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Richardson JS, Williams CJ, Chen VB, Prisant MG, Richardson DC. The bad and the good of trends in model building and refinement for sparse-data regions: pernicious forms of overfitting versus good new tools and predictions. Acta Crystallogr D Struct Biol 2023; 79:1071-1078. [PMID: 37921807 PMCID: PMC10833350 DOI: 10.1107/s2059798323008847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 10/09/2023] [Indexed: 11/04/2023] Open
Abstract
Model building and refinement, and the validation of their correctness, are very effective and reliable at local resolutions better than about 2.5 Å for both crystallography and cryo-EM. However, at local resolutions worse than 2.5 Å both the procedures and their validation break down and do not ensure reliably correct models. This is because in the broad density at lower resolution, critical features such as protein backbone carbonyl O atoms are not just less accurate but are not seen at all, and so peptide orientations are frequently wrongly fitted by 90-180°. This puts both backbone and side chains into the wrong local energy minimum, and they are then worsened rather than improved by further refinement into a valid but incorrect rotamer or Ramachandran region. On the positive side, new tools are being developed to locate this type of pernicious error in PDB depositions, such as CaBLAM, EMRinger, Pperp diagnosis of ribose puckers, and peptide flips in PDB-REDO, while interactive modeling in Coot or ISOLDE can help to fix many of them. Another positive trend is that artificial intelligence predictions such as those made by AlphaFold2 contribute additional evidence from large multiple sequence alignments, and in high-confidence parts they provide quite good starting models for loops, termini or whole domains with otherwise ambiguous density.
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Affiliation(s)
- Jane S. Richardson
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina, USA
| | | | - Vincent B. Chen
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina, USA
| | - Michael G. Prisant
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina, USA
| | - David C. Richardson
- Department of Biochemistry, Duke University Medical Center, Durham, North Carolina, USA
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2
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Okada T, Tomoike F. Distance-based global analysis of consistent cis-bonds in protein backbones. Heliyon 2023; 9:e18598. [PMID: 37576297 PMCID: PMC10413078 DOI: 10.1016/j.heliyon.2023.e18598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/13/2023] [Accepted: 07/21/2023] [Indexed: 08/15/2023] Open
Abstract
Biological polypeptides are known to contain cis-linkage in their main chain as a minor but important feature. Such anomalous connection of amino acids has different structural and functional effects on proteins. Experimental evidence of cis-bonds in proteins is mainly obtained using X-ray crystallography and other methods in the field of structural biology. To date, extensive analyses have been carried out on the experimentally found cis-bonds using the Protein Data Bank (PDB) entry-wise or residue-wise; however, their consistency in each protein has not been examined on a global scale. Data accumulation and advances in computational methodology enable the use of new approaches from a proteomic point of view. Here, we sought to carry out protein-wise analysis and describe a simple procedure for the detection and confirmation of cis-bonds from a set of experimental PDB chains for a protein to discriminate this type of bond from isomerizable and/or misassigned bonds. The resulting set of consistent cis bonds (found at identical positions in multiple chains) provides unprecedented insights into the trend of "high cis content" proteins and the upper limit of consistent cis bonds per polypeptide length. Recognizing such limit would not only be important for a practical check of upcoming structures, but also for the design of novel protein folds beyond the evolutionally-acquired repertoire.
