Discrete molecular dynamics can predict helical prestructured motifs in disordered proteins.

Intrinsically disordered proteins (IDPs) lack a stable tertiary structure, but their short binding regions termed Pre-Structured Motifs (PreSMo) can form transient secondary structure elements in solution. Although disordered proteins are crucial in many biological processes and designing strategies to modulate their function is highly important, both experimental and computational tools to describe their conformational ensembles and the initial steps of folding are sparse. Here we report that discrete molecular dynamics (DMD) simulations combined with replica exchange (RX) method efficiently samples the conformational space and detects regions populating alpha-helical conformational states in disordered protein regions. While the available computational methods predict secondary structural propensities in IDPs based on the observation of protein-protein interactions, our ab initio method rests on physical principles of protein folding and dynamics. We show that RX-DMD predicts alpha-PreSMos with high confidence confirmed by comparison to experimental NMR data. Moreover, the method also can dissect alpha-PreSMos in close vicinity to each other and indicate helix stability. Importantly, simulations with disordered regions forming helices in X-ray structures of complexes indicate that a preformed helix is frequently the binding element itself, while in other cases it may have a role in initiating the binding process. Our results indicate that RX-DMD provides a breakthrough in the structural and dynamical characterization of disordered proteins by generating the structural ensembles of IDPs even when experimental data are not available

Differences in the Number of Intrinsically Disordered Regions between Yeast Duplicated Proteins, and Their Relationship with Functional Divergence

BACKGROUND: Intrinsically disordered regions are enriched in short interaction motifs that play a critical role in many protein-protein interactions. Since new short interaction motifs may easily evolve, they have the potential to rapidly change protein interactions and cellular signaling. In this work we examined the dynamics of gain and loss of intrinsically disordered regions in duplicated proteins to inspect if changes after genome duplication can create functional divergence. For this purpose we used Saccharomyces cerevisiae and the outgroup species Lachancea kluyveri. PRINCIPAL FINDINGS: We find that genes duplicated as part of a genome duplication (ohnologs) are significantly more intrinsically disordered than singletons (p<2.2(e)-16, Wilcoxon), reflecting a preference for retaining intrinsically disordered proteins in duplicate. In addition, there have been marked changes in the extent of intrinsic disorder following duplication. A large number of duplicated genes have more intrinsic disorder than their L. kluyveri ortholog (29% for duplicates versus 25% for singletons) and an even greater number have less intrinsic disorder than the L. kluyveri ortholog (37% for duplicates versus 25% for singletons). Finally, we show that the number of physical interactions is significantly greater in the more intrinsically disordered ohnolog of a pair (p = 0.003, Wilcoxon). CONCLUSION: This work shows that intrinsic disorder gain and loss in a protein is a mechanism by which a genome can also diverge and innovate. The higher number of interactors for proteins that have gained intrinsic disorder compared with their duplicates may reflect the acquisition of new interaction partners or new functional roles

On the Need to Develop Guidelines for Characterizing and Reporting Intrinsic Disorder in Proteins

Since the early 2000s, numerous computational tools have been created and used to predict intrinsic disorder in proteins. At present, the output from these algorithms is difficult to interpret in the absence of standards or references for comparison. There are many reasons to establish a set of standard‐based guidelines to evaluate computational protein disorder predictions. This viewpoint explores a handful of these reasons, including standardizing nomenclature to improve communication, rigor and reproducibility, and making it easier for newcomers to enter the field. An approach for reporting predicted disorder in single proteins with respect to whole proteomes is discussed. The suggestions are not intended to be formulaic; they should be viewed as a starting point to establish guidelines for interpreting and reporting computational protein disorder predictions.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148366/1/pmic13071_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148366/2/pmic13071.pd

Validation of Inflammopathic, Adaptive, and Coagulopathic Sepsis Endotypes in Coronavirus Disease 2019

Objectives: Complex critical syndromes like sepsis and coronavirus disease 2019 may be composed of underling &quot;endotypes,&quot;which may respond differently to treatment. The aim of this study was to test whether a previously defined bacterial sepsis endotypes classifier recapitulates the same clinical and immunological endotypes in coronavirus disease 2019. Design: Prospective single-center observational cohort study. Setting: Patients were enrolled in Athens, Greece, and blood was shipped to Inflammatix (Burlingame, CA) for analysis. Patients: Adult patients within 24 hours of hospital admission with coronavirus disease 2019 confirmed by polymerase chain reaction and chest radiography. Interventions: None. Measurements and Main results: We studied 97 patients with coronavirus disease 2019, of which 50 went on to severe respiratory failure (SRF) and 16 died. We applied a previously defined 33-messenger RNA classifier to assign endotype (Inflammopathic, Adaptive, or Coagulopathic) to each patient. We tested endotype status against other clinical parameters including laboratory values, severity scores, and outcomes. Patients were assigned as Inflammopathic (29%), Adaptive (44%), or Coagulopathic (27%), similar to our prior study in bacterial sepsis. Adaptive patients had lower rates of SRF and no deaths. Coagulopathic and Inflammopathic endotypes had 42% and 18% mortality rates, respectively. The Coagulopathic group showed highest d-dimers, and the Inflammopathic group showed highest C-reactive protein and interleukin-6 levels. Conclusions: Our predefined 33-messenger RNA endotypes classifier recapitulated immune phenotypes in viral sepsis (coronavirus disease 2019) despite its prior training and validation only in bacterial sepsis. Further work should focus on continued validation of the endotypes and their interaction with immunomodulatory therapy. © 2020 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved