1
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Rozano L, Mukuka YM, Hane JK, Mancera RL. Ab Initio Modelling of the Structure of ToxA-like and MAX Fungal Effector Proteins. Int J Mol Sci 2023; 24:ijms24076262. [PMID: 37047233 PMCID: PMC10094246 DOI: 10.3390/ijms24076262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/09/2023] [Accepted: 03/21/2023] [Indexed: 03/29/2023] Open
Abstract
Pathogenic fungal diseases in crops are mediated by the release of effector proteins that facilitate infection. Characterising the structure of these fungal effectors is vital to understanding their virulence mechanisms and interactions with their hosts, which is crucial in the breeding of plant cultivars for disease resistance. Several effectors have been identified and validated experimentally; however, their lack of sequence conservation often impedes the identification and prediction of their structure using sequence similarity approaches. Structural similarity has, nonetheless, been observed within fungal effector protein families, creating interest in validating the use of computational methods to predict their tertiary structure from their sequence. We used Rosetta ab initio modelling to predict the structures of members of the ToxA-like and MAX effector families for which experimental structures are known to validate this method. An optimised approach was then used to predict the structures of phenotypically validated effectors lacking known structures. Rosetta was found to successfully predict the structure of fungal effectors in the ToxA-like and MAX families, as well as phenotypically validated but structurally unconfirmed effector sequences. Interestingly, potential new effector structural families were identified on the basis of comparisons with structural homologues and the identification of associated protein domains.
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2
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Borges RJ, Salvador GHM, Pimenta DC, Dos Santos LD, Fontes MRM, Usón I. SEQUENCE SLIDER: integration of structural and genetic data to characterize isoforms from natural sources. Nucleic Acids Res 2022; 50:e50. [PMID: 35104880 PMCID: PMC9122596 DOI: 10.1093/nar/gkac029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 01/05/2022] [Accepted: 01/30/2022] [Indexed: 12/28/2022] Open
Abstract
Proteins isolated from natural sources can be composed of a mixture of isoforms with similar physicochemical properties that coexist in the final steps of purification. Yet, even where unverified, the assumed sequence is enforced throughout the structural studies. Herein, we propose a novel perspective to address the usually neglected sequence heterogeneity of natural products by integrating biophysical, genetic and structural data in our program SEQUENCE SLIDER. The aim is to assess the evidence supporting chemical composition in structure determination. Locally, we interrogate the experimental map to establish which side chains are supported by the structural data, and the genetic information relating sequence conservation is integrated into this statistic. Hence, we build a constrained peptide database, containing most probable sequences to interpret mass spectrometry data (MS). In parallel, we perform MS de novo sequencing with genomic-based algorithms to detect point mutations. We calibrated SLIDER with Gallus gallus lysozyme, whose sequence is unequivocally established and numerous natural isoforms are reported. We used SLIDER to characterize a metalloproteinase and a phospholipase A2-like protein from the venom of Bothrops moojeni and a crotoxin from Crotalus durissus collilineatus. This integrated approach offers a more realistic structural descriptor to characterize macromolecules isolated from natural sources.
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Affiliation(s)
- Rafael J Borges
- Departament of Biophysics and Pharmacology, Biosciences Institute, São Paulo State University (UNESP), Botucatu, São Paulo 18618-689, Brazil.,Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB-CSIC), Barcelona 08028, Spain
| | - Guilherme H M Salvador
- Departament of Biophysics and Pharmacology, Biosciences Institute, São Paulo State University (UNESP), Botucatu, São Paulo 18618-689, Brazil
| | - Daniel C Pimenta
- Biochemistry and Biophysics Laboratory, Butantan Institute, São Paulo, São Paulo 05503-900, Brazil
| | - Lucilene D Dos Santos
- Graduate Program in Tropical Diseases, Botucatu Medical School (FMB), São Paulo State University (UNESP), Botucatu, São Paulo 18618-687, Brazil.,Biotechnology Institute (IBTEC), São Paulo State University (UNESP), Botucatu, São Paulo 18607-440, Brazil
| | - Marcos R M Fontes
- Departament of Biophysics and Pharmacology, Biosciences Institute, São Paulo State University (UNESP), Botucatu, São Paulo 18618-689, Brazil
| | - Isabel Usón
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB-CSIC), Barcelona 08028, Spain.,ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Spain
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3
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Rodríguez FS, Mesdaghi S, Simpkin AJ, Burgos-Mármol JJ, Murphy DL, Uski V, Keegan RM, Rigden DJ. ConPlot: Web-based application for the visualisation of protein contact maps integrated with other data. Bioinformatics 2021; 37:2763-2765. [PMID: 34499718 PMCID: PMC8428603 DOI: 10.1093/bioinformatics/btab049] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 12/18/2020] [Accepted: 01/21/2021] [Indexed: 12/15/2022] Open
Abstract
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.
