dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2014). FRAGON   (Jenkins, 2018 ) also uses fragment libraries, rapidly processing them in Phaser and then attempting to improve the resulting phases with ACORN  (Foadi et al. , 2000; Yao et al. , 2005). Another developing trend in unconventional methods is the brute-force trialling of a library of all (or a nonredundant subset) of the set of known structures in MR against a target. This approach requires a large amount of computing power and has been implemented as a grid-based service through the SBGrid Wide Research Molecular Replacement service (Stokes-Rees & Sliz, 2010) using Phaser to perform the MR trials. CCP4 distributes an application to perform a similar task, SIMBAD  (Simpkin et al. , 2017) , which uses the rotationsearch step in AMoRe to rank all of the entries in the nonredundant PDB domain database distributed with the MoRDa pipeline against the target data set.
The pipeline has also been deployed as an online service through CCP4 online. As shown in Fig. 1 , MrBUMP has two main modes of operation. The program can be run in a fully automated way from model search through to model rebuilding, or it can be terminated after the model-search and model-preparation steps have completed. The latter mode is useful in cases where a user wishes to carry out MR directly with Phaser, MOLREP or another MR application using the models generated by MrBUMP. The steps taken in this mode are also very quick, taking less than a minute on a single core for a typical target sequence. To enhance the usability of this feature, we combined it with the ability to inspect and modify the produced search models graphically using the CCP4mg molecular-graphics application  (McNicholas et al.
The MrBUMP pipeline implementation 2. 1. Finding a suitable search model for MR Apart from the scenario where an existing structure of the target is available, the first step in conventional MR is to identify a suitable homologue or set of homologues, usually by a sequence-based search against the PDB. The result of the alignment of a sequence against a database of sequences will differ according to the program and accompanying parameters used. In the context of MR, it is an attempt to infer structural similarity from the amino-acid sequence similarity. Fortunately for crystallographers, the field of bioinformatics has seen the development of sensitive sequence-alignment tools designed to exploit the explosion of sequence information being produced by the genomics field. MrBUMP now has the option to use two such programs: phmmer and HHpred.
2. 1. 1. phmmer. phmmer is part of the HMMER suite of programs  (Eddy, 2011)  designed to search sequence databases for sequence homologues. Its methods use probabilistic models called hidden Markov models (HMMs) to help to find distant homologues for a given sequence profile or individual sequence. Similarly to the BLAST (Basic Local Alignment Search Tool) application  (Altschul et al. , 1990) , it searches using a single protein query sequence against a database of sequences. phmmer is distributed with the CCP4 suite and is now used (in preference to the original FASTA;  Pearson & Lipman, 1988)  as the default method in MrBUMP for identifying homologues of the target for use as search models. MrBUMP includes a curated database of sequences from known protein structures for use in the phmmer search.
Currently, this database contains entries from the PDB up to 2016. Updates to the database are made available through the CCP4 update system on an infrequent basis. Work is being performed to automate the database generation and allow updates to be made with every CCP4 update release. A particular advance in the present version of MrBUMP is the implementation of several versions of the database, each with a different level of redundancy removed from the complete list of sequences. The redundancy-level options are 100 (no redundancy removed), 95, 90, 70 and 50%. For example, in the 90% redundant set, where two or more structures with a sequence identity greater than 90% are present in the complete list only one example is retained. The sequence lists are drawn from those provided by the PDB (https://www.
rcsb. org/pdb/statistics/ clusterStatistics. do), which are created using the BLASTClust program from the BLAST package  (Altschul et al. , 1990). When removing similar sequences, the sequence with the highest resolution and the lowest R value is preferred. The addition of these nonredundant sequence lists is one of the advances that were introduced as a result of the inspection of models in the CCP4mg implementation (x3) and is a significant advance on the original implementation, which could only use the 100% set. The redundancy default is now set to 95%, enabling a broad sampling of search models to be trialled in MR, and avoids the pipeline choosing a set of essentially identical search models, such as depositions for the same protein with a series of ligands bound. Use of the latter results in a restricted survey of possible models, where all of the top ten may be essentially identical.
