In this issue: Proteomics 9/2009

In this issue of Proteomics you will find the following highlighted articles: Rafting on the pond It seems that any river with a drop of more than 20‐30 cm/km is a candidate for a commercially viable rafting business. Biochemical rafters are pickier. They need a detergent‐resistant lipid raft where they can set up their signaling system. Kim et al. examined the changes in the raft molecules involved in insulin stimulated pre‐adipocyte to adipocyte differentiation (adipogenesis). A substantial number of adipocyte raft‐specific proteins were identified by immunoblots and confirmed by 2‐DE MS. A protein of particular interest was gC1qR, specific for mature adipocyte rafts, which also binds complement C1q and a number of other extracellular proteins (vitronectin, fibrinogen, hyaluronic acids . . .). Down‐regulation of gC1qR by siRNA was paralleled by reduction of insulin signaling through gC1qR, through the insulin receptor, and prevented adipogenesis. The rafts also were home to a variety of mitochondrial proteins during adipogenesis. Kim, K.‐B. et al., Proteomics 2009, 9, 2373‐2382. E. coli chaperone SurA is recognized SurA was a sad protein. It was sad because it couldn't get promoted without proof that it had done a good job on its current assignment. But what was that assignment? Being a good little protein, it did its best to never make a mistake and its good was very good, making thousands of perfect cycles. Still, no‐one noticed. Then one day, Vertommen et al. decided to give SurA a rest (actually its clone rested). After creating the deletion clone, they fired up the proteome machines to see what had changed. The lab was quiet as the proteomers collected their results. They sat down with the data and looked and talked, studied and talked. They finally came to a conclusion: SurA was indeed a chaperone and was responsible for transport of eight important bbarrel proteins across the periplasmic space to the outer membrane! And now a publication! Vertommen, D.. et al., Proteomics 2009, 9, 2432‐2443. Aphid saliva: solvent, glue, caulk, . . . Children learn quickly that if they don't wash their faces properly, a mother's wet thumb will finish the job. If hair won't stay where it belongs, you can always use saliva. Spots on your glasses or your computer monitor? Aphids and mosquitoes extend the uses even further. Carolan et al. report on the active components of saliva of the pea aphid (Acrythosiphon pisum), an agricultural pest that attacks legumes. The researchers used mass spectrometry, RNAi, and various types of electrophoresis to identify the nine proteins secreted in pea aphid saliva. From the complete genome sequence, four proteins could be identified by homology: a metalloprotease [M2], a zinc [M1] protease, both probably cleaving plant defensive peptides, a glucose oxidoreductase that probably detoxifies phytochemicals, and a relative of regucalsin, which might suppress Ca+2 mediated defense. Three of the proteins could not be matched to any known proteins. Carolan, J. C. et al., Proteomics 2009, 9, 2457‐2467.     