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Affiliation(s)
- Filomeno Sánchez Rodríguez
- Institute of Structural, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England.,Life Science, Diamond Light Source, Harwell Science and Innovation Campus, Oxfordshire OX11 0DE, Didcot, England
| | - Shahram Mesdaghi
- Institute of Structural, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Adam J Simpkin
- Institute of Structural, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - J Javier Burgos-Mármol
- Institute of Structural, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - David L Murphy
- Institute of Structural, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Ville Uski
- UKRI-STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, England
| | - Ronan M Keegan
- UKRI-STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, England
| | - Daniel J Rigden
- Institute of Structural, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
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4
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McCoy AJ, Stockwell DH, Sammito MD, Oeffner RD, Hatti KS, Croll TI, Read RJ. Phasertng: directed acyclic graphs for crystallographic phasing. Acta Crystallogr D Struct Biol 2021; 77:1-10. [PMID: 33404520 PMCID: PMC7787104 DOI: 10.1107/s2059798320014746] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 11/06/2020] [Indexed: 12/01/2022] Open
Abstract
Crystallographic phasing strategies increasingly require the exploration and ranking of many hypotheses about the number, types and positions of atoms, molecules and/or molecular fragments in the unit cell, each with only a small chance of being correct. Accelerating this move has been improvements in phasing methods, which are now able to extract phase information from the placement of very small fragments of structure, from weak experimental phasing signal or from combinations of molecular replacement and experimental phasing information. Describing phasing in terms of a directed acyclic graph allows graph-management software to track and manage the path to structure solution. The crystallographic software supporting the graph data structure must be strictly modular so that nodes in the graph are efficiently generated by the encapsulated functionality. To this end, the development of new software, Phasertng, which uses directed acyclic graphs natively for input/output, has been initiated. In Phasertng, the codebase of Phaser has been rebuilt, with an emphasis on modularity, on scripting, on speed and on continuing algorithm development. As a first application of phasertng, its advantages are demonstrated in the context of phasertng.xtricorder, a tool to analyse and triage merged data in preparation for molecular replacement or experimental phasing. The description of the phasing strategy with directed acyclic graphs is a generalization that extends beyond the functionality of Phasertng, as it can incorporate results from bioinformatics and other crystallographic tools, and will facilitate multifaceted search strategies, dynamic ranking of alternative search pathways and the exploitation of machine learning to further improve phasing strategies.
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Affiliation(s)
- Airlie J. McCoy
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Duncan H. Stockwell
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Massimo D. Sammito
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Robert D. Oeffner
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Kaushik S. Hatti
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
- Drug Discovery Unit, Wellcome Centre for Anti-Infectives Research, School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, United Kingdom
| | - Tristan I. Croll
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Randy J. Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
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5
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Shao D, Mao W, Xing Y, Gong H. RDb2C2: an improved method to identify the residue-residue pairing in β strands. BMC Bioinformatics 2020; 21:133. [PMID: 32245403 PMCID: PMC7126467 DOI: 10.1186/s12859-020-3476-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Accepted: 03/31/2020] [Indexed: 11/17/2022] Open
Abstract
Background Despite the great advance of protein structure prediction, accurate prediction of the structures of mainly β proteins is still highly challenging, but could be assisted by the knowledge of residue-residue pairing in β strands. Previously, we proposed a ridge-detection-based algorithm RDb2C that adopted a multi-stage random forest framework to predict the β-β pairing given the amino acid sequence of a protein. Results In this work, we developed a second version of this algorithm, RDb2C2, by employing the residual neural network to further enhance the prediction accuracy. In the benchmark test, this new algorithm improves the F1-score by > 10 percentage points, reaching impressively high values of ~ 72% and ~ 73% in the BetaSheet916 and BetaSheet1452 sets, respectively. Conclusion Our new method promotes the prediction accuracy of β-β pairing to a new level and the prediction results could better assist the structure modeling of mainly β proteins. We prepared an online server of RDb2C2 at http://structpred.life.tsinghua.edu.cn/rdb2c2.html.
