Literature DB >> 30825368

Aggrescan3D standalone package for structure-based prediction of protein aggregation properties.

Aleksander Kuriata1, Valentin Iglesias2, Mateusz Kurcinski1, Salvador Ventura2, Sebastian Kmiecik1.   

Abstract

SUMMARY: Aggrescan3D (A3D) standalone is a multiplatform Python package for structure-based prediction of protein aggregation properties and rational design of protein solubility. A3D allows the re-design of protein solubility by combining structural aggregation propensity and stability predictions, as demonstrated by a recent experimental study. It also enables predicting the impact of protein conformational fluctuations on the aggregation properties. The standalone A3D version is an upgrade of the original web server implementation-it introduces a number of customizable options, automated analysis of multiple mutations and offers a flexible computational framework for merging it with other computational tools.
AVAILABILITY AND IMPLEMENTATION: A3D standalone is distributed under the MIT license, which is free for academic and non-profit users. It is implemented in Python. The A3D standalone source code, wiki with documentation and examples of use, and installation instructions for Linux, macOS and Windows are available in the A3D standalone repository at https://bitbucket.org/lcbio/aggrescan3d.
© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Year:  2019        PMID: 30825368     DOI: 10.1093/bioinformatics/btz143

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  9 in total

1.  Protocols for Rational Design of Protein Solubility and Aggregation Properties Using Aggrescan3D Standalone.

Authors:  Aleksander Kuriata; Aleksandra E Badaczewska-Dawid; Jordi Pujols; Salvador Ventura; Sebastian Kmiecik
Journal:  Methods Mol Biol       Date:  2022

2.  Bioinformatics Methods in Predicting Amyloid Propensity of Peptides and Proteins.

Authors:  Małgorzata Kotulska; Jakub W Wojciechowski
Journal:  Methods Mol Biol       Date:  2022

3.  Membrane fluidity, composition, and charge affect the activity and selectivity of the AMP ascaphin-8.

Authors:  Adriana Morales-Martínez; Brandt Bertrand; Juan M Hernández-Meza; Ramón Garduño-Juárez; Jesús Silva-Sanchez; Carlos Munoz-Garay
Journal:  Biophys J       Date:  2022-07-16       Impact factor: 3.699

4.  Computational Assessment of Bacterial Protein Structures Indicates a Selection Against Aggregation.

Authors:  Anita Carija; Francisca Pinheiro; Valentin Iglesias; Salvador Ventura
Journal:  Cells       Date:  2019-08-08       Impact factor: 6.600

5.  pH-Dependent Aggregation in Intrinsically Disordered Proteins Is Determined by Charge and Lipophilicity.

Authors:  Jaime Santos; Valentín Iglesias; Juan Santos-Suárez; Marco Mangiagalli; Stefania Brocca; Irantzu Pallarès; Salvador Ventura
Journal:  Cells       Date:  2020-01-08       Impact factor: 6.600

6.  Endolysins from Antarctic Pseudomonas Display Lysozyme Activity at Low Temperature.

Authors:  Marco Orlando; Sandra Pucciarelli; Marina Lotti
Journal:  Mar Drugs       Date:  2020-11-20       Impact factor: 5.118

7.  Enabling QTY Server for Designing Water-Soluble α-Helical Transmembrane Proteins.

Authors:  Fei Tao; Hongzhi Tang; Shuguang Zhang; Mengke Li; Ping Xu
Journal:  mBio       Date:  2022-01-18       Impact factor: 7.867

8.  DE-STRESS: a user-friendly web application for the evaluation of protein designs.

Authors:  Michael J Stam; Christopher W Wood
Journal:  Protein Eng Des Sel       Date:  2021-02-15       Impact factor: 1.650

9.  Coiled-coil inspired functional inclusion bodies.

Authors:  Marcos Gil-Garcia; Susanna Navarro; Salvador Ventura
Journal:  Microb Cell Fact       Date:  2020-06-01       Impact factor: 5.328

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.