However, sequence similarity does not always correspond to structural similarity, so in some cases it can help to use the 100% option and retain structures with very similar sequences: this can be particularly useful for a protein that can adopt a range of different conformations through hinge movements, for example. It may also help to use the 100% option when creating ensemble search models, where small variations in the alignment between a set of very similar structures can guide the identification of the most conserved core residues. This will be discussed in more detail in x2. 2. 2. His tags and sequences shorter than ten residues are removed from the databases so as to prevent search results being dominated by false-positive matches or short-fragment models. The target sequence provided to MrBUMP is used as input to phmmer, which will return a set of matches with scores based on how similar they are to the target sequence.
These scores are based on the sequence identity as well as other factors such as gap-opening and gap-extension penalties, and substitution, insertion and deletion scores. To save time in preparation and in MR, a cutoff score of 20 is used to eliminate unrelated proteins and homologues that may be too dissimilar for use as search models. However, this value can be adjusted through the MrBUMP PMRCUTOFF keyword when using the program through CCP4i or from the command line. phmmer produces a multiple sequence alignment of the target sequence and those of all of the homologues. The pairwise alignments are extracted from the phmmer alignment and are converted by MrBUMP into FASTA-and PIR-formatted alignment files. These are retained for use as a guide for the truncation of the corresponding search models in preparation for their use in MR.
It can accept a single sequence (used to search a database such as UniProt20) or a multiple alignment as an input profile and can make use of various databases such as the PDB, SCOP  (Murzin et al. , 1995) , Pfam  (Finn et al. , 2016)    The CCP4mg interface to MrBUMP, with an illustrative representation of the domain regions found during the clustering of matches from the phmmer search. The results shown here are for a search using the sequence from PDB entry 5u4p, a protein-protein complex between 26S proteasome regulatory subunit RPN8, RPN11 and ubiquitin S31  (Worden et al. , 2017). The results have been clustered into four domains, two of which consist of more than one match, making them suitable for ensemble generation. The dashed line indicates the cutoff phmmer score (default = 20) used for the selection of matches to be used as search models in MR.
Molecular replacement in MrBUMP MrBUMP uses Phaser and/or MOLREP to carry out the molecular-replacement step. Both programs are highly automated and default parameters are used for most of their options. The only program options set by MrBUMP are the anticipated number of molecules expected in the asymmetric unit, which is calculated using MATTHEWS_COEF  (Matthews, 1968; Kantardjieff & Rupp, 2003) , and the expected r. m. s. d. value for each search model given to Phaser. Values below 60 can still correspond to a correct solution but require further examination. A single, standout solution is also a good indication of success. As a general rule, the Phaser scoring system is sensitive to the accuracy of what is specified as the composition of the asymmetric unit of the target and the estimated r.
There are several program options that can be selected within the MrBUMP pipeline, namely SHELXE  (Thorn & Sheldrick, 2013 ; Uson & Sheldrick, 2018), ARP/wARP  (Langer et al. , 2008)  and Buccaneer  (Cowtan, 2012). (i) SHELXE. Where the resolution of the experimental data permits (typically better than 2. 4 A ˚), a run of SHELXE will, by default, be invoked to attempt density modification followed by polyalanine tracing into the improved electrondensity map. The initial set of phases for the target are derived by SHELXE from the refined MR solution model produced by REFMAC in the refinement step of MrBUMP. By default, 15 global cycles of density modification (20 iterations per global cycle) and polyalanine tracing are invoked. The resulting polyalanine model after each cycle is used as a new estimate for the phases of the target for the next cycle.
In the MrBUMP pipeline, options allow these programs to be run immediately after the refinement step, using the refined MR model as a starting point for the phase information, or after SHELXE, using its polyalanine trace model as the starting model. This second option is particularly useful for removing model bias at resolutions better than 2. 4 A ˚. (iii) ACORN. ACORN is a program for phase improvement using dynamic density modification and can be invoked through the USEAcorn keyword in MrBUMP. It is not currently invoked by default or made available through the interfaces owing to the overlap of its functionality with that of SHELXE, which is less restrictive in the required resolution for the observed reflection data (the recommeded lowresolution limit for ACORN is 1. 7 A ˚;  Yao et al.