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6
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Sánchez Rodríguez F, Simpkin AJ, Davies OR, Keegan RM, Rigden DJ. Helical ensembles outperform ideal helices in molecular replacement. ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY 2020; 76:962-970. [PMID: 33021498 PMCID: PMC7543657 DOI: 10.1107/s205979832001133x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 08/18/2020] [Indexed: 03/21/2023]
Abstract
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.
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Affiliation(s)
- Filomeno Sánchez Rodríguez
- Institute of Structural, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Adam J Simpkin
- Institute of Structural, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Owen R Davies
- Institute for Cell and Molecular Biosciences, Newcastle University, Framlington Place, Newcastle upon Tyne NE2 4HH, United Kingdom
| | - Ronan M Keegan
- UKRI-STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Daniel J Rigden
- Institute of Structural, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
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7
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Simpkin AJ, Thomas JMH, Simkovic F, Keegan RM, Rigden DJ. Molecular replacement using structure predictions from databases. Acta Crystallogr D Struct Biol 2019; 75:1051-1062. [PMID: 31793899 PMCID: PMC6889911 DOI: 10.1107/s2059798319013962] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 10/12/2019] [Indexed: 01/19/2023] Open
Abstract
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.
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Affiliation(s)
- Adam J. Simpkin
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Jens M. H. Thomas
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Felix Simkovic
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Ronan M. Keegan
- STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, England
| | - Daniel J. Rigden
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
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8
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Mao W, Wang T, Zhang W, Gong H. Identification of residue pairing in interacting β-strands from a predicted residue contact map. BMC Bioinformatics 2018; 19:146. [PMID: 29673311 PMCID: PMC5907701 DOI: 10.1186/s12859-018-2150-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 04/09/2018] [Indexed: 12/04/2022] Open
Abstract
Background Despite the rapid progress of protein residue contact prediction, predicted residue contact maps frequently contain many errors. However, information of residue pairing in β strands could be extracted from a noisy contact map, due to the presence of characteristic contact patterns in β-β interactions. This information may benefit the tertiary structure prediction of mainly β proteins. In this work, we propose a novel ridge-detection-based β-β contact predictor to identify residue pairing in β strands from any predicted residue contact map. Results Our algorithm RDb2C adopts ridge detection, a well-developed technique in computer image processing, to capture consecutive residue contacts, and then utilizes a novel multi-stage random forest framework to integrate the ridge information and additional features for prediction. Starting from the predicted contact map of CCMpred, RDb2C remarkably outperforms all state-of-the-art methods on two conventional test sets of β proteins (BetaSheet916 and BetaSheet1452), and achieves F1-scores of ~ 62% and ~ 76% at the residue level and strand level, respectively. Taking the prediction of the more advanced RaptorX-Contact as input, RDb2C achieves impressively higher performance, with F1-scores reaching ~ 76% and ~ 86% at the residue level and strand level, respectively. In a test of structural modeling using the top 1 L predicted contacts as constraints, for 61 mainly β proteins, the average TM-score achieves 0.442 when using the raw RaptorX-Contact prediction, but increases to 0.506 when using the improved prediction by RDb2C. Conclusion Our method can significantly improve the prediction of β-β contacts from any predicted residue contact maps. Prediction results of our algorithm could be directly applied to effectively facilitate the practical structure prediction of mainly β proteins. Availability All source data and codes are available at http://166.111.152.91/Downloads.html or the GitHub address of https://github.com/wzmao/RDb2C. Electronic supplementary material The online version of this article (10.1186/s12859-018-2150-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Wenzhi Mao
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China.,Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing, China
| | - Tong Wang
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China.,Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing, China
| | - Wenxuan Zhang
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China.,Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing, China
| | - Haipeng Gong
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China. .,Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing, China.
