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Gavalda-Garcia J, Bickel D, Roca-Martinez J, Raimondi D, Orlando G, Vranken W. Data-driven probabilistic definition of the low energy conformational states of protein residues. NAR Genom Bioinform 2024; 6:lqae082. [PMID: 38984065 PMCID: PMC11231583 DOI: 10.1093/nargab/lqae082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 06/14/2024] [Accepted: 06/26/2024] [Indexed: 07/11/2024] Open
Abstract
Protein dynamics and related conformational changes are essential for their function but difficult to characterise and interpret. Amino acids in a protein behave according to their local energy landscape, which is determined by their local structural context and environmental conditions. The lowest energy state for a given residue can correspond to sharply defined conformations, e.g. in a stable helix, or can cover a wide range of conformations, e.g. in intrinsically disordered regions. A good definition of such low energy states is therefore important to describe the behaviour of a residue and how it changes with its environment. We propose a data-driven probabilistic definition of six low energy conformational states typically accessible for amino acid residues in proteins. This definition is based on solution NMR information of 1322 proteins through a combined analysis of structure ensembles with interpreted chemical shifts. We further introduce a conformational state variability parameter that captures, based on an ensemble of protein structures from molecular dynamics or other methods, how often a residue moves between these conformational states. The approach enables a different perspective on the local conformational behaviour of proteins that is complementary to their static interpretation from single structure models.
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Affiliation(s)
- Jose Gavalda-Garcia
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium
| | - David Bickel
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium
| | - Joel Roca-Martinez
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium
| | | | | | - Wim Vranken
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium
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2
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Vincenzi M, Mercurio FA, Leone M. NMR Spectroscopy in the Conformational Analysis of Peptides: An Overview. Curr Med Chem 2021; 28:2729-2782. [PMID: 32614739 DOI: 10.2174/0929867327666200702131032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 05/21/2020] [Accepted: 05/28/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND NMR spectroscopy is one of the most powerful tools to study the structure and interaction properties of peptides and proteins from a dynamic perspective. Knowing the bioactive conformations of peptides is crucial in the drug discovery field to design more efficient analogue ligands and inhibitors of protein-protein interactions targeting therapeutically relevant systems. OBJECTIVE This review provides a toolkit to investigate peptide conformational properties by NMR. METHODS Articles cited herein, related to NMR studies of peptides and proteins were mainly searched through PubMed and the web. More recent and old books on NMR spectroscopy written by eminent scientists in the field were consulted as well. RESULTS The review is mainly focused on NMR tools to gain the 3D structure of small unlabeled peptides. It is more application-oriented as it is beyond its goal to deliver a profound theoretical background. However, the basic principles of 2D homonuclear and heteronuclear experiments are briefly described. Protocols to obtain isotopically labeled peptides and principal triple resonance experiments needed to study them, are discussed as well. CONCLUSION NMR is a leading technique in the study of conformational preferences of small flexible peptides whose structure can be often only described by an ensemble of conformations. Although NMR studies of peptides can be easily and fast performed by canonical protocols established a few decades ago, more recently we have assisted to tremendous improvements of NMR spectroscopy to investigate instead large systems and overcome its molecular weight limit.
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Affiliation(s)
- Marian Vincenzi
- Institute of Biostructures and Bioimaging, National Research Council of Italy, Via Mezzocannone 16, 80134, Naples, Italy
| | - Flavia Anna Mercurio
- Institute of Biostructures and Bioimaging, National Research Council of Italy, Via Mezzocannone 16, 80134, Naples, Italy
| | - Marilisa Leone
- Institute of Biostructures and Bioimaging, National Research Council of Italy, Via Mezzocannone 16, 80134, Naples, Italy
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3
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Orlando G, Raimondi D, Kagami LP, Vranken WF. ShiftCrypt: a web server to understand and biophysically align proteins through their NMR chemical shift values. Nucleic Acids Res 2020; 48:W36-W40. [PMID: 32459331 PMCID: PMC7319548 DOI: 10.1093/nar/gkaa391] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 04/21/2020] [Accepted: 05/04/2020] [Indexed: 02/06/2023] Open
Abstract
Nuclear magnetic resonance (NMR) spectroscopy data provides valuable information on the behaviour of proteins in solution. The primary data to determine when studying proteins are the per-atom NMR chemical shifts, which reflect the local environment of atoms and provide insights into amino acid residue dynamics and conformation. Within an amino acid residue, chemical shifts present multi-dimensional and complexly cross-correlated information, making them difficult to analyse. The ShiftCrypt method, based on neural network auto-encoder architecture, compresses the per-amino acid chemical shift information in a single, interpretable, amino acid-type independent value that reflects the biophysical state of a residue. We here present the ShiftCrypt web server, which makes the method readily available. The server accepts chemical shifts input files in the NMR Exchange Format (NEF) or NMR-STAR format, executes ShiftCrypt and visualises the results, which are also accessible via an API. It also enables the ”biophysically-based” pairwise alignment of two proteins based on their ShiftCrypt values. This approach uses Dynamic Time Warping and can optionally include their amino acid code information, and has applications in, for example, the alignment of disordered regions. The server uses a token-based system to ensure the anonymity of the users and results. The web server is available at www.bio2byte.be/shiftcrypt.
