Structural basis for the nuclease activity of a bacteriophage large terminase

The DNA-packaging motor in tailed bacteriophages requires nuclease activity to ensure that the genome is packaged correctly. This nuclease activity is tightly regulated as the enzyme is inactive for the duration of DNA translocation. Here, we report the X-ray structure of the large terminase nuclease domain from bacteriophage SPP1. Similarity with the RNase H family endonucleases allowed interactions with the DNA to be predicted. A structure-based alignment with the distantly related T4 gp17 terminase shows the conservation of an extended β-sheet and an auxiliary β-hairpin that are not found in other RNase H family proteins. The model with DNA suggests that the β-hairpin partly blocks the active site, and in vivo activity assays show that the nuclease domain is not functional in the absence of the ATPase domain. Here, we propose that the nuclease activity is regulated by movement of the β-hairpin, altering active site access and the orientation of catalytically essential residues

CCP4 Cloud for structure determination and project management in macromolecular crystallography

Nowadays, progress in the determination of three-dimensional macromolecular structures from diffraction images is achieved partly at the cost of increasing data volumes. This is due to the deployment of modern high-speed, high-resolution detectors, the increased complexity and variety of crystallographic software, the use of extensive databases and high-performance computing. This limits what can be accomplished with personal, offline, computing equipment in terms of both productivity and maintainability. There is also an issue of long-term data maintenance and availability of structure-solution projects as the links between experimental observations and the final results deposited in the PDB. In this article, CCP4 Cloud, a new front-end of the CCP4 software suite, is presented which mitigates these effects by providing an online, cloud-based environment for crystallographic computation. CCP4 Cloud was developed for the efficient delivery of computing power, database services and seamless integration with web resources. It provides a rich graphical user interface that allows project sharing and long-term storage for structure-solution projects, and can be linked to data-producing facilities. The system is distributed with the CCP4 software suite version 7.1 and higher, and an online publicly available instance of CCP4 Cloud is provided by CCP4.The following funding is acknowledged: Biotechnology and Biological Sciences Research Council (grant No. BB/L007037/1; grant No. BB/S007040/1; grant No. BB/S007083/1; grant No. BB/S005099/1; grant No. BB/S007105/1; award No. BBF020384/1); Medical Research Council (grant No.MC_UP_A025_1012; grant No. MC_U105184325); Ro¨ntgenA˚ ngstro¨m Cluster (grant No. 349-2013-597); Nederlandse Wetenschappelijke Organisatie (grant No. TKI 16219)

