Online carbohydrate 3D structure validation with the Privateer web app

Owing to the difficulties associated with working with carbohydrates, validating glycan 3D structures prior to deposition into the Protein Data Bank has become a staple of the structure-solution pipeline. The Privateer software provides integrative methods for the validation, analysis, refinement and graphical representation of 3D atomic structures of glycans, both as ligands and as protein modifiers. While Privateer is free software, it requires users to install any of the structural biology software suites that support it or to build it from source code. Here, the Privateer web app is presented, which is always up to date and available to be used online (https://privateer.york.ac.uk) without installation. This self-updating tool, which runs locally on the user's machine, will allow structural biologists to simply and quickly analyse carbohydrate ligands and protein glycosylation from a web browser whilst retaining all confidential information on their devices.

Tertiary structure assessment at CASP15

The results of tertiary structure assessment at CASP15 are reported. For the first time, recognizing the outstanding performance of AlphaFold 2 (AF2) at CASP14, all single-chain predictions were assessed together, irrespective of whether a template was available. At CASP15, there was no single stand-out group, with most of the best-scoring groups-led by PEZYFoldings, UM-TBM, and Yang Server-employing AF2 in one way or another. Many top groups paid special attention to generating deep Multiple Sequence Alignments (MSAs) and testing variant MSAs, thereby allowing them to successfully address some of the hardest targets. Such difficult targets, as well as lacking templates, were typically proteins with few homologues. Local divergence between prediction and target correlated with localization at crystal lattice or chain interfaces, and with regions exhibiting high B-factor factors in crystal structure targets, and should not necessarily be considered as representing error in the prediction. However, analysis of exposed and buried side chain accuracy showed room for improvement even in the latter. Nevertheless, a majority of groups produced high-quality predictions for most targets, which are valuable for experimental structure determination, functional analysis, and many other tasks across biology. These include those applying methods similar to those used to generate major resources such as the AlphaFold Protein Structure Database and the ESM Metagenomic atlas: the confidence estimates of the former were also notably accurate.

Predicted models and CCP4

In late 2020, the results of CASP14, the 14th event in a series of competitions to assess the latest developments in computational protein structure-prediction methodology, revealed the giant leap forward that had been made by Google's Deepmind in tackling the prediction problem. The level of accuracy in their predictions was the first instance of a competitor achieving a global distance test score of better than 90 across all categories of difficulty. This achievement represents both a challenge and an opportunity for the field of experimental structural biology. For structure determination by macromolecular X-ray crystallography, access to highly accurate structure predictions is of great benefit, particularly when it comes to solving the phase problem. Here, details of new utilities and enhanced applications in the CCP4 suite, designed to allow users to exploit predicted models in determining macromolecular structures from X-ray diffraction data, are presented. The focus is mainly on applications that can be used to solve the phase problem through molecular replacement.

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.

Using deep-learning predictions of inter-residue distances for model validation

Determination of protein structures typically entails building a model that satisfies the collected experimental observations and its deposition in the Protein Data Bank. Experimental limitations can lead to unavoidable uncertainties during the process of model building, which result in the introduction of errors into the deposited model. Many metrics are available for model validation, but most are limited to consideration of the physico-chemical aspects of the model or its match to the experimental data. The latest advances in the field of deep learning have enabled the increasingly accurate prediction of inter-residue distances, an advance which has played a pivotal role in the recent improvements observed in the field of protein ab initio modelling. Here, new validation methods are presented based on the use of these precise inter-residue distance predictions, which are compared with the distances observed in the protein model. Sequence-register errors are particularly clearly detected and the register shifts required for their correction can be reliably determined. The method is available in the ConKit package (https://www.conkit.org).

Multitasking in the gut: the X-ray structure of the multidomain BbgIII from Bifidobacterium bifidum offers possible explanations for its alternative functions

