Assessing the predicted impact of single amino acid substitutions in calmodulin for CAGI6 challenges

Paola Turina; Giuditta Dal Cortivo; Carlos A Enriquez Sandoval; Emil Alexov; David B Ascher; Giulia Babbi; Constantina Bakolitsa; Rita Casadio; Piero Fariselli; Lukas Folkman; Akash Kamandula; Panagiotis Katsonis; Dong Li; Olivier Lichtarge; Pier Luigi Martelli; Shailesh Kumar Panday; Douglas E V Pires; Stephanie Portelli; Fabrizio Pucci; Carlos H M Rodrigues; Marianne Rooman; Castrense Savojardo; Martin Schwersensky; Yang Shen; Alexey V Strokach; Yuanfei Sun; Junwoo Woo; Predrag Radivojac; Steven E Brenner; Daniele Dell'Orco; Emidio Capriotti

doi:10.1007/s00439-024-02720-y

Assessing the predicted impact of single amino acid substitutions in calmodulin for CAGI6 challenges

Hum Genet. 2024 Dec 23. doi: 10.1007/s00439-024-02720-y. Online ahead of print.

Authors

Paola Turina^#¹, Giuditta Dal Cortivo^#², Carlos A Enriquez Sandoval¹, Emil Alexov³, David B Ascher^{4

5}, Giulia Babbi¹, Constantina Bakolitsa⁶, Rita Casadio¹, Piero Fariselli⁷, Lukas Folkman⁸, Akash Kamandula⁹, Panagiotis Katsonis¹⁰, Dong Li¹¹, Olivier Lichtarge¹⁰, Pier Luigi Martelli¹, Shailesh Kumar Panday³, Douglas E V Pires¹², Stephanie Portelli^{4

5}, Fabrizio Pucci¹¹, Carlos H M Rodrigues⁴, Marianne Rooman¹¹, Castrense Savojardo¹, Martin Schwersensky¹¹, Yang Shen¹³, Alexey V Strokach¹⁴, Yuanfei Sun¹³, Junwoo Woo¹⁵, Predrag Radivojac⁹, Steven E Brenner^{6

16

17}, Daniele Dell'Orco¹⁸, Emidio Capriotti^{19

20}

Affiliations

¹ Department of Pharmacy and Biotechnology, University of Bologna, 40126, Bologna, Italy.
² Department of Neurosciences, Biomedicine, and Movement Sciences, Section of Biological Chemistry, University of Verona, 37134, Verona, Italy.
³ Department of Physics and Astronomy, Clemson University, Clemson, SC, 29634, USA.
⁴ Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, 3004, Australia.
⁵ School of Chemistry and Molecular Biosciences, Australian Centre for Ecogenomics, University of Queensland, St Lucia, QLD, 4072, Australia.
⁶ Department of Plant and Microbial Biology and Center for Computational Biology, University of California, Berkeley, CA, USA.
⁷ Department of Medical Sciences, University of Torino, Turin, Italy.
⁸ Institute for Integrated and Intelligent Systems, Griffith University, Southport, QLD, Australia.
⁹ Khoury College of Computer Sciences, Northeastern University, Boston, MA, 02115, USA.
¹⁰ Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
¹¹ Computational Biology and Bioinformatics, Université Libre de Bruxelles, 50 Roosevelt Ave, 1050, Brussels, Belgium.
¹² School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC, 3053, Australia.
¹³ Department of Electrical and Computer Engineering Texas, A&M University, College Station, TX, USA.
¹⁴ Department of Computer Science, University of Toronto, Toronto, ON, Canada.
¹⁵ , 3 Billion, Seoul, South Korea.
¹⁶ Biophysics Graduate Group, University of California, Berkeley, CA, 94720, USA.
¹⁷ Center for Computational Biology, University of California, Berkeley, CA, 94720, USA.
¹⁸ Department of Neurosciences, Biomedicine, and Movement Sciences, Section of Biological Chemistry, University of Verona, 37134, Verona, Italy. [email protected].
¹⁹ Department of Pharmacy and Biotechnology, University of Bologna, 40126, Bologna, Italy. [email protected].
²⁰ Computational Genomics Platform, IRCCS University Hospital of Bologna, 40138, Bologna, Italy. [email protected].

^# Contributed equally.

PMID: 39714488
DOI: 10.1007/s00439-024-02720-y

Abstract

Recent thermodynamic and functional studies have been conducted to evaluate the impact of amino acid substitutions on Calmodulin (CaM). The Critical Assessment of Genome Interpretation (CAGI) data provider at University of Verona (Italy) measured the melting temperature (T_m) and the percentage of unfolding (%unfold) of a set of CaM variants (CaM challenge dataset). Thermodynamic measurements for the equilibrium unfolding of CaM were obtained by monitoring far-UV Circular Dichroism as a function of temperature. These measurements were used to determine the T_m and the percentage of protein remaining unfolded at the highest temperature. The CaM challenge dataset, comprising a total of 15 single amino acid substitutions, was used to evaluate the effectiveness of computational methods in predicting the T_m and unfolding percentages associated with the variants, and categorizing them as destabilizing or not. For the sixth edition of CAGI, nine independent research groups from four continents (Asia, Australia, Europe, and North America) submitted over 52 sets of predictions, derived from various approaches. In this manuscript, we summarize the results of our assessment to highlight the potential limitations of current algorithms and provide insights into the future development of more accurate prediction tools. By evaluating the thermodynamic stability of CaM variants, this study aims to enhance our understanding of the relationship between amino acid substitutions and protein stability, ultimately contributing to more accurate predictions of the effects of genetic variants.

Abstract

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