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Affiliation(s)
- Tetsuji Okada
- Department of Life Science, Gakushuin University, 1-5-1 Mejiro, Toshima-ku, Tokyo, 171-8588, Japan
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3
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Croll TI, Williams CJ, Chen VB, Richardson DC, Richardson JS. Improving SARS-CoV-2 structures: Peer review by early coordinate release. Biophys J 2021; 120:1085-1096. [PMID: 33460600 PMCID: PMC7834719 DOI: 10.1016/j.bpj.2020.12.029] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 12/17/2020] [Accepted: 12/22/2020] [Indexed: 01/18/2023] Open
Abstract
This work builds upon the record-breaking speed and generous immediate release of new experimental three-dimensional structures of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteins and complexes, which are crucial to downstream vaccine and drug development. We have surveyed those structures to catch the occasional errors that could be significant for those important uses and for which we were able to provide demonstrably higher-accuracy corrections. This process relied on new validation and correction methods such as CaBLAM and ISOLDE, which are not yet in routine use. We found such important and correctable problems in seven early SARS-CoV-2 structures. Two of the structures were soon superseded by new higher-resolution data, confirming our proposed changes. For the other five, we emailed the depositors a documented and illustrated report and encouraged them to make the model corrections themselves and use the new option at the worldwide Protein Data Bank for depositors to re-version their coordinates without changing the Protein Data Bank code. This quickly and easily makes the better-accuracy coordinates available to anyone who examines or downloads their structure, even before formal publication. The changes have involved sequence misalignments, incorrect RNA conformations near a bound inhibitor, incorrect metal ligands, and cis-trans or peptide flips that prevent good contact at interaction sites. These improvements have propagated into nearly all related structures done afterward. This process constitutes a new form of highly rigorous peer review, which is actually faster and more strict than standard publication review because it has access to coordinates and maps; journal peer review would also be strengthened by such access.
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Affiliation(s)
| | | | - Vincent B Chen
- Department of Biochemistry, Duke University, Durham, North Carolina
| | | | - Jane S Richardson
- Department of Biochemistry, Duke University, Durham, North Carolina.
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4
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Prisant MG, Williams CJ, Chen VB, Richardson JS, Richardson DC. New tools in MolProbity validation: CaBLAM for CryoEM backbone, UnDowser to rethink "waters," and NGL Viewer to recapture online 3D graphics. Protein Sci 2020; 29:315-329. [PMID: 31724275 PMCID: PMC6933861 DOI: 10.1002/pro.3786] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 11/08/2019] [Accepted: 11/11/2019] [Indexed: 12/17/2022]
Abstract
The MolProbity web service provides macromolecular model validation to help correct local errors, for the structural biology community worldwide. Here we highlight new validation features, and also describe how we are fighting back against outside developments which compromise that mission. Our new tool called UnDowser analyzes the properties and context of clashing HOH "waters" to diagnose what they might actually represent; a dozen distinct scenarios are illustrated and described. We now treat alternate conformations more thoroughly, and switching to the Neo4j database (graphical rather than relational) enables cleaner, more comprehensive, and much larger reference datasets. A problematic outside change is that refinement software now increasingly restrains traditional validation criteria (geometry, clashes, rotamers, and even Ramachandran) in order to supplement the sparser experimental data at 3-4 Å resolutions typical of modern cryoEM. But unfortunately the broad density allows model optimization without fixing underlying problems, which means these structures often score much better on validation than they really are. CaBLAM, our tool designed for evaluating peptide orientations at lower resolutions, was described in the previous Tools issue, and here we demonstrate its effectiveness in diagnosing local errors even when other validation outliers have been artificially removed. Sophisticated hacking of the MolProbity server has required continual monitoring and various security measures short of restricting user access. The deprecation of Java applets now prevents KiNG interactive online display of outliers on the 3D model during a MolProbity run, but that important functionality has now been recaptured with a modified version of the Javascript NGL Viewer.