In this issue: Proteomics 6/2009

In this issue of Proteomics you will find the following highlighted articles: Keeping up with the lung cancers You're in good company if you smoke and develop lung cancer. The World Health Organization estimates 1.2 million new cases occur every year. On the other hand, 1.1 million people die from it every year‐bummer. One reason for the high death rate is the frequent development of resistance to several of the most commonly used drugs simultaneously. Multiple drug resistance (MDR) is the major cause of chemotherapeutic failure. Keenan et al. explored the proteomic changes associated with MDR failure (adriamycin) in a cultured lung cancer cell line (DLKP) and several subtypes. Adriamycin normally kills by blocking replication at DNA gyrase and by generating reactive oxygen species that lead to apoptosis. Proteomes were examined by 2‐D DIGE. Approximately 80 proteins displayed quantitative shifts, 32 showed a correlation with resistance, 24 being linked positively to resistance, 6 correlated negatively. Some known targets did not appear on the 2‐D maps consistently. Keenan, J. et al., Proteomics 2009, 9, 1556‐1566. An image of spit Spitting images have been around for a long time. The phrase is possibly human‐kind's first recognition of genetically transmitted traits. Proteomic analysis of saliva has only developed recently. The question raised by Walz et al. here is “What is the possible contribution of saliva to the high level of infection by Helicobacter pylori?” H. pylori is known to have extracellular adhesins that bind to a number of salivary proteins. A convenient way to detect targets of adhesins was found to be incubating 1‐D and 2‐D PAGE Western blots with an overlay of whole H. pylori. Targets detected included mucins, sialic acid‐containing glycoproteins, fucose‐containing blood group antigens and each pair of salivary glands had a different binding pattern. Walz, A. et al., Proteomics 2009, 9, 1582‐1592. Mix'em up, folks Conventional analytical chemical identifications frequently yield a characteristic spectrum of peaks for particular compounds on particular instruments. Just look up the observed spectrum in the “library” of standard spectra for identification. It is not so simple for proteins. Because of the size of a potential proteomic peptide library and the diversity of instruments used, most often the observed spectrum is compared to a theoretical spectrum for a peptide of interest. Ahrné et al. combine the two for improved performance. First they run the spectrum of interest through an exhaustive proteome search program (Phenyx), then through a sensitive library search (SpectraST) of the highest scoring sequences in the previous Phenyx search plus a number of controls. In the first (relatively simple) test, Phenyx matched 362 spectra, SpectraST made 639 matches at the same error detection level. In a more complex test, Phenyx generated >1000 hits, SpectraST 1304 hits. Ahrné, E. et al., Proteomics 2009, 9, 1731‐1736.     

In this issue: Proteomics 1/2009

In this issue of Proteomics you will find the following highlighted articles: How many tries before you get it right? British Prime Minister Benjamin Disraeli is reputed to have stated that “There are three types of lies: lies, damned lies and statistics.” As those immersed in bioinformatics have recognized, though they may be slippery characters, statistics are the only way some information can be extracted from an experimental structure. One of the recurring problems is the question of how many samples need to be tested to get a reasonable, reliable result. This is particularly important when samples are difficult to get, require arduous preparation, or yield only small amounts. These experiments are generally multidimensional. In this article Cairns et al., examine the number of mass spectrometry samples that are required for a quantitative answer in a biomarker search. They evaluate MALDI‐TOF and SELDI‐TOF data for sources and amounts of variability on a pilot scale (biological and technical particularly) which allows them to calculate the number of samples required for a valid full‐scale screen. Cairns, D. A. et al., Proteomics 2009, 9, 74‐86. Double‐barreled proteomic run on embryonic stem cell membranes Embryonic stem cells (ESC) appear to be as close to the fountain of youth as most of us can reasonably expect to get in this lifetime. How close they come to being a “silver bullet” for cancer and other diseases is yet to be determined. Intoh et al., have taken a major step forward in improving our understanding of ESC control and maintenance. They applied 2‐D DIGE and trypsin digestion + iTRAQ labeling to identify membrane and membrane‐associated proteins in mouse ESCs that had or had not been exposed to leukemia inhibitory factor, a factor which maintains pluripotency in ESCs. Some 338 membrane and membrane‐associated proteins, up‐ or down‐regulated, were identified and assigned to functional groups. Intoh, A. et al., Proteomics 2009, 9, 126‐137. H, M, L You see these three letters on a variety of simple controllers: pump speed, temperature, under‐desk foot warmers, etc. Now you can hope to see them soon on bottles in a cell mass isotope labeling kit. Schwanhäusser et al., describe here a protocol for following levels of protein expression in array volumes and numbers with array simplicity. They pulse label samples with Heavy, Medium, or Light amino acids. Pulse‐labeling has been used for determining protein turnover rates for eons but with a quantitation problem for translation: did the ratio change because the numerator changed or because the denominator changed? The answer comes from labeling the untreated control with the M amino acid, then mixing M+H or M+L samples before fractionating by SDS‐PAGE and high‐resolution LC‐MS/MS. It worked for cell fractions (HeLa) as well as whole cells (yeast). Schwanhäusser, B. et al., Proteomics 2009, 9, 205‐209.     