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Affiliation(s)
- Gabriele Orlando
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Triomflaan, Brussels 1050, Belgium.,Switch Laboratory, VIB, Leuven, Belgium
| | - Daniele Raimondi
- ESAT-STADIUS, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium
| | - Luciano Porto Kagami
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Triomflaan, Brussels 1050, Belgium
| | - Wim F Vranken
- Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Triomflaan, Brussels 1050, Belgium.,Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, Brussels 1050, Belgium.,VIB Structural Biology Research Centre, Pleinlaan 2, Brussels 1050, Belgium
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4
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Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index. Nat Commun 2019; 10:2511. [PMID: 31175284 PMCID: PMC6555786 DOI: 10.1038/s41467-019-10322-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 05/01/2019] [Indexed: 11/26/2022] Open
Abstract
Chemical shifts (CS) are determined from NMR experiments and represent the resonance frequency of the spin of atoms in a magnetic field. They contain a mixture of information, encompassing the in-solution conformations a protein adopts, as well as the movements it performs. Due to their intrinsically multi-faceted nature, CS are difficult to interpret and visualize. Classical approaches for the analysis of CS aim to extract specific protein-related properties, thus discarding a large amount of information that cannot be directly linked to structural features of the protein. Here we propose an autoencoder-based method, called ShiftCrypt, that provides a way to analyze, compare and interpret CS in their native, multidimensional space. We show that ShiftCrypt conserves information about the most common structural features. In addition, it can be used to identify hidden similarities between diverse proteins and peptides, and differences between the same protein in two different binding states. NMR chemical shift information is highly valuable in the investigation of small molecule and protein structure. Here, the authors developed a neural network approach to unify protein chemical shifts and their changes in response to changes in protein sequence, structure, and dimerization interactions.
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5
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Chen X, Smelter A, Moseley HNB. Automatic 13C chemical shift reference correction for unassigned protein NMR spectra. JOURNAL OF BIOMOLECULAR NMR 2018; 72:11-28. [PMID: 30097912 PMCID: PMC6209040 DOI: 10.1007/s10858-018-0202-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 08/01/2018] [Indexed: 05/09/2023]
Abstract
Poor chemical shift referencing, especially for 13C in protein Nuclear Magnetic Resonance (NMR) experiments, fundamentally limits and even prevents effective study of biomacromolecules via NMR, including protein structure determination and analysis of protein dynamics. To solve this problem, we constructed a Bayesian probabilistic framework that circumvents the limitations of previous reference correction methods that required protein resonance assignment and/or three-dimensional protein structure. Our algorithm named Bayesian Model Optimized Reference Correction (BaMORC) can detect and correct 13C chemical shift referencing errors before the protein resonance assignment step of analysis and without three-dimensional structure. By combining the BaMORC methodology with a new intra-peaklist grouping algorithm, we created a combined method called Unassigned BaMORC that utilizes only unassigned experimental peak lists and the amino acid sequence. Unassigned BaMORC kept all experimental three-dimensional HN(CO)CACB-type peak lists tested within ± 0.4 ppm of the correct 13C reference value. On a much larger unassigned chemical shift test set, the base method kept 13C chemical shift referencing errors to within ± 0.45 ppm at a 90% confidence interval. With chemical shift assignments, Assigned BaMORC can detect and correct 13C chemical shift referencing errors to within ± 0.22 at a 90% confidence interval. Therefore, Unassigned BaMORC can correct 13C chemical shift referencing errors when it will have the most impact, right before protein resonance assignment and other downstream analyses are started. After assignment, chemical shift reference correction can be further refined with Assigned BaMORC. These new methods will allow non-NMR experts to detect and correct 13C referencing error at critical early data analysis steps, lowering the bar of NMR expertise required for effective protein NMR analysis.
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Affiliation(s)
- Xi Chen
- Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY, 40356, USA
- Department of Statistics, University of Kentucky, Lexington, KY, 40356, USA
| | - Andrey Smelter
- Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY, 40356, USA
- Markey Cancer Center, University of Kentucky, Lexington, KY, 40356, USA
- Center for Environmental and Systems Biochemistry, University of Kentucky, Lexington, KY, 40356, USA
| | - Hunter N B Moseley
- Department of Molecular and Cellular Biochemistry, University of Kentucky, Lexington, KY, 40356, USA.
- Department of Statistics, University of Kentucky, Lexington, KY, 40356, USA.
- Markey Cancer Center, University of Kentucky, Lexington, KY, 40356, USA.
- Center for Environmental and Systems Biochemistry, University of Kentucky, Lexington, KY, 40356, USA.
- Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, 40356, USA.