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Affiliation(s)
- Michael G. Prisant
- Department of BiochemistryDuke University Medical CenterDurhamNorth Carolina
| | | | - Vincent B. Chen
- Department of BiochemistryDuke University Medical CenterDurhamNorth Carolina
| | - Jane S. Richardson
- Department of BiochemistryDuke University Medical CenterDurhamNorth Carolina
| | - David C. Richardson
- Department of BiochemistryDuke University Medical CenterDurhamNorth Carolina
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5
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Liebschner D, Afonine PV, Baker ML, Bunkóczi G, Chen VB, Croll TI, Hintze B, Hung LW, Jain S, McCoy AJ, Moriarty NW, Oeffner RD, Poon BK, Prisant MG, Read RJ, Richardson JS, Richardson DC, Sammito MD, Sobolev OV, Stockwell DH, Terwilliger TC, Urzhumtsev AG, Videau LL, Williams CJ, Adams PD. Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix. Acta Crystallogr D Struct Biol 2019; 75:861-877. [PMID: 31588918 PMCID: PMC6778852 DOI: 10.1107/s2059798319011471] [Citation(s) in RCA: 3458] [Impact Index Per Article: 691.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 08/15/2019] [Indexed: 12/16/2022] Open
Abstract
Diffraction (X-ray, neutron and electron) and electron cryo-microscopy are powerful methods to determine three-dimensional macromolecular structures, which are required to understand biological processes and to develop new therapeutics against diseases. The overall structure-solution workflow is similar for these techniques, but nuances exist because the properties of the reduced experimental data are different. Software tools for structure determination should therefore be tailored for each method. Phenix is a comprehensive software package for macromolecular structure determination that handles data from any of these techniques. Tasks performed with Phenix include data-quality assessment, map improvement, model building, the validation/rebuilding/refinement cycle and deposition. Each tool caters to the type of experimental data. The design of Phenix emphasizes the automation of procedures, where possible, to minimize repetitive and time-consuming manual tasks, while default parameters are chosen to encourage best practice. A graphical user interface provides access to many command-line features of Phenix and streamlines the transition between programs, project tracking and re-running of previous tasks.
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Affiliation(s)
- Dorothee Liebschner
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Pavel V. Afonine
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Matthew L. Baker
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Gábor Bunkóczi
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, England
| | - Vincent B. Chen
- Department of Biochemistry, Duke University, Durham, NC 27710, USA
| | - Tristan I. Croll
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, England
| | - Bradley Hintze
- Department of Biochemistry, Duke University, Durham, NC 27710, USA
| | - Li-Wei Hung
- Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Swati Jain
- Department of Biochemistry, Duke University, Durham, NC 27710, USA
| | - Airlie J. McCoy
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, England
| | - Nigel W. Moriarty
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Robert D. Oeffner
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, England
| | - Billy K. Poon
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | | | - Randy J. Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, England
| | | | | | - Massimo D. Sammito
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, England
| | - Oleg V. Sobolev
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Duncan H. Stockwell
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, England
| | - Thomas C. Terwilliger
- Los Alamos National Laboratory, Los Alamos, NM 87545, USA
- New Mexico Consortium, Los Alamos, NM 87544, USA
| | - Alexandre G. Urzhumtsev
- Centre for Integrative Biology, Institut de Génétique et de Biologie Moléculaire et Cellulaire, CNRS–INSERM–UdS, 67404 Illkirch, France
- Faculté des Sciences et Technologies, Université de Lorraine, BP 239, 54506 Vandoeuvre-lès-Nancy, France
| | | | | | - Paul D. Adams
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Department of Bioengineering, University of California Berkeley, Berkeley, CA 94720, USA
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6
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Richardson JS, Williams CJ, Videau LL, Chen VB, Richardson DC. Assessment of detailed conformations suggests strategies for improving cryoEM models: Helix at lower resolution, ensembles, pre-refinement fixups, and validation at multi-residue length scale. J Struct Biol 2018; 204:301-312. [PMID: 30107233 PMCID: PMC6163098 DOI: 10.1016/j.jsb.2018.08.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Revised: 08/01/2018] [Accepted: 08/08/2018] [Indexed: 11/17/2022]
Abstract
We find that the overall quite good methods used in the CryoEM Model Challenge could still benefit greatly from several strategies for improving local conformations. Our assessments primarily use validation criteria from the MolProbity web service. Those criteria include MolProbity's all-atom contact analysis, updated versions of standard conformational validations for protein and RNA, plus two recent additions: first, flags for cis-nonPro and twisted peptides, and second, the CaBLAM system for diagnosing secondary structure, validating Cα backbone, and validating adjacent peptide CO orientations in the context of the Cα trace. In general, automated ab initio building of starting models is quite good at backbone connectivity but often fails at local conformation or sequence register, especially at poorer than 3.5 Å resolution. However, we show that even if criteria (such as Ramachandran or rotamer) are explicitly restrained to improve refinement behavior and overall validation scores, automated optimization of a deposited structure seldom corrects specific misfittings that start in the wrong local minimum, but just hides them. Therefore, local problems should be identified, and as many as possible corrected, before starting refinement. Secondary structures are confusing at 3-4 Å but can be better recognized at 6-8 Å. In future model challenges, specific steps being tested (such as segmentation) and the required documentation (such as PDB code of starting model) should each be explicitly defined, so competing methods on a given task can be meaningfully compared. Individual local examples are presented here, to understand what local mistakes and corrections look like in 3D, how they probably arise, and what possible improvements to methodology might help avoid them. At these resolutions, both structural biologists and end-users need meaningful estimates of local uncertainty, perhaps through explicit ensembles. Fitting problems can best be diagnosed by validation that spans multiple residues; CaBLAM is such a multi-residue tool, and its effectiveness is demonstrated.
