Fahad Saeed

Dynamics of the G protein-coupled vasopressin V2 receptor signaling network revealed by quantitative phosphoproteomics

G protein-coupled receptors (GPCRs) regulate diverse physiological processes, and many human diseases are due to defects in GPCR signaling. To identify the dynamic response of a signaling network downstream from a prototypical Gs-coupled GPCR, the vasopressin V2 receptor, we have carried out multireplicate, quantitative phosphoproteomics with iTRAQ labeling at four time points following vasopressin exposure at a physiological concentration in cells isolated from rat kidney. A total of 12,167 phosphopeptides were identified from 2,783 proteins, with 273 changing significantly in abundance with vasopressin. Two-dimensional clustering of phosphopeptide time courses and Gene Ontology terms revealed that ligand binding to the V2 receptor affects more than simply the canonical cyclic adenosine monophosphate-protein kinase A and arrestin pathways under physiological conditions. The regulated proteins included key components of actin cytoskeleton remodeling, cell-cell adhesion, mitogen-activated protein kinase signaling, Wnt/β-catenin signaling, and apoptosis pathways. These data suggest that vasopressin can regulate an array of cellular functions well beyond its classical role in regulating water and solute transport. These results greatly expand the current view of GPCR signaling in a physiological context and shed new light on potential roles for this signaling network in disorders such as polycystic kidney disease. Finally, we provide an online resource of physiologically regulated phosphorylation sites with dynamic quantitative data (http://helixweb.nih.gov/ESBL/Database/TiPD/index.html).

Proteome-Wide Measurement of Protein Half-Lives and Translation Rates in Vasopressin-Sensitive Collecting Duct Cells

Vasopressin regulates water excretion, in part, by controlling the abundances of the water channel aquaporin-2 (AQP2) protein and regulatory proteins in the renal collecting duct. To determine whether vasopressin-induced alterations in protein abundance result from modulation of protein production, protein degradation, or both, we used protein mass spectrometry with dynamic stable isotope labeling in cell culture to achieve a proteome-wide determination of protein half-lives and relative translation rates in mpkCCD cells. Measurements were made at steady state in the absence or presence of the vasopressin analog, desmopressin (dDAVP). Desmopressin altered the translation rate rather than the stability of most responding proteins, but it significantly increased both the translation rate and the half-life of AQP2. In addition, proteins associated with vasopressin action, including Mal2, Akap12, gelsolin, myosin light chain kinase, annexin-2, and Hsp70, manifested altered translation rates. Interestingly, desmopressin increased the translation of seven glutathione S-transferase proteins and enhanced protein S-glutathionylation, uncovering a previously unexplored vasopressin-induced post-translational modification. Additional bioinformatic analysis of the mpkCCD proteome indicated a correlation between protein function and protein half-life. In particular, processes that are rapidly regulated, such as transcription, endocytosis, cell cycle regulation, and ubiquitylation are associated with proteins with especially short half-lives. These data extend our understanding of the mechanisms underlying vasopressin signaling and provide a broad resource for additional investigation of collecting duct function (http://helixweb.nih.gov/ESBL/Database/ProteinHalfLives/index.html).

Identifying protein kinase target preferences using mass spectrometry.

A general question in molecular physiology is how to identify candidate protein kinases corresponding to a known or hypothetical phosphorylation site in a protein of interest. It is generally recognized that the amino acid sequence surrounding the phosphorylation site provides information that is relevant to identification of the cognate protein kinase. Here, we present a mass spectrometry-based method for profiling the target specificity of a given protein kinase as well as a computational tool for the calculation and visualization of the target preferences. The mass spectrometry-based method identifies sites phosphorylated in response to in vitro incubation of protein mixtures with active recombinant protein kinases followed by standard phosphoproteomic methodologies. The computational tool, called PhosphoLogo, uses an information-theoretic algorithm to calculate position-specific amino acid preferences and anti-preferences from the mass-spectrometry data (http://helixweb.nih.gov/PhosphoLogo/). The method was tested using protein kinase A (catalytic subunit α), revealing the well-known preference for basic amino acids in positions -2 and -3 relative to the phosphorylated amino acid. It also provides evidence for a preference for amino acids with a branched aliphatic side chain in position +1, a finding compatible with known crystal structures of protein kinase A. The method was also employed to profile target preferences and anti-preferences for 15 additional protein kinases with potential roles in regulation of epithelial transport: CK2, p38, AKT1, SGK1, PKCδ, CaMK2δ, DAPK1, MAPKAPK2, PKD3, PIM1, OSR1, STK39/SPAK, GSK3β, Wnk1, and Wnk4.

