Effects of tropomyosin on actin depolymerization and actin binding by yeast Cof1 or mouse cofilin 1.

<p>A) Actin filament depolymerization was followed for 4 min at 25°C by the decrease in light-scattering at 400 nm. 5 µM yeast F-actin assembled in the presence or absence of Tpm1 was diluted to reactions containing final concentrations of 0.5 µM Cof1 or Cof1-22 and 0.5 µM F-actin. The spontaneous depolymerization of F-actin (no cofilin), with or without Tpm1 bound was monitored in parallel. B) Decrease in pyrene actin fluorescence was followed for 4 min at 25°C after dilution of F-actin (5 µM with 8% pyrene-labelled, assembled in the presence or absence of Tpm1) to a final concentration 0.5 µM F-actin with or without of 0.5 µM Cof1, Cof1-5 or Cof1-22. C) The same experiment as in (B) was carried out with muscle F-actin assembled with or without TM1, and in the presence or absence of mouse cofilin 1.</p

Yeast Cof1 mutants Cof1-5 and Cof1-22 can depolymerize F-actin with or without tropomyosin bound.

<p>A, B, C) F-actin (10 µM) polymerized without Tpm1p was incubated with 0, 10, 20 µM Cof1, Cof1-5 and Cof1-22 respectively and with 10 µM Tpm1p was incubated with 0, 2.5, 5, 10 and 20 µM Cof1, Cof1-5 and Cof1-22 respectively. The supernatants and pellets after ultracentrifugation (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003641#s4" target="_blank">Materials and Methods</a>) were analyzed on an SDS-PAGE gel. D) The cofilin/actin molar ratios in the pellet from two independent co-sedimentation experiments using 10 µM actin and 10 µM cofilin. Ratios were determined by densitometry of Coomassie-stained SDS-gels shown in (A), (B) and (C).</p

Yeast cofilin but not mouse cofilin 1 depolymerizes F-actin decorated with tropomyosin.

<p>A) Yeast F-actin (10 µM) polymerized with or without 10 µM tropomyosin was incubated with 0, 10 or 20 µM Cof1 (Tpm1-containing sample), or with 0 or 10 µM Cof1 (TM1 or TM4-containing sample). The supernatants and pellets after ultracentrifugation (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003641#s4" target="_blank">Materials and Methods</a>) were analyzed on an SDS-PAGE gel. B) F-actin (10 µM) polymerized with or without 10 µM Tpm1p was incubated with 0, 2.5, 5, 10, 20 and 40 µM Cof1. Subsequent analysis was done as in (A). C) Rabbit muscle actin (10 µM) polymerized with or without 10 µM TM1 was incubated with 0, 5, 10, 20 and 40 µM mouse cofilin 1. Subsequent analysis was done as in (A). D, E) Quantification by densitometry of actin in pellet fractions (as % of the total actin) from experiments in (B) and (C), respectively.</p

Improving Proteomics Mass Accuracy by Dynamic Offline Lock Mass

Several methods to obtain low-ppm mass accuracy have been described. In particular, online or offline lock mass approaches can use background ions, produced by electrospray under ambient conditions, as calibrants. However, background ions such as protonated and ammoniated polydimethylcyclosiloxane ions have relatively weak and fluctuating intensity. To address this issue, we implemented dynamic offline lock mass (DOLM). Within every MS1 survey spectrum, DOLM dynamically selected the strongest <i>n</i> background ions for statistical treatments and <i>m</i>/<i>z</i> recalibration. We systematically optimized the mass profile abstraction method to find one single <i>m</i>/<i>z</i> value to represent an ion and the number of calibrants. To assess the influence of the intensity of the analyte ions, we used tandem mass spectroscopy (MS/MS) datasets obtained from MudPIT analyses of two protein samples with different dynamic ranges. DOLM outperformed both external mass calibration and offline lock mass that used predetermined calibrant ions, especially in the low-ppm range. The unique dynamic feature of DOLM was able to adapt to wide variations in calibrant intensities, leading to averaged mass error center at 0.03 ± 0.50 ppm for precursor ions. Such consistently tight mass accuracies meant that a precursor mass tolerance as low as 1.5 ppm could be used to search or filter post-search DOLM-recalibrated MS/MS datasets

Yeast Cofilin binds Tpm1.

<p>A) Tpm1 binds directly to wild-type Cof1 and mutant Cof1-5 but not Cof1-22. BSA beads were used as a control. Beads coated with Tpm1 were incubated with 50 nM Cof1, Cof1-5 or Cof1-22. Bound cofilin was visualized by immunoblotting using yeast cofilin antibody <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003641#pone.0003641-Moon1" target="_blank">[39]</a>. B) Determination of dissociation constant K_d between Cof1 and Tpm1. Beads coated with 0 to 8 µM Tpm1 were incubated with 50 nM Cof1. Bound cofilin was visualized as in (A). C) Bound and free Tpm1 from (B) were quantified and plotted. The calculated K_d was 3.34±0.12 µM (mean±SD).</p

