Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity
Systemic light chain amyloidosis (AL) is caused by the production of toxic light chains and can be fatal, yet effective treatments are often not possible due to delayed diagnosis. Here the authors show that a machine learning platform analyzing light chain somatic mutations allows the prediction of...
Main Authors: | Maura Garofalo, Luca Piccoli, Margherita Romeo, Maria Monica Barzago, Sara Ravasio, Mathilde Foglierini, Milos Matkovic, Jacopo Sgrignani, Raoul De Gasparo, Marco Prunotto, Luca Varani, Luisa Diomede, Olivier Michielin, Antonio Lanzavecchia, Andrea Cavalli |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2021-06-01
|
Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/s41467-021-23880-9 |
Similar Items
-
Structural Biology of STAT3 and Its Implications for Anticancer Therapies Development
by: Jacopo Sgrignani, et al.
Published: (2018-05-01) -
AncesTree: An interactive immunoglobulin lineage tree visualizer.
by: Mathilde Foglierini, et al.
Published: (2020-07-01) -
How Computational Chemistry and Drug Delivery Techniques Can Support the Development of New Anticancer Drugs
by: Mariangela Garofalo, et al.
Published: (2020-04-01) -
Computational Identification of a Putative Allosteric Binding Pocket in TMPRSS2
by: Jacopo Sgrignani, et al.
Published: (2021-04-01) -
HCMV Envelope Glycoprotein Diversity Demystified
by: Mathilde Foglierini, et al.
Published: (2019-05-01)