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9
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Rigden DJ, Thomas JMH, Simkovic F, Simpkin A, Winn MD, Mayans O, Keegan RM. Ensembles generated from crystal structures of single distant homologues solve challenging molecular-replacement cases in AMPLE. Acta Crystallogr D Struct Biol 2018; 74:183-193. [PMID: 29533226 PMCID: PMC5947759 DOI: 10.1107/s2059798318002310] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 02/07/2018] [Indexed: 01/17/2023] Open
Abstract
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.
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Affiliation(s)
- Daniel J. Rigden
- Institute of Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, England
| | - Jens M. H. Thomas
- Institute of Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, England
| | - Felix Simkovic
- Institute of Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, England
| | - Adam Simpkin
- Institute of Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, England
| | - Martyn D. Winn
- Science and Technology Facilities Council, Daresbury Laboratory, Warrington WA4 4AD, England
| | - Olga Mayans
- Fachbereich Biologie, Universität Konstanz, 78457 Konstanz, Germany
| | - Ronan M. Keegan
- Research Complex at Harwell, STFC Rutherford Appleton Laboratory, Didcot OX11 0FA, England
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10
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Thomas JMH, Simkovic F, Keegan R, Mayans O, Zhang C, Zhang Y, Rigden DJ. Approaches to ab initio molecular replacement of α-helical transmembrane proteins. Acta Crystallogr D Struct Biol 2017; 73:985-996. [PMID: 29199978 PMCID: PMC5713875 DOI: 10.1107/s2059798317016436] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Accepted: 11/15/2017] [Indexed: 02/06/2023] Open
Abstract
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.
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Affiliation(s)
- Jens M. H. Thomas
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Felix Simkovic
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Ronan Keegan
- Research Complex at Harwell, STFC Rutherford Appleton Laboratory, Didcot OX11 0FA, England
| | - Olga Mayans
- Fachbereich Biologie, Universität Konstanz, D-78457 Konstanz, Germany
| | - Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, Department of Biological Chemistry, Medical School, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109-2218, USA
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, Department of Biological Chemistry, Medical School, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109-2218, USA
| | - Daniel J. Rigden
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
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11
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Abstract
Co-evolution techniques were originally conceived to assist in protein structure prediction by inferring pairs of residues that share spatial proximity. However, the functional relationships that can be extrapolated from co-evolution have also proven to be useful in a wide array of structural bioinformatics applications. These techniques are a powerful way to extract structural and functional information in a sequence-rich world.
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12
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Simkovic F, Ovchinnikov S, Baker D, Rigden DJ. Applications of contact predictions to structural biology. IUCRJ 2017; 4:291-300. [PMID: 28512576 PMCID: PMC5414403 DOI: 10.1107/s2052252517005115] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 04/03/2017] [Indexed: 06/07/2023]
Abstract
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 β-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.
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Affiliation(s)
- Felix Simkovic
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Sergey Ovchinnikov
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, University of Washington, Box 357370, Seattle, WA 98195, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
- Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, University of Washington, Box 357370, Seattle, WA 98195, USA
| | - Daniel J. Rigden
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
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13
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Baker JA, Simkovic F, Taylor HMC, Rigden DJ. Potential DNA binding and nuclease functions of ComEC domains characterized in silico. Proteins 2016; 84:1431-42. [PMID: 27318187 PMCID: PMC5031224 DOI: 10.1002/prot.25088] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2016] [Revised: 05/25/2016] [Accepted: 06/13/2016] [Indexed: 12/15/2022]
Abstract
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 β-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.
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Affiliation(s)
- James A Baker
- Department of Biochemistry, Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, United Kingdom
| | - Felix Simkovic
- Department of Biochemistry, Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, United Kingdom
| | - Helen M C Taylor
- Department of Biochemistry, Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, United Kingdom
| | - Daniel J Rigden
- Department of Biochemistry, Institute of Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, United Kingdom.
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