We conducted a comprehensive analysis of a manually curated human signaling network containing 1634 nodes and 5089 signaling regulatory relations by integrating cancer-associated genetically and epigenetically altered genes. We find that cancer mutated genes are enriched in positive signaling regulatory loops, whereas the cancer-associated methylated genes are enriched in negative signaling regulatory loops. We further characterized an overall picture of the cancersignaling architectural and functional organization. From the network, we extracted an oncogenesignaling map, which contains 326 nodes, 892 links and the interconnections of mutated and methylated genes. The map can be decomposed into 12 topological regions or oncogene-signaling blocks, including a few 'oncogene-signaling-dependent blocks' in which frequently used oncogenesignaling events are enriched. One such block, in which the genes are highly mutated and methylated, appears in most tumors and thus plays a central role in cancer signaling. Functional collaborations between two oncogene-signaling-dependent blocks occur in most tumors, although breast and lung tumors exhibit more complex collaborative patterns between multiple blocks than other cancer types. Benchmarking two data sets derived from systematic screening of mutations in tumors further reinforced our findings that, although the mutations are tremendously diverse and complex at the gene level, clear patterns of oncogene-signaling collaborations emerge recurrently at the network level. Finally, the mutated genes in the network could be used to discover novel cancerassociated genes and biomarkers.

There has been great interest in attempting to identify gene expression signatures that predict cancer survival. In this study a new algorithm is developed to analyse gene expression datasets that accurately classify both ER+ and ER− breast cancers into low- and high-risk groups.

Tumor genome sequencing leads to documenting thousands of DNA mutations and other genomic alterations. At present, these data cannot be analyzed adequately to aid in the understanding of tumorigenesis and its evolution. Moreover, we have little insight into how to use these data to predict clinical phenotypes and tumor progression to better design patient treatment. To meet these challenges, we discuss a cancer hallmark network framework for modeling genome sequencing data to predict cancer clonal evolution and associated clinical phenotypes. The framework includes: (1) cancer hallmarks that can be represented by a few molecular/signaling networks. 'Network operational signatures' which represent gene regulatory logics/strengths enable to quantify state transitions and measures of hallmark traits. Thus, sets of genomic alterations which are associated with network operational signatures could be linked to the state/measure of hallmark traits. The network operational signature transforms genotypic data (i.e., genomic alterations) to regulatory phenotypic profiles (i.e., regulatory logics/strengths), to cellular phenotypic profiles (i.e., hallmark traits) which lead to clinical phenotypic profiles (i.e., a collection of hallmark traits). Furthermore, the framework considers regulatory logics of the hallmark networks under tumor evolutionary dynamics and therefore also includes: (2) a self-promoting positive feedback loop that is dominated by a genomic instability network and a cell survival/proliferation network is the main driver of tumor clonal evolution. Surrounding tumor stroma and its host immune systems shape the evolutionary paths; (3) cell motility initiating metastasis is a byproduct of the above self-promoting loop activity during tumorigenesis; (4) an emerging hallmark network which triggers genome duplication dominates a feed-forward loop which in turn could act as a rate-limiting step for tumor formation; (5) mutations and other genomic alterations have specific patterns and tissue-specificity, which are driven by aging and other cancer-inducing agents. This framework represents the logics of complex cancer biology as a myriad of phenotypic complexities governed by a limited set of underlying organizing principles. It therefore adds to our understanding of tumor evolution and tumorigenesis, and moreover, potential usefulness of predicting tumors' evolutionary paths and clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for cancer patients, as well as cancer risks for healthy individuals are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial impact on timely diagnosis, personalized treatment and personalized prevention of cancer.

The cap structure and the poly(A) tail of eukaryotic mRNAs act synergistically to enhance translation. This effect is mediated by a direct interaction of eukaryotic initiation factor 4G and poly (

The C-terminal domain of poly(A)-binding protein (PABC) is a peptide-binding domain found in poly(A)-binding proteins (PABPs) and a HECT (homologous to E6-AP Cterminus) family E3 ubiquitin ligase. In protein synthesis, the PABC domain of PABP functions to recruit several translation factors possessing the PABP-interacting motif 2 (PAM2) to the mRNA poly(A) tail. We have determined the solution structure of the human PABC domain in complex with two peptides from PABP-interacting protein-1 (Paip1) and Paip2. The structures show a novel mode of peptide recognition, in which the peptide binds as a pair of b-turns with extensive hydrophobic, electrostatic and aromatic stacking interactions. Mutagenesis of PABC and peptide residues was used to identify key protein-peptide interactions and quantified by isothermal calorimetry, surface plasmon resonance and GST pulldown assays. The results provide insight into the specificity of PABC in mediating PABP-protein interactions.

No abstract

We have de novo designed a heterodimeric coiled-coil formed by two peptides as a capture/ delivery system that can be used in applications such as affinity tag purification, immobilization in biosensors, etc. The two strands are designated as K coil (KVSALKE heptad sequence) and E coil (EVSALEK heptad sequence), where positively charged or negatively charged residues occupy positions e and g of the heptad repeat. In this study, for each E coil or K coil, three peptides were synthesized with lengths varying from three to five heptads. The effect of the chain length of each partner upon the kinetic and thermodynamic constants of interaction were determined using a surface plasmon resonance-based biosensor. Global fitting of the interactions revealed that the E5 coil interacted with the K5 coil according to a simple binding model. All the other interactions involving shorter coils were better described by a more complex kinetic model involving a rate-limiting reorganization of the coiled-coil structure. The affinities of these de novo designed coiled-coil interactions were found to range from 60 pM (E5/K5) to 30 µM (E3/K3). From these K d values, we were able to determine the free energy contribution of each heptad, depending on its relative position within the coiled-coils. We found that the free energy contribution of a heptad occupying a central position was 3-fold higher than that of a heptad at either end of the coiled-coil. The wide range of stabilities and affinities for the E/K coil system provides considerable flexibility for protein engineering and biotechnological applications.The R-helical coiled-coil is an oligomerization domain that is naturally present in a wide variety of proteins such as transcription factors, cytoskeletal proteins, motor proteins, and viral fusion proteins (1-3). The structural properties of coiled-coils have been extensively characterized from numerous high-resolution crystal and NMR structures (3-5). The formation of coiled-coils requires the wrapping of two or more amphipathic R-helices around each other in a lefthanded supercoil fashion; the helices may be aligned in either a parallel or antiparallel manner. The R-helices composing the coiled-coil structure are defined by a heptad repeat (denoted abcdefg) in which hydrophobic residues occupy the positions a and d and pack in a characteristic "knobs-intoholes" manner (6), forming the hydrophobic core ( Figure 1). Many studies (1,(7)(8)(9)(10)(11)(12) emphasized the role of charged residues (typically in the e and g positions) in controlling the specificity of oligomerization and opened up the way to the de novo design of heterodimeric coiled-coils (13-17). Their relatively small size, in addition to their well-defined structure, make coiled-coils a very attractive system for protein engineering and biotechnological applications (5, 18) such as the construction of miniaturized antibodies, the control of signaling protein domain oligomerization, and the specific targeting of GFP to cytoskeletal proteins (see ref 4 for ...

The CSS sets substantially outperformed other prognostic predictors of stage 2 CRC. They are more accurate and robust for prognostic predictions and facilitate the identification of patients with stage II disease who could gain survival benefit from fluorouracil-based adjuvant chemotherapy.