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6
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Nerli S, McShan AC, Sgourakis NG. Chemical shift-based methods in NMR structure determination. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2018; 106-107:1-25. [PMID: 31047599 PMCID: PMC6788782 DOI: 10.1016/j.pnmrs.2018.03.002] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 03/09/2018] [Accepted: 03/09/2018] [Indexed: 05/08/2023]
Abstract
Chemical shifts are highly sensitive probes harnessed by NMR spectroscopists and structural biologists as conformational parameters to characterize a range of biological molecules. Traditionally, assignment of chemical shifts has been a labor-intensive process requiring numerous samples and a suite of multidimensional experiments. Over the past two decades, the development of complementary computational approaches has bolstered the analysis, interpretation and utilization of chemical shifts for elucidation of high resolution protein and nucleic acid structures. Here, we review the development and application of chemical shift-based methods for structure determination with a focus on ab initio fragment assembly, comparative modeling, oligomeric systems, and automated assignment methods. Throughout our discussion, we point out practical uses, as well as advantages and caveats, of using chemical shifts in structure modeling. We additionally highlight (i) hybrid methods that employ chemical shifts with other types of NMR restraints (residual dipolar couplings, paramagnetic relaxation enhancements and pseudocontact shifts) that allow for improved accuracy and resolution of generated 3D structures, (ii) the utilization of chemical shifts to model the structures of sparsely populated excited states, and (iii) modeling of sidechain conformations. Finally, we briefly discuss the advantages of contemporary methods that employ sparse NMR data recorded using site-specific isotope labeling schemes for chemical shift-driven structure determination of larger molecules. With this review, we aim to emphasize the accessibility and versatility of chemical shifts for structure determination of challenging biological systems, and to point out emerging areas of development that lead us towards the next generation of tools.
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Affiliation(s)
- Santrupti Nerli
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA 95064, United States; Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA 95064, United States
| | - Andrew C McShan
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA 95064, United States
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA 95064, United States.
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7
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Klukowski P, Augoff M, Zamorski M, Gonczarek A, Walczak MJ. Application of Dirichlet process mixture model to the identification of spin systems in protein NMR spectra. JOURNAL OF BIOMOLECULAR NMR 2018; 71:11-18. [PMID: 29777498 DOI: 10.1007/s10858-018-0185-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Accepted: 05/10/2018] [Indexed: 06/08/2023]
Abstract
Analysis of structure, function and interactions of proteins by NMR spectroscopy usually requires the assignment of resonances to the corresponding nuclei in protein. This task, although automated by methods such as FLYA or PINE, is still frequently performed manually. To facilitate the manual sequence-specific chemical shift assignment of complex proteins, we propose a method based on Dirichlet process mixture model (DPMM) that performs automated matching of groups of signals observed in NMR spectra to corresponding nuclei in protein sequence. The model has been extensively tested on 80 proteins retrieved from the BMRB database and has shown superior performance to the reference method.
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Affiliation(s)
- Piotr Klukowski
- Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland.
| | - Michał Augoff
- Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland
| | - Maciej Zamorski
- Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland
| | - Adam Gonczarek
- Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland
- Alphamoon Ltd., ul. Włodkowica 21/3, 50-072, Wrocław, Poland
| | - Michał J Walczak
- Captor Therapeutics Ltd., ul. Duńska 11, 54-427, Wrocław, Poland
- Alphamoon Ltd., ul. Włodkowica 21/3, 50-072, Wrocław, Poland
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Automated NMR resonance assignments and structure determination using a minimal set of 4D spectra. Nat Commun 2018; 9:384. [PMID: 29374165 PMCID: PMC5786013 DOI: 10.1038/s41467-017-02592-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2017] [Accepted: 12/12/2017] [Indexed: 12/22/2022] Open
Abstract
Automated methods for NMR structure determination of proteins are continuously becoming more robust. However, current methods addressing larger, more complex targets rely on analyzing 6-10 complementary spectra, suggesting the need for alternative approaches. Here, we describe 4D-CHAINS/autoNOE-Rosetta, a complete pipeline for NOE-driven structure determination of medium- to larger-sized proteins. The 4D-CHAINS algorithm analyzes two 4D spectra recorded using a single, fully protonated protein sample in an iterative ansatz where common NOEs between different spin systems supplement conventional through-bond connectivities to establish assignments of sidechain and backbone resonances at high levels of completeness and with a minimum error rate. The 4D-CHAINS assignments are then used to guide automated assignment of long-range NOEs and structure refinement in autoNOE-Rosetta. Our results on four targets ranging in size from 15.5 to 27.3 kDa illustrate that the structures of proteins can be determined accurately and in an unsupervised manner in a matter of days.
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9
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Dashti H, Tonelli M, Lee W, Westler WM, Cornilescu G, Ulrich EL, Markley JL. Probabilistic validation of protein NMR chemical shift assignments. JOURNAL OF BIOMOLECULAR NMR 2016; 64:17-25. [PMID: 26724815 PMCID: PMC4744101 DOI: 10.1007/s10858-015-0007-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Accepted: 12/20/2015] [Indexed: 05/05/2023]
Abstract
Data validation plays an important role in ensuring the reliability and reproducibility of studies. NMR investigations of the functional properties, dynamics, chemical kinetics, and structures of proteins depend critically on the correctness of chemical shift assignments. We present a novel probabilistic method named ARECA for validating chemical shift assignments that relies on the nuclear Overhauser effect data . ARECA has been evaluated through its application to 26 case studies and has been shown to be complementary to, and usually more reliable than, approaches based on chemical shift databases. ARECA is available online at http://areca.nmrfam.wisc.edu/.
