pgex-4t1 Search Results


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Addgene inc pgex 4ti shp2 wt plasmid
(a) Schematic for in silico pK a prediction method for proteins with solved structures (see text and methods for details). Briefly, all available structures in the protein database are curated, electrostatic properties are calculated using PROPKA, results are filtered for ionizable residues with physiologically relevant predicted pK a values, and data are visualized in a 3D structure or a 2D residue interaction network. (b) Crystal structure of <t>SHP2</t> shown in cartoon and surface format (PDB ID:2SHP). Protein tyrosine phosphatase (PTP) domain colored in grey, SH2 domains colored in yellow. (c) Structure of SHP2 (PDB ID:2SHP) with protein tyrosine phosphatase (PTP) domain in grey and SH2 domains in yellow. Residues identified through in silico ionizable network prediction pipeline shown in spheres. Residues with predicted pK a shifts (cyan) cluster with ionizable interactors (magenta) across the phosphatase-SH2 domain interaction interface of SHP2. (d) Table of predicted pK a s for cyan residues identified using in silico ionizable network prediction pipeline on 47 SHP2 structures (mean ± SD). (e) Residue interaction network of residues with predicted pK a shifts (cyan) and their ionizable interactors (magenta). Length of edges reflect the strength of the coulombic interaction, with stronger coulombic interactions having shorter edge lengths (f) Zoom of SHP2 structure at the PTP-SH2 interaction interface. Networked residues from a and b are shown in stick. Residues with predicted pK a shifts in cyan and ionizing interactors in magenta.
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(a) Schematic for in silico pK a prediction method for proteins with solved structures (see text and methods for details). Briefly, all available structures in the protein database are curated, electrostatic properties are calculated using PROPKA, results are filtered for ionizable residues with physiologically relevant predicted pK a values, and data are visualized in a 3D structure or a 2D residue interaction network. (b) Crystal structure of <t>SHP2</t> shown in cartoon and surface format (PDB ID:2SHP). Protein tyrosine phosphatase (PTP) domain colored in grey, SH2 domains colored in yellow. (c) Structure of SHP2 (PDB ID:2SHP) with protein tyrosine phosphatase (PTP) domain in grey and SH2 domains in yellow. Residues identified through in silico ionizable network prediction pipeline shown in spheres. Residues with predicted pK a shifts (cyan) cluster with ionizable interactors (magenta) across the phosphatase-SH2 domain interaction interface of SHP2. (d) Table of predicted pK a s for cyan residues identified using in silico ionizable network prediction pipeline on 47 SHP2 structures (mean ± SD). (e) Residue interaction network of residues with predicted pK a shifts (cyan) and their ionizable interactors (magenta). Length of edges reflect the strength of the coulombic interaction, with stronger coulombic interactions having shorter edge lengths (f) Zoom of SHP2 structure at the PTP-SH2 interaction interface. Networked residues from a and b are shown in stick. Residues with predicted pK a shifts in cyan and ionizing interactors in magenta.
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(a) Schematic for in silico pK a prediction method for proteins with solved structures (see text and methods for details). Briefly, all available structures in the protein database are curated, electrostatic properties are calculated using PROPKA, results are filtered for ionizable residues with physiologically relevant predicted pK a values, and data are visualized in a 3D structure or a 2D residue interaction network. (b) Crystal structure of <t>SHP2</t> shown in cartoon and surface format (PDB ID:2SHP). Protein tyrosine phosphatase (PTP) domain colored in grey, SH2 domains colored in yellow. (c) Structure of SHP2 (PDB ID:2SHP) with protein tyrosine phosphatase (PTP) domain in grey and SH2 domains in yellow. Residues identified through in silico ionizable network prediction pipeline shown in spheres. Residues with predicted pK a shifts (cyan) cluster with ionizable interactors (magenta) across the phosphatase-SH2 domain interaction interface of SHP2. (d) Table of predicted pK a s for cyan residues identified using in silico ionizable network prediction pipeline on 47 SHP2 structures (mean ± SD). (e) Residue interaction network of residues with predicted pK a shifts (cyan) and their ionizable interactors (magenta). Length of edges reflect the strength of the coulombic interaction, with stronger coulombic interactions having shorter edge lengths (f) Zoom of SHP2 structure at the PTP-SH2 interaction interface. Networked residues from a and b are shown in stick. Residues with predicted pK a shifts in cyan and ionizing interactors in magenta.
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(a) Schematic for in silico pK a prediction method for proteins with solved structures (see text and methods for details). Briefly, all available structures in the protein database are curated, electrostatic properties are calculated using PROPKA, results are filtered for ionizable residues with physiologically relevant predicted pK a values, and data are visualized in a 3D structure or a 2D residue interaction network. (b) Crystal structure of <t>SHP2</t> shown in cartoon and surface format (PDB ID:2SHP). Protein tyrosine phosphatase (PTP) domain colored in grey, SH2 domains colored in yellow. (c) Structure of SHP2 (PDB ID:2SHP) with protein tyrosine phosphatase (PTP) domain in grey and SH2 domains in yellow. Residues identified through in silico ionizable network prediction pipeline shown in spheres. Residues with predicted pK a shifts (cyan) cluster with ionizable interactors (magenta) across the phosphatase-SH2 domain interaction interface of SHP2. (d) Table of predicted pK a s for cyan residues identified using in silico ionizable network prediction pipeline on 47 SHP2 structures (mean ± SD). (e) Residue interaction network of residues with predicted pK a shifts (cyan) and their ionizable interactors (magenta). Length of edges reflect the strength of the coulombic interaction, with stronger coulombic interactions having shorter edge lengths (f) Zoom of SHP2 structure at the PTP-SH2 interaction interface. Networked residues from a and b are shown in stick. Residues with predicted pK a shifts in cyan and ionizing interactors in magenta.