In this issue: Proteomics 8/2009

In this issue of Proteomics you will find the following highlighted articles: Are you sick or are you just getting old: Does it matter? We all joke about the hazards of aging: various systems that break down, some you didn't even know you had. But then there's the alternative of not aging. Hmmm. Now what if aging were a disease? If so, it is worse than any cold I've had. Zürbig et al. have found that the proteome of the aging kidney has many markers in common with chronic kidney disease. The degree of match among small peptide markers ranged from 4% to 22% for IgA nephropathy to diabetic nephropathy, respectively. From these data they developed an age estimating scale that revealed some individuals had kidneys apparently “older” than their bodies. If these findings hold up, they could offer new approaches to diagnosis and therapy of chronic kidney diseases. Zürbig, P. et al., Proteomics 2009, 9, 2108‐2117. Sharing your niche with an unrelated species Anyone who's ever lived with a roommate knows the pain of dividing up the refrigerator space and the cleaning duties as well as the rent. Is it based on number of people, the size of bedrooms, or size of biceps? Many “free‐living” bacteria share their living space with other species in stable consortia to which each member contributes. Bobadilla Fazzini et al. use proteomic and other tools to examine the changes resulting from shifts in limiting carbon sources. Their system is a continuous culture of 9:1 Pseudomonas reinekei (MT1): Achromobacter xylosoxidans (MT3), cultured from a contaminated stream and able to grow on 4‐chlorosalicylate, an intermediate in the degradation of toxic furans and dioxins. MT1 OprF, the outer membrane protein and homolog of E. coli OmpA, is a “slow porin” that contributes to toxin resistance. After a shift in carbon sources, MT1 OprF was up‐regulated ～11‐fold in mixed culture vs. pure culture. Bobadilla Fazzini, R. A. et al., Proteomics 2009, 9, 2273‐2285. Heart to heart: Biomarkers for MACE Mace is a spice, not an herb. It is a badge of office and a weapon (albeit now of a defensive sort). It is also an acronym for a Major Adverse Cardiac Event, otherwise known as a big heart attack, something you want to know is coming and to prevent. So what to do? Biomarkers to the rescue. Currently the FDA has approved one prospective test: the CardioMPO? ELISA test for myeloperoxidase. The MPO marker is >60% accurate in predicting a MACE over 30 days and 6 months. Zhou et al. propose an alternative statistical method for evaluating a panel of mass spectrometry markers. An improved preprocessing procedure utilizes low‐level signal processing and spectrum cleanup routines followed by partial least squares logistic regression and support vector machine classifier to select the markers. The prediction is done by an improved genetic algorithm with local optimization. Using seven markers yields >75% accuracy. Zhou, X. et al., Proteomics 2009, 9, 2286‐2294.     