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Affiliation(s)
| | | | - Lizbeth L Videau
- Department of Biochemistry, Duke University, Durham, NC 27710, USA
| | - Vincent B Chen
- Department of Biochemistry, Duke University, Durham, NC 27710, USA
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7
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Croll TI. ISOLDE: a physically realistic environment for model building into low-resolution electron-density maps. Acta Crystallogr D Struct Biol 2018; 74:519-530. [PMID: 29872003 PMCID: PMC6096486 DOI: 10.1107/s2059798318002425] [Citation(s) in RCA: 917] [Impact Index Per Article: 152.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2017] [Accepted: 02/09/2018] [Indexed: 01/19/2023] Open
Abstract
This paper introduces ISOLDE, a new software package designed to provide an intuitive environment for high-fidelity interactive remodelling/refinement of macromolecular models into electron-density maps. ISOLDE combines interactive molecular-dynamics flexible fitting with modern molecular-graphics visualization and established structural biology libraries to provide an immersive interface wherein the model constantly acts to maintain physically realistic conformations as the user interacts with it by directly tugging atoms with a mouse or haptic interface or applying/removing restraints. In addition, common validation tasks are accelerated and visualized in real time. Using the recently described 3.8 Å resolution cryo-EM structure of the eukaryotic minichromosome maintenance (MCM) helicase complex as a case study, it is demonstrated how ISOLDE can be used alongside other modern refinement tools to avoid common pitfalls of low-resolution modelling and improve the quality of the final model. A detailed analysis of changes between the initial and final model provides a somewhat sobering insight into the dangers of relying on a small number of validation metrics to judge the quality of a low-resolution model.
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Affiliation(s)
- Tristan Ian Croll
- Cambridge Institute for Medical Research, University of Cambridge, Wellcome Trust/MRC Building, Cambridge CB2 0XY, England
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8
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Richardson JS, Williams CJ, Hintze BJ, Chen VB, Prisant MG, Videau LL, Richardson DC. Model validation: local diagnosis, correction and when to quit. Acta Crystallogr D Struct Biol 2018; 74:132-142. [PMID: 29533239 PMCID: PMC5947777 DOI: 10.1107/s2059798317009834] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Accepted: 07/03/2017] [Indexed: 02/07/2023] Open
Abstract
Traditionally, validation was considered to be a final gatekeeping function, but refinement is smoother and results are better if model validation actively guides corrections throughout structure solution. This shifts emphasis from global to local measures: primarily geometry, conformations and sterics. A fit into the wrong local minimum conformation usually produces outliers in multiple measures. Moving to the right local minimum should be prioritized, rather than small shifts across arbitrary borderlines. Steric criteria work best with all explicit H atoms. `Backrub' motions should be used for side chains and `P-perp' diagnostics to correct ribose puckers. A `water' may actually be an ion, a relic of misfitting or an unmodeled alternate. Beware of wishful thinking in modeling ligands. At high resolution, internally consistent alternate conformations should be modeled and geometry in poor density should not be downweighted. At low resolution, CaBLAM should be used to diagnose protein secondary structure and ERRASER to correct RNA backbone. All atoms should not be forced inside density, beware of sequence misalignment, and very rare conformations such as cis-non-Pro peptides should be avoided. Automation continues to improve, but the crystallographer still must look at each outlier, in the context of density, and correct most of them. For the valid few with unambiguous density and something that is holding them in place, a functional reason should be sought. The expectation is a few outliers, not zero.