The CCP4 suite: integrative software for macromolecular crystallography

The Collaborative Computational Project No. 4 (CCP4) is a UK-led international collective with a mission to develop, test, distribute and promote software for macromolecular crystallography. The CCP4 suite is a multiplatform collection of programs brought together by familiar execution routines, a set of common libraries and graphical interfaces. The CCP4 suite has experienced several considerable changes since its last reference article, involving new infrastructure, original programs and graphical interfaces. This article, which is intended as a general literature citation for the use of the CCP4 software suite in structure determination, will guide the reader through such transformations, offering a general overview of the new features and outlining future developments. As such, it aims to highlight the individual programs that comprise the suite and to provide the latest references to them for perusal by crystallographers around the world.Jon Agirre is a Royal Society University Research Fellow (UF160039 and URF\R\221006). Mihaela Atanasova is funded by the UK Engineering and Physical Sciences Research Council (EPSRC; EP/R513386/1). Haroldas Bagdonas is funded by The Royal Society (RGF/R1/181006). Jose´ Javier Burgos-Ma´rmol and Daniel J. Rigden are supported by the BBSRC (BB/S007105/1). Robbie P. Joosten is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 871037 (iNEXTDiscovery) and by CCP4. This work was supported by the Medical Research Council as part of United Kingdom Research and Innovation, also known as UK Research and Innovation: MRC file reference No. MC_UP_A025_1012 to Garib N. Murshudov, which also funded Keitaro Yamashita, Paul Emsley and Fei Long. Robert A. Nicholls is funded by the BBSRC (BB/S007083/1). Soon Wen Hoh is funded by the BBSRC (BB/T012935/1). Kevin D. Cowtan and Paul S. Bond are funded in part by the BBSRC (BB/S005099/1). John Berrisford and Sameer Velankar thank the European Molecular Biology Laboratory–European Bioinformatics Institute, who supported this work. Andrea Thorn was supported in the development of AUSPEX by the German Federal Ministry of Education and Research (05K19WWA and 05K22GU5) and by Deutsche Forschungsgemeinschaft (TH2135/2-1). Petr Kolenko and Martin Maly´ are funded by the MEYS CR (CZ.02.1.01/0.0/0.0/16_019/0000778). Martin Maly´ is funded by the Czech Academy of Sciences (86652036) and CCP4/STFC (521862101). Anastassis Perrakis acknowledges funding from iNEXT (grant No. 653706), iNEXT-Discovery (grant No. 871037), West-Life (grant No. 675858) and EOSC-Life (grant No. 824087) funded by the Horizon 2020 program of the European Commission. Robbie P. Joosten has been the recipient of a Veni grant (722.011.011) and a Vidi grant (723.013.003) from the Netherlands Organization for Scientific Research (NWO). Maarten L. Hekkelman, Robbie P. Joosten and Anastassis Perrakis thank the Research High Performance Computing facility of the Netherlands Cancer Institute for providing and maintaining computation resources and acknowledge the institutional grant from the Dutch Cancer Society and the Dutch Ministry of Health, Welfare and Sport. Tarik R. Drevon is funded by the BBSRC (BB/S007040/1). Randy J. Read is supported by a Principal Research Fellowship from the Wellcome Trust (grant 209407/Z/17/Z). Atlanta G. Cook is supported by a Wellcome Trust SRF (200898) and a Wellcome Centre for Cell Biology core grant (203149). Isabel Uso´n acknowledges support from STFC-UK/CCP4: ‘Agreement for the integration of methods into the CCP4 software distribution, ARCIMBOLDO_LOW’ and Spanish MICINN/AEI/FEDER/UE (PID2021-128751NB-I00). Pavol Skubak and Navraj Pannu were funded by the NWO Applied Sciences and Engineering Domain and CCP4 (grant Nos. 13337 and 16219). Bernhard Lohkamp was supported by the Ro¨ntgen A˚ ngstro¨m Cluster (grant 349-2013-597). Nicholas Pearce is currently funded by the SciLifeLab and Wallenberg Data Driven Life Science Program (grant KAW 2020.0239) and has previously been funded by a Veni Fellowship (VI.Veni.192.143) from the Dutch Research Council (NWO), a Long-term EMBO fellowship (ALTF 609-2017) and EPSRC grant EP/G037280/1. David M. Lawson received funding from BBSRC Institute Strategic Programme Grants (BB/P012523/1 and BB/P012574/1). Lucrezia Catapano is the recipient of an STFC/CCP4-funded PhD studentship (Agreement No: 7920 S2 2020 007).Peer reviewe

The CCP4 suite: integrative software for macromolecular crystallography

The Collaborative Computational Project No. 4 (CCP4) is a UK-led international collective with a mission to develop, test, distribute and promote software for macromolecular crystallography. The CCP4 suite is a multiplatform collection of programs brought together by familiar execution routines, a set of common libraries and graphical interfaces. The CCP4 suite has experienced several considerable changes since its last reference article, involving new infrastructure, original programs and graphical interfaces. This article, which is intended as a general literature citation for the use of the CCP4 software suite in structure determination, will guide the reader through such transformations, offering a general overview of the new features and outlining future developments. As such, it aims to highlight the individual programs that comprise the suite and to provide the latest references to them for perusal by crystallographers around the world

