Using Phaser and ensembles to improve the performance of SIMBAD.

The conventional approach to search-model identification in molecular replacement (MR) is to screen a database of known structures using the target sequence. However, this strategy is not always effective, for example when the relationship between sequence and structural similarity fails or when the crystal contents are not those expected. An alternative approach is to identify suitable search models directly from the experimental data. SIMBAD is a sequence-independent MR pipeline that uses either a crystal lattice search or MR functions to directly locate suitable search models from databases. The previous version of SIMBAD used the fast AMoRe rotation-function search. Here, a new version of SIMBAD which makes use of Phaser and its likelihood scoring to improve the sensitivity of the pipeline is presented. It is shown that the additional compute time potentially required by the more sophisticated scoring is counterbalanced by the greater sensitivity, allowing more cases to trigger early-termination criteria, rather than running to completion. Using Phaser solved 17 out of 25 test cases in comparison to the ten solved with AMoRe, and it is shown that use of ensemble search models produces additional performance benefits.

Molecular replacement using structure predictions from databases.

Molecular replacement (MR) is the predominant route to solution of the phase problem in macromolecular crystallography. Where the lack of a suitable homologue precludes conventional MR, one option is to predict the target structure using bioinformatics. Such modelling, in the absence of homologous templates, is called ab initio or de novo modelling. Recently, the accuracy of such models has improved significantly as a result of the availability, in many cases, of residue-contact predictions derived from evolutionary covariance analysis. Covariance-assisted ab initio models representing structurally uncharacterized Pfam families are now available on a large scale in databases, potentially representing a valuable and easily accessible supplement to the PDB as a source of search models. Here, the unconventional MR pipeline AMPLE is employed to explore the value of structure predictions in the GREMLIN and PconsFam databases. It was tested whether these deposited predictions, processed in various ways, could solve the structures of PDB entries that were subsequently deposited. The results were encouraging: nine of 27 GREMLIN cases were solved, covering target lengths of 109-355 residues and a resolution range of 1.4-2.9 Å, and with target-model shared sequence identity as low as 20%. The cluster-and-truncate approach in AMPLE proved to be essential for most successes. For the overall lower quality structure predictions in the PconsFam database, remodelling with Rosetta within the AMPLE pipeline proved to be the best approach, generating ensemble search models from single-structure deposits. Finally, it is shown that the AMPLE-obtained search models deriving from GREMLIN deposits are of sufficiently high quality to be selected by the sequence-independent MR pipeline SIMBAD. Overall, the results help to point the way towards the optimal use of the expanding databases of ab initio structure predictions.

Production of monodisperse polyurea microcapsules using microfluidics.

Methods to make microcapsules - used in a broad range of healthcare and energy applications - currently suffer from poor size control, limiting the establishment of size/property relationships. Here, we use microfluidics to produce monodisperse polyurea microcapsules (PUMC) with a limonene core. Using varied flow rates and a commercial glass chip, we produce capsules with mean diameters of 27, 30, 32, 34, and 35 µm, achieving narrow capsule size distributions of ±2 µm for each size. We describe an automated method of sizing droplets as they are produced using video recording and custom Python code. The sustainable generation of such size-controlled PUMCs, potential replacements for commercial encapsulated systems, will allow new insights into the effect of particle size on performance.

SIMBAD: a sequence-independent molecular-replacement pipeline.

The conventional approach to finding structurally similar search models for use in molecular replacement (MR) is to use the sequence of the target to search against those of a set of known structures. Sequence similarity often correlates with structure similarity. Given sufficient similarity, a known structure correctly positioned in the target cell by the MR process can provide an approximation to the unknown phases of the target. An alternative approach to identifying homologous structures suitable for MR is to exploit the measured data directly, comparing the lattice parameters or the experimentally derived structure-factor amplitudes with those of known structures. Here, SIMBAD, a new sequence-independent MR pipeline which implements these approaches, is presented. SIMBAD can identify cases of contaminant crystallization and other mishaps such as mistaken identity (swapped crystallization trays), as well as solving unsequenced targets and providing a brute-force approach where sequence-dependent search-model identification may be nontrivial, for example because of conformational diversity among identifiable homologues. The program implements a three-step pipeline to efficiently identify a suitable search model in a database of known structures. The first step performs a lattice-parameter search against the entire Protein Data Bank (PDB), rapidly determining whether or not a homologue exists in the same crystal form. The second step is designed to screen the target data for the presence of a crystallized contaminant, a not uncommon occurrence in macromolecular crystallography. Solving structures with MR in such cases can remain problematic for many years, since the search models, which are assumed to be similar to the structure of interest, are not necessarily related to the structures that have actually crystallized. To cater for this eventuality, SIMBAD rapidly screens the data against a database of known contaminant structures. Where the first two steps fail to yield a solution, a final step in SIMBAD can be invoked to perform a brute-force search of a nonredundant PDB database provided by the MoRDa MR software. Through early-access usage of SIMBAD, this approach has solved novel cases that have otherwise proved difficult to solve.

Recent developments in MrBUMP: better search-model preparation, graphical interaction with search models, and solution improvement and assessment.