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(a) Schematic for in silico pK a prediction method for proteins with solved structures (see text and methods for details). Briefly, all available structures in the protein database are curated, electrostatic properties are calculated using PROPKA, results are filtered for ionizable residues with physiologically relevant predicted pK a values, and data are visualized in a 3D structure or a 2D residue interaction network. (b) Crystal structure of <t>SHP2</t> shown in cartoon and surface format (PDB ID:2SHP). Protein tyrosine phosphatase (PTP) domain colored in grey, SH2 domains colored in yellow. (c) Structure of SHP2 (PDB ID:2SHP) with protein tyrosine phosphatase (PTP) domain in grey and SH2 domains in yellow. Residues identified through in silico ionizable network prediction pipeline shown in spheres. Residues with predicted pK a shifts (cyan) cluster with ionizable interactors (magenta) across the phosphatase-SH2 domain interaction interface of SHP2. (d) Table of predicted pK a s for cyan residues identified using in silico ionizable network prediction pipeline on 47 SHP2 structures (mean ± SD). (e) Residue interaction network of residues with predicted pK a shifts (cyan) and their ionizable interactors (magenta). Length of edges reflect the strength of the coulombic interaction, with stronger coulombic interactions having shorter edge lengths (f) Zoom of SHP2 structure at the PTP-SH2 interaction interface. Networked residues from a and b are shown in stick. Residues with predicted pK a shifts in cyan and ionizing interactors in magenta.
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(a) Schematic for in silico pK a prediction method for proteins with solved structures (see text and methods for details). Briefly, all available structures in the protein database are curated, electrostatic properties are calculated using PROPKA, results are filtered for ionizable residues with physiologically relevant predicted pK a values, and data are visualized in a 3D structure or a 2D residue interaction network. (b) Crystal structure of <t>SHP2</t> shown in cartoon and surface format (PDB ID:2SHP). Protein tyrosine phosphatase (PTP) domain colored in grey, SH2 domains colored in yellow. (c) Structure of SHP2 (PDB ID:2SHP) with protein tyrosine phosphatase (PTP) domain in grey and SH2 domains in yellow. Residues identified through in silico ionizable network prediction pipeline shown in spheres. Residues with predicted pK a shifts (cyan) cluster with ionizable interactors (magenta) across the phosphatase-SH2 domain interaction interface of SHP2. (d) Table of predicted pK a s for cyan residues identified using in silico ionizable network prediction pipeline on 47 SHP2 structures (mean ± SD). (e) Residue interaction network of residues with predicted pK a shifts (cyan) and their ionizable interactors (magenta). Length of edges reflect the strength of the coulombic interaction, with stronger coulombic interactions having shorter edge lengths (f) Zoom of SHP2 structure at the PTP-SH2 interaction interface. Networked residues from a and b are shown in stick. Residues with predicted pK a shifts in cyan and ionizing interactors in magenta.
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(a) Schematic for in silico pK a prediction method for proteins with solved structures (see text and methods for details). Briefly, all available structures in the protein database are curated, electrostatic properties are calculated using PROPKA, results are filtered for ionizable residues with physiologically relevant predicted pK a values, and data are visualized in a 3D structure or a 2D residue interaction network. (b) Crystal structure of <t>SHP2</t> shown in cartoon and surface format (PDB ID:2SHP). Protein tyrosine phosphatase (PTP) domain colored in grey, SH2 domains colored in yellow. (c) Structure of SHP2 (PDB ID:2SHP) with protein tyrosine phosphatase (PTP) domain in grey and SH2 domains in yellow. Residues identified through in silico ionizable network prediction pipeline shown in spheres. Residues with predicted pK a shifts (cyan) cluster with ionizable interactors (magenta) across the phosphatase-SH2 domain interaction interface of SHP2. (d) Table of predicted pK a s for cyan residues identified using in silico ionizable network prediction pipeline on 47 SHP2 structures (mean ± SD). (e) Residue interaction network of residues with predicted pK a shifts (cyan) and their ionizable interactors (magenta). Length of edges reflect the strength of the coulombic interaction, with stronger coulombic interactions having shorter edge lengths (f) Zoom of SHP2 structure at the PTP-SH2 interaction interface. Networked residues from a and b are shown in stick. Residues with predicted pK a shifts in cyan and ionizing interactors in magenta.
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(a) Schematic for in silico pK a prediction method for proteins with solved structures (see text and methods for details). Briefly, all available structures in the protein database are curated, electrostatic properties are calculated using PROPKA, results are filtered for ionizable residues with physiologically relevant predicted pK a values, and data are visualized in a 3D structure or a 2D residue interaction network. (b) Crystal structure of <t>SHP2</t> shown in cartoon and surface format (PDB ID:2SHP). Protein tyrosine phosphatase (PTP) domain colored in grey, SH2 domains colored in yellow. (c) Structure of SHP2 (PDB ID:2SHP) with protein tyrosine phosphatase (PTP) domain in grey and SH2 domains in yellow. Residues identified through in silico ionizable network prediction pipeline shown in spheres. Residues with predicted pK a shifts (cyan) cluster with ionizable interactors (magenta) across the phosphatase-SH2 domain interaction interface of SHP2. (d) Table of predicted pK a s for cyan residues identified using in silico ionizable network prediction pipeline on 47 SHP2 structures (mean ± SD). (e) Residue interaction network of residues with predicted pK a shifts (cyan) and their ionizable interactors (magenta). Length of edges reflect the strength of the coulombic interaction, with stronger coulombic interactions having shorter edge lengths (f) Zoom of SHP2 structure at the PTP-SH2 interaction interface. Networked residues from a and b are shown in stick. Residues with predicted pK a shifts in cyan and ionizing interactors in magenta.