In this issue: Proteomics 5/2009

In this issue of Proteomics you will find the following highlighted articles: Heart (pump) broken? Hearts are pumps within pumps within channels and pumps. Calcium is pumped, potassium, sodium, amino acids, and electrons are all pumped, channeled or driven until, finally, blood is pumped. Failure of one or more pumps leads to a heart attack. This report from Zlatkovic et al. looks at the sub‐proteome associated with hypertensive failure of the K⁺ATP channel and associated cardiomyopathy that develops in KIR6.2 knock‐out mice. Out of >900 reproducible 2‐DE spots, 81 displayed significant over‐ or under‐expression, a number of which validated previously proposed interactions with the Kir6.2 channel. Two‐thirds were down‐regulations, including creatine kinase, adenylate kinase, and lactate dehydrogenase. A total of 114 proteins were ontologically mapped into the K⁺ATP‐dependent sub‐proteome and a role in hypertensive heart failure. Interaction mapping found >240 nodes and >1200 interactions/edges. A good foundation for future work. Zlatkovic, J. et al., Proteomics 2009, 9, 1314‐1325. The deeper you dig, the more you find A classical biochemist interested in protein‐protein interactions purifies his protein away from other proteins, seeking the highest “‐fold purification”. A proteomicist, on the other hand, looks for “consistent contamination” – i.e. association – of the protein of interest with other proteins. This requires high resolution separations and high accuracy concentration determinations. You can only work with species with concentrations above the detection limit (DL) for the detection method. 2‐DE MS has a DL of approximately 10^?8 M, LC‐MS/MS is ～10^?10 M and saturating Cy5 dye method is ～10^?13 M. Archakov et al. report on an atomic force microscope technique that can yield a DL of 10^?16 M when the target is irreversibly fixed to the bait to avoid the losses due to dissociation kinetics. At that level, over 1 000 000 different proteins can be seen in human plasma. How many biomarkers do you want? Math warning: more equations than figures. Archakov, A. et al., Proteomics 2009, 9, 1326‐1343. Unexplored territory: a catfish pathogen's proteome As genomic and proteomic tools become more powerful and cheaper per base or peptide, we can expect to see more papers like this one by Dumpala et al., focused on an organism of modest economic value. Each paper will, however, contribute a new niche with alternative adaptations for survival. In this case, we are introduced to Edwardsiella ictaluri, a Gram negative pathogen of farm‐raised channel catfish. Enteric septicemia of catfish is the most frequent disease of the commercially farmed catfish and appears in acute and chronic forms. For the work reported here, the bacteria were grown in culture, washed, lysed and separated by 2‐DE TOF/TOF or 2‐D LC‐MS/MS for peptide identification. The combined methods identified 788 unique proteins, including 73 ribosomal proteins, several protein synthesis factors, tRNA synthases and a number of other proteins that could be assigned by orthology to Escherichia coli or Edwardsiella tarda. Dumpala, P. R. et al., Proteomics 2009, 9, 1353‐1363.     

In this issue: Proteomics 7/2008

In this issue of Proteomics you will find the following highlighted articles: Modified amino peptides step out of line, reveal identity In thriller movies and spy stories, you can often tell which character is a bad guy if his “confession” changes under pressure or depends on the inquisitor. Likewise for peptides with modifications. Staes et al. use a similar technique to find α‐amino blocked peptides. After chromatography of a digest over a C₁₈ reverse phase column, fractions were treated with TNBS and re‐chromatographed on the same column, under the same conditions. The peptides that had trypsin‐exposed amino groups became much more hydrophobic in the second round because of the addition of the TNBS. The technique (COFRADIC) was also improved by preceding the C₁₈ column by use of a strong cation exchange for fractionation and using a kit for removal of any pyrrolidone carboxylic acid termini from peptides. The revised protocol raised the yield of true amino termini from 60% to 95%. Staes, A. et al., Proteomics 2008, 8, 1362–1370. Decrypting Cryptosporidium parvum: Proteome data revealed by triple analysis As hikers in North America and normal people in many parts of the world know, Cryptosporidium parvum is a protozoan parasite that causes an unpleasant intestinal infection in humans. It also infects livestock species, which leads to widespread waterborne transmission unless effective water treatment is employed. When the oocytes enter the gastrointestinal tract, they are stimulated to undergo excystation, releasing four sporozoites that enter the epithelial cells. There they undergo asexual reproduction and begin a complex series of steps before reproduction is complete and oocytes are released. Although the genome has been completely sequenced, many of the proteins predicted did not have recognizable functions. Sanderson et al. used a tissue culture system of excystation to collect enough sporozoites for proteomic analysis by MuDPIT and LC‐MS/MS after (a) 2‐DE and (b) 1‐DE. Over 1200 unique proteins were identified, representing >30% of the predicted organism proteome, >200 of which had transmembrane domains. Sanderson, S. J. et al., Proteomics 2008, 8, 1398–1414. Oxidized proteins in serum: Inside job or outside contractor? Reactive oxygen species (ROS) seem to be involved in a variety of diseases, including Alzheimer's, Parkinson's, cancer and heart disease. Searches for biomarkers for these diseases have most commonly been done in blood plasma, which contains proteins from essentially every cell type and tissue in the organism. Mirzaei et al. explore questions of cause and effect in rat plasma by trapping ROS‐caused carbonylation points with biotin hydrazide, followed by avidin affinity chromatography and proteomic analysis (LC‐MS/MS). Of 146 proteins identified in four rats, 44 had at least one carbonylation site and 38 had two or more sites. Over 30% of the proteins were membrane proteins, suggesting a major source of ROS was external, a hypothesis supported by the observation that mitochondrial proteins are not affected, despite their proximity to endogenous ROS. On the other hand, 13% were nuclear proteins. Another surprise: virtually no (2%) plasma proteins were found. Mirzaei, H. et al., Proteomics 2008, 8, 1516–1527.     