Quantitative Phosphoproteomics in Nuclei of Vasopressin-Sensitive Renal Collecting Duct Cells

Vasopressin regulates transport across the collecting duct epithelium in part via effects on gene transcription. Transcriptional regulation occurs partially via changes in phosphorylation of transcription factors, transcriptional coactivators, and protein kinases in the nucleus. To test whether vasopressin alters the nuclear phosphoproteome of vasopressin-sensitive cultured mouse mpkCCD cells, we used stable isotope labeling and mass spectrometry to quantify thousands of phosphorylation sites in nuclear extracts and nuclear pellet fractions. Measurements were made in the presence and absence of the vasopressin analog dDAVP. Of the 1,251 sites quantified, 39 changed significantly in response to dDAVP. Network analysis of the regulated proteins revealed two major clusters (cell-cell adhesion and transcriptional regulation) that were connected to known elements of the vasopressin signaling pathway. The hub proteins for these two clusters were the transcriptional coactivator β-catenin and the transcription factor c-Jun. Phosphorylation of β-catenin at Ser552 was increased by dDAVP [log(2)(dDAVP/vehicle) = 1.79], and phosphorylation of c-Jun at Ser73 was decreased [log(2)(dDAVP/vehicle) = -0.53]. The β-catenin site is known to be targeted by either protein kinase A or Akt, both of which are activated in response to vasopressin. The c-Jun site is a canonical target for the MAP kinase Jnk2, which is downregulated in response to vasopressin in the collecting duct. The data support the idea that vasopressin-mediated control of transcription in collecting duct cells involves selective changes in the nuclear phosphoproteome. All data are available to users at http://helixweb.nih.gov/ESBL/Database/mNPPD/.

CPhos: A program to calculate and visualize evolutionarily conserved functional phosphorylation sites

Profiling using high‐throughput MS has discovered an overwhelming number of novel protein phosphorylation sites (“phosphosites”). However, the functional relevance of these sites is not always clear. In light of recent studies on the evolutionary mechanism of phosphorylation, we have developed CPhos, a Java program that can assess the conservation of phosphosites among species using an information theory‐based approach. The degree of conservation established using CPhos can be used to assess the functional significance of phosphosites. CPhos has a user friendly graphical user interface and is available both as a web service and as a standalone Java application to assist phosphoproteomic researchers in analyzing and prioritizing lists of phosphosites for further experimental validation. CPhos can be accessed or downloaded at http://helixweb.nih.gov/CPhos/.

NHLBI-AbDesigner: an online tool for design of peptide-directed antibodies

Investigation of physiological mechanisms at a cellular level often requires production of high-quality antibodies, frequently using synthetic peptides as immunogens. Here we describe a new, web-based software tool called NHLBI-AbDesigner that allows the user to visualize the information needed to choose optimal peptide sequences for peptide-directed antibody production (http://helixweb.nih.gov/AbDesigner/). The choice of an immunizing peptide is generally based on a need to optimize immunogenicity, antibody specificity, multispecies conservation, and robustness in the face of posttranslational modifications (PTMs). AbDesigner displays information relevant to these criteria as follows: 1) Immunogenicity Score, based on hydropathy and secondary structure prediction; 2) Uniqueness Score, a predictor of specificity of an antibody against all proteins expressed in the same species; 3) Conservation Score, a predictor of ability of the antibody to recognize orthologs in other animal species; and 4) Protein Features that show structural domains, variable regions, and annotated PTMs that may affect antibody performance. AbDesigner displays the information online in an interactive graphical user interface, which allows the user to recognize the trade-offs that exist for alternative synthetic peptide choices and to choose the one that is best for a proposed application. Several examples of the use of AbDesigner for the display of such trade-offs are presented, including production of a new antibody to Slc9a3. We also used the program in large-scale mode to create a database listing the 15-amino acid peptides with the highest Immunogenicity Scores for all known proteins in five animal species, one plant species (Arabidopsis thaliana), and Saccharomyces cerevisiae.