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Affiliation(s)
- Hesam Dashti
- Graduate Program in Biophysics, Biochemistry Department, University of Wisconsin-Madison, Madison, WI, USA
- Biochemistry Department, National Magnetic Resonance Facility at Madison, University of Wisconsin-Madison, Madison, WI, USA
| | - Marco Tonelli
- Biochemistry Department, National Magnetic Resonance Facility at Madison, University of Wisconsin-Madison, Madison, WI, USA
| | - Woonghee Lee
- Biochemistry Department, National Magnetic Resonance Facility at Madison, University of Wisconsin-Madison, Madison, WI, USA
| | - William M Westler
- Biochemistry Department, National Magnetic Resonance Facility at Madison, University of Wisconsin-Madison, Madison, WI, USA
| | - Gabriel Cornilescu
- Biochemistry Department, National Magnetic Resonance Facility at Madison, University of Wisconsin-Madison, Madison, WI, USA
| | - Eldon L Ulrich
- BioMagResBank, Biochemistry Department, University of Wisconsin-Madison, Madison, WI, USA
| | - John L Markley
- Biochemistry Department, National Magnetic Resonance Facility at Madison, University of Wisconsin-Madison, Madison, WI, USA.
- BioMagResBank, Biochemistry Department, University of Wisconsin-Madison, Madison, WI, USA.
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10
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Dashti H, Lee W, Tonelli M, Cornilescu CC, Cornilescu G, Assadi-Porter FM, Westler WM, Eghbalnia HR, Markley JL. NMRFAM-SDF: a protein structure determination framework. JOURNAL OF BIOMOLECULAR NMR 2015; 62:481-95. [PMID: 25900069 PMCID: PMC4569665 DOI: 10.1007/s10858-015-9933-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2015] [Accepted: 04/15/2015] [Indexed: 05/21/2023]
Abstract
The computationally demanding nature of automated NMR structure determination necessitates a delicate balancing of factors that include the time complexity of data collection, the computational complexity of chemical shift assignments, and selection of proper optimization steps. During the past two decades the computational and algorithmic aspects of several discrete steps of the process have been addressed. Although no single comprehensive solution has emerged, the incorporation of a validation protocol has gained recognition as a necessary step for a robust automated approach. The need for validation becomes even more pronounced in cases of proteins with higher structural complexity, where potentially larger errors generated at each step can propagate and accumulate in the process of structure calculation, thereby significantly degrading the efficacy of any software framework. This paper introduces a complete framework for protein structure determination with NMR--from data acquisition to the structure determination. The aim is twofold: to simplify the structure determination process for non-NMR experts whenever feasible, while maintaining flexibility by providing a set of modules that validate each step, and to enable the assessment of error propagations. This framework, called NMRFAM-SDF (NMRFAM-Structure Determination Framework), and its various components are available for download from the NMRFAM website (http://nmrfam.wisc.edu/software.htm).
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Affiliation(s)
- Hesam Dashti
- National Magnetic Resonance Facility at Madison, Biochemistry Department, University of Wisconsin-Madison, 433 Babcock Drive, Madison, WI, USA
| | - Woonghee Lee
- National Magnetic Resonance Facility at Madison, Biochemistry Department, University of Wisconsin-Madison, 433 Babcock Drive, Madison, WI, USA
| | - Marco Tonelli
- National Magnetic Resonance Facility at Madison, Biochemistry Department, University of Wisconsin-Madison, 433 Babcock Drive, Madison, WI, USA
| | - Claudia C Cornilescu
- National Magnetic Resonance Facility at Madison, Biochemistry Department, University of Wisconsin-Madison, 433 Babcock Drive, Madison, WI, USA
| | - Gabriel Cornilescu
- National Magnetic Resonance Facility at Madison, Biochemistry Department, University of Wisconsin-Madison, 433 Babcock Drive, Madison, WI, USA
| | - Fariba M Assadi-Porter
- National Magnetic Resonance Facility at Madison, Biochemistry Department, University of Wisconsin-Madison, 433 Babcock Drive, Madison, WI, USA
| | - William M Westler
- National Magnetic Resonance Facility at Madison, Biochemistry Department, University of Wisconsin-Madison, 433 Babcock Drive, Madison, WI, USA
| | - Hamid R Eghbalnia
- National Magnetic Resonance Facility at Madison, Biochemistry Department, University of Wisconsin-Madison, 433 Babcock Drive, Madison, WI, USA
| | - John L Markley
- National Magnetic Resonance Facility at Madison, Biochemistry Department, University of Wisconsin-Madison, 433 Babcock Drive, Madison, WI, USA.
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Ragan TJ, Fogh RH, Tejero R, Vranken W, Montelione GT, Rosato A, Vuister GW. Analysis of the structural quality of the CASD-NMR 2013 entries. JOURNAL OF BIOMOLECULAR NMR 2015; 62:527-40. [PMID: 26032236 PMCID: PMC4569653 DOI: 10.1007/s10858-015-9949-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2015] [Accepted: 05/20/2015] [Indexed: 05/30/2023]
Abstract
We performed a comprehensive structure validation of both automated and manually generated structures of the 10 targets of the CASD-NMR-2013 effort. We established that automated structure determination protocols are capable of reliably producing structures of comparable accuracy and quality to those generated by a skilled researcher, at least for small, single domain proteins such as the ten targets tested. The most robust results appear to be obtained when NOESY peak lists are used either as the primary input data or to augment chemical shift data without the need to manually filter such lists. A detailed analysis of the long-range NOE restraints generated by the different programs from the same data showed a surprisingly low degree of overlap. Additionally, we found that there was no significant correlation between the extent of the NOE restraint overlap and the accuracy of the structure. This result was surprising given the importance of NOE data in producing good quality structures. We suggest that this could be explained by the information redundancy present in NOEs between atoms contained within a fixed covalent network.
