DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability
By Carlos H. M. Rodrigues, Douglas E. V. Pires, David B. Ascher. Nucleic Acids Res., 2018
926 citations recorded by the source corpus.
Abstract Proteins are highly dynamic molecules, whose function is intrinsically linked to their molecular motions. Despite the pivotal role of protein dynamics, their computational simulation cost has led to most structure-based approaches for assessing the impact of mutations on protein structure and function relying upon static structures. Here we present DynaMut, a web server implementing two distinct, well established normal mode approaches, which can be used to analyze and visualize protein dynamics by sampling conformations and assess the impact of mutations on protein dynamics and stability resulting from vibrational entropy changes. DynaMut integrates our graph-based signatures along with normal mode dynamics to generate a consensus prediction of the impact of a mutation on protein stability. We demonstrate our approach outperforms alternative approaches to predict the effects of mutations on protein stability and flexibility (P-value < 0.001), achieving a correlation of up to 0.70 on blind tests. DynaMut also provides a comprehensive suite for protein motion and flexibility analysis and visualization via a freely available, user friendly web server at http://biosig.unimelb.edu.au/dynamut/.
Subfields: Materials Chemistry, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/cc57ec7fb8c2cf0586d8160440863976cc05e85a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Materials Chemistry
- Douglas E. V. Pires
- Carlos H. M. Rodrigues
- David B. Ascher
- Arne Elofsson
- Janet M. Thornton
- Sergey Ovchinnikov
- Roman A. Laskowski
- Thanh-Binh Nguyen
In Molecular Biology
- Carlos H. M. Rodrigues
- Douglas E. V. Pires
- David B. Ascher
- Arne Elofsson
- Gabriele Pozzati
- Janet M. Thornton
- Sergey Ovchinnikov
- Yang Zhang
Citations in the corpus
- Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures (2022, methodology): “This dataset has been extensively used over the past decade as a benchmark for computational method development aiming to assess mutation effects on stability [44,46,47,70] and, therefore, has been extensively curated and manually inspected.”
- A structural biology community assessment of AlphaFold2 applications (2021, methodology): “These were compared against predictions made with DynaMut2, FoldX and Rosetta 30,32 (see also Supplementary Methods).”
- DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations (2020, methodology): “We previously showed that by considering both the mutation environment and the protein dynamics, we could more accurately predict the effects of single-point missense mutations.(38) Most predictive tools, however, have been limited to single point missense variants, and the inclusion of protein dyna”
- mCSM-PPI2: predicting the effects of mutations on protein–protein interactions (2019, background): “Our previously described graph-based signatures concept has proven to be a powerful approach and has been widely applied to the study of protein structure, including how mutations alter protein stability (20,26), dynamics (27) and interactions with other molecules (20,28–34).”