Reproducibility of (i) and (ii) with another column generally adds confidence to the results. protein isolation, protein digestion, sample preparation, LC-MS/MS parameter optimization, method validation, and sample analysis. In particular, bioinformatic tools used in method development and sample analysis are discussed in detail. Numerous pre-analytical and analytical sources of CUDC-305 (DEBIO-0932 ) variability that should be considered during transporter quantification are highlighted. All these actions are illustrated using P-glycoprotein (P-gp) as a case example. Greater use of quantitative transporter proteomics will lead to a better understanding of the role of drug transporters in drug disposition. maximum transport velocity (to (tissue) transporter expression data. Using such adjusted clearance (WESTERN BLOTTING FOR PROTEIN QUANTIFICATION MRM proteomics has now become the platinum standard for protein quantification [27, 31, 32]. MRM proteomics is usually superior over traditional Western blotting in several ways including selectivity, velocity, ease of use, quality of data, and ability to confirm results (Table?I). For example, MRM proteomics can quantify multiple proteins in a short period of time, liquid chromatography tandem mass spectrometry, multiple reaction monitoring MRM METHOD DEVELOPMENT AND VALIDATION Surrogate Peptide Selection Selection of surrogate peptides for any protein is the first and most crucial step in MRM proteomics. Until few years back, the surrogate peptide designing was based on experimental approach involving tedious protein fractionation by SDS-PAGE gel electrophoresis followed by capillary LC nano-electrospray ionization quadruple time-of-flight (nano-ESI-Q-TOF) or Orbitrap MS instruments [23, 24, 35]. While this approach is accurate, it is a laborious, cost-intensive procedure, and requires a protein standard with reasonable purity and amount. To overcome these shortcomings, approach, first adopted by Kamiie [27] for drug transporters, is preferred these days. The uniqueness, LC retention, MS response, peptide stability, quantitative reliability, and signal quality are the important aspects that are considered for surrogate peptide selection. While integrated software tools, National Center for Biotechnology Information, Basic CUDC-305 (DEBIO-0932 ) Local Alignment Search Tool, Global Paleomagnetic Database, National Institute of Standards and Technology Open in a separate window Fig. 3 P-gp protein sequence (http://www.uniprot.org/uniprot/P08183). Residues highlighted in represent predicted transmembrane regions. residues represent either unstable residues, SNPs, ragged ends, or sequence conflicts digestion tools listed in Table?II. The parameters like protein accession number, corresponding database (prediction, it is important that only those candidates that are suitable for quantitative work are selected. Only peptides with 7C22 amino acids should be considered as this mass range is detectable in triple quadrupole MS. Also, potential (or known) transmembrane regions are excluded as they can affect protein digestion of transporter proteins. Where experimental information on the location of the transmembrane regions is not available, nonexperimental methods can be used to predict these regions. For this, Uniprot marks transmembrane regions as (i) potential regions indicating some conclusive evidence, (ii) probable regions with at least some experimental data, and (iii) regions by similarity indicating that some experimental information is available on a similar protein (or part of it). Uniprot predicted 12 transmembrane regions for MDR1, (P3)360C367449.7699.5536.4770.5407.412.512513hMDR124.3 (SIL-P3)453.8707.5544.412.5ILKGLNLK (P4)409C416449.8672.4785.5544.3752.510.312513hMDR1, rMdr1b, rMdr3, mMdr324.2ILKGLNLK (SIL-P4)453.8680.4793.510.3 (P5)1,086C1,093455.7485.3600.3426.3763.413.312513hMDR121.7 (SIL-P5)459.7493.3608.313.3ILSSFTDK (P6)235C242455.8684.4597.4510.4797.510.612513hMDR1, mMdr1a20.6ILSSFTDK (SIL-P6)459.8692.4719.410.6NTTGALTTR (P7)809C817467.8719.4618.4490.3561.36.912513hMDR1, mMdr1a, rMdr110.4NTTGALTTR (SIL-P7)472.5729.4628.46.9IAIARALVR (P8)539C547491.8685.4614.4458.3798.514.613014hMDR1, rMdr1,(P10)1077C1085522.8757.5658.4530.3417.213.913014hMDR1, rMdr1, mMdr1a/b27.1 (SIL-P10)527.8767.5668.413.9IATEAIENFR (P11)896?905582.3749.5678.5565.4436.312.214016hMDR1, rMdr1, mMdr1a/b25.7IATEAIENFR (SIL-P11)587.3759.5688.512.2 Open in a separate window Key: Residues shown in bold letters (retention time, Sequence specific retention time (tools, it is important to test whether this peptide works in the real sample. One can procure the synthetic surrogate peptides at this stage and tune LC-MS/MS parameters using standards. However, peptide synthesis generally takes 4C6?weeks, and sometime the selected surrogate peptide turns out to be less sensitive in MS. Therefore, an alternative approach that is independent of peptide standard can be used to facilitate method development. The latter involves screening potential quantitative surrogate peptides in the real samples (membrane fractions from tissues or cells) using generic LC and Rabbit Polyclonal to 14-3-3 zeta (phospho-Ser58) MS conditions. For transporter protein, vesicles or cells expressing transporters are preferred for qualification as they generally have higher transporter expression. For example, 11 surrogate peptides of P-gp were detected in cell expression P-gp in a single CUDC-305 (DEBIO-0932 ) dynamic MRM method (Fig.?4). The peptide signal can be qualified by (i) superimposability of multiple product ion chromatograms and (ii) elution of peptides at predicted RT.

Reproducibility of (i) and (ii) with another column generally adds confidence to the results