Characteristics of the psychoemotional sphere among Evenk children

Background: In Russia, there is an active ongoing process of national revival of the indigenous small-numbered peoples of the North Siberia, such as the Evenks. Revival of the younger generation, in particular, remains a priority. The state helps to solve the problems of education and adaptation of Evenk children to modern life. This is necessary because parents, hunters, and reindeer herders have a nomadic lifestyle. The educational feature of Evenk children is to study and live in a boarding school after elementary school graduation. Success of adaptation largely depends on the ability to account for the ethnopsychological specificity of Evenk children. Aim of the study: To study characteristics of the psychoemotional sphere and identify ethnospecific indicators for the adaptation of Evenk children to a boarding school. Material and methods: Pupils (N = 409) aged 10–16 from the village boarding school of Evenkia, Krasnoyarsk Territory, Russia were examined. Pupils consisted of 132 Evenk children and 277 Russian children. The emotional sphere was evaluated according to Eysenck Personality Inventory (EPI) and lateral phenotype was evaluated according to Bragina & Dobrokhotova. Results: We found that Evenk children show a predominant pattern of left laterality (p = 0.024). In addition, relative to Russian children, Evenk children are more likely to show the introverted personality type (p = 0.035). Relative to Russian children, Evenk children are more restrained in their emotional manifestations, have greater difficulty in communicating with strangers, answer with monosyllables, and show a less vivid emotional reaction to praise. Further, relative to Russian children, Evenk children are more likely to show a high level of emotional stability (i.e., 9–10 points; p = 0.001). Conclusions: The present study examined the psychoemotional characteristics of Evenk children. We identified ethnospecific indicators, including an introversion personality type combined with emotional stability and left laterality. Identification of these characteristics allowed us to form a risk group of children in adaptation. Ethnospecific indicators of the psycho-emotional sphere should be considered for effective management of the adaptation of children in a boarding school

Biology of Crocidura sibirica Dukelsky, 1930 in the southern West Siberia

<p>Our paper reflects the data of a comprehensive study of the main biological characteristics of the Siberian shrew <i>Crocidura sibirica</i> Dukelsky, 1930. 921 specimens were examined for the period 1978–2020. It has been found that the Siberian shrew is attracted to habitats that have been significantly disturbed by human activity (logging sites, hayfields, reclaimed coal dumps, burned areas), but avoids completely degraded areas and urban ecosystems. It reaches its maximum abundance in the low-mountain belt of the Kuznetsk Alatau in hay meadows. The population of the Siberian shrew is subject to cyclic fluctuations with a frequency of 3–4 years. Seasonal activity peaks in mid-August and September, with breeding in the second half of summer and early fall. Among the one-year-old animals, the predominance of males is observed. The diurnal activity of the Siberian shrew is polyphasic, mainly nocturnal. Peaks of highest activity were observed at 23–24 hours and 6–9 hours. In terms of running speed, digging ability, and swimming ability, the Siberian shrew is significantly inferior to its trophic competitors, the other shrews. In interspecific encounters, neutral, friendly interactions predominate; aggression is ritualized. In intraspecific encounters with large shrews, the Siberian shrew will occupy a shelter and attempt to drive an opponent from it. The food spectrum is based on the imaginal and larval stages of insects, arachnids, and centipedes. Among insects, ground beetle larvae, Brachycera, and Hymenoptera are the most preferred foods. The identified food spectrum corresponds to the biotopic distribution of invertebrates, indicating the absence of food selectivity. The trophic spectrum of the <i>C. sibirica</i> overlaps significantly with that of sympatric species of other shrews. Given the significant overlap of the spatial ecological niche, it can be assumed that the Siberian shrew avoids competitive interactions for food resources due to the mismatch of the peak of seasonal activity. Under the influence of competitive interactions with numerous species of the genus <i>Sorex</i>, the main features of the biology of the <i>C. sibirica</i> were formed.</p&gt