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Jacquelyn S Fetrow

Publications and source records attributed to Jacquelyn S Fetrow.

11 recordsLinked to original sources
PubMed2006

Insights into correlated motions and long-range interactions in CheY derived from molecular dynamics simulations.

CheY is a response regulator protein involved in bacterial chemotaxis. Much is known about its active and inactive conformations, but little is known about the mechanisms underlying long-range interactions or correlated motions. To investigate these events, molecular dynamics simulations were performed on the unphosphorylated, inactive structure from Salmonella typhimurium and the CheY-BeF(3)(-) active mimic structure (with BeF(3)(-) removed) from Escherichia coli. Simulations utilized both sequences in each conformation to discriminate sequence- and structure-specific behavior. The previously identified conformational differences between the inactive and active conformations of the strand-4-helix-4 loop, which are present in these simulations, arise from the structural, and not the sequence, differences. The simulations identify previously unreported structure-specific flexibility features in this loop and sequence-specific flexibility features in other regions of the protein. Both structure- and sequence-specific long-range interactions are observed in the active and inactive ensembles. In the inactive ensemble, two distinct mechanisms based on Thr-87 or Ile-95 rotameric forms, are observed for the previously identified g+ and g- rotamer sampling by Tyr-106. These molecular dynamics simulations have thus identified both sequence- and structure-specific differences in flexibility, long-range interactions, and rotameric form of key residues. Potential biological consequences of differential flexibility and long-range correlated motion are discussed.

Amino Acid Sequence↗
PubMed2006

Mutations in alpha-helical solvent-exposed sites of eglin c have long-range effects: evidence from molecular dynamics simulations.

Eglin c is a small protease inhibitor whose structural and thermodynamic properties have been well studied. Previous thermodynamic measurements on mutants at solvent-accessible positions in the protein's helix have shown the unexpected result that the data could be best fit by the inclusion of residue- and position-specific parameters to the model. To explore the origins of this surprising result, long molecular dynamics simulations in explicit solvent have been performed. These simulations indicate specific long-range interactions between the solvent-exposed residues in the eglin c alpha-helix and binding loop, an unexpected observation for such a small protein. The residues involved in the interaction are on opposite sides of the protein, about 25 A apart. Simulations of alanine substitutions at the solvent-exposed helix positions, arginine 22, glutamic acid 23, threonine 26, and leucine 27, show both small and large perturbations of eglin c dynamics. Two mutations exhibit large impacts on the long-range helix-loop interactions. Previous stability measurements (Yi et al., Biochemistry 2003;42:7594-7603) had indicated that an alanine substitution at position 27 was less stabilizing than at other solvent-exposed positions in the helix. The L27A mutation effects observed in these simulations suggest that the position-dependent loss of stability measured in wet bench experiments is derived from changes in dynamics that involve long-range interactions; thus, these simulations support the hypothesis that solvent-exposed positions in helices are not always equivalent.

Computational Biology↗
PubMed2005

Algebraic dependency models of protein signal transduction networks from time-series data.

Signal transduction networks are crucial for inter- and intra-cellular signaling. Signals are often transmitted via covalent modification of protein structure, with phosphorylation/dephosphorylation as the primary example. In this paper, we apply a recently described method of computational algebra to the modeling of signaling networks, based on time-course protein modification data. Computational algebraic techniques are employed to construct next-state functions. A Monte Carlo method is used to approximate the Deegan-Packel Index of Power corresponding to the respective variables. The Deegan-Packel Index of Power is used to conjecture dependencies in the cellular signaling networks. We apply this method to two examples of protein modification time-course data available in the literature. These experiments identified protein carbonylation upon exposure of cells to sub-lethal concentrations of copper. We demonstrate that this method can identify protein dependencies that might correspond to regulatory mechanisms to shut down glycolysis in a reverse, step-wise fashion in response to copper-induced oxidative stress in yeast. These examples show that the computational algebra approach can identify dependencies that may outline signaling networks involved in the response of glycolytic enzymes to the oxidative stress caused by copper.

Animals↗
PubMed2005

Chemical and structural diversity in cyclooxygenase protein active sites.

A major pharmaceutical problem is designing diverse and selective lead compounds. The human genome sequence provides opportunities to discover compounds that are protein selective if we can develop methods to identify specificity determinants from sequence alone. We have analyzed sequence and structural diversity of sheep COX-1 and mouse COX-2 proteins by Active Site Profiling (ASP). Eleven residues that should serve as specificity determinants between COX-1 and COX-2 were identified; however, the literature suggests that only one has been utilized in structure-based discovery. ASP was used to create a position-specific scoring matrix, which was used to identify possible cross-reacting proteins from the human sequences. This method proved selective for cyclooxygenases, comparing well with results using BLAST. The methods identify a probable misannotation of a cyclooxygenase in which there is high sequence similarity scores using BLAST, but ASP shows it does not contain the residues necessary for cyclooxygenase function. ASP Analysis of human COX proteins suggests that some specificity determinants that distinguish COX-1 and COX-2 proteins are similar between sheep COX-1/mouse COX-2 and human COX-1/COX2; however, residue identities at those positions are not necessarily conserved. Our results lay groundwork for development of family-specific pattern recognition methods to selectively match compounds with proteins.

Amino Acid Sequence↗
PubMed2003

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 trade mark or FFFs trade mark ) 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.

Algorithms↗
PubMed2003

Structure-based active site profiles for genome analysis and functional family subclassification.

In previous work, structure-based functional site descriptors, fuzzy functional forms (FFFs), were developed to recognize structurally conserved active sites in proteins. These descriptors identify members of protein families according to active-site structural similarity, rather than overall sequence or structure similarity. FFFs are defined by a minimal number of highly conserved residues and their three-dimensional arrangement. This approach is advantageous for function assignment across broad families, but is limited when applied to detailed subclassification within these families. In the work described here, we developed a method of three-dimensional, or structure-based, active-site profiling that utilizes FFFs to identify residues located in the spatial environment around the active site. Three-dimensional active-site profiling reveals similarities and differences among active sites across protein families. Using this approach, active-site profiles were constructed from known structures for 193 functional families, and these profiles were verified as distinct and characteristic. To achieve this result, a scoring function was developed that discriminates between true functional sites and those that are geometrically most similar, but do not perform the same function. In a large-scale retrospective analysis of human genome sequences, this profile score was shown to identify specific functional families correctly. The method is effective at recognizing the likely subtype of structurally uncharacterized members of the diverse family of protein kinases, categorizing sequences correctly that were misclassified by global sequence alignment methods. Subfamily information provided by this three-dimensional active-site profiling method yields key information for specific and selective inhibitor design for use in the pharmaceutical industry.

Algorithms↗
PubMed2003

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.

Amino Acid Sequence↗
PubMed2002

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.

Pharmacology↗