A neural-network-based method for predicting protein stability changes upon single point mutations (original) (raw)
Journal Article
,
Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy
Search for other works by this author on:
,
Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy
Search for other works by this author on:
Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy
Search for other works by this author on:
Received:
15 January 2004
Published:
04 August 2004
Cite
Emidio Capriotti, Piero Fariselli, Rita Casadio, A neural-network-based method for predicting protein stability changes upon single point mutations, Bioinformatics, Volume 20, Issue suppl_1, August 2004, Pages i63–i68, https://doi.org/10.1093/bioinformatics/bth928
Close
Navbar Search Filter Mobile Enter search term Search
Abstract
Motivation: One important requirement for protein design is to be able to predict changes of protein stability upon mutation. Different methods addressing this task have been described and their performance tested considering global linear correlation between predicted and experimental data. Neither is direct statistical evaluation of their prediction performance available, nor is a direct comparison among different approaches possible. Recently, a significant database of thermodynamic data on protein stability changes upon single point mutation has been generated (ProTherm). This allows the application of machine learning techniques to predicting free energy stability changes upon mutation starting from the protein sequence.
Results: In this paper, we present a neural-network-based method to predict if a given mutation increases or decreases the protein thermodynamic stability with respect to the native structure. Using a dataset consisting of 1615 mutations, our predictor correctly classifies >80% of the mutations in the database. On the same task and using the same data, our predictor performs better than other methods available on the Web. Moreover, when our system is coupled with energy-based methods, the joint prediction accuracy increases up to 90%, suggesting that it can be used to increase also the performance of pre-existing methods, and generally to improve protein design strategies.
Availability: The server is under construction and will be available at http://www.biocomp.unibo.it
*
To whom correspondence should be addressed.
Bioinformatics 20(Suppl. 1) © Oxford University Press 2004; all rights reserved.
Citations
Views
Altmetric
Metrics
Total Views 1,314
507 Pageviews
807 PDF Downloads
Since 11/1/2016
Month: | Total Views: |
---|---|
November 2016 | 4 |
December 2016 | 3 |
January 2017 | 5 |
February 2017 | 10 |
March 2017 | 7 |
April 2017 | 5 |
May 2017 | 23 |
June 2017 | 8 |
July 2017 | 9 |
August 2017 | 6 |
September 2017 | 5 |
October 2017 | 16 |
November 2017 | 5 |
December 2017 | 20 |
January 2018 | 16 |
February 2018 | 15 |
March 2018 | 24 |
April 2018 | 23 |
May 2018 | 8 |
June 2018 | 3 |
July 2018 | 10 |
August 2018 | 2 |
September 2018 | 6 |
October 2018 | 5 |
November 2018 | 11 |
December 2018 | 9 |
January 2019 | 11 |
February 2019 | 7 |
March 2019 | 14 |
April 2019 | 31 |
May 2019 | 15 |
June 2019 | 13 |
July 2019 | 19 |
August 2019 | 15 |
September 2019 | 17 |
October 2019 | 9 |
November 2019 | 16 |
December 2019 | 13 |
January 2020 | 19 |
February 2020 | 10 |
March 2020 | 20 |
April 2020 | 19 |
May 2020 | 12 |
June 2020 | 19 |
July 2020 | 5 |
August 2020 | 8 |
September 2020 | 4 |
October 2020 | 11 |
November 2020 | 11 |
December 2020 | 13 |
January 2021 | 13 |
February 2021 | 8 |
March 2021 | 18 |
April 2021 | 11 |
May 2021 | 14 |
June 2021 | 10 |
July 2021 | 7 |
August 2021 | 11 |
September 2021 | 5 |
October 2021 | 10 |
November 2021 | 7 |
December 2021 | 8 |
January 2022 | 5 |
February 2022 | 8 |
March 2022 | 9 |
April 2022 | 14 |
May 2022 | 11 |
June 2022 | 11 |
July 2022 | 13 |
August 2022 | 10 |
September 2022 | 11 |
October 2022 | 11 |
November 2022 | 16 |
December 2022 | 14 |
January 2023 | 18 |
February 2023 | 9 |
March 2023 | 33 |
April 2023 | 7 |
May 2023 | 19 |
June 2023 | 7 |
July 2023 | 19 |
August 2023 | 30 |
September 2023 | 18 |
October 2023 | 33 |
November 2023 | 40 |
December 2023 | 25 |
January 2024 | 22 |
February 2024 | 44 |
March 2024 | 18 |
April 2024 | 20 |
May 2024 | 27 |
June 2024 | 19 |
July 2024 | 16 |
August 2024 | 17 |
September 2024 | 21 |
October 2024 | 8 |
Citations
143 Web of Science
×
Email alerts
Citing articles via
More from Oxford Academic