In this issue: Proteomics 22/2008

In this issue of Proteomics you will find the following highlighted articles: Man bites dog! Noise improves signal! Yes, the right kind of noise does improve the signal (by about 10‐fold in the LC/MS case described here). Scheltema et al. used the noise generated by the ions remaining in the sample from the LC step as internal standards to standardize and calibrate the mass spectrum of interest. Given a set of well characterized contaminants at very low, but detectable levels, the researchers were able to appropriately stretch or compress spectra by comparison to a reference spectrum of contaminants expected in a particular sample. The demonstration was performed on a Thermo Fisher LTQ Orbitrap system which, run conventionally, yielded a mass accuracy of 1 to 2 parts per million. When the noise method was applied to the same data, the mass accuracy was 0.21 ppm. Scheltema, R. A. et al., Proteomics 2008, 8, 4647–4656. Rafting down the Melanoma river When the subject is rafts, Mark Twain's story of Tom Sawyer and Huckleberry Finn rafting down the Mississippi comes immediately to mind for most Americans. A raft of interest to life scientists is associated with detergent resistant membranes found in malignant melanoma cell lines. Made of predominantly cholesterol and sphingolipids, the raft and associated proteins have been shown to participate in signal regulation and protein trafficking as well as several diseases. Working from this information, Baruthio et al. have looked at the lipid raft proteome as a function of melanoma malignancy stage using LC‐MS/MS: radial growth phase, (pre‐metastatic); early vertical growth phase, (non‐metastatic); and fully transformed. They found >175 proteins total in all stages, the most abundant was AHNAK, a large membrane protein. Groups of potential stage markers were detected, although with some difficulty in reproducibility of extraction. Functions found included vacuolar ATPases, adhesion molecules, and signaling pathway regulators. Baruthio, F. et al., Proteomics 2008, 8, 4733–4747. Hot peppers maker confusing soup Capsaicin is the naturally occurring compound that gives chili peppers their “heat.” It is also a component of the pepper's arsenal, deterring some types of attacks. Another of its roles is in regulation of programmed cell death, apoptosis: sometimes it promotes it, sometimes it inhibits it and it always seems to involve reactive oxygen species (ROS). To look at its function as a potential anti‐cancer agent, Baek et al. compared its effect on two human cancer cell lines. HepG2, a hepatoblastoma and SK‐N‐SH, a neuroblastoma, were examined for proteomic changes after exposure to capsaicin at various levels and for various times. Both blastomas responded but in markedly different fashions. Apoptosis was induced in both cell lines, but the ROS levels were up in HepG2 and down in SK‐N‐SH. A number of ROS enzymes exhibited anomalous expression level changes, possibly due to the number of enzymes involved. Baek, Y. M. et al., Proteomics 2008, 8, 4748–4767.     