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Affiliation(s)
- Timothy J Ragan
- Department of Biochemistry, School of Biological Sciences, University of Leicester, Henry Wellcome Building, Lancaster Road, Leicester, LE1 9HN, UK
| | - Rasmus H Fogh
- Department of Biochemistry, School of Biological Sciences, University of Leicester, Henry Wellcome Building, Lancaster Road, Leicester, LE1 9HN, UK
| | - Roberto Tejero
- Departamento de Química Física, Universidad de Valencia, Avda. Dr. Moliner 50, 46100, Burjassot (Valencia), Spain
| | - Wim Vranken
- Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, Belgium
- (IB)2 Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Triomflaan, 1050, Brussels, Belgium
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Robert Wood Johnson Medical School, Piscataway, NJ, 08854, USA
| | - Antonio Rosato
- Magnetic Resonance Center, Department of Chemistry, University of Florence, 50019, Sesto Fiorentino, Italy
| | - Geerten W Vuister
- Department of Biochemistry, School of Biological Sciences, University of Leicester, Henry Wellcome Building, Lancaster Road, Leicester, LE1 9HN, UK.
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12
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Vuister GW, Fogh RH, Hendrickx PMS, Doreleijers JF, Gutmanas A. An overview of tools for the validation of protein NMR structures. JOURNAL OF BIOMOLECULAR NMR 2014; 58:259-285. [PMID: 23877928 DOI: 10.1007/s10858-013-9750-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2013] [Accepted: 06/04/2013] [Indexed: 06/02/2023]
Abstract
Biomolecular structures at atomic resolution present a valuable resource for the understanding of biology. NMR spectroscopy accounts for 11% of all structures in the PDB repository. In response to serious problems with the accuracy of some of the NMR-derived structures and in order to facilitate proper analysis of the experimental models, a number of program suites are available. We discuss nine of these tools in this review: PROCHECK-NMR, PSVS, GLM-RMSD, CING, Molprobity, Vivaldi, ResProx, NMR constraints analyzer and QMEAN. We evaluate these programs for their ability to assess the structural quality, restraints and their violations, chemical shifts, peaks and the handling of multi-model NMR ensembles. We document both the input required by the programs and output they generate. To discuss their relative merits we have applied the tools to two representative examples from the PDB: a small, globular monomeric protein (Staphylococcal nuclease from S. aureus, PDB entry 2kq3) and a small, symmetric homodimeric protein (a region of human myosin-X, PDB entry 2lw9).
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Affiliation(s)
- Geerten W Vuister
- Department of Biochemistry, School of Biological Sciences, University of Leicester, Henry Wellcome Building, Lancaster Road, Leicester, LE1 9HN, UK,
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Gutmanas A, Alhroub Y, Battle GM, Berrisford JM, Bochet E, Conroy MJ, Dana JM, Fernandez Montecelo MA, van Ginkel G, Gore SP, Haslam P, Hatherley R, Hendrickx PMS, Hirshberg M, Lagerstedt I, Mir S, Mukhopadhyay A, Oldfield TJ, Patwardhan A, Rinaldi L, Sahni G, Sanz-García E, Sen S, Slowley RA, Velankar S, Wainwright ME, Kleywegt GJ. PDBe: Protein Data Bank in Europe. Nucleic Acids Res 2013; 42:D285-91. [PMID: 24288376 PMCID: PMC3965016 DOI: 10.1093/nar/gkt1180] [Citation(s) in RCA: 105] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The Protein Data Bank in Europe (pdbe.org) is a founding member of the Worldwide PDB consortium (wwPDB; wwpdb.org) and as such is actively engaged in the deposition, annotation, remediation and dissemination of macromolecular structure data through the single global archive for such data, the PDB. Similarly, PDBe is a member of the EMDataBank organisation (emdatabank.org), which manages the EMDB archive for electron microscopy data. PDBe also develops tools that help the biomedical science community to make effective use of the data in the PDB and EMDB for their research. Here we describe new or improved services, including updated SIFTS mappings to other bioinformatics resources, a new browser for the PDB archive based on Gene Ontology (GO) annotation, updates to the analysis of Nuclear Magnetic Resonance-derived structures, redesigned search and browse interfaces, and new or updated visualisation and validation tools for EMDB entries.
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Affiliation(s)
- Aleksandras Gutmanas
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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14
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Rosato A, Tejero R, Montelione GT. Quality assessment of protein NMR structures. Curr Opin Struct Biol 2013; 23:715-24. [PMID: 24060334 DOI: 10.1016/j.sbi.2013.08.005] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2013] [Accepted: 08/14/2013] [Indexed: 10/26/2022]
Abstract
Biomolecular NMR structures are now routinely used in biology, chemistry, and bioinformatics. Methods and metrics for assessing the accuracy and precision of protein NMR structures are beginning to be standardized across the biological NMR community. These include both knowledge-based assessment metrics, parameterized from the database of protein structures, and model versus data assessment metrics. On line servers are available that provide comprehensive protein structure quality assessment reports, and efforts are in progress by the world-wide Protein Data Bank (wwPDB) to develop a biomolecular NMR structure quality assessment pipeline as part of the structure deposition process. These quality assessment metrics and standards will aid NMR spectroscopists in determining more accurate structures, and increase the value and utility of these structures for the broad scientific community.
