Premium
Evaluating the predictions of the protein stability change upon single amino acid substitutions for the FXN CAGI5 challenge
Author(s) -
Savojardo Castrense,
Petrosino Maria,
Babbi Giulia,
Bovo Samuele,
CorbiVerge Carles,
Casadio Rita,
Fariselli Piero,
Folkman Lukas,
Garg Aditi,
Karimi Mostafa,
Katsonis Panagiotis,
Kim Philip M.,
Lichtarge Olivier,
Martelli Pier Luigi,
Pasquo Alessandra,
Pal Debnath,
Shen Yang,
Strokach Alexey V.,
Turina Paola,
Zhou Yaoqi,
Andreoletti Gaia,
Brenner Steven E.,
Chiaraluce Roberta,
Consalvi Valerio,
Capriotti Emidio
Publication year - 2019
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.23843
Subject(s) - frataxin , biology , computational biology , amino acid , genetics , genome , protein stability , stability (learning theory) , gene , biochemistry , iron binding proteins , computer science , machine learning
Frataxin (FXN) is a highly conserved protein found in prokaryotes and eukaryotes that is required for efficient regulation of cellular iron homeostasis. Experimental evidence associates amino acid substitutions of the FXN to Friedreich Ataxia, a neurodegenerative disorder. Recently, new thermodynamic experiments have been performed to study the impact of somatic variations identified in cancer tissues on protein stability. The Critical Assessment of Genome Interpretation (CAGI) data provider at the University of Rome measured the unfolding free energy of a set of variants (FXN challenge data set) with far‐UV circular dichroism and intrinsic fluorescence spectra. These values have been used to calculate the change in unfolding free energy between the variant and wild‐type proteins at zero concentration of denaturant ( Δ Δ G H 2 O ) . The FXN challenge data set, composed of eight amino acid substitutions, was used to evaluate the performance of the current computational methods for predicting the Δ Δ G H 2 Ovalue associated with the variants and to classify them as destabilizing and not destabilizing. For the fifth edition of CAGI, six independent research groups from Asia, Australia, Europe, and North America submitted 12 sets of predictions from different approaches. In this paper, we report the results of our assessment and discuss the limitations of the tested algorithms.