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(a) Schematic for in silico pK a prediction method for proteins with solved structures (see text and methods for details). Briefly, all available structures in the protein database are curated, electrostatic properties are calculated using PROPKA, results are filtered for ionizable residues with physiologically relevant predicted pK a values, and data are visualized in a 3D structure or a 2D residue interaction network. (b) Crystal structure of <t>SHP2</t> shown in cartoon and surface format (PDB ID:2SHP). Protein tyrosine phosphatase (PTP) domain colored in grey, SH2 domains colored in yellow. (c) Structure of SHP2 (PDB ID:2SHP) with protein tyrosine phosphatase (PTP) domain in grey and SH2 domains in yellow. Residues identified through in silico ionizable network prediction pipeline shown in spheres. Residues with predicted pK a shifts (cyan) cluster with ionizable interactors (magenta) across the phosphatase-SH2 domain interaction interface of SHP2. (d) Table of predicted pK a s for cyan residues identified using in silico ionizable network prediction pipeline on 47 SHP2 structures (mean ± SD). (e) Residue interaction network of residues with predicted pK a shifts (cyan) and their ionizable interactors (magenta). Length of edges reflect the strength of the coulombic interaction, with stronger coulombic interactions having shorter edge lengths (f) Zoom of SHP2 structure at the PTP-SH2 interaction interface. Networked residues from a and b are shown in stick. Residues with predicted pK a shifts in cyan and ionizing interactors in magenta.
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Figure 2. <t>eIF4G1</t> accelerates eIF4E–mRNA binding in an mRNA-dependent manner and allows the interaction to persist on the translation initiation timescale. (A) eIF4E-mRNA association rates in the absence (grey) and presence (red) of full-length yeast eIF4G1. (B) Fold-stimulation of eIF4E–mRNA association rate by eIF4G1, as a function of mRNA length. (C) Kinetics of eIF4E–mRNA dissociation for transient binding events in the presence (blue) and absence (grey) of eIF4G1. (D) Representative single-molecule fluorescence trace for eIF4E–mRNA binding in the presence of eIF4G1. The inset shows representative transient and prolonged events on an expanded time axis. (E) Cumulative probability distributions of eIF4E–NCE102 mRNA event durations in the absence (grey) and presence (blue) of eIF4G1, showing appearance of slowly-dissociating events when eIF4G1 is present. (F) eIF4E– mRNA dissociation rates for long-lived binding events in the presence of eIF4G1. (G) Apparent equilibrium dissociation constants for the eIF4E–mRNA interaction in the presence of eIF4G1. (H) eIF4E–mRNA association rates in the presence (red) and absence (grey) of eIF4G11–452.
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Figure 2. <t>eIF4G1</t> accelerates eIF4E–mRNA binding in an mRNA-dependent manner and allows the interaction to persist on the translation initiation timescale. (A) eIF4E-mRNA association rates in the absence (grey) and presence (red) of full-length yeast eIF4G1. (B) Fold-stimulation of eIF4E–mRNA association rate by eIF4G1, as a function of mRNA length. (C) Kinetics of eIF4E–mRNA dissociation for transient binding events in the presence (blue) and absence (grey) of eIF4G1. (D) Representative single-molecule fluorescence trace for eIF4E–mRNA binding in the presence of eIF4G1. The inset shows representative transient and prolonged events on an expanded time axis. (E) Cumulative probability distributions of eIF4E–NCE102 mRNA event durations in the absence (grey) and presence (blue) of eIF4G1, showing appearance of slowly-dissociating events when eIF4G1 is present. (F) eIF4E– mRNA dissociation rates for long-lived binding events in the presence of eIF4G1. (G) Apparent equilibrium dissociation constants for the eIF4E–mRNA interaction in the presence of eIF4G1. (H) eIF4E–mRNA association rates in the presence (red) and absence (grey) of eIF4G11–452.
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Figure 2. <t>eIF4G1</t> accelerates eIF4E–mRNA binding in an mRNA-dependent manner and allows the interaction to persist on the translation initiation timescale. (A) eIF4E-mRNA association rates in the absence (grey) and presence (red) of full-length yeast eIF4G1. (B) Fold-stimulation of eIF4E–mRNA association rate by eIF4G1, as a function of mRNA length. (C) Kinetics of eIF4E–mRNA dissociation for transient binding events in the presence (blue) and absence (grey) of eIF4G1. (D) Representative single-molecule fluorescence trace for eIF4E–mRNA binding in the presence of eIF4G1. The inset shows representative transient and prolonged events on an expanded time axis. (E) Cumulative probability distributions of eIF4E–NCE102 mRNA event durations in the absence (grey) and presence (blue) of eIF4G1, showing appearance of slowly-dissociating events when eIF4G1 is present. (F) eIF4E– mRNA dissociation rates for long-lived binding events in the presence of eIF4G1. (G) Apparent equilibrium dissociation constants for the eIF4E–mRNA interaction in the presence of eIF4G1. (H) eIF4E–mRNA association rates in the presence (red) and absence (grey) of eIF4G11–452.
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Image Search Results


