Literature DB >> 27284085

Solubis: a webserver to reduce protein aggregation through mutation.

Joost Van Durme1, Greet De Baets1, Rob Van Der Kant1, Meine Ramakers1, Ashok Ganesan1, Hannah Wilkinson1, Rodrigo Gallardo1, Frederic Rousseau2, Joost Schymkowitz2.   

Abstract

Protein aggregation is a major factor limiting the biotechnological and therapeutic application of many proteins, including enzymes and monoclonal antibodies. The molecular principles underlying aggregation are by now sufficiently understood to allow rational redesign of natural polypeptide sequences for decreased aggregation tendency, and hence potentially increased expression and solubility. Given that aggregation-prone regions (APRs) tend to contribute to the stability of the hydrophobic core or to functional sites of the protein, mutations in these regions have to be carefully selected in order not to disrupt protein structure or function. Therefore, we here provide access to an automated pipeline to identify mutations that reduce protein aggregation by reducing the intrinsic aggregation propensity of the sequence (using the TANGO algorithm), while taking care not to disrupt the thermodynamic stability of the native structure (using the empirical force-field FoldX). Moreover, by providing a plot of the intrinsic aggregation propensity score of APRs corrected by the local stability of that region in the folded structure, we allow users to prioritize those regions in the protein that are most in need of improvement through protein engineering. The method can be accessed at http://solubis.switchlab.org/.
© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  protein aggregation; protein design; structural bioinformatics

Mesh:

Substances:

Year:  2016        PMID: 27284085     DOI: 10.1093/protein/gzw019

Source DB:  PubMed          Journal:  Protein Eng Des Sel        ISSN: 1741-0126            Impact factor:   1.650


  20 in total

1.  In vitro and in silico assessment of the developability of a designed monoclonal antibody library.

Authors:  Adriana-Michelle Wolf Pérez; Pietro Sormanni; Jonathan Sonne Andersen; Laila Ismail Sakhnini; Ileana Rodriguez-Leon; Jais Rose Bjelke; Annette Juhl Gajhede; Leonardo De Maria; Daniel E Otzen; Michele Vendruscolo; Nikolai Lorenzen
Journal:  MAbs       Date:  2019-01-18       Impact factor: 5.857

Review 2.  Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies.

Authors:  Rahmad Akbar; Habib Bashour; Puneet Rawat; Philippe A Robert; Eva Smorodina; Tudor-Stefan Cotet; Karine Flem-Karlsen; Robert Frank; Brij Bhushan Mehta; Mai Ha Vu; Talip Zengin; Jose Gutierrez-Marcos; Fridtjof Lund-Johansen; Jan Terje Andersen; Victor Greiff
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 5.857

Review 3.  Protein Design: From the Aspect of Water Solubility and Stability.

Authors:  Rui Qing; Shilei Hao; Eva Smorodina; David Jin; Arthur Zalevsky; Shuguang Zhang
Journal:  Chem Rev       Date:  2022-08-03       Impact factor: 72.087

Review 4.  Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics.

Authors:  Rahul Khetan; Robin Curtis; Charlotte M Deane; Johannes Thorling Hadsund; Uddipan Kar; Konrad Krawczyk; Daisuke Kuroda; Sarah A Robinson; Pietro Sormanni; Kouhei Tsumoto; Jim Warwicker; Andrew C R Martin
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 5.857

5.  Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods.

Authors:  Adriana-Michelle Wolf Pérez; Nikolai Lorenzen; Michele Vendruscolo; Pietro Sormanni
Journal:  Methods Mol Biol       Date:  2022

6.  Rational identification of aggregation hotspots based on secondary structure and amino acid hydrophobicity.

Authors:  Daisuke Matsui; Shogo Nakano; Mohammad Dadashipour; Yasuhisa Asano
Journal:  Sci Rep       Date:  2017-08-25       Impact factor: 4.379

7.  Prediction and Reduction of the Aggregation of Monoclonal Antibodies.

Authors:  Rob van der Kant; Anne R Karow-Zwick; Joost Van Durme; Michaela Blech; Rodrigo Gallardo; Daniel Seeliger; Kerstin Aßfalg; Pieter Baatsen; Griet Compernolle; Ann Gils; Joey M Studts; Patrick Schulz; Patrick Garidel; Joost Schymkowitz; Frederic Rousseau
Journal:  J Mol Biol       Date:  2017-03-18       Impact factor: 5.469

8.  Using extensional flow to reveal diverse aggregation landscapes for three IgG1 molecules.

Authors:  Leon F Willis; Amit Kumar; John Dobson; Nicholas J Bond; David Lowe; Richard Turner; Sheena E Radford; Nikil Kapur; David J Brockwell
Journal:  Biotechnol Bioeng       Date:  2018-02-04       Impact factor: 4.530

9.  Solubility and Aggregation of Selected Proteins Interpreted on the Basis of Hydrophobicity Distribution.

Authors:  Magdalena Ptak-Kaczor; Mateusz Banach; Katarzyna Stapor; Piotr Fabian; Leszek Konieczny; Irena Roterman
Journal:  Int J Mol Sci       Date:  2021-05-08       Impact factor: 5.923

Review 10.  Using protein engineering to understand and modulate aggregation.

Authors:  Jessica S Ebo; Nicolas Guthertz; Sheena E Radford; David J Brockwell
Journal:  Curr Opin Struct Biol       Date:  2020-02-19       Impact factor: 6.809

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