Susan M. Baxter

Scientific Software Development Is Not an Oxymoron

Susan M. Baxter*, Steven W. Day, Jacquelyn S. Fetrow, Stephanie J. Reisinger‘‘Many scientists and engineers spend much of their lives writing,debugging, and maintaining software, but only a handful have everbeen taught how to do this effectively: after a couple of introductorycourses, they are left to rediscover (or reinvent) the rest of programmingon their own. The result? Most spend far too much time wrestling withsoftware, instead of doing research, but have no idea how reliable orefficient their programs are.’’ —Greg Wilson [1]

Synergistic Computational and Experimental Proteomics Approaches for More Accurate Detection of Active Serine Hydrolases in Yeast

An analysis of the structurally and catalytically diverse serine hydrolase protein family in the Saccharomyces cerevisiae proteome was undertaken using two independent but complementary, large-scale approaches. The first approach is based on computational analysis of serine hydrolase active site structures; the second utilizes the chemical reactivity of the serine hydrolase active site in complex mixtures. These proteomics approaches share the ability to fractionate the complex proteome into functional subsets. Each method identified a significant number of sequences, but 15 proteins were identified by both methods. Eight of these were unannotated in the Saccharomyces Genome Database at the time of this study and are thus novel serine hydrolase identifications. Three of the previously uncharacterized proteins are members of a eukaryotic serine hydrolase family, designated as Fsh (family of serine hydrolase), identified here for the first time. OVCA2, a potential human tumor suppressor, and DYR—SCHPO, a dihydrofolate reductase from Schizosaccharomyces pombe, are members of this family. Comparing the combined results to results of other proteomic methods showed that only four of the 15 proteins were identified in a recent large-scale, “shotgun” proteomic analysis and eight were identified using a related, but similar, approach (neither identifies function). Only 10 of the 15 were annotated using alternate motif-based computational tools. The results demonstrate the precision derived from combining complementary, function-based approaches to extract biological information from complex proteomes. The chemical proteomics technology indicates that a functional protein is being expressed in the cell, while the computational proteomics technology adds details about the specific type of function and residue that is likely being labeled. The combination of synergistic methods facilitates analysis, enriches true positive results, and increases confidence in novel identifications. This work also highlights the risks inherent in annotation transfer and the use of scoring functions for determination of correct annotations.

Protein tyrosine phosphatases: Strategies for distinguishing proteins in a family containing multiple drug targets and anti-targets

PTP1B, but also proteins that are essential to cell development and survival. The availability of sequences and representative structures for the PTP family allows better identification of anti-targets, closely related family members likely to cross-react with directed inhibitors. Eight PTP subfamilies, classified by active site information and overall PTP catalytic domain structure similarity, are reviewed here: 1) the tyrosine-specific PTPs, 2) the dual-specificity PTPs, 3) the cdc25 subclass; 4) the Pten subclass; 5) the myotubularins, 6) the PRL subclass, 7) the low molecular weight PTPs, and 8) the newly defined cdc14 subclass. PTP subfamily classification and structure information can be incorporated into design strategies aimed at identifying potent and selective small molecule inhibitors. The accumulating inhibition data for compounds screened against panels of PTPs is reviewed. The in vitro data can yield clues to specificity so that individual subfamilies can be matched with effective scaffolds to jumpstart lead design and reduce false starts.

Prediction of deleterious functional effects of amino acid mutations using a library of structure‐based function descriptors

An automated, active site‐focused, computational method is described for use in predicting the effects of engineered amino acid mutations on enzyme catalytic activity. The method uses structure‐based function descriptors (Fuzzy Functional Forms™ or FFFs™) to automatically identify enzyme functional sites in proteins. Three‐dimensional sequence profiles are created from the surrounding active site structure. The computationally derived active site profile is used to analyze the effect of each amino acid change by defining three key features: proximity of the change to the active site, degree of amino acid conservation at the position in related proteins, and compatibility of the change with residues observed at that position in similar proteins. The features were analyzed using a data set of individual amino acid mutations occurring at 128 residue positions in 14 different enzymes. The results show that changes at key active site residues and at highly conserved positions are likely to have deleterious effects on the catalytic activity, and that non‐conservative mutations at highly conserved residues are even more likely to be deleterious. Interestingly, the study revealed that amino acid substitutions at residues in close contact with the key active site residues are not more likely to have deleterious effects than mutations more distant from the active site. Utilization of the FFF‐derived structural information yields a prediction method that is accurate in 79–83% of the test cases. The success of this method across all six EC classes suggests that it can be used generally to predict the effects of mutations and nsSNPs for enzymes. Future applications of the approach include automated, large‐scale identification of deleterious nsSNPs in clinical populations and in large sets of disease‐associated nsSNPs, and identification of deleterious nsSNPs in drug targets and drug metabolizing enzymes. Proteins 2003.

Function first: a powerful approach to post-genomic drug discovery

In the post-genomic era, pharmaceutical researchers must evaluate vast numbers of protein sequences and formulate novel, intelligent strategies for identifying valid targets and discovering leads against them. The identification of small molecules that selectively target proteins or protein families will be aided by knowing the function and/or the structure of the target(s). By identifying protein function first, efficiencies are gained that allow subsequent focus of resources on particular protein families of interest. This article reviews current proteomic-scale approaches to identifying function as a way of accelerating lead discovery.

Identification of the Archaeoglobus fulgidus endonuclease III DNA interaction surface using heteronuclear NMR methods

BACKGROUND Endonuclease III is the prototype for a family of DNA-repair enzymes that recognize and remove damaged and mismatched bases from DNA via cleavage of the N-glycosidic bond. Crystal structures for endonuclease III, which removes damaged pyrimidines, and MutY, which removes mismatched adenines, show a highly conserved structure. Although there are several models for DNA binding by this family of enzymes, no experimental structures with bound DNA exist for any member of the family. RESULTS Nuclear magnetic resonance (NMR) spectroscopy chemical-shift perturbation of backbone nuclei (1H, 15N, 13CO) has been used to map the DNA-binding site on Archaeoglobus fulgidus endonuclease III. The experimentally determined interaction surface includes five structural elements: the helix-hairpin-helix (HhH) motif, the iron-sulfur cluster loop (FCL) motif, the pseudo helix-hairpin-helix motif, the helix B-helix C loop, and helix H. The elements form a continuous surface that spans the active site of the enzyme. CONCLUSIONS The enzyme-DNA interaction surface for endonuclease III contains five elements of the protein structure and suggests that DNA damage recognition may require several specific interactions between the enzyme and the DNA substrate. Because the target DNA used in this study contained a generic apurinic/apyrimidinic (AP) site, the binding interactions we observed for A. fulgidus endonuclease III should apply to all members of the endonuclease III family and several interactions could apply to the endonuclease III/AlkA (3-methyladenine DNA glycosylase) superfamily.