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Affiliation(s)
- Antonio Rosato
- Magnetic Resonance Center and Department of Chemistry, University of Florence, 50019 Sesto Fiorentino, Italy
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15
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Montelione GT, Nilges M, Bax A, Güntert P, Herrmann T, Richardson JS, Schwieters CD, Vranken WF, Vuister GW, Wishart DS, Berman HM, Kleywegt GJ, Markley JL. Recommendations of the wwPDB NMR Validation Task Force. Structure 2013; 21:1563-70. [PMID: 24010715 PMCID: PMC3884077 DOI: 10.1016/j.str.2013.07.021] [Citation(s) in RCA: 132] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2013] [Revised: 07/19/2013] [Accepted: 07/29/2013] [Indexed: 11/25/2022]
Abstract
As methods for analysis of biomolecular structure and dynamics using nuclear magnetic resonance spectroscopy (NMR) continue to advance, the resulting 3D structures, chemical shifts, and other NMR data are broadly impacting biology, chemistry, and medicine. Structure model assessment is a critical area of NMR methods development, and is an essential component of the process of making these structures accessible and useful to the wider scientific community. For these reasons, the Worldwide Protein Data Bank (wwPDB) has convened an NMR Validation Task Force (NMR-VTF) to work with wwPDB partners in developing metrics and policies for biomolecular NMR data harvesting, structure representation, and structure quality assessment. This paper summarizes the recommendations of the NMR-VTF, and lays the groundwork for future work in developing standards and metrics for biomolecular NMR structure quality assessment.
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Affiliation(s)
- Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
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16
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van der Schot G, Zhang Z, Vernon R, Shen Y, Vranken WF, Baker D, Bonvin AMJJ, Lange OF. Improving 3D structure prediction from chemical shift data. JOURNAL OF BIOMOLECULAR NMR 2013; 57:27-35. [PMID: 23912841 DOI: 10.1007/s10858-013-9762-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2013] [Accepted: 07/16/2013] [Indexed: 05/22/2023]
Abstract
We report advances in the calculation of protein structures from chemical shift nuclear magnetic resonance data alone. Our previously developed method, CS-Rosetta, assembles structures from a library of short protein fragments picked from a large library of protein structures using chemical shifts and sequence information. Here we demonstrate that combination of a new and improved fragment picker and the iterative sampling algorithm RASREC yield significant improvements in convergence and accuracy. Moreover, we introduce improved criteria for assessing the accuracy of the models produced by the method. The method was tested on 39 proteins in the 50-100 residue size range and yields reliable structures in 70 % of the cases. All structures that passed the reliability filter were accurate (<2 Å RMSD from the reference).
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Affiliation(s)
- Gijs van der Schot
- Computational Structural Biology, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands
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17
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Dehof AK, Loew S, Lenhof HP, Hildebrandt A. NightShift: NMR shift inference by general hybrid model training--a framework for NMR chemical shift prediction. BMC Bioinformatics 2013; 14:98. [PMID: 23496927 PMCID: PMC3682865 DOI: 10.1186/1471-2105-14-98] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2012] [Accepted: 02/27/2013] [Indexed: 11/11/2022] Open
Abstract
Background NMR chemical shift prediction plays an important role in various applications in computational biology. Among others, structure determination, structure optimization, and the scoring of docking results can profit from efficient and accurate chemical shift estimation from a three-dimensional model. A variety of NMR chemical shift prediction approaches have been presented in the past, but nearly all of these rely on laborious manual data set preparation and the training itself is not automatized, making retraining the model, e.g., if new data is made available, or testing new models a time-consuming manual chore. Results In this work, we present the framework NightShift (NMR Shift Inference by General Hybrid Model Training), which enables automated data set generation as well as model training and evaluation of protein NMR chemical shift prediction. In addition to this main result – the NightShift framework itself – we describe the resulting, automatically generated, data set and, as a proof-of-concept, a random forest model called Spinster that was built using the pipeline. Conclusion By demonstrating that the performance of the automatically generated predictors is at least en par with the state of the art, we conclude that automated data set and predictor generation is well-suited for the design of NMR chemical shift estimators. The framework can be downloaded from https://bitbucket.org/akdehof/nightshift. It requires the open source Biochemical Algorithms Library (BALL), and is available under the conditions of the GNU Lesser General Public License (LGPL). We additionally offer a browser-based user interface to our NightShift instance employing the Galaxy framework via https://ballaxy.bioinf.uni-sb.de/.
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18
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Hendrickx PMS, Gutmanas A, Kleywegt GJ. Vivaldi: visualization and validation of biomacromolecular NMR structures from the PDB. Proteins 2013. [PMID: 23180575 PMCID: PMC3618379 DOI: 10.1002/prot.24213] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We describe Vivaldi (VIsualization and VALidation DIsplay; http://pdbe.org/vivaldi), a web-based service for the analysis, visualization, and validation of NMR structures in the Protein Data Bank (PDB). Vivaldi provides access to model coordinates and several types of experimental NMR data using interactive visualization tools, augmented with structural annotations and model-validation information. The service presents information about the modeled NMR ensemble, validation of experimental chemical shifts, residual dipolar couplings, distance and dihedral angle constraints, as well as validation scores based on empirical knowledge and databases. Vivaldi was designed for both expert NMR spectroscopists and casual non-expert users who wish to obtain a better grasp of the information content and quality of NMR structures in the public archive. © Proteins 2013. © 2012 Wiley Periodicals, Inc.