(a) Schematic for in silico pK a prediction method for proteins with solved structures (see text and methods for details). Briefly, all available structures in the protein database are curated, electrostatic properties are calculated using PROPKA, results are filtered for ionizable residues with physiologically relevant predicted pK a values, and data are visualized in a 3D structure or a 2D residue interaction network. (b) Crystal structure of SHP2 shown in cartoon and surface format (PDB ID:2SHP). Protein tyrosine phosphatase (PTP) domain colored in grey, SH2 domains colored in yellow. (c) Structure of SHP2 (PDB ID:2SHP) with protein tyrosine phosphatase (PTP) domain in grey and SH2 domains in yellow. Residues identified through in silico ionizable network prediction pipeline shown in spheres. Residues with predicted pK a shifts (cyan) cluster with ionizable interactors (magenta) across the phosphatase-SH2 domain interaction interface of SHP2. (d) Table of predicted pK a s for cyan residues identified using in silico ionizable network prediction pipeline on 47 SHP2 structures (mean ± SD). (e) Residue interaction network of residues with predicted pK a shifts (cyan) and their ionizable interactors (magenta). Length of edges reflect the strength of the coulombic interaction, with stronger coulombic interactions having shorter edge lengths (f) Zoom of SHP2 structure at the PTP-SH2 interaction interface. Networked residues from a and b are shown in stick. Residues with predicted pK a shifts in cyan and ionizing interactors in magenta.