A domain decomposition strategy for alignment of multiple biological sequences on multiprocessor platforms

Multiple Sequences Alignment (MSA) of biological sequences is a fundamental problem in computational biology due to its critical significance in wide ranging applications including haplotype reconstruction, sequence homology, phylogenetic analysis, and prediction of evolutionary origins. The MSA problem is considered NP-hard and known heuristics for the problem do not scale well with increasing numbers of sequences. On the other hand, with the advent of a new breed of fast sequencing techniques it is now possible to generate thousands of sequences very quickly. For rapid sequence analysis, it is therefore desirable to develop fast MSA algorithms that scale well with an increase in the dataset size. In this paper, we present a novel domain decomposition based technique to solve the MSA problem on multiprocessing platforms. The domain decomposition based technique, in addition to yielding better quality, gives enormous advantages in terms of execution time and memory requirements. The proposed strategy allows one to decrease the time complexity of any known heuristic of O(N)^x complexity by a factor of O(1/p)^x, where N is the number of sequences, x depends on the underlying heuristic approach, and p is the number of processing nodes. In particular, we propose a highly scalable algorithm, Sample-Align-D, for aligning biological sequences using Muscle system as the underlying heuristic. The proposed algorithm has been implemented on a cluster of workstations using the MPI library. Experimental results for different problem sizes are analyzed in terms of quality of alignment, execution time and speed-up.

An efficient dynamic programming algorithm for phosphorylation site assignment of large-scale mass spectrometry data

Phosphorylation site assignment of large-scale data from high throughput tandem mass spectrometry (LC-MS/MS) data is an important aspect of phosphoproteomics. Correct assignment of phosphorylated residue(s) is important for functional interpretation of the data within a biological context. Common search algorithms (Sequest etc.) for mass spectrometry data are not designed for accurate site assignment; thus, additional algorithms are needed. In this paper, we propose a linear-time and linear-space dynamic programming strategy for phosphorylation site assignment. The algorithm, referred to as PhosSA, optimizes the objective function defined as the summation of peak intensities that are associated with theoretical phosphopeptide fragmentation ions. Quality control is achieved through the use of a post-processing criteria whose value is indicative of the signal-to-noise (S/N) properties and redundancy of the fragmentation spectra. The algorithm is tested using experimentally generated data sets of peptides with known phosphorylation sites while varying the fragmentation strategy (CID or HCD) and molar amounts of the peptides. The algorithm is also compatible with various peptide labeling strategies including SILAC and iTRAQ. PhosSA is shown to achieve > 99% accuracy with a high degree of sensitivity. The algorithm is extremely fast and scalable (able to process up to 0.5 million peptides in an hour). The implemented algorithm is freely available at http://helixweb.nih.gov/ESBL/PhosSA/ for academic purposes.

CAMS-RS: clustering algorithm for large-scale mass spectrometry data using restricted search space and intelligent random sampling

High-throughput mass spectrometers can produce massive amounts of redundant data at an astonishing rate with many of them having poor signal-to-noise (S/N) ratio. These low S/N ratio spectra may not get interpreted using conventional spectra-to-database matching techniques. In this paper, we present an efficient algorithm, CAMS-RS (Clustering Algorithm for Mass Spectra using Restricted Space and Sampling) for clustering of raw mass spectrometry data. CAMS-RS utilizes a novel metric (called F-set) that exploits the temporal and spatial patterns to accurately assess similarity between two given spectra. The F-set similarity metric is independent of the retention time and allows clustering of mass spectrometry data from independent LC-MS/MS runs. A novel restricted search space strategy is devised to limit the comparisons of the number of spectra. An intelligent sampling method is executed on individual bins that allow merging of the results to make the final clusters. Our experiments, using experimentally generated data sets, show that the proposed algorithm is able to cluster spectra with high accuracy and is helpful in interpreting low S/N ratio spectra. The CAMS-RS algorithm is highly scalable with increasing number of spectra and our implementation allows clustering of up to a million spectra within minutes.

An efficient algorithm for clustering of large-scale mass spectrometry data

High-throughput spectrometers are capable of producing data sets containing thousands of spectra for a single biological sample. These data sets contain a substantial amount of redundancy from peptides that may get selected multiple times in a LC-MS/MS experiment. In this paper, we present an efficient algorithm, CAMS (Clustering Algorithm for Mass Spectra) for clustering mass spectrometry data which increases both the sensitivity and confidence of spectral assignment. CAMS utilizes a novel metric, called F-set, that allows accurate identification of the spectra that are similar. A graph theoretic framework is defined that allows the use of F-set metric efficiently for accurate cluster identifications. The accuracy of the algorithm is tested on real HCD and CID data sets with varying amounts of peptides. Our experiments show that the proposed algorithm is able to cluster spectra with very high accuracy in a reasonable amount of time for large spectral data sets. Thus, the algorithm is able to decrease the computational time by compressing the data sets while increasing the throughput of the data by interpreting low S/N spectra.