SARS-CoV-2 structural coverage map reveals viral protein assembly, mimicry, and hijacking mechanisms

Abstract We modeled 3D structures of all SARS‐CoV‐2 proteins, generating 2,060 models that span 69% of the viral proteome and provide details not available elsewhere. We found that ˜6% of the proteome mimicked human proteins, while ˜7% was implicated in hijacking mechanisms that reverse post‐translational modifications, block host translation, and disable host defenses; a further ˜29% self‐assembled into heteromeric states that provided insight into how the viral replication and translation complex forms. To make these 3D models more accessible, we devised a structural coverage map, a novel visualization method to show what is—and is not—known about the 3D structure of the viral proteome. We integrated the coverage map into an accompanying online resource (https://aquaria.ws/covid) that can be used to find and explore models corresponding to the 79 structural states identified in this work. The resulting Aquaria‐COVID resource helps scientists use emerging structural data to understand the mechanisms underlying coronavirus infection and draws attention to the 31% of the viral proteome that remains structurally unknown or dark

DDRprot: a database of DNA damage response-related proteins

The DNA Damage Response (DDR) signalling network is an essential system that protects the genome’s integrity. The DDRprot database presented here is a resource that integrates manually curated information on the human DDR network and its sub-pathways. For each particular DDR protein, we present detailed information about its function. If involved in post-translational modifications (PTMs) with each other, we depict the position of the modified residue/s in the three-dimensional structures, when resolved structures are available for the proteins. All this information is linked to the original publication from where it was obtained. Phylogenetic information is also shown, including time of emergence and conservation across 47 selected species, family trees and sequence alignments of homologues. The DDRprot database can be queried by different criteria: pathways, species, evolutionary age or involvement in (PTM). Sequence searches using hidden Markov models can be also used.E.A.-L. was supported by the European Commission grant [FP7-REGPOT-2012-2013-1; A.A. was partially supported by the Spanish Ministry of Science and Innovation grant [PS09/02111].Peer reviewe

Tools and data services registry: a community effort to document bioinformatics resources

Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand. Here we present a community-driven curation effort, supported by ELIXIR—the European infrastructure for biological information—that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners. As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools

Peer-to-Peer Overlays and Data Integration in a Life Science Grid

Abstract. Databases and Grid computing are a good match. With the service orientation of Grid computing, the complexity of maintaining and integrating databases can be kept away from the actual users. Data access and integration is performed via services, which also allow to employ an access control. While it is our perception that many proposed Grid applications rely on a centralized and static infrastructure, Peer-to-Peer (P2P) technologies might help to dynamically scale and enhance Grid applications. The focus does not lie on publicly available P2P networks here, but on the self-organizing capabilities of P2P networks in general. A P2P overlay could, e.g., be used to improve the distribution of queries in a data Grid. For studying the combination of these three technologies, Grid computing, databases, and P2P, in this paper, we use an existing application from the life sciences, drug target validation, as an example. In its current form, this system has several drawbacks. We believe that they can be alleviated by using a combination of the service-based architecture of Grid computing and P2P technologies for implementing the services. The work presented in this paper is in progress. We mainly focus on the description of the current system state, its problems and the proposed new architecture. For a better understanding, we also outline the main topics related to the work presented here.

Differentially regulated genes under different growth conditions.

<p>(A) Example of a transcriptionally and translationally upregulated gene in LB compared to BHI control. The novel gene XECs170 is highlighted in pink. The transcription of XECs170 is increased 2.7-fold and translation 9.8-fold. (B) Summary of differentially regulated genes in LB compared to BHI control. For all three gene categories, downregulation dominates. (C) Example of a transcriptionally and translationally downregulated gene in BHI COS compared to BHI control. Transcription of XECs197 is 5.5-fold and translation is 129-fold reduced at the stress condition. (D) Summary of differentially regulated genes in BHI COS compared to BHI control. Downregulation at the translational level clearly dominates for all gene categories.</p