@article {67964, title = {Many dissimilar protein domains switch between α-helix and β-sheet folds}, journal = {Nature Communications}, volume = {13}, year = {2022}, month = {2022 Jul01}, abstract = {

Hundreds of millions of structured proteins sustain life through chemical interactions and catalytic reactions1. Though dynamic, these proteins are assumed to be built upon fixed scaffolds of secondary structure, α-helices and β-sheets. Experimentally determined structures of over \>58,000 non-redundant proteins support this assumption, though it has recently been challenged by \~{}100 fold-switching proteins2. These {\textquotedblleft}metamorphic3{\textquotedblright} proteins, though ostensibly rare, raise the question of how many uncharacterized proteins have shapeshifting{\textendash}rather than fixed{\textendash}secondary structures. To address this question, we developed a comparative sequence-based approach that predicts fold-switching proteins from differences in secondary structure propensity. We applied this approach to the universally conserved NusG transcription factor family of \~{}15,000 proteins, one of which has a 50-residue regulatory subunit experimentally shown to switch between α-helical and β-sheet folds4. Our approach predicted that 25\% of the sequences in this family undergo similar α-helix ⇌ β-sheet transitions, a frequency two orders of magnitude larger than previously observed. Our predictions evade state-of-the-art computational methods but were confirmed experimentally by circular dichroism and nuclear magnetic resonance spectroscopy for all 10 assiduously chosen dissimilar variants. These results suggest that fold switching is a pervasive mechanism of transcriptional regulation in all kingdoms of life and imply that numerous uncharacterized proteins may also switch folds.

}, doi = {10.1101/2021.06.10.447921}, url = {https://doi.org/10.1101/2021.06.10.447921}, author = {Lauren L. Porter and Allen K. Kim and Swechha Rimal and Loren L. Looger and Ananya Majumdar and Brett D. Mensh and Mary Starich} } @article {67702, title = {Pervasive fold switching in a ubiquitous protein superfamily.}, journal = {bioRxiv}, year = {2021}, month = {2021 Jun 11}, abstract = {

Fold-switching proteins challenge the one-sequence-one-structure paradigm by adopting multiple stable folds. Nevertheless, it is uncertain whether fold switchers are naturally pervasive or rare exceptions to the well-established rule. To address this question, we developed a predictive method and applied it to the NusG superfamily of \>15,000 transcription factors. We predicted that a substantial population (25\%) of the proteins in this family switch folds. Circular dichroism and nuclear magnetic resonance spectroscopies of 10 sequence-diverse variants confirmed our predictions. Thus, we leveraged family-wide predictions to determine both conserved contacts and taxonomic distributions of fold-switching proteins. Our results indicate that fold switching is pervasive in the NusG superfamily and that the single-fold paradigm significantly biases structure-prediction strategies.

}, doi = {10.1101/2021.06.10.447921}, author = {Lauren L. Porter and Allen K. Kim and Loren L. Looger and Anaya Majumdar and Mary Starich} } @article {67703, title = {A sequence-based method for predicting extant fold switchers that undergo α-helix <-> β-strand transitions}, journal = {Biopolymers}, volume = {112}, year = {2021}, month = {2021 Oct 01}, abstract = {

Extant fold-switching proteins remodel their secondary structures and change their functions in response to cellular stimuli, regulating biological processes and affecting human health. In spite of their biological importance, these proteins remain understudied. Few representative examples of fold switchers are available in the Protein Data Bank, and they are difficult to predict. In fact, all 96 experimentally validated examples of extant fold switchers were stumbled upon by chance. Thus, predictive methods are needed to expedite the process of discovering and characterizing more of these shapeshifting proteins. Previous approaches require a solved structure or all-atom simulations, greatly constraining their use. Here, we propose a high-throughput sequence-based method for predicting extant fold switchers that transition from α-helix in one conformation to β-strand in the other. This method leverages two previous observations: (1) α-helix \<-\> β-strand prediction discrepancies from JPred4 are a robust predictor of fold switching, and (2) the fold-switching regions (FSRs) of some extant fold switchers have different secondary structure propensities when expressed in isolation (isolated FSRs) than when expressed within the context of their parent protein (contextualized FSRs). Combining these two observations, we ran JPred4 on the sequences of isolated and contextualized FSRs from 14 known extant fold switchers and found α-helix \<-\>β-strand prediction discrepancies in every case. To test the overall robustness of this finding, we randomly selected regions of proteins not expected to switch folds (single-fold proteins) and found significantly fewer α-helix \<-\> β-strand prediction discrepancies (p \< 4.2*10-20, Kolmogorov-Smirnov test). Combining these discrepancies with the overall percentage of predicted secondary structure, we developed a classifier that often robustly identifies extant fold switchers (Matthews Correlation Coefficient of 0.70). Although this classifier had a high false negative rate (6/14), its false positive rate was very low (1/211), suggesting that it can be used to predict a subset of extant fold switchers from billions of available genomic sequences.

}, doi = {10.1101/2021.01.14.426714}, author = {Soumya Mishra and Loren L. Looger and Lauren L. Porter} } @article {64999, title = {A sequence-based approach for identifying protein fold switchers.}, journal = {bioRxiv}, year = {2018}, month = {2018 Nov 05}, abstract = {

Although most proteins conform to the classical one-structure/one-function paradigm, an increasing number of proteins with dual structures and functions are emerging. These fold-switching proteins remodel their secondary structures in response to cellular stimuli, fostering multi-functionality and tight cellular control. Accurate predictions of fold-switching proteins could both suggest underlying mechanisms for uncharacterized biological processes and reveal potential drug targets. Previously, we developed a prediction method for fold-switching proteins based on secondary structure predictions and structure-based thermodynamic calculations. Given the large number of genomic sequences without homologous experimentally characterized structures, however, we sought to predict fold-switching proteins from their sequences alone. To do this, we leveraged state-of-the-art secondary structure predictions, which require only amino acid sequences but are not currently designed to identify structural duality in proteins. Thus, we hypothesized that incorrect and inconsistent secondary structure predictions could be good initial predictors of fold-switching proteins. We found that secondary structure predictions of fold-switching proteins with solved structures are indeed less accurate than secondary structure predictions of non-fold-switching proteins with solved structures. These inaccuracies result largely from the conformations of fold-switching proteins that are underrepresented in the Protein Data Bank (PDB), and, consequently, the training sets of secondary structure predictors. Given that secondary structure predictions are homology-based, we hypothesized that decontextualizing the inaccurately-predicted regions of fold-switching proteins could weaken the homology relationships between these regions and their overpopulated structural representatives. Thus, we reran secondary structure predictions on these regions in isolation and found that they were significantly more inconsistent than in regions of non-fold-switching proteins. Thus, inconsistent secondary structure predictions can serve as a preliminary marker of fold switching. These findings have implications for genomics and the future development of secondary structure predictors.

}, doi = {10.1101/462606}, author = {Soumya Mishra and Loren L. Looger and Lauren L. Porter} }