Literature DB >> 31979981

CPAD 2.0: a repository of curated experimental data on aggregating proteins and peptides.

Puneet Rawat1, R Prabakaran1, R Sakthivel1, A Mary Thangakani1, Sandeep Kumar2, M Michael Gromiha1,3.   

Abstract

The Curated Protein Aggregation Database (CPAD) is a manually curated and open-access database dedicated to providing comprehensive information related to mechanistic, kinetic and structural aspects of protein and peptide aggregation. The database has been updated to CPAD 2.0 by significantly expanding datasets and improving the user-interface. Key features of CPAD 2.0 are (i) 83,098 data points on aggregation kinetics experiments, (ii) 565 structures related to aggregation, which are classified into proteins, fibrils, and protein-ligand complexes, (iii) 2031 aggregating/non-aggregating peptides with pre-calculated aggregation properties, and (iv) 912 aggregation-prone regions in amyloidogenic proteins. This database will help the scientific community (a) by facilitating research leading to improved understanding of protein aggregation, (b) by helping develop, validate and benchmark mechanistic and kinetic models of protein aggregation, and (c) by assisting experimentalists with design of their investigations and dissemination of data generated by their studies. CPAD 2.0 can be accessed at https://web.iitm.ac.in/bioinfo2/cpad2/index.html.

Entities:  

Keywords:  Protein aggregation; aggregation-prone regions; amyloid; database; fibrils

Mesh:

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Year:  2020        PMID: 31979981     DOI: 10.1080/13506129.2020.1715363

Source DB:  PubMed          Journal:  Amyloid        ISSN: 1350-6129            Impact factor:   7.141


  8 in total

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

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

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

3.  AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning.

Authors:  Phasit Charoenkwan; Saeed Ahmed; Chanin Nantasenamat; Julian M W Quinn; Mohammad Ali Moni; Pietro Lio'; Watshara Shoombuatong
Journal:  Sci Rep       Date:  2022-05-11       Impact factor: 4.996

Review 4.  Protein aggregation: in silico algorithms and applications.

Authors:  R Prabakaran; Puneet Rawat; A Mary Thangakani; Sandeep Kumar; M Michael Gromiha
Journal:  Biophys Rev       Date:  2021-01-17

5.  Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics.

Authors:  Pin-Kuang Lai; Austin Gallegos; Neil Mody; Hasige A Sathish; Bernhardt L Trout
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 5.857

6.  Solubility of proteins.

Authors:  Mauno Vihinen
Journal:  ADMET DMPK       Date:  2020-06-28

7.  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

8.  Exploring the sequence features determining amyloidosis in human antibody light chains.

Authors:  Puneet Rawat; R Prabakaran; Sandeep Kumar; M Michael Gromiha
Journal:  Sci Rep       Date:  2021-07-02       Impact factor: 4.379

  8 in total

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