Exploring the sequence features determining amyloidosis in human antibody light chains

Abstract The light chain (AL) amyloidosis is caused by the aggregation of light chain of antibodies into amyloid fibrils. There are plenty of computational resources available for the prediction of short aggregation-prone regions within proteins. However, it is still a challenging task to predict th...

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Autores principales:	Puneet Rawat, R. Prabakaran, Sandeep Kumar, M. Michael Gromiha
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4fa62a20ac6040f6894d6f47565edcc3
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spelling	oai:doaj.org-article:4fa62a20ac6040f6894d6f47565edcc32021-12-02T14:33:57ZExploring the sequence features determining amyloidosis in human antibody light chains10.1038/s41598-021-93019-92045-2322https://doaj.org/article/4fa62a20ac6040f6894d6f47565edcc32021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93019-9https://doaj.org/toc/2045-2322Abstract The light chain (AL) amyloidosis is caused by the aggregation of light chain of antibodies into amyloid fibrils. There are plenty of computational resources available for the prediction of short aggregation-prone regions within proteins. However, it is still a challenging task to predict the amyloidogenic nature of the whole protein using sequence/structure information. In the case of antibody light chains, common architecture and known binding sites can provide vital information for the prediction of amyloidogenicity at physiological conditions. Here, in this work, we have compared classical sequence-based, aggregation-related features (such as hydrophobicity, presence of gatekeeper residues, disorderness, β-propensity, etc.) calculated for the CDR, FR or VL regions of amyloidogenic and non-amyloidogenic antibody light chains and implemented the insights gained in a machine learning-based webserver called “VLAmY-Pred” ( https://web.iitm.ac.in/bioinfo2/vlamy-pred/ ). The model shows prediction accuracy of 79.7% (sensitivity: 78.7% and specificity: 79.9%) with a ROC value of 0.88 on a dataset of 1828 variable region sequences of the antibody light chains. This model will be helpful towards improved prognosis for patients that may likely suffer from diseases caused by light chain amyloidosis, understanding origins of aggregation in antibody-based biotherapeutics, large-scale in-silico analysis of antibody sequences generated by next generation sequencing, and finally towards rational engineering of aggregation resistant antibodies.Puneet RawatR. PrabakaranSandeep KumarM. Michael GromihaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Puneet Rawat R. Prabakaran Sandeep Kumar M. Michael Gromiha Exploring the sequence features determining amyloidosis in human antibody light chains
description	Abstract The light chain (AL) amyloidosis is caused by the aggregation of light chain of antibodies into amyloid fibrils. There are plenty of computational resources available for the prediction of short aggregation-prone regions within proteins. However, it is still a challenging task to predict the amyloidogenic nature of the whole protein using sequence/structure information. In the case of antibody light chains, common architecture and known binding sites can provide vital information for the prediction of amyloidogenicity at physiological conditions. Here, in this work, we have compared classical sequence-based, aggregation-related features (such as hydrophobicity, presence of gatekeeper residues, disorderness, β-propensity, etc.) calculated for the CDR, FR or VL regions of amyloidogenic and non-amyloidogenic antibody light chains and implemented the insights gained in a machine learning-based webserver called “VLAmY-Pred” ( https://web.iitm.ac.in/bioinfo2/vlamy-pred/ ). The model shows prediction accuracy of 79.7% (sensitivity: 78.7% and specificity: 79.9%) with a ROC value of 0.88 on a dataset of 1828 variable region sequences of the antibody light chains. This model will be helpful towards improved prognosis for patients that may likely suffer from diseases caused by light chain amyloidosis, understanding origins of aggregation in antibody-based biotherapeutics, large-scale in-silico analysis of antibody sequences generated by next generation sequencing, and finally towards rational engineering of aggregation resistant antibodies.
format	article
author	Puneet Rawat R. Prabakaran Sandeep Kumar M. Michael Gromiha
author_facet	Puneet Rawat R. Prabakaran Sandeep Kumar M. Michael Gromiha
author_sort	Puneet Rawat
title	Exploring the sequence features determining amyloidosis in human antibody light chains
title_short	Exploring the sequence features determining amyloidosis in human antibody light chains
title_full	Exploring the sequence features determining amyloidosis in human antibody light chains
title_fullStr	Exploring the sequence features determining amyloidosis in human antibody light chains
title_full_unstemmed	Exploring the sequence features determining amyloidosis in human antibody light chains
title_sort	exploring the sequence features determining amyloidosis in human antibody light chains
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4fa62a20ac6040f6894d6f47565edcc3
work_keys_str_mv	AT puneetrawat exploringthesequencefeaturesdeterminingamyloidosisinhumanantibodylightchains AT rprabakaran exploringthesequencefeaturesdeterminingamyloidosisinhumanantibodylightchains AT sandeepkumar exploringthesequencefeaturesdeterminingamyloidosisinhumanantibodylightchains AT mmichaelgromiha exploringthesequencefeaturesdeterminingamyloidosisinhumanantibodylightchains
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Exploring the sequence features determining amyloidosis in human antibody light chains

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