Proteins that are known only at a sequence level outnumber those with an experimental characterization by orders of magnitude. Classifying protein regions (domains) into homologous families can generate testable functional hypotheses for yet unannotated sequences. Existing domain family resources typically use at least some degree of manual curation: they grow slowly over time and leave a large fraction of the protein sequence space unclassified. We here describe automatic clustering by Density Peak Clustering of UniRef50 v. 2017_07, a protein sequence database including approximately 23M sequences. We performed a radical re-implementation of a pipeline we previously developed in order to allow handling millions of sequences and data volumes of the order of 3 TeraBytes. The modified pipeline, which we call DPCfam, finds ∼ 45,000 protein clusters in UniRef50. Our automatic classification is in close correspondence to the ones of the Pfam and ECOD resources: in particular, about 81% of medium-large Pfam families and 72% of ECOD families can be mapped to clusters generated by DPCfam. In addition, our protocol finds more than 14,000 clusters constituted of protein regions with no Pfam annotation, which are therefore candidates for representing novel protein families. These results are made available to the scientific community through a dedicated repository.

ExaNest is one of three European projects that support a ground-breaking computing architecture for exascale-class systems built upon power-efficient 64-bit ARM processors. This group of projects share an "everything-close" and "share-anything" paradigm, which trims down the power consumption -- by shortening the distance of signals for most data transfers -- as well as the cost and footprint area of the installation -- by reducing the number of devices needed to meet performance targets. In ExaNeSt, we will design and implement: (i) a physical rack prototype and its liquid-cooling subsystem providing ultra-dense compute packaging, (ii) a storage architecture with distributed (in-node) non-volatile memory (NVM) devices, (iii) a unified, low-latency interconnect, designed to efficiently uphold desired Quality-of-Service guarantees for a mix of storage with inter-processor flows, and (iv) efficient rack-level memory sharing, where each page is cacheable at only a single node. Our target is to test alternative storage and interconnect options on actual hardware, using real-world HPC applications. The ExaNeSt consortium brings together technology, skills, and knowledge across the entire value chain, from computing IP, packaging, and system deployment, all the way up to operating systems, storage, HPC, big data frameworks, and cutting-edge applications.

Even though the Italian Grid Infrastructure (IGI) is a general purpose distributed platform, in the past it has been used mainly for serial computations. Parallel applications have been typically executed on supercomputer facilities or, in case of ``not high-end'' HPC applications, on local commodity parallel clusters. Nowadays, with the availability of multiple cores processors, Grid computing is becoming very attractive also for parallel applications but some problems exist in supporting of HPC applications on Grid environment. Here we describe the work made to set up a HPC testbed for ``not high-end'' HPC applications, based on IGI Grid technologies, to find solutions to those problems. Participating sites have been selected among the ones running HPC clusters in Grid environment. Each of them contributed with their specific HPC experience and their available resources to the present test, which encompasses an unprecedented large set of applications from different disciplines in the fields of astronomy, astrophysics, chemistry, climatology, material science and oceanography. In addition to computing resources sharing, the main contribution of each participant was the identification of the real requirements of his application also related to the current middleware limitations and then the realization of a test platform enhanced with additional HPC solutions and configurations developed in a tight collaboration between HPC administrators, users and IGI managers. The main work was on computational resources selection, data management and the definition, the deployment and the documentation of the software execution environment. The outcoming results of the testbed represent the basis of the HPC support in the IGI production infrastructure.

SUMMARY: The PiSQRD web resource can be used to subdivide protein structures in quasi-rigid dynamical domains. The latter are groups of amino acids behaving as approximately rigid units in the course of protein equilibrium fluctuations. The PiSQRD server takes as input a biomolecular structure and the desired fraction of protein internal fluctuations that must be accounted for by the relative rigid-body motion of the dynamical domains. Next, the lowest energy modes of fluctuation of the protein (optionally provided by the user) are calculated and used to identify the rigid subunits. The resulting optimal subdivision is returned through a web page containing both interactive graphics and detailed data output. AVAILABILITY: The PiSQRD web server, which requires Java, is available free of charge for academic users at http://pisqrd.escience-lab.org.