000075811 001__ 75811 000075811 005__ 20200217133052.0 000075811 0247_ $$2doi$$a10.1038/s41598-018-32986-y 000075811 0248_ $$2sideral$$a108519 000075811 037__ $$aART-2018-108519 000075811 041__ $$aeng 000075811 100__ $$aClavero-Alvarez, A. 000075811 245__ $$aHumanization of Antibodies using a Statistical Inference Approach 000075811 260__ $$c2018 000075811 5060_ $$aAccess copy available to the general public$$fUnrestricted 000075811 5203_ $$aAntibody humanization is a key step in the preclinical phase of the development of therapeutic antibodies, originally developed and tested in non-human models (most typically, in mouse). The standard technique of Complementarity-Determining Regions (CDR) grafting into human Framework Regions of germline sequences has some important drawbacks, in that the resulting sequences often need further back-mutations to ensure functionality and/or stability. Here we propose a new method to characterize the statistical distribution of the sequences of the variable regions of human antibodies, that takes into account phenotypical correlations between pairs of residues, both within and between chains. We define a "humanness score" of a sequence, comparing its performance in distinguishing human from murine sequences, with that of some alternative scores in the literature. We also compare the score with the experimental immunogenicity of clinically used antibodies. Finally, we use the humanness score as an optimization function and perform a search in the sequence space, starting from different murine sequences and keeping the CDR regions unchanged. Our results show that our humanness score outperforms other methods in sequence classification, and the optimization protocol is able to generate humanized sequences that are recognized as human by standard homology modelling tools. 000075811 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E24-3$$9info:eu-repo/grantAgreement/ES/MINECO/FIS2014-55867-P$$9info:eu-repo/grantAgreement/ES/MINECO/FIS2015-65078-C2-2-P$$9info:eu-repo/grantAgreement/ES/MINECO/FIS2017-87519-P 000075811 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000075811 590__ $$a4.011$$b2018 000075811 591__ $$aMULTIDISCIPLINARY SCIENCES$$b14 / 69 = 0.203$$c2018$$dQ1$$eT1 000075811 592__ $$a1.414$$b2018 000075811 593__ $$aMultidisciplinary$$c2018$$dQ1 000075811 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000075811 700__ $$aDi Mambro, T. 000075811 700__ $$0(orcid)0000-0001-8425-7345$$aPerez-Gaviro, S.$$uUniversidad de Zaragoza 000075811 700__ $$aMagnani, M. 000075811 700__ $$0(orcid)0000-0002-5833-8798$$aBruscolini, P.$$uUniversidad de Zaragoza 000075811 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica 000075811 773__ $$g8 (2018), 14820 [11 pp]$$pSci. rep.$$tSCIENTIFIC REPORTS$$x2045-2322 000075811 8564_ $$s1516520$$uhttps://zaguan.unizar.es/record/75811/files/texto_completo.pdf$$yVersión publicada 000075811 8564_ $$s117091$$uhttps://zaguan.unizar.es/record/75811/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000075811 909CO $$ooai:zaguan.unizar.es:75811$$particulos$$pdriver 000075811 951__ $$a2020-02-17-12:43:33 000075811 980__ $$aARTICLE