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ANALYTICAL CURRENTS Deducing protein–protein interaction networks To understand a protein’s function, it’s often important to know which proteins it interacts with and in what order. This is a difficult task, particularly for peptide recognition molecules, which mediate many protein–protein interactions. But it might become easier with a new computational and experimental method developed by Stanley Fields, Charles Boone, Gianni Cesareni, and colleagues at the University of Washington, the University of Toronto (Canada), the University of Rome Tor Vergata (Italy), and Mount Sinai Hospital–Toronto (Canada). To demonstrate the new strategy, the researchers chose the SH3 domains from the yeast Saccharomyces cerevisiae. Proteins that bear an SH3 domain are typically involved in signal transduction or reorganization of the cytoskeleton. First, the researchers screened a library of random 9-amino-acid-long peptides to see which ones would bind to various SH3 domains. From these results, the researchers derived a set of patterns, which
Vrp1 Bni1 Bnr1
Bck1 Ubp7
Yhl002w Prk1
Srv2
were purported to define sePbs1 Ykl105c Ark1 quences that would bind to Ydl146w Abp1 Fun21 Myo5 SH3 domains. The researchers Myo3 Fus1 Ydr409w Sho1 searched the yeast genome for Yfr024c Bzz1 Las17 naturally occurring sequences Ypr171w Sla1 Yir003w Bbc1 that fit these patterns. These Acf2 Ygr136w candidates, along with some Yer158c Ygr268c functional information, were fed Rvs167 Ysc84 into software that could deterBoi1 Ynl094w mine the relationships among Yjr083c Ypr154w these sequences and construct a Ymr192w network of interacting proteins. Ypl249c Yor197w Next, the researchers attacked the same problem from a differ- Overlap of the two protein–protein interaction networks. This final network contains 59 interactions and 39 proent angle. They used a different teins. (Adapted with permission. Copyright 2001 Ameritechnique to screen the entire can Association for the Advancement of Science.) yeast genome for peptides that would bind to another set of SH3 domains and derived a second protein network. this logic, the researchers mined the Finally, the researchers looked for biological literature to confirm that the overlap between the two networks. They postulated interactions really occur. Their reasoned that each approach would yield combined approach yielded 3- to 5-fold different types of false positive results, more validated interactions than either so looking for the overlap would elimiapproach by itself. (Science 2002, 295, nate many of the false positives. To test 321–324)
Protein complexes without solvent
Abundance
Most of the sophisticated new analytical methods for studying protein–protein interactions begin by forming complexes in solution. Not so the new method from Scott McLuckey’s group at Purdue Uni-
4e4
C8+ (2C)3+
3e4 2e4 1e4 1.0
C4+ C5+ C3+ 3.0
5.0
C2+ 7.0 9.0 m/z (kDa)
C+ 11.0
13.0
Positive ions resulting from the reaction of (C)8+ with (C)5–.
versity. Using MS techniques, they form their complexes solely in the gas phase, creating a new tool for studying intrinsic aspects of protein–protein interactions. The key to the Purdue method is that they form positively and negatively charged protein ions via nanoelectrospray and collectively store them in a quadrupole ion trap in the presence of a 1-mTorr atmosphere of helium. Under these conditions, the researchers observe product ions that are primarily the result of single ion–ion collisions with little evidence of sequential ion–ion reactions. They demonstrate their approach by reacting charge-state-selected ions of
the proteins bovine cytochrome c (C) and bovine ubiquitin (U) in the gas phase. For example, they follow the reactions of (C)8+ with (C)5– and of (U)8+ with (C)5–. A kinetic scheme based on complex formation, proton transfer, and loss of excess energy is proposed. Because protein transfer competes with complex formation, data on these reactions may reflect the binding strengths of complexes. Moreover, the method offers a unique way to compare stabilities and reactivities of complexes formed in solution with those generated solely in the gas phase. (J. Am. Chem. Soc. 2001, 123, 12,428–12,429)
F E B R U A R Y 1 , 2 0 0 2 / A N A LY T I C A L C H E M I S T R Y
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ANALYTICAL CURRENTS Roughing it in the real world 0 Other scientists have Electrophoresis previously proposed that AFM interactions between ideal–25 ly smooth silica sheets involve non-DLVO repulsive forces, such as steric –50 or hydration, which may dominate the silica–silica interaction at short range –75 and reduce the significance of van der Waals forces. The two researchers –100 not only propose and demonstrate that surface –125 roughness explains the ab0.1 1 10 100 1000 0.01 sence of an attractive reKNO (mM) 3 gion in the force–separaComparison of electrostatic potential of silica colloids by tion data but also note particle electrophoresis (circles) and by fitting force-versusthat other short-range separation data with Derjaguin, Landau, Verwey, and Overforces would be weakbeek theory (diamonds) at pH ~8 as a function of electrolyte ened in systems that have concentration. nanometer-scale surface roughness. Drummond and Consiplane of shear from electrophoretic modine also report that the experimental bilities at low electrolyte concentration decay length of the repulsive force is (