In this issue: Proteomics 5/2008

In this issue of Proteomics you will find the following highlighted articles: When is a stain not a stain? When it's dyeing! [Dumb proteomics joke!] This silly riddle is actually relat­ed to a recurrent question in proteomics: when is the best time to apply detection reagents to proteins for quantitative analysis? (a) pre‐electrophoresis labeling with DIGE/Cy‐type of covalent stains, or (b) post‐electrophoresis staining with silver, Sypro Ruby or Deep Purple? Karp et al. explore the question using a bacterial extract as a typical sample, DIGE Cy labels, and Deep Purple. It gets more complex when they have to deal with the “missingness” of spots: just because a spot doesn’t show up doesn’t mean it is not there, there just may not be enough to detect. Progenesis SameSpots software was used to analyze images for missing spots. In the end, DIGE gave better sensitivity as previously reported, and fewer missing spots. Deep Purple was more competitive when analyzed with SameSpots software. Karp, N. A. et al., Proteomics 2008, 8, 948–960. Your own best enemy? If there weren’t one maverick, black sheep, rebel, outlaw, eccentric, or rotten apple in most families, a lot of novels would never have been written. Mammalian immune systems seem to have the same structure – they mostly target enemies of the body but there always seem to be a few maverick antibodies that are targeted at their own body’s antigens. Servettaz et al. take up proteomic tools to identify the targets of the anti‐self antibodies expressed by apparently healthy individuals. Using umbilical cord endothelial cells as a source of antigens, the authors found 884 spots by ­2‐­DE, and 61 ± 25 of those were recognized by serum IgGs. All 12 sera tested recognized 11 antigens derived from 6 proteins. There were 3 cytoskeletal, 2 glycolytic, and 1 disulfide isomerase protein seen. These were confirmed by immunoblotting of 2‐D gels and identification by in‐gel tryptic digestion and MALDI‐TOF MS. Servettaz, A. et al., Proteomics 2008, 8, 1000–1008. Signature in scraps from kidney growth stages You can tell a lot about the quality of a new building, residential or commercial, by what doesn’t go into it. The scraps of lumber, pieces of masonry, lengths and varieties of cables are all revealing. Lee et al. watch the final maturation of the rat urinary tract by proteomic analysis of the debris found in urine over time. Taking special care not to mix adult and neonatal urine, they examined four samples over 2 weeks after birth and one at maturity, >30 d. Using nano‐ESI‐LC‐MS/MS technology, six proteins were found in all samples, 30 were adult specific. Proteins were further characterized by large format 1‐ and 2‐DE, immunoblots, and immunofluorescent analysis of tissue sections. Days 1, 3, and 7 had 37% of proteins in common whereas days 7, 14 and >30 shared only 7.4% of proteins. Levels of fibronectin and location of E‐cadherin expression shifted during maturation. Lee, R. S. et al., Proteomics 2008, 8, 1097–1112.     

In this issue: Proteomics 1/2008

In this issue of Proteomics you will find the following highlighted articles: Arachnophilia: A Charlotte working on the web In the children’s book Charlotte’s Web, a spider communicates with a pig by weaving messages into her web. In this Technical Brief, Mayer’s spider is the intermediate, a program taking queries about the protein world and weaving relevant information from the www’s libraries and databases into spreadsheets. PIC (Protein Information Crawler) can link directly to a number of databases including BLAST, SMART, PROSITE, and CDD. Selected data is deposited in an Excel spreadsheet or HTML table for sorting and browsing. The system is customizable to anyone with minimal programming skills in LabView G, an easy‐to‐learn graphical language. Using PIC reduced the initial data search for a system of ～1000 neural proteins from 8 wks to 2 days. The software is free. Mayer, U., Proteomics 2008, 8, 42–44. Hard heart, soft heart: analyzing tropomyosin links to types of cardiomyopathy I don’t know if the type of a heart patient’s cardiomyopathy has been diagnosed by behavioral observations but Warren et al. examined the behavior of tropomyosin on improved 2‐D PAGE and 2‐D DIGE separations. First dimension separations were run on 18‐cm long narrow range (pH 4.5 to pH 5.5) IPG strips. Second dimension gels were 16 cm wide, 1 mm thick, and 8 cm long. Ends of the IPG strips were trimmed off to fit the vertical gel. The equilibrated strip was put in place without agarose on top of stacking and resolving gels that included 10% glycerol and, in the stacking gel, 15% N,N’‐diallyltartardiamide to ensure efficient transfer of the protein from the first‐ to the second‐dimension gel. With these changes they were able to distinguish wild type tropomyosin from an E54K mutant and phosphorylated from unphosphorylated tropomyosin, potentially key prognostic clues. Warren, C. M. et al., Proteomics 2008, 8, 100–105. Moo‐ving into ART: Cows lead the way Cow ART is not the product of a bovine Moonet or Moodigliani, it is “Assist­ed Reproductive Technology.” Not simply artificial insemination, ART includes somatic cell nuclear transfer and other advanced techniques which are critical to creating breeding herds with “elite” genetics. But the success rate is not what was expected or required for effective use. Riding et al. apply proteome analysis techniques to establish a foundation for pregnancy progress biomarkers. Ruminants have two fluid‐filled sacs, amniotic and allantoic, that are critical to fetal development. After developing an improved sample prep procedure, the 5–50 kDa fraction of the allantoic proteome was analyzed. Some 139 proteins were identified and ontologically classified into nine functional groups. Too little amniotic fluid was recovered for thorough analysis but the two fluids were clearly distinguishable at 45 days post‐conception. Riding, G. et al., Proteomics 2008, 8, 160–177.     

