CIDER: Resources to Analyze Sequence-Ensemble Relationships of Intrinsically Disordered Proteins

Holehouse, Alex S.; Das, Rahul K.; Ahad, James; Richardson, Mary O.G.; Pappu, Rohit V.

doi:10.1016/j.bpj.2016.11.3200

Cited by 435 publications

(494 citation statements)

References 45 publications

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“…Net charge per residue for OCT4 was determined by computing the average amino acid charge along the OCT4 amino acid sequence in a 5 amino acid sliding window using the localCIDER package (Holehouse et al, 2017). …”

Section: Star Methodsmentioning

confidence: 99%

Transcription Factors Activate Genes through the Phase-Separation Capacity of Their Activation Domains

Boija

Klein

Sabari

et al. 2018

Cell

1,450

1,301

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Gene expression is controlled by transcription factors (TFs) that consist of DNA-binding domains (DBDs) and activation domains (ADs). The DBDs have been well-characterized, but little is known about the mechanisms by which ADs effect gene activation. Here we report that diverse ADs form phase-separated condensates with the Mediator coactivator. For the OCT4 and GCN4 TFs, we show that the ability to form phase-separated droplets with Mediator in vitro and the ability to activate genes in vivo are dependent on the same amino acid residues. For the estrogen receptor (ER), a ligand-dependent activator, we show that estrogen enhances phase separation with Mediator, again linking phase separation with gene activation. These results suggest that diverse TFs can interact with Mediator through the phase-separating capacity of their ADs and that formation of condensates with Mediator is involved in gene activation.

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Section: Star Methodsmentioning

confidence: 99%

Transcription Factors Activate Genes through the Phase-Separation Capacity of Their Activation Domains

Boija

Klein

Sabari

et al. 2018

Cell

1,450

1,301

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“…Recovered immune receptor, V(D)J recombination reads, representing either TRA or TRB, were translated into an aa sequence, and the CDR3 domain, defined at the beginning by the conserved cysteine at the end of all V regions, and at the end by the first aa in the conserved Phe/Trp‐Gly‐X‐Gly J‐region motif, was analyzed for net charge per residue (NCPR) using the LOCALCIDER python package (http://pappulab.github.io/localCIDER/). ‘Mutect’ mutation data were obtained from Genomic Data Commons.…”

Section: Methodsmentioning

confidence: 99%

A scoring system for the electrostatic complementarities of T‐cell receptors and cancer‐mutant amino acids: multi‐cancer analyses of associated survival rates

et al. 2020

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Summary The anti‐tumor immune response is considered to be due to the T‐cell receptor (TCR) binding to tumor antigens, which can be either wild‐type, early stem cell proteins, presumably foreign to a developed immune system; or mutant peptides, foreign to the immune system because of a mutant amino acid (aa) or otherwise somatically altered aa sequence. Recently, very large numbers of TCR complementarity‐determining region‐3 (CDR3) aa sequences obtained from tumor specimens have become available. We developed a novel algorithm for assessing the complementarity of tumor mutant peptides and TCR CDR3s, based on the retrieval of TCR CDR3 aa sequences from both tumor specimen and patient blood exomes and by using an automated process of assessing CDR3 and mutant aa electrical charges. Results indicated many instances where high electrostatic complementarity was associated with a higher survival rate. In particular, our approach led to the identification of specific genes contributing significantly to the complementary, TCR CDR3‐mutant aa. These results suggest a novel approach to tumor immunoscoring and may lead to the identification of high‐priority neo‐antigen, peptide vaccines; or to the identification of ex vivo stimulants of tumor‐infiltrating lymphocytes.

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“…We performed all-atom Metropolis Monte Carlo thermal replica exchange simulations for five of the IDPs, using the ABSINTH implicit solvation model and force-field paradigm (24). This combination has proved to be useful for the analysis of conformationally heterogeneous IDPs (42,44). Details of the simulations are described in SI Appendix, Note S6.…”

Section: Source Of the Discrepant Inferences Regarding The Extent Ofmentioning

confidence: 99%

Decoupling of size and shape fluctuations in heteropolymeric sequences reconciles discrepancies in SAXS vs. FRET measurements

Fuertes

Banterle

Ruff

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Unfolded states of proteins and native states of intrinsically disordered proteins (IDPs) populate heterogeneous conformational ensembles in solution. The average sizes of these heterogeneous systems, quantified by the radius of gyration (R G ), can be measured by small-angle X-ray scattering (SAXS). Another parameter, the mean dye-to-dye distance (R E ) for proteins with fluorescently labeled termini, can be estimated using single-molecule Förster resonance energy transfer (smFRET). A number of studies have reported inconsistencies in inferences drawn from the two sets of measurements for the dimensions of unfolded proteins and IDPs in the absence of chemical denaturants. These differences are typically attributed to the influence of fluorescent labels used in smFRET and to the impact of high concentrations and averaging features of SAXS. By measuring the dimensions of a collection of labeled and unlabeled polypeptides using smFRET and SAXS, we directly assessed the contributions of dyes to the experimental values R G and R E . For chemically denatured proteins we obtain mutual consistency in our inferences based on R G and R E , whereas for IDPs under native conditions, we find substantial deviations. Using computations, we show that discrepant inferences are neither due to methodological shortcomings of specific measurements nor due to artifacts of dyes. Instead, our analysis suggests that chemical heterogeneity in heteropolymeric systems leads to a decoupling between R E and R G that is amplified in the absence of denaturants. Therefore, joint assessments of R G and R E combined with measurements of polymer shapes should provide a consistent and complete picture of the underlying ensembles.single-molecule FRET | intrinsically disordered proteins | denatured-state ensemble | protein folding | polymer theory Q uantitative characterizations of the sizes, shapes, and amplitudes of conformational fluctuations of unfolded proteins under denaturing and native conditions are directly relevant to advancing our understanding of the collapse transition during protein folding. These types of studies are also relevant to furthering our understanding of the functions and interactions of intrinsically disordered proteins (IDPs) in physiologically relevant conditions (1). Polymer physics theories provide the conceptual foundations for analyzing conformationally heterogeneous systems such as IDPs and unfolded ensembles of autonomously foldable proteins (2-4). Specifically, order parameters in theories of coil-toglobule transitions and analytical descriptions of conformational ensembles (5, 6) are based on ensemble-averaged values of radii of gyration (R G ) and amplitudes of fluctuations measured by end-toend distances (R E ).Estimates of R G are accessible through small-angle X-ray scattering (SAXS) measurements because scattering intensities are directly related to the global protein size (Fig. 1) (7, 8). At finite concentrations, assuming the absence of intermolecular interactions, R G is proportional to the square root o...

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CIDER: Resources to Analyze Sequence-Ensemble Relationships of Intrinsically Disordered Proteins

Cited by 435 publications

References 45 publications

Transcription Factors Activate Genes through the Phase-Separation Capacity of Their Activation Domains

Transcription Factors Activate Genes through the Phase-Separation Capacity of Their Activation Domains

A scoring system for the electrostatic complementarities of T‐cell receptors and cancer‐mutant amino acids: multi‐cancer analyses of associated survival rates

Decoupling of size and shape fluctuations in heteropolymeric sequences reconciles discrepancies in SAXS vs. FRET measurements

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