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M Waltham

Publications and source records attributed to M Waltham.

21 records · Page 2Linked to original sources

Rapid mass spectrometric identification of proteins from two-dimensional polyacrylamide gels after in gel proteolytic digestion.

We report a rapid method for identifying proteins resolved by two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) using matrix-assisted laser desorption ionization-mass spectrometry (MALDI-MS). In-gel digestion was performed in a way such that the volume ratio of trypsin solution to gel plug was quantitatively controlled to promote reproducible digestion and to maximize the digestion yield. To make the digestion samples more compatible with MALDI-MS, the volatile salt ammonium bicarbonate in the digestion buffer was largely removed prior to peptide extraction. Samples of mixed tryptic peptides from in-gel digestion were used without purification to obtain molecular weights by MALDI-MS with alpha-cyano, 4-hydroxy-cinnamic acid as the matrix. Modifications of MALDI sample loading procedures improved the detection sensitivity by one half to one order of magnitude. The peptide mass peaks in MALDI-MS spectra were distinguished from those of impurities by using several types of controls, and masses were corrected by using trypsin autodigestion fragments as internal calibration standards. Two different peptide-matching computer programs were used to interrogate sequence databases and identify proteins. Identification was enhanced by generation of orthogonal data sets (by using different proteases) and by including experimental values of isoelectric point (pI) and molecular weight to exclude false entries in the candidate lists. Approximately 1% of the material from a spot was used in each sample loading, and nine protein spots from rat liver 2-D PAGE gels were identified correctly, as judged by comparison with identification results previously obtained from Edman sequencing. A previously identified low-abundance spot was not identified by MALDI-MS, presumably because there was insufficient material in a single gel. The sample handling procedure reported here should permit us to identify many 2-D PAGE protein spots of medium abundance.

Acrylic Resins↗

A protein expression database for the molecular pharmacology of cancer.

In the last six years, the Developmental Therapeutics Program (DTP) of the US National Cancer Institute (NCI) has screened over 60,000 chemical compounds and a larger number of natural product extracts for their ability to inhibit growth of 60 different cancer cell lines representing different organs of origin. Whereas inhibition of the growth of one cancer cell type gives no information on drug specificity, the relative growth inhibitory activities against 60 different cells constitute patterns that encode detailed information on mechanisms of action and resistance (as reviewed in Boyd and Paull, Drug Devel. Res. 1995, 34, 19-109 and Weinstein et al., Science 1997, 275, 343-349). In order to correlate the patterns of activity with properties of the cells, we and other laboratories are characterizing the cells with respect to a large number of factors at the DNA, mRNA, and protein levels. As part of that effort, we have developed a two-dimensional gel electrophoresis (2-DE) protein expression database covering all 60 cell types (Buolamwini et al., submitted). Here we present analyses of the correlations among protein spots (i) in terms of their patterns of expression and (ii) in terms of their apparent relationships to the pharmacology of a set of 3989 screened compounds. The correlations tend to be stronger for the latter than for the former, suggesting that the spots have more robust signatures in terms of the pharmacology than in terms of expression levels. Links to pertinent databases and tools of analysis will be updated progressively at http:@www.nci.nih.gov/intra/lmp/jnwbio.htm and http:@epnwsl.ncifcrf.gov:2345/dis3d/dtp.++ +html.

Antineoplastic Agents↗

Mining and visualizing large anticancer drug discovery databases.

In order to find more effective anticancer drugs, the U.S. National Cancer Institute (NCI) screens a large number of compounds in vitro against 60 human cancer cell lines from different organs of origin. About 70,000 compounds have been tested in the program since 1990, and each tested compound can be characterized by a vector (i.e., "fingerprint") of 60 anticancer activity, or -[log(GI50)], values. GI50 is the concentration required to inhibit cell growth by 50% compared with untreated controls. Although cell growth inhibitory activity for a single cell line is not very informative, activity patterns across the 60 cell lines can provide incisive information on the mechanisms of action of screened compounds and also on molecular targets and modulators of activity within the cancer cells. Various statistical and artificial intelligence methods, including principal component analysis, hierarchical cluster analysis, stepwise linear regression, multidimensional scaling, neural network modeling, and genetic function approximation, among others, can be used to analyze this large activity database. Mining the database can provide useful information: (a) for the development of anticancer drugs; (b) for a better understanding of the molecular pharmacology of cancer; and (c) for improvement of the drug discovery process.

Algorithms↗