Protein acetyltransferases and deacetylases have been implicated in oncogenesis, apoptosis and cell cycle regulation. Most of the protein acetyltransferases described acetylate epsilon-amino groups of lysine residues within proteins. Mouse ARD1 (homologue of yeast Ard1p, where Ard1p stands for arrest defective 1 protein) is the only known protein acetyltransferase catalysing acetylation of proteins at both alpha-(N-terminus) and epsilon-amino groups. Yeast Ard1p interacts with Nat1p (N-acetyltransferase 1 protein) to form a functional NAT (N-acetyltransferase). We now describe the human homologue of Nat1p, NATH (NAT human), as the partner of the hARD1 (human ARD1) protein. Included in the characterization of the NATH and hARD1 proteins is the following: (i) endogenous NATH and hARD1 proteins are expressed in human epithelial, glioma and promyelocytic cell lines; (ii) NATH and hARD1 form a stable complex, as investigated by reciprocal immunoprecipitations followed by MS analysis; (iii) NATH-hARD1 complex expresses N-terminal acetylation activity; (iv) NATH and hARD1 interact with ribosomal subunits, indicating a co-translational acetyltransferase function; (v) NATH is localized in the cytoplasm, whereas hARD1 localizes both to the cytoplasm and nucleus; (vi) hARD1 partially co-localizes in nuclear spots with the transcription factor HIF-1alpha (hypoxia-inducible factor 1alpha), a known epsilon-amino substrate of ARD1; (vii) NATH and hARD1 are cleaved during apoptosis, resulting in a decreased NAT activity. This study identifies the human homologues of the yeast Ard1p and Nat1p proteins and presents new aspects of the NATH and hARD1 proteins relative to their yeast homologues.

Shotgun tandem mass spectrometry-based peptide sequencing using programs such as SEQUEST allows high-throughput identification of peptides, which in turn allows the identification of corresponding proteins. We have applied a machine learning algorithm, called the support vector machine, to discriminate between correctly and incorrectly identified peptides using SEQUEST output. Each peptide was characterized by SEQUEST-calculated features such as delta Cn and Xcorr, measurements such as precursor ion current and mass, and additional calculated parameters such as the fraction of matched MS/MS peaks. The trained SVM classifier performed significantly better than previous cutoff-based methods at separating positive from negative peptides. Positive and negative peptides were more readily distinguished in training set data acquired on a QTOF, compared to an ion trap mass spectrometer. The use of 13 features, including four new parameters, significantly improved the separation between positive and negative peptides. Use of the support vector machine and these additional parameters resulted in a more accurate interpretation of peptide MS/MS spectra and is an important step toward automated interpretation of peptide tandem mass spectrometry data in proteomics.

Let ∆ be the Okounkov body of a divisor D on a projective variety X. We describe a geometric criterion for ∆ to be a lattice polytope, and show that in this situation X admits a flat degeneration to the corresponding toric variety. This degeneration is functorial in an appropriate sense. IntroductionLet X be a projective algebraic variety of dimension d over an algebraically closed field k, and let D be a big divisor on X. (Following [LM], all divisors are Cartier in this article.) As part of his proof of the log-concavity of the multiplicity function for representations of a reductive group, Okounkov showed how to associate to D a convex bodyr is a subvariety of codimension r in X which is nonsingular at the point Y d . One uses the flag to define a valuation ν = ν Y• , which in turn defines a graded semigroup Γ Y• ⊆ N × N d ; the convex body ∆ = ∆ Y• (D) is the intersection of {1} × R d with the closure of the convex hull of Γ Y• in R × R d . The details of this construction will be reviewed in §3.Recently, Kaveh-Khovanskii [KK2] and Lazarsfeld-Mustaţȃ [LM] have systematically developed this construction, and exploited it to show that ∆ Y• (D) captures much of the geometry of D. For example, the volume of D (as a divisor) is equal to the Euclidean volume of ∆ (up to a normalizing factor of d!), and one can use this to prove continuity of the volume function, as a map N 1 (X) R → R (see [LM, Theorem B] and the references given there). Many intersection-theoretic notions can also be defined and generalized using the convex bodies ∆(D); this is discussed at length in [KK2].These Okounkov bodies-as ∆ Y• (D) is called in the literature stemming from [LM]-are generally quite difficult to compute. They are often not polyhedral; when polyhedral, they are often not rational; and even if ∆ is a rational polyhedron, the semigroup used to define it need not be finitely Date: October 9, 2012.