MrBUMP through CCP4mg Having the capability to view and modify search models can be useful in difficult cases. Such cases might include, for example, instances where the homologous structures found by phmmer have multiple domains and no structural alignment can adequately superpose all of the domains. For such proteins the user may wish to specify which domain to use as the search model, or to use more than one domain with different structural alignments. In other instances, only distant homologues may be found and it may be necessary to use only part of the structures in MR. Usage of this option can be performed directly from CCP4mg or from the Bioinformatics task menu in the CCP4i2 graphical interface. With a user-supplied sequence and a chosen nonredundancy level for the homologue search, MrBUMP uses phmmer to search the PDB sequence database and download homologous structures.
The results window allows the user to show and hide any of the found domains. When a domain is chosen, the 'GESAMT variance slider' is provided to allow the selection of atoms based on the structural alignment of that domain. It is alternatively possible to display and select the models before the Sculptor step of MrBUMP. When satisfied with the displayed atoms, the user saves them to an ensemble PDB file suitable for input to Phaser or MOLREP. A normal PDB file can in addition be saved for each individual structure. When using the CCP4mg/MrBUMP procedure through the CCP4i2 graphical interface, the recommended action is to save the files to the CCP4i2 database for seamless integration with the other tasks. The full set of log files produced by MrBUMP can be inspected from within CCP4mg by clicking on the appropriate button in the results window (Fig.
MrBUMP web application CCP4 online  (Krissinel et al. , 2018 ) is a web-based portal facilitating the execution of compute-intensive components of the CCP4 suite on the CCP4 compute clusters based at the Research Complex at Harwell (RCaH), and MrBUMP is one of the services it provides. The deployment of the service in this way has several advantages to the user and the developers. The processing of each search model in MR, refinement and model building is farmed out to the cluster, allowing more rapid calculation of results. The service also uses the HHpred search tools and databases to identify potential search models. The search programs in HHpred require large databases of tens of gigabytes in size, which are large to download and are not always practical for local installation. The other advantage is that developers can deploy the latest stable versions on the centralized installation, removing the need for users to keep their local installations up to date.
Exploring the solution for PDB entry 5cml using CCP4mg/MrBUMP As discussed in x3, the CCP4mg interface to MrBUMP allows a user to view and modify the set of search models found and prepared by the pipeline. The large set of model-selection and model-manipulation tools available in CCP4mg, including the ability to truncate the ensembled search models, allow a user to generate any number of permutations of the original model set. For example, a user may choose to truncate away the most variable 10% of the ensemble using the slider tool and save the reduced ensemble as a search model for MR. Alternatively, they could select only the highest scoring member of the reduced ensemble and use that as a search model. To evaluate how best to use the capabilities of this method of running MrBUMP, we explored its use for the case of PDB Statistics from 497 recently completed jobs run on the MrBUMP web service.
Results are separated for input data resolutions better and worse than 2. 5 A ˚. Solutions that achieve the stated scores tend to overlap; each row represents a subset of the row above. However, there are several cases where SHELXE achieves a CC of >25 where the scoring criteria for other programs have not been achieved, particularly when the data resolution is 2. entry 5cml, a protein-domain structure from the bacterium Rhodothermus marinus  (Jensen et al. , 2016 ). We chose this example as it has the characteristics of a difficult case for MR. When originally solved, the only homologues available had sequence identities in the range 20-30%. This put it in the boundary zone of where sequence identity can be relied upon to be indicative of structural similarity. The original solution required the generation of an ensemble search model derived from PDB entries 2fuk, 3trd and 3pf9.