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Affiliation(s)
| | | | | | - Vincent B. Chen
- Department of Biochemistry, Duke University, Durham, NC 27710, USA
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9
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Williams CJ, Headd JJ, Moriarty NW, Prisant MG, Videau LL, Deis LN, Verma V, Keedy DA, Hintze BJ, Chen VB, Jain S, Lewis SM, Arendall WB, Snoeyink J, Adams PD, Lovell SC, Richardson JS, Richardson DC. MolProbity: More and better reference data for improved all-atom structure validation. Protein Sci 2018; 27:293-315. [PMID: 29067766 PMCID: PMC5734394 DOI: 10.1002/pro.3330] [Citation(s) in RCA: 2321] [Impact Index Per Article: 386.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Revised: 10/19/2017] [Accepted: 10/23/2017] [Indexed: 12/27/2022]
Abstract
This paper describes the current update on macromolecular model validation services that are provided at the MolProbity website, emphasizing changes and additions since the previous review in 2010. There have been many infrastructure improvements, including rewrite of previous Java utilities to now use existing or newly written Python utilities in the open-source CCTBX portion of the Phenix software system. This improves long-term maintainability and enhances the thorough integration of MolProbity-style validation within Phenix. There is now a complete MolProbity mirror site at http://molprobity.manchester.ac.uk. GitHub serves our open-source code, reference datasets, and the resulting multi-dimensional distributions that define most validation criteria. Coordinate output after Asn/Gln/His "flip" correction is now more idealized, since the post-refinement step has apparently often been skipped in the past. Two distinct sets of heavy-atom-to-hydrogen distances and accompanying van der Waals radii have been researched and improved in accuracy, one for the electron-cloud-center positions suitable for X-ray crystallography and one for nuclear positions. New validations include messages at input about problem-causing format irregularities, updates of Ramachandran and rotamer criteria from the million quality-filtered residues in a new reference dataset, the CaBLAM Cα-CO virtual-angle analysis of backbone and secondary structure for cryoEM or low-resolution X-ray, and flagging of the very rare cis-nonProline and twisted peptides which have recently been greatly overused. Due to wide application of MolProbity validation and corrections by the research community, in Phenix, and at the worldwide Protein Data Bank, newly deposited structures have continued to improve greatly as measured by MolProbity's unique all-atom clashscore.
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Affiliation(s)
| | - Jeffrey J. Headd
- Department of BiochemistryDuke UniversityDurhamNC27710USA
- Present address:
Janssen Research and DevelopmentSpring HousePA19477USA
| | - Nigel W. Moriarty
- Molecular Biosciences and Integrated BioimagingLawrence Berkeley National LaboratoryBerkeleyCA94720USA
| | | | | | - Lindsay N. Deis
- Department of BiochemistryDuke UniversityDurhamNC27710USA
- Present address:
Department of BiochemistryStanford University, StanfordCA95126USA
| | - Vishal Verma
- Department of Computer ScienceUniversity of North CarolinaChapel HillNC27599USA
| | - Daniel A. Keedy
- Department of BiochemistryDuke UniversityDurhamNC27710USA
- Present address:
Structural Biology Initiative and Department of Chemistry & BiochemistryCUNY Advanced Science Research Center, City University of New YorkNew YorkNY10031USA
| | | | | | - Swati Jain
- Department of BiochemistryDuke UniversityDurhamNC27710USA
- Present address:
Department of ChemistryNew York UniversityNew YorkNYUSA
| | - Steven M. Lewis
- Department of BiochemistryDuke UniversityDurhamNC27710USA
- Present address:
Cyrus Biotechnology, 500 Union Street, Suite 320SeattleWA98101USA
| | | | - Jack Snoeyink
- Department of Computer ScienceUniversity of North CarolinaChapel HillNC27599USA
| | - Paul D. Adams
- Molecular Biosciences and Integrated BioimagingLawrence Berkeley National LaboratoryBerkeleyCA94720USA
| | - Simon C. Lovell
- School of Biological SciencesUniversity of ManchesterManchesterM13 9PTUK
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10
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Hintze BJ, Richardson JS, Richardson DC. Mismodeled purines: implicit alternates and hidden Hoogsteens. Acta Crystallogr D Struct Biol 2017; 73:852-859. [PMID: 28994414 PMCID: PMC5633910 DOI: 10.1107/s2059798317013729] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Accepted: 09/25/2017] [Indexed: 12/14/2022] Open
