RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding

Abstract

Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and proteinprotein interaction. Advances in experimental mutational scans allow highthroughput 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-Valentina Sora and Adrian Otamendi Laspiur equally contributed to this study.

ASM Variants in the Spotlight: A Structure-Based Atlas for Unraveling Pathogenic Mechanisms in Lysosomal Acid Sphingomyelinase

“…In the STABILITY module, we used foldx5 and MutateX, along with Rosetta cartddg2020 69 protocol and ref2015 70 energy function with RosettaDDGPrediction 71 estimate of changes in folding free energy. We used a consensus approach among the two methods to classify the variants, as explained in the original publication 18 .…”

TRAP1 S-nitrosylation as a model of population-shift mechanism to study the effects of nitric oxide on redox-sensitive oncoproteins

“…The only exception was the TP53 DNA binding domain (DBD). We used as starting structure a pre-processed AlphaFold model as described elsewhere [ 94 ]. In the p53 CABS-flex runs, we decided to restrain the residues that make up the protein’s zinc ion (Zn 2+ ) coordination site in the experimental structures binding site to ensure that its conformation would stay similar to one of the experimental structures, as CABS-flex doesn’t support cofactors such as zinc, to the best of our knowledge.…”

“…For TP53, we used Cancermuts to include also mutations from other sources [ 60 ]. We then applied the STABILITY module of MAVISp to classify the effects of the mutations in unknown, destabilizing, stabilizing, or neutral according to changes in the folding free energy as estimated with the MutateX protocol [ 100 ] with Foldx5 [ 101 ], as well as with RosettaDDGPrediction [ 94 ] with the cartddg2020 protocol [ 102 ] and the ref2015 energy function [ 103 ]. We retrieved the classification for the variants of interest as benign, pathogenic, or variants of unknown significance from ClinVar [ 62 ].…”

AVENGERS: Analysis of Variant Effects using Next Generation sequencing to EnhanceBRCA2Stratification

“…We applied the simple mode of the MAVISp framework 49 to retrieve and aggregate missense mutations in COSMIC 50 , cBioPortal 51 , and ClinVar 36 for BRCA2 using the Uniprot identifier P51587 and the RefSeq identifier NP_000050. Within the MAVISp framework, we used free energy calculations to predict the effects of the variants on folding/unfolding or binding free energies 57,58 . In addition, folding free energy data for all potential mutations within BRCA2 region spanning residues 2470-3185 were obtained using MutateX and RosettaDDGP prediction with the cartesian2020 protocol and ref2015 energy function 57,58 .…”

“…Within the MAVISp framework, we used free energy calculations to predict the effects of the variants on folding/unfolding or binding free energies 57,58 . In addition, folding free energy data for all potential mutations within BRCA2 region spanning residues 2470-3185 were obtained using MutateX and RosettaDDGP prediction with the cartesian2020 protocol and ref2015 energy function 57,58 . We classified the mutations for changes in folding or binding free energy according to a consensus approach between the two protocols applied for the free energy calculations, as previously described 49 .…”