Search-model selection To simulate a novel structure solution, we used a reduced version of the PDB only containing entries that have accession codes commencing with 1, 2, 3 and 4. This corresponds to a separation of several months between the most recent entry that can be used as a search model and the time of deposition for PDB entry 5cml (deposited in July 2015). We used the full database of sequences (no redundancy removal) from the reduced version of the PDB in the homologue-search step. The best homologue found in the phmmer search was chain A of PDB entry 3pf8 (deposited in October 2010), with a phmmer score of 53. 9. According to a PSI-BLAST search, this structure has a low sequence identity of 26% to the target with a query coverage of 78%, values that are indicative of a challenging problem for MR.
An additional nine structures (Fig. 3 ), including chain B of PDB entry 3pf8, scored similarly and exhibited very similar folds when aligned structurally. Table  3  shows the sequence identity (according to PSI-BLAST), the pairwise aligned r. m. s. d. against chain A of PDB entry 5cml (using GESAMT) and the the phmmer score for each of the ten structures. A multiple alignment of all ten using GESAMT gives a mean r. m. s. d. of 0. 38 A ˚from a calculated centroid for the aligned C -atom positions.
To demonstrate the effectiveness of ensembling and truncating the search models, we produced ten derived ensembles from the initial base ensemble. These models were created using the truncation slider tool in the CCP4mg graphical window, moving it in steps of 10% and exporting the displayed Table  3  phmmer search results for the sequence of PDB entry 5cml. Sequence-identity details are taken from PSI-BLAST results for the same sequence. The calculated r. m. s. d. from GESAMT for the core residues from a pairwise alignment with the structure of PDB entry 5cml is also presented. The multiple alignment of these ten models is the base alignment for ensemble generation. (Note that the final character in each model name is a domain identifier. ) coordinates to a PDB file. Note that the same set of search models can be produced when running MrBUMP in its automated mode.
Each of these ensembles is a reduced version of the base, with a C -atom position variance tolerance ranging from 10 to 100% of the GESAMT core residues. The base ensemble, as well as ensembles truncated at the 80 and 40% levels, are shown in Fig. 4. For the sake of comparison with a single-model search in MR, we extracted the components deriving from the highest scoring homologue (chain A of PDB entry 3pf8) from each of the 11 ensembles and used them as additional search models. It is possible that some of the other members of the ensemble may yield a solution where chain A of PDB entry 3pf8 does not. However, we wish to demonstrate that producing an ensemble search model from the full set of homologues can be more effective in MR than an individual component search model on its own. PSI.
Molecular replacement Each of these ensembles, including the base ensemble, along with the corresponding single models, were used as MR search models for the 5cml target using Phaser (v. 2. 7. 17). We instructed Phaser to search for two copies of the search model. Each model was tested in Phaser at each of the r. m. s. d. settings between 0. 1 and 2. 4 A ˚. A total of 496 individual tests were run using the CCP4 Linux cluster at the RCaH: 24 values for the input Phaser r. m. s. d. applied to 11 truncation levels (including the base level) for both the ensemble and the single search models.
REFMAC final R free The final R free values from 100 cycles of restrained refinement with jelly-body restraints are presented in Figs. 5(e) and 5( f). The benefits of refining the MR solutions are made clear in the single-model tests (Fig. 5f  ), where using the original nontruncated search model and an input r. m. s. d. for Phaser in the range 0. 2-2. 4 A ˚(with the exception of 1. 5 A ˚) results in R free values between 0. 47 and 0. 5. The mean mapCC value for these solutions (Fig. 6d ) is 0. 57, up from 0. 34 after the Phaser step. Use of the single-chain model (chain A of PDB entry 3pf8) worked on its own and leads to clear solutions in the SHELXE step. It is possible that recent improvements in Phaser, such as the introduction of the VRMS or using Sculptor to prepare the search model, have made it possible to solve the case with this single chain rather than having to use an ensemble as was performed in the original structure solution of PDB entry 5cml.