Abstract
Hoogsteen base pairs are seen in DNA crystal structures, but only rarely. This study tests whether Hoogsteens or other syn purines are either under-modeled or over-modeled, which are known problems for rare conformations. Candidate purines needing a syn/anti 180° flip were identified by diagnostic patterns of difference electron-density peaks. Manual inspection narrowed 105 flip candidates to 20 convincing cases, all at ≤2.7 Å resolution. Rebuilding and refinement confirmed that 14 of these were authentic purine flips. Seven examples are modeled as Watson-Crick base pairs but should be Hoogsteens (commonest at duplex termini), and three had the opposite issue. Syn/anti flips were also needed for some single-stranded purines. Five of the 20 convincing cases arose from an unmodeled alternate duplex running in the opposite direction. These are in semi-palindromic DNA sequences bound by a homodimeric protein and show flipped-purine-like difference peaks at residues where the palindrome is imperfect. This study documents types of incorrect modeling which are worth avoiding. However, the primary conclusions are that such mistakes are infrequent, the bias towards fitting anti purines is very slight, and the occurrence rate of Hoogsteen base pairs in DNA crystal structures remains unchanged from earlier estimates at ∼0.3%.
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11
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Abstract
Macromolecular structure is governed by the strict rules of stereochemistry. Several approaches to the validation of the correctness of the interpretation of crystallographic and NMR data that underlie the models deposited in the PDB are utilized in practice. The stereochemical rules applicable to macromolecular structures are discussed in this chapter. Practical, computer-based methods and tools of verification of how well the models adhere to those established structural principles to assure their quality are summarized.
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12
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Vermaas JV, Trebesch N, Mayne CG, Thangapandian S, Shekhar M, Mahinthichaichan P, Baylon JL, Jiang T, Wang Y, Muller MP, Shinn E, Zhao Z, Wen PC, Tajkhorshid E. Microscopic Characterization of Membrane Transporter Function by In Silico Modeling and Simulation. Methods Enzymol 2016; 578:373-428. [PMID: 27497175 PMCID: PMC6404235 DOI: 10.1016/bs.mie.2016.05.042] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Membrane transporters mediate one of the most fundamental processes in biology. They are the main gatekeepers controlling active traffic of materials in a highly selective and regulated manner between different cellular compartments demarcated by biological membranes. At the heart of the mechanism of membrane transporters lie protein conformational changes of diverse forms and magnitudes, which closely mediate critical aspects of the transport process, most importantly the coordinated motions of remotely located gating elements and their tight coupling to chemical processes such as binding, unbinding and translocation of transported substrate and cotransported ions, ATP binding and hydrolysis, and other molecular events fueling uphill transport of the cargo. An increasing number of functional studies have established the active participation of lipids and other components of biological membranes in the function of transporters and other membrane proteins, often acting as major signaling and regulating elements. Understanding the mechanistic details of these molecular processes require methods that offer high spatial and temporal resolutions. Computational modeling and simulations technologies empowered by advanced sampling and free energy calculations have reached a sufficiently mature state to become an indispensable component of mechanistic studies of membrane transporters in their natural environment of the membrane. In this article, we provide an overview of a number of major computational protocols and techniques commonly used in membrane transporter modeling and simulation studies. The article also includes practical hints on effective use of these methods, critical perspectives on their strengths and weak points, and examples of their successful applications to membrane transporters, selected from the research performed in our own laboratory.