β-Galactosidases catalyse the hydrolysis of lactose into galactose and glucose; as an alternative reaction, some β-galactosidases also catalyse the formation of galactooligosaccharides by transglycosylation. Both reactions have industrial importance: lactose hydrolysis is used to produce lactose-free milk, while galactooligosaccharides have been shown to act as prebiotics. For some multi-domain β-galactosidases, the hydrolysis/transglycosylation ratio can be modified by the truncation of carbohydrate-binding modules. Here, an analysis of BbgIII, a multidomain β-galactosidase from Bifidobacterium bifidum, is presented. The X-ray structure has been determined of an intact protein corresponding to a gene construct of eight domains. The use of evolutionary covariance-based predictions made sequence docking in low-resolution areas of the model spectacularly easy, confirming the relevance of this rapidly developing deep-learning-based technique for model building. The structure revealed two alternative orientations of the CBM32 carbohydrate-binding module relative to the GH2 catalytic domain in the six crystallographically independent chains. In one orientation the CBM32 domain covers the entrance to the active site of the enzyme, while in the other orientation the active site is open, suggesting a possible mechanism for switching between the two activities of the enzyme, namely lactose hydrolysis and transgalactosylation. The location of the carbohydrate-binding site of the CBM32 domain on the opposite site of the module to where it comes into contact with the catalytic GH2 domain is consistent with its involvement in adherence to host cells. The role of the CBM32 domain in switching between hydrolysis and transglycosylation modes offers protein-engineering opportunities for selective β-galactosidase modification for industrial purposes in the future.

Evaluation of model refinement in CASP14

We report here an assessment of the model refinement category of the 14th round of Critical Assessment of Structure Prediction (CASP14). As before, predictors submitted up to five ranked refinements, along with associated residue-level error estimates, for targets that had a wide range of starting quality. The ability of groups to accurately rank their submissions and to predict coordinate error varied widely. Overall, only four groups out-performed a "naïve predictor" corresponding to the resubmission of the starting model. Among the top groups, there are interesting differences of approach and in the spread of improvements seen: some methods are more conservative, others more adventurous. Some targets were "double-barreled" for which predictors were offered a high-quality AlphaFold 2 (AF2)-derived prediction alongside another of lower quality. The AF2-derived models were largely unimprovable, many of their apparent errors being found to reside at domain and, especially, crystal lattice contacts. Refinement is shown to have a mixed impact overall on structure-based function annotation methods to predict nucleic acid binding, spot catalytic sites, and dock protein structures.

In silico prediction of structure and function for a large family of transmembrane proteins that includes human Tmem41b

Background: Recent strides in computational structural biology have opened up an opportunity to understand previously uncharacterised proteins.  The under-representation of transmembrane proteins in the Protein Data Bank highlights the need to apply new and advanced bioinformatics methods to shed light on their structure and function.  This study focuses on a family of transmembrane proteins containing the Pfam domain PF09335 ('SNARE_ASSOC'/ 'VTT '/'Tvp38'/'DedA'). One prominent member, Tmem41b, has been shown to be involved in early stages of autophagosome formation and is vital in mouse embryonic development as well as being identified as a viral host factor of SARS-CoV-2. Methods: We used evolutionary covariance-derived information to construct and validate ab initio models, make domain boundary predictions and infer local structural features.  Results: The results from the structural bioinformatics analysis of Tmem41b and its homologues showed that they contain a tandem repeat that is clearly visible in evolutionary covariance data but much less so by sequence analysis.  Furthermore, cross-referencing of other prediction data with covariance analysis showed that the internal repeat features two-fold rotational symmetry.  Ab initio modelling of Tmem41b and homologues reinforces these structural predictions.  Local structural features predicted to be present in Tmem41b were also present in Cl ^-/H ⁺ antiporters.  Conclusions: The results of this study strongly point to Tmem41b and its homologues being transporters for an as-yet uncharacterised substrate and possibly using H ⁺ antiporter activity as its mechanism for transport.

In silico prediction of structure and function for a large family of transmembrane proteins that includes human Tmem41b

Background: Recent strides in computational structural biology have opened up an opportunity to understand previously uncharacterised proteins.  The under-representation of transmembrane proteins in the Protein Data Bank highlights the need to apply new and advanced bioinformatics methods to shed light on their structure and function.  This study focuses on a family of transmembrane proteins containing the Pfam domain PF09335 ('SNARE_ASSOC'/ 'VTT '/'Tvp38'). One prominent member, Tmem41b, has been shown to be involved in early stages of autophagosome formation and is vital in mouse embryonic development as well as being identified as a viral host factor of SARS-CoV-2. Methods: We used evolutionary covariance-derived information to construct and validate ab initio models, make domain boundary predictions and infer local structural features.  Results: The results from the structural bioinformatics analysis of Tmem41b and its homologues showed that they contain a tandem repeat that is clearly visible in evolutionary covariance data but much less so by sequence analysis.  Furthermore, cross-referencing of other prediction data with covariance analysis showed that the internal repeat features two-fold rotational symmetry.  Ab initio modelling of Tmem41b and homologues reinforces these structural predictions.  Local structural features predicted to be present in Tmem41b were also present in Cl ^-/H ⁺ antiporters.  Conclusions: The results of this study strongly point to Tmem41b and its homologues being transporters for an as-yet uncharacterised substrate and possibly using H ⁺ antiporter activity as its mechanism for transport.

