RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding. (28th December 2022)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Protein science. Volume 32:Number 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-28
• Subjects: binding free energy -- folding free energy -- free energy calculations -- Rosetta; Proteins -- Periodicals; 572.6
• DOI: 10.1002/pro.4527
• Languages: English
• ISSNs: 0961-8368

Abstract

Abstract: Reliable prediction of free energy changes upon amino acid substitutions (ΔΔ G s) 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 ΔΔ G calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔ G s 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 ΔΔ G 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 ΔΔ G s 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 .