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Affiliation(s)
- J V Vermaas
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, United States; University of Illinois at Urbana-Champaign, Urbana, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - N Trebesch
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, United States; University of Illinois at Urbana-Champaign, Urbana, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - C G Mayne
- University of Illinois at Urbana-Champaign, Urbana, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - S Thangapandian
- University of Illinois at Urbana-Champaign, Urbana, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - M Shekhar
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, United States; University of Illinois at Urbana-Champaign, Urbana, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - P Mahinthichaichan
- University of Illinois at Urbana-Champaign, Urbana, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - J L Baylon
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, United States; University of Illinois at Urbana-Champaign, Urbana, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - T Jiang
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, United States; University of Illinois at Urbana-Champaign, Urbana, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Y Wang
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, United States; University of Illinois at Urbana-Champaign, Urbana, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - M P Muller
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, United States; University of Illinois at Urbana-Champaign, Urbana, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States; College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - E Shinn
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, United States; University of Illinois at Urbana-Champaign, Urbana, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Z Zhao
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, United States; University of Illinois at Urbana-Champaign, Urbana, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - P-C Wen
- University of Illinois at Urbana-Champaign, Urbana, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - E Tajkhorshid
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, United States; University of Illinois at Urbana-Champaign, Urbana, IL, United States; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States; College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, United States.
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13
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McGreevy R, Teo I, Singharoy A, Schulten K. Advances in the molecular dynamics flexible fitting method for cryo-EM modeling. Methods 2016; 100:50-60. [PMID: 26804562 PMCID: PMC4848153 DOI: 10.1016/j.ymeth.2016.01.009] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Revised: 01/16/2016] [Accepted: 01/20/2016] [Indexed: 02/02/2023] Open
Abstract
Molecular Dynamics Flexible Fitting (MDFF) is an established technique for fitting all-atom structures of molecules into corresponding cryo-electron microscopy (cryo-EM) densities. The practical application of MDFF is simple but requires a user to be aware of and take measures against a variety of possible challenges presented by each individual case. Some of these challenges arise from the complexity of a molecular structure or the limited quality of available structural models and densities to be interpreted, while others stem from the intricacies of MDFF itself. The current article serves as an overview of the strategies that have been developed since MDFF's inception to overcome common challenges and successfully perform MDFF simulations.
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Affiliation(s)
- Ryan McGreevy
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, USA
| | - Ivan Teo
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Abhishek Singharoy
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, USA
| | - Klaus Schulten
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, USA; Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
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14
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Touw WG, Joosten RP, Vriend G. Detection of trans-cis flips and peptide-plane flips in protein structures. ACTA CRYSTALLOGRAPHICA. SECTION D, BIOLOGICAL CRYSTALLOGRAPHY 2015; 71:1604-14. [PMID: 26249342 PMCID: PMC4528797 DOI: 10.1107/s1399004715008263] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2015] [Accepted: 04/27/2015] [Indexed: 11/13/2022]
Abstract
A coordinate-based method is presented to detect peptide bonds that need correction either by a peptide-plane flip or by a trans-cis inversion of the peptide bond. When applied to the whole Protein Data Bank, the method predicts 4617 trans-cis flips and many thousands of hitherto unknown peptide-plane flips. A few examples are highlighted for which a correction of the peptide-plane geometry leads to a correction of the understanding of the structure-function relation. All data, including 1088 manually validated cases, are freely available and the method is available from a web server, a web-service interface and through WHAT_CHECK.
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Affiliation(s)
- Wouter G. Touw
- Centre for Molecular and Biomolecular Informatics, Radboud University Medical Center, Geert Grooteplein-Zuid 26-28, 6525 GA Nijmegen, The Netherlands
| | - Robbie P. Joosten
- Department of Biochemistry, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Gert Vriend
- Centre for Molecular and Biomolecular Informatics, Radboud University Medical Center, Geert Grooteplein-Zuid 26-28, 6525 GA Nijmegen, The Netherlands
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