Increasing sophistication in molecular-replacement (MR) software and the rapid expansion of the PDB in recent years have allowed the technique to become the dominant method for determining the phases of a target structure in macromolecular X-ray crystallography. In addition, improvements in bioinformatic techniques for finding suitable homologous structures for use as MR search models, combined with developments in refinement and model-building techniques, have pushed the applicability of MR to lower sequence identities and made weak MR solutions more amenable to refinement and improvement. MrBUMP is a CCP4 pipeline which automates all stages of the MR procedure. Its scope covers everything from the sourcing and preparation of suitable search models right through to rebuilding of the positioned search model. Recent improvements to the pipeline include the adoption of more sensitive bioinformatic tools for sourcing search models, enhanced model-preparation techniques including better ensembling of homologues, and the use of phase improvement and model building on the resulting solution. The pipeline has also been deployed as an online service through CCP4 online, which allows its users to exploit large bioinformatic databases and coarse-grained parallelism to speed up the determination of a possible solution. Finally, the molecular-graphics application CCP4mg has been combined with MrBUMP to provide an interactive visual aid to the user during the process of selecting and manipulating search models for use in MR. Here, these developments in MrBUMP are described with a case study to explore how some of the enhancements to the pipeline and to CCP4mg can help to solve a difficult case.

Ensembles generated from crystal structures of single distant homologues solve challenging molecular-replacement cases in AMPLE.

Molecular replacement (MR) is the predominant route to solution of the phase problem in macromolecular crystallography. Although routine in many cases, it becomes more effortful and often impossible when the available experimental structures typically used as search models are only distantly homologous to the target. Nevertheless, with current powerful MR software, relatively small core structures shared between the target and known structure, of 20-40% of the overall structure for example, can succeed as search models where they can be isolated. Manual sculpting of such small structural cores is rarely attempted and is dependent on the crystallographer's expertise and understanding of the protein family in question. Automated search-model editing has previously been performed on the basis of sequence alignment, in order to eliminate, for example, side chains or loops that are not present in the target, or on the basis of structural features (e.g. solvent accessibility) or crystallographic parameters (e.g. B factors). Here, based on recent work demonstrating a correlation between evolutionary conservation and protein rigidity/packing, novel automated ways to derive edited search models from a given distant homologue over a range of sizes are presented. A variety of structure-based metrics, many readily obtained from online webservers, can be fed to the MR pipeline AMPLE to produce search models that succeed with a set of test cases where expertly manually edited comparators, further processed in diverse ways with MrBUMP, fail. Further significant performance gains result when the structure-based distance geometry method CONCOORD is used to generate ensembles from the distant homologue. To our knowledge, this is the first such approach whereby a single structure is meaningfully transformed into an ensemble for the purposes of MR. Additional cases further demonstrate the advantages of the approach. CONCOORD is freely available and computationally inexpensive, so these novel methods offer readily available new routes to solve difficult MR cases.

Approaches to ab initio molecular replacement of α-helical transmembrane proteins.

α-Helical transmembrane proteins are a ubiquitous and important class of proteins, but present difficulties for crystallographic structure solution. Here, the effectiveness of the AMPLE molecular replacement pipeline in solving α-helical transmembrane-protein structures is assessed using a small library of eight ideal helices, as well as search models derived from ab initio models generated both with and without evolutionary contact information. The ideal helices prove to be surprisingly effective at solving higher resolution structures, but ab initio-derived search models are able to solve structures that could not be solved with the ideal helices. The addition of evolutionary contact information results in a marked improvement in the modelling and makes additional solutions possible.

Applications of contact predictions to structural biology.

Evolutionary pressure on residue interactions, intramolecular or intermolecular, that are important for protein structure or function can lead to covariance between the two positions. Recent methodological advances allow much more accurate contact predictions to be derived from this evolutionary covariance signal. The practical application of contact predictions has largely been confined to structural bioinformatics, yet, as this work seeks to demonstrate, the data can be of enormous value to the structural biologist working in X-ray crystallo-graphy, cryo-EM or NMR. Integrative structural bioinformatics packages such as Rosetta can already exploit contact predictions in a variety of ways. The contribution of contact predictions begins at construct design, where structural domains may need to be expressed separately and contact predictions can help to predict domain limits. Structure solution by molecular replacement (MR) benefits from contact predictions in diverse ways: in difficult cases, more accurate search models can be constructed using ab initio modelling when predictions are available, while intermolecular contact predictions can allow the construction of larger, oligomeric search models. Furthermore, MR using supersecondary motifs or large-scale screens against the PDB can exploit information, such as the parallel or antiparallel nature of any ß-strand pairing in the target, that can be inferred from contact predictions. Contact information will be particularly valuable in the determination of lower resolution structures by helping to assign sequence register. In large complexes, contact information may allow the identity of a protein responsible for a certain region of density to be determined and then assist in the orientation of an available model within that density. In NMR, predicted contacts can provide long-range information to extend the upper size limit of the technique in a manner analogous but complementary to experimental methods. Finally, predicted contacts can distinguish between biologically relevant interfaces and mere lattice contacts in a final crystal structure, and have potential in the identification of functionally important regions and in foreseeing the consequences of mutations.

