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Evaluating the predictions of the protein stability change upon single amino acid substitutions for the FXN CAGI5 challenge

Savojardo, Castrense and Petrosino, Maria and Babbi, Giulia and Bovo, Samuele and Corbi-Verge, Caries and Casadio, Rita and Fariselli, Piero and Folkman, Lukas and Garg, Aditi and Karimi, Mostafa and Katsonis, Panagiotis and Kim, Philip M and Lichtarge, Olivier and Martelli, Pier Luigi and Pasquo, Alessandra and Pal, Debnath and Shen, Yang and Strokach, Alexey and Turin, Paola and Zhou, Yaoqi and Andreoletti, Gaia and Brenner, Steven E and Chiaraluce, Roberta and Consalvi, Valerio and Capriotti, Emidio (2019) Evaluating the predictions of the protein stability change upon single amino acid substitutions for the FXN CAGI5 challenge. In: HUMAN MUTATION, 40 (9, SI). pp. 1392-1399.

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Official URL: https://dx.doi.org/10.1002/humu.23843


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 (Delta Delta GH2O). 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 Delta Delta GH2O value 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.

Item Type: Journal Article
Publisher: WILEY
Additional Information: copyright for this article belongs to WILEY
Keywords: machine learning; protein folding; protein stability; single amino acid variant; free energy change
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 21 Oct 2019 10:00
Last Modified: 21 Oct 2019 10:00
URI: http://eprints.iisc.ac.in/id/eprint/63730

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