ConPlot: Web-based application for the visualisation of protein contact maps integrated with other data

Summary

Covariance-based predictions of residue contacts and inter-residue distances are an increasingly popular data type in protein bioinformatics. Here we present ConPlot, a web-based application for convenient display and analysis of contact maps and distograms. Integration of predicted contact data with other predictions is often required to facilitate inference of structural features. ConPlot can therefore use the empty space near the contact map diagonal to display multiple coloured tracks representing other sequence-based predictions. Popular file formats are natively read and bespoke data can also be flexibly displayed. This novel visualization will enable easier interpretation of predicted contact maps.

Availability and implementation

available online at www.conplot.org, along with documentation and examples. Alternatively, ConPlot can be installed and used locally using the docker image from the project’s Docker Hub repository. ConPlot is licensed under the BSD 3-Clause.

Supplementary information

Supplementary data are available at Bioinformatics online.

Helical ensembles outperform ideal helices in molecular replacement

Helical ensembles solve more structures by MR with AMPLE than do ideal helices and at no greater CPU cost.

The conventional approach in molecular replacement is the use of a related structure as a search model. However, this is not always possible as the availability of such structures can be scarce for poorly characterized families of proteins. In these cases, alternative approaches can be explored, such as the use of small ideal fragments that share high, albeit local, structural similarity with the unknown protein. Earlier versions of AMPLE enabled the trialling of a library of ideal helices, which worked well for largely helical proteins at suitable resolutions. Here, the performance of libraries of helical ensembles created by clustering helical segments is explored. The impacts of different B-factor treatments and different degrees of structural heterogeneity are explored. A 30% increase in the number of solutions obtained by AMPLE was observed when using this new set of ensembles compared with the performance with ideal helices. The boost in performance was notable across three different fold classes: transmembrane, globular and coiled-coil structures. Furthermore, the increased effectiveness of these ensembles was coupled to a reduction in the time required by AMPLE to reach a solution. AMPLE users can now take full advantage of this new library of search models by activating the ‘helical ensembles’ mode.

Initial Poor Function in the Age of Old Donors: Prognostic Factors

INTRODUCTION

The current donor selection criteria have changed from the past years. Primary nonfunction is a serious complication after liver transplantation, but initial poor function (IPF), which occurs from 2% to 23%, also has an increased morbidity and mortality. We analyzed prognostic factors associated with IPF.

MATERIALS AND METHOD

This retrospective study of 551 liver transplants performed from January 2000 to December 2005 excluded retransplantations and transplants by classic surgery. The study cohort was attentified according to the presence or the absence of IPF. The variables included were (1) donor age, gender, cause of death, length of stay in Critical Care Medicine, noradrenaline use, sodium levels, and cardiorespiratory arrest, (2) from the standpoint of surgery: we included ischemia time (IT), intervention time, units of packet red cells (PRC), volume of blood autotransfusion (VBA), postreperfusion syndrome (PRS), and vasoactive drugs within the surgery procedure (VAD); (3) from the recipient's: view we examined age, gender, etiology, functional state, and covermittant pathology; (4) During the postoperative period we noted the presence of postoperative hemorrhage. Statistical analysis used chi-square test, Student t test, multiple logistic regression with significance set at P < .05.

RESULTS

Differences were found in IT (P = .001), VBA (P = .001), PRS (P = .012), VAD (P = .03), fulminant hepatic failure as the cause the transplantation (P = .002), and chronic obstructive pulmonary disease (P = .007). A regression model retained the following variables: IT, VBA, PRS, fulminant hepatic failure, and chronic obstructive pulmonary disease.

CONCLUSIONS

The prognostic factors for IPF need to be modified together with donor selection criteria in liver transplantation.

[Pattern of antimicrobial susceptibility in uropathogen isolates from children]

The bacteriological profile in children with culture-positive bacteriuria was analyzed during a 5-year period. Escherichia coli was the most common cause of urinary tract infections (57%), followed by Streptococcus faecalis (11%), Pseudomonas aeruginosa (8%), and Proteus mirabilis (6%). Results of antimicrobial susceptibility testing indicated that cephalosporins (first, second and third generation) and nitrofurantoin are the best empirical oral treatment for urinary infections in children. Fosfomycin is a valid option in some cases. In hospitalized children treatment must be initiated with a third-generation cephalosporin, and gentamicin can be added in severely ill inpatients. These treatments can be modified when microbiological results become available.