ConKit: a python interface to contact predictions.

SUMMARY: Recent advances in protein residue contact prediction algorithms have led to the emergence of many new methods and a variety of file formats. We present ConKit , an open source, modular and extensible Python interface which allows facile conversion between formats and provides an interface to analyses of sequence alignments and sets of contact predictions. AVAILABILITY AND IMPLEMENTATION: ConKit is available via the Python Package Index. The documentation can be found at http://www.conkit.org . ConKit is licensed under the BSD 3-Clause. CONTACT: hlfsimko@liverpool.ac.uk or drigden@liverpool.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Residue contacts predicted by evolutionary covariance extend the application of ab initio molecular replacement to larger and more challenging protein folds.

For many protein families, the deluge of new sequence information together with new statistical protocols now allow the accurate prediction of contacting residues from sequence information alone. This offers the possibility of more accurate ab initio (non-homology-based) structure prediction. Such models can be used in structure solution by molecular replacement (MR) where the target fold is novel or is only distantly related to known structures. Here, AMPLE, an MR pipeline that assembles search-model ensembles from ab initio structure predictions ('decoys'), is employed to assess the value of contact-assisted ab initio models to the crystallographer. It is demonstrated that evolutionary covariance-derived residue-residue contact predictions improve the quality of ab initio models and, consequently, the success rate of MR using search models derived from them. For targets containing ß-structure, decoy quality and MR performance were further improved by the use of a ß-strand contact-filtering protocol. Such contact-guided decoys achieved 14 structure solutions from 21 attempted protein targets, compared with nine for simple Rosetta decoys. Previously encountered limitations were superseded in two key respects. Firstly, much larger targets of up to 221 residues in length were solved, which is far larger than the previously benchmarked threshold of 120 residues. Secondly, contact-guided decoys significantly improved success with ß-sheet-rich proteins. Overall, the improved performance of contact-guided decoys suggests that MR is now applicable to a significantly wider range of protein targets than were previously tractable, and points to a direct benefit to structural biology from the recent remarkable advances in sequencing.

Potential DNA binding and nuclease functions of ComEC domains characterized in silico.

Bacterial competence, which can be natural or induced, allows the uptake of exogenous double stranded DNA (dsDNA) into a competent bacterium. This process is known as transformation. A multiprotein assembly binds and processes the dsDNA to import one strand and degrade another yet the underlying molecular mechanisms are relatively poorly understood. Here distant relationships of domains in Competence protein EC (ComEC) of Bacillus subtilis (Uniprot: P39695) were characterized. DNA-protein interactions were investigated in silico by analyzing models for structural conservation, surface electrostatics and structure-based DNA binding propensity; and by data-driven macromolecular docking of DNA to models. Our findings suggest that the DUF4131 domain contains a cryptic DNA-binding OB fold domain and that the ß-lactamase-like domain is the hitherto cryptic competence nuclease. Proteins 2016; 84:1431-1442. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

Titin kinase is an inactive pseudokinase scaffold that supports MuRF1 recruitment to the sarcomeric M-line.

Striated muscle tissues undergo adaptive remodelling in response to mechanical load. This process involves the myofilament titin and, specifically, its kinase domain (TK; titin kinase) that translates mechanical signals into regulatory pathways of gene expression in the myofibril. TK mechanosensing appears mediated by a C-terminal regulatory tail (CRD) that sterically inhibits its active site. Allegedly, stretch-induced unfolding of this tail during muscle function releases TK inhibition and leads to its catalytic activation. However, the cellular pathway of TK is poorly understood and substrates proposed to date remain controversial. TK's best-established substrate is Tcap, a small structural protein of the Z-disc believed to link TK to myofibrillogenesis. Here, we show that TK is a pseudokinase with undetectable levels of catalysis and, therefore, that Tcap is not its substrate. Inactivity is the result of two atypical residues in TK's active site, M34 and E147, that do not appear compatible with canonical kinase patterns. While not mediating stretch-dependent phospho-transfers, TK binds the E3 ubiquitin ligase MuRF1 that promotes sarcomeric ubiquitination in a stress-induced manner. Given previous evidence of MuRF2 interaction, we propose that the cellular role of TK is to act as a conformationally regulated scaffold that functionally couples the ubiquitin ligases MuRF1 and MuRF2, thereby coordinating muscle-specific ubiquitination pathways and myofibril trophicity. Finally, we suggest that an evolutionary dichotomy of kinases/pseudokinases has occurred in TK-like kinases, where invertebrate members are active enzymes but vertebrate counterparts perform their signalling function as pseudokinase scaffolds.

Mechanistic and functional diversity in the mechanosensory kinases of the titin-like family.

The giant cytoskeletal kinases of the titin-like family are emerging as key mediators of stretch-sensing in muscle. It is thought that their elastic conformational deformation during muscle function regulates both their catalysis and the recruitment of regulatory proteins to signalosomes that assemble in their vicinity. In the present article, we discuss the speciation of mechanosensory mechanisms in titin-like kinases, their scaffolding properties and the kinase/pseudokinase domain variations that define a rich functional diversity across the family.