[Abstract unavailable

Understanding the evolution of a protein, including both close and distant relationships, often reveals insight into its structure and function. Fast and easy access to such up-to-date information facilitates research. We have developed a hierarchical evolutionary classification of all proteins with experimentally determined spatial structures, and presented it as an interactive and updatable online database. ECOD (Evolutionary Classification of protein Domains) is distinct from other structural classifications in that it groups domains primarily by evolutionary relationships (homology), rather than topology (or “fold”). This distinction highlights cases of homology between domains of differing topology to aid in understanding of protein structure evolution. ECOD uniquely emphasizes distantly related homologs that are difficult to detect, and thus catalogs the largest number of evolutionary links among structural domain classifications. Placing distant homologs together underscores the ancestral similarities of these proteins and draws attention to the most important regions of sequence and structure, as well as conserved functional sites. ECOD also recognizes closer sequence-based relationships between protein domains. Currently, approximately 100,000 protein structures are classified in ECOD into 9,000 sequence families clustered into close to 2,000 evolutionary groups. The classification is assisted by an automated pipeline that quickly and consistently classifies weekly releases of PDB structures and allows for continual updates. This synchronization with PDB uniquely distinguishes ECOD among all protein classifications. Finally, we present several case studies of homologous proteins not recorded in other classifications, illustrating the potential of how ECOD can be used to further biological and evolutionary studies.

We present an overview of the ninth round of Critical Assessment of Protein Structure Prediction (CASP9) ‘Template free modeling’ category (FM). Prediction models were evaluated using a combination of established structural and sequence comparison measures and a novel automated method designed to mimic manual inspection by capturing both global and local structural features. These scores were compared to those assigned manually over a diverse subset of target domains. Scores were combined to compare overall performance of participating groups and to estimate rank significance. Moreover, we discuss a few examples of free modeling targets to highlight the progress and bottlenecks of current prediction methods. Notably, a server prediction model for a single target (T0581) improved significantly over the closest structure template (44% GDT increase). This accomplishment represents the ‘winner’ of the CASP9 FM category. A number of human expert groups submitted slight variations of this model, highlighting a trend for human experts to act as “meta predictors” by correctly selecting among models produced by the top-performing automated servers. The details of evaluation are available at http://prodata.swmed.edu/CASP9/

The Critical Assessment of Protein Structure Prediction round 9 (CASP9) aimed to evaluate predictions for 129 experimentally determined protein structures. To assess tertiary structure predictions, these target structures were divided into domain-based evaluation units that were then classified into two assessment categories: template based modeling (TBM) and template free modeling (FM). CASP9 targets were split into domains of structurally compact evolutionary modules. For the targets with more than one defined domain, the decision to split structures into domains for evaluation was based on server performance. Target domains were categorized based on their evolutionary relatedness to existing templates as well as their difficulty levels indicated by server performance. Those target domains with sequence-related templates and high server prediction performance were classified as TMB, while those targets without identifiable templates and low server performance were classified as FM. However, using these generalizations for classification resulted in a blurred boundary between CASP9 assessment categories. Thus, the FM category included those domains without sequence detectable templates (25 target domains) as well as some domains with difficult to detect templates whose predictions were as poor as those without templates (5 target domains). Several interesting examples are discussed, including targets with sequence related templates that exhibit unusual structural differences, targets with homologous or analogous structure templates that are not detectable by sequence, and targets with new folds.

Signet-ring cell carcinoma (SRCC) has specific epidemiology and oncogenesis in gastric cancer, however, with no systematical investigation for prognostic genomic features. Here we report a systematic investigation conducted in 1868 Chinese gastric cancer patients indicating that signet-ring cells content was related to multiple clinical characteristics and treatment outcomes. We thus perform whole-genome sequencing on 32 pairs of SRC samples, and identify frequent CLDN18-ARHGAP26/6 fusion (25%). With 797 additional patients for validation, prevalence of CLDN18-ARHGAP26/6 fusion is noticed to be associated with signet-ring cell content, age at diagnosis, female/male ratio, and TNM stage. Importantly, patients with CLDN18-ARHGAP26/6 fusion have worse survival outcomes, and get no benefit from oxaliplatin/fluoropyrimidines-based chemotherapy, which is consistent with the fact of chemo-drug resistance acquired in CLDN18-ARHGAP26 introduced cell lines. Overall, this study provides insights into the clinical and genomic features of SRCC, and highlights the importance of frequent CLDN18-ARHGAP26/6 fusions in chemotherapy response for SRCC.

