | vauthors = Tran NH, Rahman MZ, He L, Xin L, Shan B, Li M | title = Complete De Novo Assembly of Monoclonal Antibody Sequences | journal = Scientific Reports | volume = 6 | pages = 31730 | date = August 2016 | pmid = 27562653 | pmc = 4999880 | doi = 10.1038/srep31730 | bibcode = 2016NatSR...631730T }}</ref>