In this issue: Proteomics 10/2008

In this issue of Proteomics you will find the following highlighted articles: Open‐pit mining for compatible neighbors Open pit mines are an explosive topic in some parts of the US and elsewhere around the world. In this case, however, it is information, not coal or copper, that is being mined from computer files. Ahmad et al. are looking for patterns in the sequence of amino acids that surround a landmark, an amino acid that is frequently modified by phosphorylation or glycosylation. If an appropriate set of rules can be found, it becomes feasible to predict sites of post‐translational modification (PTM) and possibly winners in conflicts from overlapping sites. Algorithms (MAPRes) for O‐glycosylation (GalNAc) and O‐phosphorylation have been implemented that show good fit, correlating well with patterns predicted by existing software. The MAPRes software should also be useful in creating patterns for features such as protease targets and secondary protein structures. Ahmad, I. et al., Proteomics 2008, 8, 1954–1958. Synthetic sequence steals enzyme‐specific (PKCα) spot It is interesting that evolution has optimized, rather than maximized, many interactions. It was only after we maximized these interactions artificially that we began to recognize the subtleties possible with control systems that were not pushed to the max full time. On the other hand, less than 100% is not satisfactory if we are trying to clean out metastasizing tumor cells. Kang et al. are looking for maximum discrimination between protein kinase C (PKC) isozymes for diagnostic and therapeutic applications. PKCα is normally involved in differentiation, growth, and programmed death of many cell types. The researchers began by designing and screening a set of >1700 PKCα target peptides. They selected the one with the highest efficiency of being labeled and characterized it further for kinetics (K_m, and V_max) with 11 PKC isozymes. They also used it for Western blot evaluation of enzyme levels in tumor and normal tissues. Kang, J.‐H. et al., Proteomics 2008, 8, 2006–2011. Subtleties of B. subtilis biological labeling Bacilllus subtilis is a workhorse bacterium, if you'll allow a mixed metaphor. My grad school friends who worked with it always claimed it was a “higher organism” than E. coli because it could differentiate, sort of like yeast. Because it is well studied genetically and physiologically, it has been adopted as a useful model system for the study of stress responses. Dreisbach et al. wanted to extend proteome analysis to membrane proteins under different starvation conditions that generated the stringent response. Conventional methods (e.g. 2‐DE) were not quantitative enough, or had unacceptable error rates (in vitro labelling). They found in vivo labelling with either specific amino acids (SILAC with lysine) or general metabolic labelling (14N/15N‐metabolic) to meet their needs. Samples could be mixed with controls prior to extraction and digestion to markedly reduce technical error rates. Both methods were considered suitable for quantitative proteomic analysis of membrane proteins. Dreisbach, A. et al., Proteomics 2008, 8, 2062–2076.