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Affiliation(s)
- Pieter M S Hendrickx
- Protein Data Bank in Europe, EMBL-European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
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19
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Doreleijers JF, Sousa da Silva AW, Krieger E, Nabuurs SB, Spronk CAEM, Stevens TJ, Vranken WF, Vriend G, Vuister GW. CING: an integrated residue-based structure validation program suite. JOURNAL OF BIOMOLECULAR NMR 2012; 54:267-83. [PMID: 22986687 PMCID: PMC3483101 DOI: 10.1007/s10858-012-9669-7] [Citation(s) in RCA: 77] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2012] [Accepted: 08/31/2012] [Indexed: 05/03/2023]
Abstract
We present a suite of programs, named CING for Common Interface for NMR Structure Generation that provides for a residue-based, integrated validation of the structural NMR ensemble in conjunction with the experimental restraints and other input data. External validation programs and new internal validation routines compare the NMR-derived models with empirical data, measured chemical shifts, distance- and dihedral restraints and the results are visualized in a dynamic Web 2.0 report. A red-orange-green score is used for residues and restraints to direct the user to those critiques that warrant further investigation. Overall green scores below ~20 % accompanied by red scores over ~50 % are strongly indicative of poorly modelled structures. The publically accessible, secure iCing webserver ( https://nmr.le.ac.uk ) allows individual users to upload the NMR data and run a CING validation analysis.
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Affiliation(s)
- Jurgen F. Doreleijers
- CMBI, Radboud University Medical Centre, Geert Grooteplein 26-28, 6525 GA Nijmegen, The Netherlands
| | | | - Elmar Krieger
- YASARA Biosciences GmbH, Wagramer Strasse 25/3/45, 1220 Vienna, Austria
| | - Sander B. Nabuurs
- CMBI, Radboud University Medical Centre, Geert Grooteplein 26-28, 6525 GA Nijmegen, The Netherlands
| | | | - Tim J. Stevens
- Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA UK
| | - Wim F. Vranken
- Department of Structural Biology, VIB, Building E, 4th Floor, Pleinlaan 2, 1050 Brussels, Belgium
- Structural Biology Brussels, Vrije Universiteit Brussel, Building E, 4th Floor, Pleinlaan 2, 1050 Brussels, Belgium
| | - Gert Vriend
- CMBI, Radboud University Medical Centre, Geert Grooteplein 26-28, 6525 GA Nijmegen, The Netherlands
| | - Geerten W. Vuister
- Department of Biochemistry, University of Leicester, Henry Wellcome Building, Lancaster Road, Leicester, LE1 9HN UK
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20
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Doreleijers JF, Vranken WF, Schulte C, Markley JL, Ulrich EL, Vriend G, Vuister GW. NRG-CING: integrated validation reports of remediated experimental biomolecular NMR data and coordinates in wwPDB. Nucleic Acids Res 2011; 40:D519-24. [PMID: 22139937 PMCID: PMC3245154 DOI: 10.1093/nar/gkr1134] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
For many macromolecular NMR ensembles from the Protein Data Bank (PDB) the experiment-based restraint lists are available, while other experimental data, mainly chemical shift values, are often available from the BioMagResBank. The accuracy and precision of the coordinates in these macromolecular NMR ensembles can be improved by recalculation using the available experimental data and present-day software. Such efforts, however, generally fail on half of all NMR ensembles due to the syntactic and semantic heterogeneity of the underlying data and the wide variety of formats used for their deposition. We have combined the remediated restraint information from our NMR Restraints Grid (NRG) database with available chemical shifts from the BioMagResBank and the Common Interface for NMR structure Generation (CING) structure validation reports into the weekly updated NRG-CING database (http://nmr.cmbi.ru.nl/NRG-CING). Eleven programs have been included in the NRG-CING production pipeline to arrive at validation reports that list for each entry the potential inconsistencies between the coordinates and the available experimental NMR data. The longitudinal validation of these data in a publicly available relational database yields a set of indicators that can be used to judge the quality of every macromolecular structure solved with NMR. The remediated NMR experimental data sets and validation reports are freely available online.
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Affiliation(s)
- Jurgen F Doreleijers
- IMM, Radboud University Nijmegen, Geert Grooteplein 26-28, 6525 GA Nijmegen, The Netherlands.
