BioAssemblyModeler (BAM): User-Friendly Homology Modeling of Protein Homo- and Heterooligomers

Shapovalov, Maxim V.; Wang, Qiang; Xu, Qifang; Andrake, Mark D.; Dunbrack, Roland L.

doi:10.1371/journal.pone.0098309

Cited by 17 publications

(13 citation statements)

References 29 publications

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“…The SOAR2 domain of the STIM2 protein was modeled as a dimer using Biological Assembly Modeler (BAM) 49 which allows simultaneous modeling of both chains based on a single template (PDB code 3TEQ). Modeling also utilized MolIDE software 50 .…”

Section: Methodsmentioning

confidence: 99%

Distinct Orai-coupling domains in STIM1 and STIM2 define the Orai-activating site

et al. 2014

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STIM1 and STIM2 are widely expressed endoplasmic reticulum (ER) Ca2+ sensor proteins able to translocate within the ER membrane to physically couple with and gate plasma membrane Orai Ca2+ channels. Although structurally similar, we reveal critical differences in the function of the short STIM-Orai activating regions (SOAR) from STIM1 and STIM2. We narrowed these differences in Orai1 gating to a strategically exposed phenylalanine residue (Phe-394) in SOAR1, which in SOAR2 is substituted by a leucine residue. Remarkably, in full-length STIM1, replacement of Phe-394 with the dimensionally similar but polar histidine headgroup, prevents both Orai1 binding and gating, creating an Orai1 non-agonist. Thus, this residue is critical in tuning the efficacy of Orai activation. While STIM1 is a full Orai1-agonist, leucine-replacement of this crucial residue in STIM2 endows it with partial agonist properties, which may be critical for limiting Orai1 activation stemming from its enhanced sensitivity to store-depletion.

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Section: Methodsmentioning

confidence: 99%

Distinct Orai-coupling domains in STIM1 and STIM2 define the Orai-activating site

et al. 2014

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“…This result is consistent with earlier studies reporting 14-15% error rates in the quaternary-structure assignment of protein homooligomers in the PDB (Ponstingl et al, 2003;Levy, 2007). Correcting these errors is important not only for individual users of the PDB, but also for servers that use the PDB's biological assemblies, such as BioAssemblyModeler (Shapovalov et al, 2014) and the Protein Biological Unit Database (Xu et al, 2006).…”

Section: Using Power Laws To Identify Correct and Prevent Misannotatmentioning

confidence: 99%

Empirical power laws for the radii of gyration of protein oligomers

Tanner

2016

Acta Crystallogr D Struct Biol

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The radius of gyration is a fundamental structural parameter that is particularly useful for describing polymers. It has been known since Flory's seminal work in the mid-20th century that polymers show a power-law dependence, where the radius of gyration is proportional to the number of residues raised to a power. The power-law exponent has been measured experimentally for denatured proteins and derived empirically for folded monomeric proteins using crystal structures. Here, the biological assemblies in the Protein Data Bank are surveyed to derive the power-law parameters for protein oligomers having degrees of oligomerization of 2–6 and 8. The power-law exponents for oligomers span a narrow range of 0.38–0.41, which is close to the value of 0.40 obtained for monomers. This result shows that protein oligomers exhibit essentially the same power-law behavior as monomers. A simple power-law formula is provided for estimating the oligomeric state from an experimental measurement of the radius of gyration. Several proteins in the Protein Data Bank are found to deviate substantially from power-law behavior by having an atypically large radius of gyration. Some of the outliers have highly elongated structures, such as coiled coils. For coiled coils, the radius of gyration does not follow a power law and instead scales linearly with the number of residues in the oligomer. Other outliers are proteins whose oligomeric state or quaternary structure is incorrectly annotated in the Protein Data Bank. The power laws could be used to identify such errors and help prevent them in future depositions.

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“…We searched protein‐protein interaction databases such as STRING and BioGrid, given in the Uniprot pages for each CASP target for possible PPIs that could be modeled for any of the CASP targets. We found 44 CASP11 targets with protein interactions that were assigned high confidence of physical interactions in these databases, and used our program BioAssemblyModeler (BAM) to search the PDB for templates, which might contain Pfam domains from the CASP target and its interacting proteins. BAM works by assigning Pfams to a query consisting of up to six different protein sequences, and then searches our PDBfam database for any structures, which contain one or more of the Pfams in the query proteins.…”

Section: Resultsmentioning

confidence: 99%

Biological function derived from predicted structures in CASP11

Huwe

Shapovalov

et al. 2016

Proteins

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In CASP11, the organizers sought to bring the biological inferences from predicted structures to the fore. To accomplish this, we assessed the models for their ability to perform quantifiable tasks related to biological function. First, for 10 targets that were probable homodimers, we measured the accuracy of docking the models into homodimers as a function of GDT-TS of the monomers, which produced characteristic L-shaped plots. At low GDT-TS, none of the models could be docked correctly as homodimers. Above GDT-TS of~60%, some models formed correct homodimers in one of the largest docked clusters, while many other models at the same values of GDT-TS did not. Docking was more successful when many of the templates shared the same homodimer. Second, we docked a ligand from an experimental structure into each of the models of one of the targets. Docking to the models with two different programs produced poor ligand RMSDs with the experimental structure. Measures that evaluated similarity of contacts were reasonable for some of the models, although there was not a significant correlation with model accuracy. Finally, we assessed whether models would be useful in predicting the phenotypes of missense mutations in three human targets by comparing features calculated from the models with those calculated from the experimental structures. The models were successful in reproducing accessible surface areas but there was little correlation of model accuracy with calculation of FoldX evaluation of the change in free energy between the wild-type and the mutant.

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BioAssemblyModeler (BAM): User-Friendly Homology Modeling of Protein Homo- and Heterooligomers

Cited by 17 publications

References 29 publications

Distinct Orai-coupling domains in STIM1 and STIM2 define the Orai-activating site

Distinct Orai-coupling domains in STIM1 and STIM2 define the Orai-activating site

Empirical power laws for the radii of gyration of protein oligomers

Biological function derived from predicted structures in CASP11

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