RosettaDDGPrediction for high-throughput mutational scans: From stability to binding
Research output: Contribution to journal › Journal article › Research › peer-review
Standard
RosettaDDGPrediction for high-throughput mutational scans : From stability to binding. / Sora, Valentina; Laspiur, Adrian Otamendi; Degn, Kristine; Arnaudi, Matteo; Utichi, Mattia; Beltrame, Ludovica; De Menezes, Dayana; Orlandi, Matteo; Stoltze, Ulrik Kristoffer; Rigina, Olga; Sackett, Peter Wad; Wadt, Karin; Schmiegelow, Kjeld; Tiberti, Matteo; Papaleo, Elena.
In: Protein Science, Vol. 32, No. 1, e4527, 2023.Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - RosettaDDGPrediction for high-throughput mutational scans
T2 - From stability to binding
AU - Sora, Valentina
AU - Laspiur, Adrian Otamendi
AU - Degn, Kristine
AU - Arnaudi, Matteo
AU - Utichi, Mattia
AU - Beltrame, Ludovica
AU - De Menezes, Dayana
AU - Orlandi, Matteo
AU - Stoltze, Ulrik Kristoffer
AU - Rigina, Olga
AU - Sackett, Peter Wad
AU - Wadt, Karin
AU - Schmiegelow, Kjeld
AU - Tiberti, Matteo
AU - Papaleo, Elena
N1 - Publisher Copyright: © 2022 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.
PY - 2023
Y1 - 2023
N2 - Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein–protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.
AB - Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein–protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.
KW - binding free energy
KW - folding free energy
KW - free energy calculations
KW - Rosetta
U2 - 10.1002/pro.4527
DO - 10.1002/pro.4527
M3 - Journal article
C2 - 36461907
AN - SCOPUS:85145458554
VL - 32
JO - Protein Science
JF - Protein Science
SN - 0961-8368
IS - 1
M1 - e4527
ER -
ID: 334303856