Journal: bioRxiv

Article Title: Ionizable networks mediate pH-dependent allostery in SH2 signaling proteins

doi: 10.1101/2024.08.21.608875

Figure Lengend Snippet: (a) Schematic for in silico pK a prediction method for proteins with solved structures (see text and methods for details). Briefly, all available structures in the protein database are curated, electrostatic properties are calculated using PROPKA, results are filtered for ionizable residues with physiologically relevant predicted pK a values, and data are visualized in a 3D structure or a 2D residue interaction network. (b) Crystal structure of SHP2 shown in cartoon and surface format (PDB ID:2SHP). Protein tyrosine phosphatase (PTP) domain colored in grey, SH2 domains colored in yellow. (c) Structure of SHP2 (PDB ID:2SHP) with protein tyrosine phosphatase (PTP) domain in grey and SH2 domains in yellow. Residues identified through in silico ionizable network prediction pipeline shown in spheres. Residues with predicted pK a shifts (cyan) cluster with ionizable interactors (magenta) across the phosphatase-SH2 domain interaction interface of SHP2. (d) Table of predicted pK a s for cyan residues identified using in silico ionizable network prediction pipeline on 47 SHP2 structures (mean ± SD). (e) Residue interaction network of residues with predicted pK a shifts (cyan) and their ionizable interactors (magenta). Length of edges reflect the strength of the coulombic interaction, with stronger coulombic interactions having shorter edge lengths (f) Zoom of SHP2 structure at the PTP-SH2 interaction interface. Networked residues from a and b are shown in stick. Residues with predicted pK a shifts in cyan and ionizing interactors in magenta.

Article Snippet: Full-length SHP2 variants were cloned into the pGEX-4TI SHP2 WT plasmid , pGEX-4T1 SHP2 WT was a gift from Ben Neel (Addgene plasmid # 8322 ; http://n2t.net/addgene:8322 ; RRID:Addgene_8322)) and expressed in E. Coli for purification.