Without exception, the successes based on a SHELXE CC of !25 correlate directly with results with high mapCC (!0. 88). With few exceptions, results with ACL ! 35 correlate with the same set of mapCC results. factor amplitudes. This is to match the default in Phaser, where intensities are used in preference to amplitudes to account better for measurement error in the experiment in its likelihood functions  (Read & McCoy, 2016). Other enhancements will include the automatic provision of updates to the phmmer sequence databases and the deployment of MrBUMP through the CCP4 Cloud development jsCoFE (JavaScriptpowered Cloud Front End) described by  Krissinel et al. (2018). This has the potential to eventually replace CCP4 online, giving users the ability to run MrBUMP within a project-based environment online, rather than as a standalone application.
Acknowledgements We thank  Eugene Krissinel  for making changes to the  GESAMT  software to support the generation of ensemble search models and  Andrey Lebedev  for useful discussions on molecular replacement, and we wish to acknowledge the support of the  Research Complex at Harwell  for providing the computing resources for testing and the provision of the MrBUMP web application. We also thank the developers of the underlying software in the MrBUMP pipeline as well as the users of  MrBUMP  for providing useful feedback.
Conclusions The MrBUMP automated pipeline for molecular replacement has been significantly enhanced in recent years, with improvements in its search-model selection and preparation steps, and post-MR refinement, as well as the use of density modification and automated model building to improve the positioned search model. Taken together, these changes improve the performance, user-friendliness and success indicators of the software. Its integration with the CCP4mg molecular-graphics program provides a valuable tool for the visual examination and manipulation of search models for MR. Users can also benefit from using MrBUMP through the CCP4 online web application. Advantages of the latter include access to the HHpred software and databases for a more sensitive detection of potential search models and a manyfold speedup through the distribution of search-model processing in MR onto a compute cluster.
e. , consensus motifs were not found to be separated by about 10 nucleotides (one helix turn), other than in one case (Table  II ). A refinement of such an analysis by using PWMs instead of the consensus sequence overcomes the constraint of requiring the presence of the exact consensus sequence. This can be done, e. g. , via available open source programs which, given a PWM, allow one to score sequences and return a p-value for each position detected as significant  [Korhonen et al. 2009 ]. In our specific case, we wrote a shell script to scan PWMs against the 21 Arabidopsis IAA upstream sequences. For completeness, we compared the obtained scores with those generated by scanning the same PWMs against reshuffled sequences: the program selects then as "true AuxRE" all those positions whose scores were higher than the highest random one.
ACKNOWLEDGMENTS The authors would like to thank  S. Alt ,  L. Band ,  M. Bennett ,  E. Farcot ,  J. Friml ,  V. Hakim ,  N. Mellor ,  C. Rechenmann  and  G. Salbreux  for enlightening suggestions and interesting discussions. Special thanks go to  P. Sollich  and  T. Vernoux  who followed this work since its beginning and provided valuable guidance. This work has been supported by the  Marie Curie Training Network NETADIS  ( FP7 , grant  290038 ). S. G. further acknowledges the  Francis Crick Institute  which receives its core funding from  Cancer Research UK  ( FC001317 ), the  UK Medical Research Council  ( FC001317 ) and the  Wellcome Trust  ( FC001317 ). B. B. acknowledges also the  Simons Foundation  Grant No. 454953.
M. setense CSUR47 was cultured on Middlebrook 7H11 agar supplemented with 10% (vol/vol) oleic acid-albumin-dextrose-catalase (Becton, Dickinson, Sparks, MD, USA). Genomic DNA was then sequenced with MiSeq technology (Illumina, Inc. , San Diego, CA, USA) using a mate pair library. The index representation for M. setense CSUR47 was determined to be 10. 10%. A total of 1,538,022 paired-end reads were filtered per the read qualities and then assembled using the Velvet tool (3). Contigs were combined using SSPACE (4) assisted by manual finishing and GapFiller (5). This yielded a 6,278,097-bp draft genome with a 66. 40% GC content and 19 scaffolds composed of 63 contigs. Prodigal (6) was used with default parameters to predict open reading frames (ORFs). Functional annotation was achieved using a BLASTp search against the GenBank (7) and Clusters of Orthologous Groups (COGs) databases (8).