Browsing by Author "Parra, R. Gonzalo"

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    A contact-based analysis of local energetic frustration dynamics identifies key residues enabling RfaH fold-switch
    (2024) Gonzalez-Higueras, Jorge; Freiberger, Maria Ines; Galaz-Davison, Pablo; Parra, R. Gonzalo; Ramirez-Sarmiento, Cesar A.
    Fold-switching enables metamorphic proteins to reversibly interconvert between two highly dissimilar native states to regulate their protein functions. While about 100 proteins have been identified to undergo fold-switching, unveiling the key residues behind this mechanism for each protein remains challenging. Reasoning that fold-switching in proteins is driven by dynamic changes in local energetic frustration, we combined fold-switching simulations generated using simplified structure-based models with frustration analysis to identify key residues involved in this process based on the change in the density of minimally frustrated contacts during refolding. Using this approach to analyze the fold-switch of the bacterial transcription factor RfaH, we identified 20 residues that significantly change their frustration during its fold-switch, some of which have been experimentally and computationally reported in previous works. Our approach, which we developed as an additional module for the FrustratometeR package, highlights the role of local frustration dynamics in protein fold-switching and offers a robust tool to enhance our understanding of other proteins with significant conformational shifts.
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    Local energetic frustration conservation in protein families and superfamilies
    (2023) Freiberger, Maria I.; Ruiz-Serra, Victoria; Pontes, Camila; Romero-Durana, Miguel; Galaz-Davison, Pablo; Ramirez-Sarmiento, Cesar A.; Schuster, Claudio D.; Marti, Marcelo A.; Wolynes, Peter G.; Ferreiro, Diego U.; Parra, R. Gonzalo; Valencia, Alfonso
    Energetic local frustration offers a biophysical perspective to interpret the effects of sequence variability on protein families. Here we present a methodology to analyze local frustration patterns within protein families and superfamilies that allows us to uncover constraints related to stability and function, and identify differential frustration patterns in families with a common ancestry. We analyze these signals in very well studied protein families such as PDZ, SH3, alpha and beta globins and RAS families. Recent advances in protein structure prediction make it possible to analyze a vast majority of the protein space. An automatic and unsupervised proteome-wide analysis on the SARS-CoV-2 virus demonstrates the potential of our approach to enhance our understanding of the natural phenotypic diversity of protein families beyond single protein instances. We apply our method to modify biophysical properties of natural proteins based on their family properties, as well as perform unsupervised analysis of large datasets to shed light on the physicochemical signatures of poorly characterized proteins such as the ones belonging to emergent pathogens.