Management of High-Throughput DNA Sequencing Projects: Alpheus.

High-throughput DNA sequencing has enabled systems biology to begin to address areas in health, agricultural and basic biological research. Concomitant with the opportunities is an absolute necessity to manage significant volumes of high-dimensional and inter-related data and analysis. Alpheus is an analysis pipeline, database and visualization software for use with massively parallel DNA sequencing technologies that feature multi-gigabase throughput characterized by relatively short reads, such as Illumina-Solexa (sequencing-by-synthesis), Roche-454 (pyrosequencing) and Applied Biosystem's SOLiD (sequencing-by-ligation). Alpheus enables alignment to reference sequence(s), detection of variants and enumeration of sequence abundance, including expression levels in transcriptome sequence. Alpheus is able to detect several types of variants, including non-synonymous and synonymous single nucleotide polymorphisms (SNPs), insertions/deletions (indels), premature stop codons, and splice isoforms. Variant detection is aided by the ability to filter variant calls based on consistency, expected allele frequency, sequence quality, coverage, and variant type in order to minimize false positives while maximizing the identification of true positives. Alpheus also enables comparisons of genes with variants between cases and controls or bulk segregant pools. Sequence-based differential expression comparisons can be developed, with data export to SAS JMP Genomics for statistical analysis.

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.

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.

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.

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.

Engineered improvements in DNA-binding function of the MATa1 homeodomain reveal structural changes involved in combinatorial control.

We have engineered enhanced DNA-binding function into the a1 homeodomain by making changes in a loop distant from the DNA-binding surface. Comparison of the free and bound a1 structures suggested a mechanism linking van der Waals stacking changes in this loop to the ordering of a final turn in the DNA-binding helix of a1. Inspection of the protein sequence revealed striking differences in amino acid identity at positions 24 and 25 compared to related homeodomain proteins. These positions lie in the loop connecting helix-1 and helix-2, which is involved in heterodimerization with the alpha 2 protein. A series of single and double amino acid substitutions (a1-Q24R, a1-S25Y, a1-S25F and a1-Q24R/S25Y) were engineered, expressed and purified for biochemical and biophysical study. Calorimetric measurements and HSQC NMR spectra confirm that the engineered variants are folded and are equally or more stable than the wild-type a1 homeodomain. NMR analysis of a1-Q24R/S25Y demonstrates that the DNA recognition helix (helix-3) is extended by at least one turn as a result of the changes in the loop connecting helix-1 and helix-2. As shown by EMSA, the engineered variants bind DNA with enhanced affinity (16-fold) in the absence of the alpha 2 cofactor and the variant alpha 2/a1 heterodimers bind cognate DNA with specificity and affinity reflective of the enhanced a1 binding affinity. Importantly, in vivo assays demonstrate that the a1-Q24R/S25Y protein binds with fivefold greater affinity than wild-type a1 and is able to partially suppress defects in repression by alpha 2 mutants. As a result of these studies, we show how subtle differences in residues at a surface distant from the functional site code for a conformational switch that allows the a1 homeodomain to become active in DNA binding in association with its cofactor alpha 2.

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.