Literature DB >> 33747245

Protein aggregation: in silico algorithms and applications.

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

Abstract

Protein aggregation is a topic of immense interest to the scientific community due to its role in several neurodegenerative diseases/disorders and industrial importance. Several in silico techniques, tools, and algorithms have been developed to predict aggregation in proteins and understand the aggregation mechanisms. This review attempts to provide an essence of the vast developments in in silico approaches, resources available, and future perspectives. It reviews aggregation-related databases, mechanistic models (aggregation-prone region and aggregation propensity prediction), kinetic models (aggregation rate prediction), and molecular dynamics studies related to aggregation. With a multitude of prediction models related to aggregation already available to the scientific community, the field of protein aggregation is rapidly maturing to tackle new applications. © International Union for Pure and Applied Biophysics (IUPAB) and Springer-Verlag GmbH Germany, part of Springer Nature 2021.

Entities:  

Keywords:  Aggregation kinetics; Aggregation propensity; Algorithm; Molecular dynamics; Peptide assembly; Prediction; Protein aggregation

Year:  2021        PMID: 33747245      PMCID: PMC7930180          DOI: 10.1007/s12551-021-00778-w

Source DB:  PubMed          Journal:  Biophys Rev        ISSN: 1867-2450


  159 in total

1.  Sequence determinants of amyloid fibril formation.

Authors:  Manuela López de la Paz; Luis Serrano
Journal:  Proc Natl Acad Sci U S A       Date:  2003-12-22       Impact factor: 11.205

Review 2.  The Zyggregator method for predicting protein aggregation propensities.

Authors:  Gian Gaetano Tartaglia; Michele Vendruscolo
Journal:  Chem Soc Rev       Date:  2008-05-27       Impact factor: 54.564

Review 3.  Prediction of amyloid aggregation in vivo.

Authors:  Mattia Belli; Matteo Ramazzotti; Fabrizio Chiti
Journal:  EMBO Rep       Date:  2011-07-01       Impact factor: 8.807

4.  De novo design of a biologically active amyloid.

Authors:  Rodrigo Gallardo; Meine Ramakers; Frederik De Smet; Filip Claes; Ladan Khodaparast; Laleh Khodaparast; José R Couceiro; Tobias Langenberg; Maxime Siemons; Sofie Nyström; Laurence J Young; Romain F Laine; Lydia Young; Enrico Radaelli; Iryna Benilova; Manoj Kumar; An Staes; Matyas Desager; Manu Beerens; Petra Vandervoort; Aernout Luttun; Kris Gevaert; Guy Bormans; Mieke Dewerchin; Johan Van Eldere; Peter Carmeliet; Greetje Vande Velde; Catherine Verfaillie; Clemens F Kaminski; Bart De Strooper; Per Hammarström; K Peter R Nilsson; Louise Serpell; Joost Schymkowitz; Frederic Rousseau
Journal:  Science       Date:  2016-11-11       Impact factor: 47.728

5.  A method for probing the mutational landscape of amyloid structure.

Authors:  Charles W O'Donnell; Jérôme Waldispühl; Mieszko Lis; Randal Halfmann; Srinivas Devadas; Susan Lindquist; Bonnie Berger
Journal:  Bioinformatics       Date:  2011-07-01       Impact factor: 6.937

6.  FISH Amyloid - a new method for finding amyloidogenic segments in proteins based on site specific co-occurrence of aminoacids.

Authors:  Pawel Gasior; Malgorzata Kotulska
Journal:  BMC Bioinformatics       Date:  2014-02-24       Impact factor: 3.169

7.  ABodyBuilder: Automated antibody structure prediction with data-driven accuracy estimation.

Authors:  Jinwoo Leem; James Dunbar; Guy Georges; Jiye Shi; Charlotte M Deane
Journal:  MAbs       Date:  2016-07-08       Impact factor: 5.857

8.  Aggrescan3D (A3D) 2.0: prediction and engineering of protein solubility.

Authors:  Aleksander Kuriata; Valentin Iglesias; Jordi Pujols; Mateusz Kurcinski; Sebastian Kmiecik; Salvador Ventura
Journal:  Nucleic Acids Res       Date:  2019-07-02       Impact factor: 16.971

9.  Prediction and analysis of antibody amyloidogenesis from sequences.

Authors:  Chyn Liaw; Chun-Wei Tung; Shinn-Ying Ho
Journal:  PLoS One       Date:  2013-01-07       Impact factor: 3.240

10.  MetAmyl: a METa-predictor for AMYLoid proteins.

Authors:  Mathieu Emily; Anthony Talvas; Christian Delamarche
Journal:  PLoS One       Date:  2013-11-19       Impact factor: 3.240

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  6 in total

Review 1.  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 2.  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

3.  Understanding the mutational frequency in SARS-CoV-2 proteome using structural features.

Authors:  Puneet Rawat; Divya Sharma; Medha Pandey; R Prabakaran; M Michael Gromiha
Journal:  Comput Biol Med       Date:  2022-06-07       Impact factor: 6.698

Review 4.  Computational models for studying physical instabilities in high concentration biotherapeutic formulations.

Authors:  Marco A Blanco
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 5.857

5.  Characterization of a Stable Form of Carboxypeptidase G2 (Glucarpidase), a Potential Biobetter Variant, From Acinetobacter sp. 263903-1.

Authors:  Issa Sadeghian; Shiva Hemmati
Journal:  Mol Biotechnol       Date:  2021-07-15       Impact factor: 2.695

6.  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

  6 in total

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