Evaluating the predictions of the protein stability change upon single amino acid substitutions for the FXN CAGI5 challenge
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Petrosino, Maria
Babbi, Giulia
Bovo, Samuele
Corbi-Verge, Caries
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
Turina, Paola
Zhou, Yaoqi
Andreoletti, Gaia
Brenner, Steven E
Chiaraluce, Roberta
Consalvi, Valerio
Capriotti, Emidio
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Abstract
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 (Formula presented.). 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 (Formula presented.) 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.
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Human Mutation
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40
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9
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Genetics
Clinical sciences
Science & Technology
Life Sciences & Biomedicine
Genetics & Heredity
machine learning
protein folding
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Savojardo, C; Petrosino, M; Babbi, G; Bovo, S; Corbi-Verge, C; Casadio, R; Fariselli, P; Folkman, L; Garg, A; Karimi, M; Katsonis, P; Kim, PM; Lichtarge, O; Martelli, PL; Pasquo, A; Pal, D; Shen, Y; Strokach, A; Turina, P; Zhou, Y; Andreoletti, G; Brenner, SE; Chiaraluce, R; Consalvi, V; Capriotti, E, Evaluating the predictions of the protein stability change upon single amino acid substitutions for the FXN CAGI5 challenge, Human Mutation, 2019, 40 (9), pp. 1392-1399