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21
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Velankar S, Alhroub Y, Best C, Caboche S, Conroy MJ, Dana JM, Fernandez Montecelo MA, van Ginkel G, Golovin A, Gore SP, Gutmanas A, Haslam P, Hendrickx PMS, Heuson E, Hirshberg M, John M, Lagerstedt I, Mir S, Newman LE, Oldfield TJ, Patwardhan A, Rinaldi L, Sahni G, Sanz-García E, Sen S, Slowley R, Suarez-Uruena A, Swaminathan GJ, Symmons MF, Vranken WF, Wainwright M, Kleywegt GJ. PDBe: Protein Data Bank in Europe. Nucleic Acids Res 2011; 40:D445-52. [PMID: 22110033 PMCID: PMC3245096 DOI: 10.1093/nar/gkr998] [Citation(s) in RCA: 74] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
The Protein Data Bank in Europe (PDBe; pdbe.org) is a partner in the Worldwide PDB organization (wwPDB; wwpdb.org) and as such actively involved in managing the single global archive of biomacromolecular structure data, the PDB. In addition, PDBe develops tools, services and resources to make structure-related data more accessible to the biomedical community. Here we describe recently developed, extended or improved services, including an animated structure-presentation widget (PDBportfolio), a widget to graphically display the coverage of any UniProt sequence in the PDB (UniPDB), chemistry- and taxonomy-based PDB-archive browsers (PDBeXplore), and a tool for interactive visualization of NMR structures, corresponding experimental data as well as validation and analysis results (Vivaldi).
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Affiliation(s)
- S Velankar
- Protein Data Bank in Europe, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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22
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Sahakyan AB, Vranken WF, Cavalli A, Vendruscolo M. Structure-based prediction of methyl chemical shifts in proteins. JOURNAL OF BIOMOLECULAR NMR 2011; 50:331-46. [PMID: 21748266 DOI: 10.1007/s10858-011-9524-2] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2011] [Accepted: 05/17/2011] [Indexed: 05/07/2023]
Abstract
Protein methyl groups have recently been the subject of much attention in NMR spectroscopy because of the opportunities that they provide to obtain information about the structure and dynamics of proteins and protein complexes. With the advent of selective labeling schemes, methyl groups are particularly interesting in the context of chemical shift based protein structure determination, an approach that to date has exploited primarily the mapping between protein structures and backbone chemical shifts. In order to extend the scope of chemical shifts for structure determination, we present here the CH3Shift method of performing structure-based predictions of methyl chemical shifts. The terms considered in the predictions take account of ring current, magnetic anisotropy, electric field, rotameric type, and dihedral angle effects, which are considered in conjunction with polynomial functions of interatomic distances. We show that the CH3Shift method achieves an accuracy in the predictions that ranges from 0.133 to 0.198 ppm for (1)H chemical shifts for Ala, Thr, Val, Leu and Ile methyl groups. We illustrate the use of the method by assessing the accuracy of side-chain structures in structural ensembles representing the dynamics of proteins.
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Affiliation(s)
- Aleksandr B Sahakyan
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
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23
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Velankar S, Kleywegt GJ. The Protein Data Bank in Europe (PDBe): bringing structure to biology. ACTA CRYSTALLOGRAPHICA SECTION D: BIOLOGICAL CRYSTALLOGRAPHY 2011; 67:324-30. [PMID: 21460450 PMCID: PMC3069747 DOI: 10.1107/s090744491004117x] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2010] [Accepted: 10/13/2010] [Indexed: 12/30/2022]
Abstract
The Protein Data Bank in Europe (PDBe) is the European partner in the Worldwide PDB and as such handles depositions of X-ray, NMR and EM data and structure models. PDBe also provides advanced bioinformatics services based on data from the PDB and related resources. Some of the challenges facing the PDB and its guardians are discussed, as well as some of the areas on which PDBe activities will focus in the future (advanced services, ligands, integration, validation and experimental data). Finally, some recent developments at PDBe are described.
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Affiliation(s)
- Sameer Velankar
- Protein Data Bank in Europe (PDBe), EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge, England
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24
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Velankar S, Alhroub Y, Alili A, Best C, Boutselakis HC, Caboche S, Conroy MJ, Dana JM, van Ginkel G, Golovin A, Gore SP, Gutmanas A, Haslam P, Hirshberg M, John M, Lagerstedt I, Mir S, Newman LE, Oldfield TJ, Penkett CJ, Pineda-Castillo J, Rinaldi L, Sahni G, Sawka G, Sen S, Slowley R, Sousa da Silva AW, Suarez-Uruena A, Swaminathan GJ, Symmons MF, Vranken WF, Wainwright M, Kleywegt GJ. PDBe: Protein Data Bank in Europe. Nucleic Acids Res 2010; 39:D402-10. [PMID: 21045060 PMCID: PMC3013808 DOI: 10.1093/nar/gkq985] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
The Protein Data Bank in Europe (PDBe; pdbe.org) is actively involved in managing the international archive of biomacromolecular structure data as one of the partners in the Worldwide Protein Data Bank (wwPDB; wwpdb.org). PDBe also develops new tools to make structural data more widely and more easily available to the biomedical community. PDBe has developed a browser to access and analyze the structural archive using classification systems that are familiar to chemists and biologists. The PDBe web pages that describe individual PDB entries have been enhanced through the introduction of plain-English summary pages and iconic representations of the contents of an entry (PDBprints). In addition, the information available for structures determined by means of NMR spectroscopy has been expanded. Finally, the entire web site has been redesigned to make it substantially easier to use for expert and novice users alike. PDBe works closely with other teams at the European Bioinformatics Institute (EBI) and in the international scientific community to develop new resources with value-added information. The SIFTS initiative is an example of such a collaboration—it provides extensive mapping data between proteins whose structures are available from the PDB and a host of other biomedical databases. SIFTS is widely used by major bioinformatics resources.
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Affiliation(s)
- Sameer Velankar
- Protein Data Bank in Europe, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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