Techniques: In Silico, Residue

(a) Wild-type (WT) SHP2 in vitro phosphatase activity curves with increasing concentrations of generic substrate p-Nitrophenyl Phosphate (PNPP) at buffer pH ranging from 6.1 to 8.0. (mean ± SEM; N=3 from ≥2 different protein preparations) (b) Double mutant (H116A/E252A) SHP2 in vitro phosphatase activity, assays performed as in A. (mean ± SEM; N=3 from ≥2 different protein preparations) (c) Plot of k cat vs. pH for WT and double mutant (H116A/E252A) SHP2 activity. Calculated from activity curves in a and b. (mean ± SEM) (d) Single-mutant H116A-SHP2 in vitro phosphatase activity, assays performed as in a. (mean ± SEM; N=3 from ≥2 different protein preparations) (e) Single-mutant E252A-SHP2 in vitro phosphatase activity, assays performed as in a. (mean ± SEM; N=3, from ≥2 different protein preparations) (f) Plot of K cat vs. pH for WT and double mutant (H116A/E252A) SHP2 activity. Calculated from activity curves in a, d and e. (mean ± SEM) (g) Proposed pH-sensing mechanism where SH2 domain (yellow) binding to catalytic domain (grey) is titratable by pH. (h) CpHMD (see methods for details) was performed on SHP2 at pH values from 4.0-10.0 (see supplemental videos). Shown are overlapping views of SHP2 structures at the start of CpHMD simulation (t = 0 ns) and end of simulation (t = 8 ns) for pH values 4.5 (pink), 5.5 (purple), 6.5 (blue) and 8.5 (yellow).

Journal: bioRxiv

Article Title: Ionizable networks mediate pH-dependent allostery in SH2 signaling proteins

doi: 10.1101/2024.08.21.608875

Figure Lengend Snippet: (a) Wild-type (WT) SHP2 in vitro phosphatase activity curves with increasing concentrations of generic substrate p-Nitrophenyl Phosphate (PNPP) at buffer pH ranging from 6.1 to 8.0. (mean ± SEM; N=3 from ≥2 different protein preparations) (b) Double mutant (H116A/E252A) SHP2 in vitro phosphatase activity, assays performed as in A. (mean ± SEM; N=3 from ≥2 different protein preparations) (c) Plot of k cat vs. pH for WT and double mutant (H116A/E252A) SHP2 activity. Calculated from activity curves in a and b. (mean ± SEM) (d) Single-mutant H116A-SHP2 in vitro phosphatase activity, assays performed as in a. (mean ± SEM; N=3 from ≥2 different protein preparations) (e) Single-mutant E252A-SHP2 in vitro phosphatase activity, assays performed as in a. (mean ± SEM; N=3, from ≥2 different protein preparations) (f) Plot of K cat vs. pH for WT and double mutant (H116A/E252A) SHP2 activity. Calculated from activity curves in a, d and e. (mean ± SEM) (g) Proposed pH-sensing mechanism where SH2 domain (yellow) binding to catalytic domain (grey) is titratable by pH. (h) CpHMD (see methods for details) was performed on SHP2 at pH values from 4.0-10.0 (see supplemental videos). Shown are overlapping views of SHP2 structures at the start of CpHMD simulation (t = 0 ns) and end of simulation (t = 8 ns) for pH values 4.5 (pink), 5.5 (purple), 6.5 (blue) and 8.5 (yellow).

Article Snippet: Full-length SHP2 variants were cloned into the pGEX-4TI SHP2 WT plasmid , pGEX-4T1 SHP2 WT was a gift from Ben Neel (Addgene plasmid # 8322 ; http://n2t.net/addgene:8322 ; RRID:Addgene_8322)) and expressed in E. Coli for purification.

Techniques: In Vitro, Activity Assay, Mutagenesis, Binding Assay

(a) pHi measurements of MCF10A cells. Cells were treated with 25 μM EIPA + 30 μM S0858 for 1 hour to lower pH to 7.10. To raise pHi, cells were treated with 30 mM ammonium chloride for 1 hour to raise pH to 8.00. Untreated cells had a pHi of 7.45. Scatter plot shows (median ± interquartile range, N =10) (b) Representative immunoblots of SHP2, Gab1, and phospho-SHP2 (pY542) from SHP2 immune complexes (SHP2 IP) or whole cell lysates (Cell Lysate) isolated from MCF10A cells prepared as in A. (c) Quantification of replicate data collected as in B. Data was normalized to control in each biological replicate. Scatter plot shows (mean ± SEM, N=4). I (d) Quantification of Co-IP of SHP2 shown in b. Immunoblot intensities in the treatment conditions were normalized to control in each biological replicate. Scatter plot shows (mean ± SEM, N=7). (e) Representative images of MCF10A cells expressing the SHP2 activity reporter (Grb2 TagBFP) pseudocolored on an intensiometric scale. Images show cells prior to manipulating pHi with nigericin buffer (Pre Nigericin) (see methods for details), 50s - after manipulating pHi, and 900s after manipulating pHi. Scale bars: 25μm (f) Quantification of images as in E. Membrane intensity of SHP2 activity reporter was photobleach-corrected and then normalized to initial intensity over time. Line trace shows from single-cell data (mean ± SEM) (6.7 pH, n=30, 7.4 pH, n=30, 7.8 pH, n=25, control, n=28) collected across N=3 biological replicates. (g) Quantification of endpoint membrane intensities of single cells collected as described in f. Scatter plot shows (median ± interquartile range, N = 5) (h) Representative immunoblot of lysates prepared from MCF10A cells expressing either WT SHP2 or H116A/E252A SHP2 and treated as described in a. Immunoblots show total and pY542-SHP2 under low, control, and high pHi conditions. (i) Quantification of replicate data collected as in h. Scatter plots show (mean ± SEM, N=3). Intensities were normalized to the corresponding control condition. For a and g significance was determined using the Kruskal-Wallis test. For c, d, and i significance was determined using a ratio paired t-test to compare between treatment conditions and a one-sample t-test to compare to control. * p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Journal: bioRxiv