Improving Proteomics Mass Accuracy by Dynamic Offline Lock Mass

Several methods to obtain low-ppm mass accuracy have been described. In particular, online or offline lock mass approaches can use background ions, produced by electrospray under ambient conditions, as calibrants. However, background ions such as protonated and ammoniated polydimethylcyclosiloxane ions have relatively weak and fluctuating intensity. To address this issue, we implemented dynamic offline lock mass (DOLM). Within every MS1 survey spectrum, DOLM dynamically selected the strongest <i>n</i> background ions for statistical treatments and <i>m</i>/<i>z</i> recalibration. We systematically optimized the mass profile abstraction method to find one single <i>m</i>/<i>z</i> value to represent an ion and the number of calibrants. To assess the influence of the intensity of the analyte ions, we used tandem mass spectroscopy (MS/MS) datasets obtained from MudPIT analyses of two protein samples with different dynamic ranges. DOLM outperformed both external mass calibration and offline lock mass that used predetermined calibrant ions, especially in the low-ppm range. The unique dynamic feature of DOLM was able to adapt to wide variations in calibrant intensities, leading to averaged mass error center at 0.03 ± 0.50 ppm for precursor ions. Such consistently tight mass accuracies meant that a precursor mass tolerance as low as 1.5 ppm could be used to search or filter post-search DOLM-recalibrated MS/MS datasets

Improving Proteomics Mass Accuracy by Dynamic Offline Lock Mass

Several methods to obtain low-ppm mass accuracy have been described. In particular, online or offline lock mass approaches can use background ions, produced by electrospray under ambient conditions, as calibrants. However, background ions such as protonated and ammoniated polydimethylcyclosiloxane ions have relatively weak and fluctuating intensity. To address this issue, we implemented dynamic offline lock mass (DOLM). Within every MS1 survey spectrum, DOLM dynamically selected the strongest <i>n</i> background ions for statistical treatments and <i>m</i>/<i>z</i> recalibration. We systematically optimized the mass profile abstraction method to find one single <i>m</i>/<i>z</i> value to represent an ion and the number of calibrants. To assess the influence of the intensity of the analyte ions, we used tandem mass spectroscopy (MS/MS) datasets obtained from MudPIT analyses of two protein samples with different dynamic ranges. DOLM outperformed both external mass calibration and offline lock mass that used predetermined calibrant ions, especially in the low-ppm range. The unique dynamic feature of DOLM was able to adapt to wide variations in calibrant intensities, leading to averaged mass error center at 0.03 ± 0.50 ppm for precursor ions. Such consistently tight mass accuracies meant that a precursor mass tolerance as low as 1.5 ppm could be used to search or filter post-search DOLM-recalibrated MS/MS datasets

High-scoring binders of Tpm1 affinity purifications identified by MudPIT.

a<p>Proteins were first ranked based on their frequency of detection in the Tmp1 affinity purifications and their absence (or presence at much lower levels based on NSAF values) in the BSA negative controls. Proteins were then ranked based on their NSAF values averaged across the three Tmp1 experiments.</p>b<p>NSAF (normalized spectral abundance factor) values measured in the Urea or KCl eluted samples and negative controls, as well as NSAF values calculated when spectral counts and proteins from all four runs were merged (ALL_NSAF).</p>c<p>All_P, All_sS, and All_uS are respectively the peptide, shared spectral, and unique spectral counts when the four runs were merged, while All_SC is the final sequence coverage.</p

Severing of Tpm1-bound yeast F-actin by Cof1 but not Cof1-22.

<p>A) Representative confocal images of F-actin (5 µM), assembled without (upper panels) or with Tpm1 (lower panels), after incubation with 50 nM Cof1 for lengths of time as indicated (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003641#s4" target="_blank">Materials and Methods</a> for more details). B) Representative confocal images of F-actin (5 µM), assembled without (upper panels) or with 5 µM Tpm1 (lower panels), after incubation with 50 nM Cof1-5 (left panels) or Cof1-22 (right panels) for 2 min. C) Measurements of actin filaments length from images recorded in experiments in (A) and (B). Shown are averages of filament length measurements from three fields per sample with error bars representing standard deviations. D) Representative confocal images of F-actin (5 µM), assembled with 5 µM Tpm1, after incubation with 50 nM Cof1-22 for lengths of time as indicated.</p

Improving Proteomics Mass Accuracy by Dynamic Offline Lock Mass

Several methods to obtain low-ppm mass accuracy have been described. In particular, online or offline lock mass approaches can use background ions, produced by electrospray under ambient conditions, as calibrants. However, background ions such as protonated and ammoniated polydimethylcyclosiloxane ions have relatively weak and fluctuating intensity. To address this issue, we implemented dynamic offline lock mass (DOLM). Within every MS1 survey spectrum, DOLM dynamically selected the strongest <i>n</i> background ions for statistical treatments and <i>m</i>/<i>z</i> recalibration. We systematically optimized the mass profile abstraction method to find one single <i>m</i>/<i>z</i> value to represent an ion and the number of calibrants. To assess the influence of the intensity of the analyte ions, we used tandem mass spectroscopy (MS/MS) datasets obtained from MudPIT analyses of two protein samples with different dynamic ranges. DOLM outperformed both external mass calibration and offline lock mass that used predetermined calibrant ions, especially in the low-ppm range. The unique dynamic feature of DOLM was able to adapt to wide variations in calibrant intensities, leading to averaged mass error center at 0.03 ± 0.50 ppm for precursor ions. Such consistently tight mass accuracies meant that a precursor mass tolerance as low as 1.5 ppm could be used to search or filter post-search DOLM-recalibrated MS/MS datasets