a b s t r a c tEven though an estimated 10e20 million people worldwide are infected with the oncogenic retrovirus, human T-lymphotropic virus type 1 (HTLV-1), its epidemiology is poorly understood, and little effort has been made to reduce its prevalence. In response to this situation, the Global Virus Network launched a taskforce in 2014 to develop new methods of prevention and treatment of HTLV-1 infection and promote basic research. HTLV-1 is the etiological agent of two life-threatening diseases, adult T-cell leukemia and HTLV-associated myelopathy/tropical spastic paraparesis, for which no effective therapy is currently available. Although the modes of transmission of HTLV-1 resemble those of the more familiar HIV-1, routine diagnostic methods are generally unavailable to support the prevention of new infections. In * Corresponding author. Molecular and Cellular Epigenetics, Interdisciplinary Cluster for Applied Genoproteomics (GIGA) of University of Liege (ULg), B34, 1 Avenue de l'Hôpital, 4000, Sart-Tilman, Liege, Belgium.E-mail address: luc.willems@ulg.ac.be (L. Willems). Contents lists available at ScienceDirect Antiviral Researchj o u rn a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / a n t iv i r a l the present article, the Taskforce proposes a series of actions to expand epidemiological studies; increase research on mechanisms of HTLV-1 persistence, replication and pathogenesis; discover effective treatments; and develop prophylactic and therapeutic vaccines.

We present an overview of the fifth round of Critical Assessment of Protein Structure Prediction (CASP5) fold recognition category. Prediction models were evaluated by using six different structural measures and four different alignment measures, and these scores were compared to those assigned manually over a diverse subset of target domains. Scores were combined to compare overall performance of participating groups and to estimate rank significance. The methods used by a few groups outperformed all other methods in terms of the evaluated criteria and could be considered state-of-the-art in structure prediction. We discuss a few examples of difficult fold recognition targets to highlight the progress of ab initio-type methods on difficult structure analogs and the difficulties of predicting multidomain targets and selecting prediction models. We also compared the results of manual groups to those of automatic servers evaluated in parallel by CAFASP, showing that the top performing automated server structure predictions approached those of the best manual predictors.

Tax, an oncogenic viral protein encoded by human T cell leukemia virus type 1 (HTLV-1), induces cellular transformation of T lymphocytes by modulating a variety of cellular gene expressions [1]. Identifying cellular partners that interact with Tax constitutes the first step toward elucidating the molecular basis of Tax-induced transformation. Here, we report a novel Tax-interacting protein, hTid-1. hTid-1, a human homolog of the Drosophila tumor suppressor protein Tid56, was initially characterized based on its interaction with the HPV-16 E7 oncoprotein [2]. hTid-1 and Tid56 are members of the DnaJ family [2,3], which contains a highly conserved signature J domain that regulates the activities of heat shock protein 70 (Hsp70) by serving as cochaperone [4-6]. In this context, the molecular chaperone complex is involved in cellular signaling pathways linked to apoptosis, protein folding, and membrane translocation and in modulation of the activities of tumor suppressor proteins, including retinoblastoma, p53, and WT1[7-12]. We find that expression of hTid-1 inhibits the transformation phenotype of two human lung adenocarcinoma cell lines. We show that Tax interacts with hTid-1 via a central cysteine-rich domain of hTid-1 while a signature J domain of hTid-1 mediates its binding to Hsp70 in HEK cells. Importantly, Tax associates with the molecular chaperone complex containing both hTid-1 and Hsp70 and alters the cellular localization of hTid-1 and Hsp70. In the absence of Tax, expression of the hTid-1/Hsp70 molecular complex is targeted to perinuclear mitochondrial clusters. In the presence of Tax, hTid-1 and its associated Hsp70 are sequestered within a cytoplasmic "hot spot" structure, a subcellular distribution that is characteristic of Tax in HEK cells.