Article Title: Ionizable networks mediate pH-dependent allostery in SH2 signaling proteins

doi: 10.1101/2024.08.21.608875

Figure Lengend Snippet: (a) pHi measurements of MCF10A cells. Cells were treated with 25 μM EIPA + 30 μM S0858 for 1 hour to lower pH to 7.10. To raise pHi, cells were treated with 30 mM ammonium chloride for 1 hour to raise pH to 8.00. Untreated cells had a pHi of 7.45. Scatter plot shows (median ± interquartile range, N =10) (b) Representative immunoblots of SHP2, Gab1, and phospho-SHP2 (pY542) from SHP2 immune complexes (SHP2 IP) or whole cell lysates (Cell Lysate) isolated from MCF10A cells prepared as in A. (c) Quantification of replicate data collected as in B. Data was normalized to control in each biological replicate. Scatter plot shows (mean ± SEM, N=4). I (d) Quantification of Co-IP of SHP2 shown in b. Immunoblot intensities in the treatment conditions were normalized to control in each biological replicate. Scatter plot shows (mean ± SEM, N=7). (e) Representative images of MCF10A cells expressing the SHP2 activity reporter (Grb2 TagBFP) pseudocolored on an intensiometric scale. Images show cells prior to manipulating pHi with nigericin buffer (Pre Nigericin) (see methods for details), 50s - after manipulating pHi, and 900s after manipulating pHi. Scale bars: 25μm (f) Quantification of images as in E. Membrane intensity of SHP2 activity reporter was photobleach-corrected and then normalized to initial intensity over time. Line trace shows from single-cell data (mean ± SEM) (6.7 pH, n=30, 7.4 pH, n=30, 7.8 pH, n=25, control, n=28) collected across N=3 biological replicates. (g) Quantification of endpoint membrane intensities of single cells collected as described in f. Scatter plot shows (median ± interquartile range, N = 5) (h) Representative immunoblot of lysates prepared from MCF10A cells expressing either WT SHP2 or H116A/E252A SHP2 and treated as described in a. Immunoblots show total and pY542-SHP2 under low, control, and high pHi conditions. (i) Quantification of replicate data collected as in h. Scatter plots show (mean ± SEM, N=3). Intensities were normalized to the corresponding control condition. For a and g significance was determined using the Kruskal-Wallis test. For c, d, and i significance was determined using a ratio paired t-test to compare between treatment conditions and a one-sample t-test to compare to control. * p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Article Snippet: Full-length SHP2 variants were cloned into the pGEX-4TI SHP2 WT plasmid , pGEX-4T1 SHP2 WT was a gift from Ben Neel (Addgene plasmid # 8322 ; http://n2t.net/addgene:8322 ; RRID:Addgene_8322)) and expressed in E. Coli for purification.

Techniques: Western Blot, Isolation, Control, Co-Immunoprecipitation Assay, Expressing, Activity Assay, Membrane

(a) Schematic of pH-driven activation and inhibition of SHP2. At low pH, the SH2 domain is unbound and SHP2 becomes signaling active with increased phosphorylation of Y542, increased GAB1 binding, and increased Grb2 recruitment. (b) Schematic of pH-driven activation and inhibition of Src. At low pH, the SH2 domain is unbound and c-Src becomes signaling active with increased phosphorylation of Y416, decreased phosphorylation of Y527, and increased membrane recruitment.

