TOP-IDP-scale: a new amino acid scale measuring propensity for intrinsic disorder.

Intrinsically disordered proteins carry out various biological functions while lacking ordered secondary and/or tertiary structure. In order to find general intrinsic properties of amino acid residues that are responsible for the absence of ordered structure in intrinsically disordered proteins we surveyed 517 amino acid scales. Each of these scales was taken as an independent attribute for the subsequent analysis. For a given attribute value X, which is averaged over a consecutive string of amino acids, and for a given data set having both ordered and disordered segments, the conditional probabilities P(s(o) | x) and P(s(d) | x) for order and disorder, respectively, can be determined for all possible values of X. Plots of the conditional probabilities P(s(o) | x) and P(s(o) | x) versus X give a pair of curves. The area between these two curves divided by the total area of the graph gives the area ratio value (ARV), which is proportional to the degree of separation of the two probability curves and, therefore, provides a measure of the given attribute's power to discriminate between order and disorder. As ARV falls between zero and one, larger ARV corresponds to the better discrimination between order and disorder. Starting from the scale with the highest ARV, we applied a simulated annealing procedure to search for alternative scale values and have managed to increase the ARV by more than 10%. The ranking of the amino acids in this new TOP-IDP scale is as follows (from order promoting to disorder promoting): W, F, Y, I, M, L, V, N, C, T, A, G, R, D, H, Q, K, S, E, P. A web-based server has been created to apply the TOP-IDP scale to predict intrinsically disordered proteins (http://www.disprot.org/dev/disindex.php).

Identification of a gene signature in cell cycle pathway for breast cancer prognosis using gene expression profiling data.

BACKGROUND: Numerous studies have used microarrays to identify gene signatures for predicting cancer patient clinical outcome and responses to chemotherapy. However, the potential impact of gene expression profiling in cancer diagnosis, prognosis and development of personalized treatment may not be fully exploited due to the lack of consensus gene signatures and poor understanding of the underlying molecular mechanisms. METHODS: We developed a novel approach to derive gene signatures for breast cancer prognosis in the context of known biological pathways. Using unsupervised methods, cancer patients were separated into distinct groups based on gene expression patterns in one of the following pathways: apoptosis, cell cycle, angiogenesis, metastasis, p53, DNA repair, and several receptor-mediated signaling pathways including chemokines, EGF, FGF, HIF, MAP kinase, JAK and NF-kappaB. The survival probabilities were then compared between the patient groups to determine if differential gene expression in a specific pathway is correlated with differential survival. RESULTS: Our results revealed expression of cell cycle genes is strongly predictive of breast cancer outcomes. We further confirmed this observation by building a cell cycle gene signature model using supervised methods. Validated in multiple independent datasets, the cell cycle gene signature is a more accurate predictor for breast cancer clinical outcome than the previously identified Amsterdam 70-gene signature that has been developed into a FDA approved clinical test MammaPrint. CONCLUSION: Taken together, the gene expression signature model we developed from well defined pathways is not only a consistently powerful prognosticator but also mechanistically linked to cancer biology. Our approach provides an alternative to the current methodology of identifying gene expression markers for cancer prognosis and drug responses using the whole genome gene expression data.

DGEM--a microarray gene expression database for primary human disease tissues.

Gene expression patterns can reflect gene regulations in human tissues under normal or pathologic conditions. Gene expression profiling data from studies of primary human disease samples are particularly valuable since these studies often span many years in order to collect patient clinical information and achieve a large sample size. Disease-to-Gene Expression Mapper (DGEM) provides a beneficial community resource to access and analyze these data; it currently includes Affymetrix oligonucleotide array datasets for more than 40 human diseases and 1400 samples. The data are normalized to the same scale and stored in a relational database. A statistical-analysis pipeline was implemented to identify genes abnormally expressed in disease tissues or genes whose expressions are associated with clinical parameters such as cancer patient survival. Data-mining results can be queried through a web-based interface at http://dgem.dhcp.iupui.edu/. The query tool enables dynamic generation of graphs and tables that are further linked to major gene and pathway resources that connect the data to relevant biology, including Entrez Gene and Kyoto Encyclopedia of Genes and Genomes (KEGG). In summary, DGEM provides scientists and physicians a valuable tool to study disease mechanisms, to discover potential disease biomarkers for diagnosis and prognosis, and to identify novel gene targets for drug discovery. The source code is freely available for non-profit use, on request to the authors.

Evolutionary rate heterogeneity in proteins with long disordered regions.

The dominant view in protein science is that a three-dimensional (3-D) structure is a prerequisite for protein function. In contrast to this dominant view, there are many counterexample proteins that fail to fold into a 3-D structure, or that have local regions that fail to fold, and yet carry out function. Protein without fixed 3-D structure is called intrinsically disordered. Motivated by anecdotal accounts of higher rates of sequence evolution in disordered protein than in ordered protein we are exploring the molecular evolution of disordered proteins. To test whether disordered protein evolves more rapidly than ordered protein, pairwise genetic distances were compared between the ordered and the disordered regions of 26 protein families having at least one member with a structurally characterized region of disorder of 30 or more consecutive residues. For five families, there were no significant differences in pairwise genetic distances between ordered and disordered sequences. The disordered region evolved significantly more rapidly than the ordered region for 19 of the 26 families. The functions of these disordered regions are diverse, including binding sites for protein, DNA, or RNA and also including flexible linkers. The functions of some of these regions are unknown. The disordered regions evolved significantly more slowly than the ordered regions for the two remaining families. The functions of these more slowly evolving disordered regions include sites for DNA binding. More work is needed to understand the underlying causes of the variability in the evolutionary rates of intrinsically ordered and disordered protein.