Journal: bioRxiv

Article Title: Ionizable networks mediate pH-dependent allostery in SH2 signaling proteins

doi: 10.1101/2024.08.21.608875

Figure Lengend Snippet: (a) Schematic of pH-driven activation and inhibition of SHP2. At low pH, the SH2 domain is unbound and SHP2 becomes signaling active with increased phosphorylation of Y542, increased GAB1 binding, and increased Grb2 recruitment. (b) Schematic of pH-driven activation and inhibition of Src. At low pH, the SH2 domain is unbound and c-Src becomes signaling active with increased phosphorylation of Y416, decreased phosphorylation of Y527, and increased membrane recruitment.

Article Snippet: Full-length SHP2 variants were cloned into the pGEX-4TI SHP2 WT plasmid , pGEX-4T1 SHP2 WT was a gift from Ben Neel (Addgene plasmid # 8322 ; http://n2t.net/addgene:8322 ; RRID:Addgene_8322)) and expressed in E. Coli for purification.

Techniques: Activation Assay, Inhibition, Phospho-proteomics, Binding Assay, Membrane

Figure 2. eIF4G1 accelerates eIF4E–mRNA binding in an mRNA-dependent manner and allows the interaction to persist on the translation initiation timescale. (A) eIF4E-mRNA association rates in the absence (grey) and presence (red) of full-length yeast eIF4G1. (B) Fold-stimulation of eIF4E–mRNA association rate by eIF4G1, as a function of mRNA length. (C) Kinetics of eIF4E–mRNA dissociation for transient binding events in the presence (blue) and absence (grey) of eIF4G1. (D) Representative single-molecule fluorescence trace for eIF4E–mRNA binding in the presence of eIF4G1. The inset shows representative transient and prolonged events on an expanded time axis. (E) Cumulative probability distributions of eIF4E–NCE102 mRNA event durations in the absence (grey) and presence (blue) of eIF4G1, showing appearance of slowly-dissociating events when eIF4G1 is present. (F) eIF4E– mRNA dissociation rates for long-lived binding events in the presence of eIF4G1. (G) Apparent equilibrium dissociation constants for the eIF4E–mRNA interaction in the presence of eIF4G1. (H) eIF4E–mRNA association rates in the presence (red) and absence (grey) of eIF4G11–452.

Journal: Nucleic acids research

Article Title: mRNA- and factor-driven dynamic variability controls eIF4F-cap recognition for translation initiation.

doi: 10.1093/nar/gkac631

Figure Lengend Snippet: Figure 2. eIF4G1 accelerates eIF4E–mRNA binding in an mRNA-dependent manner and allows the interaction to persist on the translation initiation timescale. (A) eIF4E-mRNA association rates in the absence (grey) and presence (red) of full-length yeast eIF4G1. (B) Fold-stimulation of eIF4E–mRNA association rate by eIF4G1, as a function of mRNA length. (C) Kinetics of eIF4E–mRNA dissociation for transient binding events in the presence (blue) and absence (grey) of eIF4G1. (D) Representative single-molecule fluorescence trace for eIF4E–mRNA binding in the presence of eIF4G1. The inset shows representative transient and prolonged events on an expanded time axis. (E) Cumulative probability distributions of eIF4E–NCE102 mRNA event durations in the absence (grey) and presence (blue) of eIF4G1, showing appearance of slowly-dissociating events when eIF4G1 is present. (F) eIF4E– mRNA dissociation rates for long-lived binding events in the presence of eIF4G1. (G) Apparent equilibrium dissociation constants for the eIF4E–mRNA interaction in the presence of eIF4G1. (H) eIF4E–mRNA association rates in the presence (red) and absence (grey) of eIF4G11–452.

Article Snippet: The plasmid containing recombinant full-length eIF4G1 was a gift from Sarah Walker (also available from Addgene as plasmid #122248).

Techniques: Binding Assay, Fluorescence