1
|
Analyzing Fluctuation Properties in Protein Elastic Networks with Sequence-Specific and Distance-Dependent Interactions. Biomolecules 2019; 9:biom9100549. [PMID: 31575003 PMCID: PMC6843209 DOI: 10.3390/biom9100549] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2019] [Revised: 09/20/2019] [Accepted: 09/24/2019] [Indexed: 01/26/2023] Open
Abstract
Simple protein elastic networks which neglect amino-acid information often yield reasonable predictions of conformational dynamics and are broadly used. Recently, model variants which incorporate sequence-specific and distance-dependent interactions of residue pairs have been constructed and demonstrated to improve agreement with experimental data. We have applied the new variants in a systematic study of protein fluctuation properties and compared their predictions with those of conventional anisotropic network models. We find that the quality of predictions is frequently linked to poor estimations in highly flexible protein regions. An analysis of a large set of protein structures shows that fluctuations of very weakly connected network residues are intrinsically prone to be significantly overestimated by all models. This problem persists in the new models and is not resolved by taking into account sequence information. The effect becomes even enhanced in the model variant which takes into account very soft long-ranged residue interactions. Beyond these shortcomings, we find that model predictions are largely insensitive to the integration of chemical information, at least regarding the fluctuation properties of individual residues. One can furthermore conclude that the inherent drawbacks may present a serious hindrance when improvement of elastic network models are attempted.
Collapse
|
2
|
Wako H, Endo S. Normal mode analysis as a method to derive protein dynamics information from the Protein Data Bank. Biophys Rev 2017; 9:877-893. [PMID: 29103094 DOI: 10.1007/s12551-017-0330-2] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Accepted: 10/04/2017] [Indexed: 11/30/2022] Open
Abstract
Normal mode analysis (NMA) can facilitate quick and systematic investigation of protein dynamics using data from the Protein Data Bank (PDB). We developed an elastic network model-based NMA program using dihedral angles as independent variables. Compared to the NMA programs that use Cartesian coordinates as independent variables, key attributes of the proposed program are as follows: (1) chain connectivity related to the folding pattern of a polypeptide chain is naturally embedded in the model; (2) the full-atom system is acceptable, and owing to a considerably smaller number of independent variables, the PDB data can be used without further manipulation; (3) the number of variables can be easily reduced by some of the rotatable dihedral angles; (4) the PDB data for any molecule besides proteins can be considered without coarse-graining; and (5) individual motions of constituent subunits and ligand molecules can be easily decomposed into external and internal motions to examine their mutual and intrinsic motions. Its performance is illustrated with an example of a DNA-binding allosteric protein, a catabolite activator protein. In particular, the focus is on the conformational change upon cAMP and DNA binding, and on the communication between their binding sites remotely located from each other. In this illustration, NMA creates a vivid picture of the protein dynamics at various levels of the structures, i.e., atoms, residues, secondary structures, domains, subunits, and the complete system, including DNA and cAMP. Comparative studies of the specific protein in different states, e.g., apo- and holo-conformations, and free and complexed configurations, provide useful information for studying structurally and functionally important aspects of the protein.
Collapse
Affiliation(s)
- Hiroshi Wako
- School of Social Sciences, Waseda University, Tokyo, 169-8050, Japan.
| | - Shigeru Endo
- Department of Physics, School of Science, Kitasato University, Sagamihara, 252-0373, Japan
| |
Collapse
|
3
|
López-Blanco JR, Chacón P. New generation of elastic network models. Curr Opin Struct Biol 2015; 37:46-53. [PMID: 26716577 DOI: 10.1016/j.sbi.2015.11.013] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Revised: 11/23/2015] [Accepted: 11/26/2015] [Indexed: 12/16/2022]
Abstract
The intrinsic flexibility of proteins and nucleic acids can be grasped from remarkably simple mechanical models of particles connected by springs. In recent decades, Elastic Network Models (ENMs) combined with Normal Model Analysis widely confirmed their ability to predict biologically relevant motions of biomolecules and soon became a popular methodology to reveal large-scale dynamics in multiple structural biology scenarios. The simplicity, robustness, low computational cost, and relatively high accuracy are the reasons behind the success of ENMs. This review focuses on recent advances in the development and application of ENMs, paying particular attention to combinations with experimental data. Successful application scenarios include large macromolecular machines, structural refinement, docking, and evolutionary conservation.
Collapse
Affiliation(s)
- José Ramón López-Blanco
- Department of Biological Chemical Physics, Rocasolano Physical Chemistry Institute C.S.I.C., Serrano 119, 28006 Madrid, Spain
| | - Pablo Chacón
- Department of Biological Chemical Physics, Rocasolano Physical Chemistry Institute C.S.I.C., Serrano 119, 28006 Madrid, Spain.
| |
Collapse
|
4
|
Van Benschoten AH, Afonine PV, Terwilliger TC, Wall ME, Jackson CJ, Sauter NK, Adams PD, Urzhumtsev A, Fraser JS. Predicting X-ray diffuse scattering from translation-libration-screw structural ensembles. ACTA CRYSTALLOGRAPHICA. SECTION D, BIOLOGICAL CRYSTALLOGRAPHY 2015; 71:1657-67. [PMID: 26249347 PMCID: PMC4528799 DOI: 10.1107/s1399004715007415] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2014] [Accepted: 04/15/2015] [Indexed: 01/01/2023]
Abstract
Identifying the intramolecular motions of proteins and nucleic acids is a major challenge in macromolecular X-ray crystallography. Because Bragg diffraction describes the average positional distribution of crystalline atoms with imperfect precision, the resulting electron density can be compatible with multiple models of motion. Diffuse X-ray scattering can reduce this degeneracy by reporting on correlated atomic displacements. Although recent technological advances are increasing the potential to accurately measure diffuse scattering, computational modeling and validation tools are still needed to quantify the agreement between experimental data and different parameterizations of crystalline disorder. A new tool, phenix.diffuse, addresses this need by employing Guinier's equation to calculate diffuse scattering from Protein Data Bank (PDB)-formatted structural ensembles. As an example case, phenix.diffuse is applied to translation-libration-screw (TLS) refinement, which models rigid-body displacement for segments of the macromolecule. To enable the calculation of diffuse scattering from TLS-refined structures, phenix.tls_as_xyz builds multi-model PDB files that sample the underlying T, L and S tensors. In the glycerophosphodiesterase GpdQ, alternative TLS-group partitioning and different motional correlations between groups yield markedly dissimilar diffuse scattering maps with distinct implications for molecular mechanism and allostery. These methods demonstrate how, in principle, X-ray diffuse scattering could extend macromolecular structural refinement, validation and analysis.
Collapse
Affiliation(s)
- Andrew H. Van Benschoten
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Pavel V. Afonine
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | | | - Michael E. Wall
- Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Colin J. Jackson
- Research School of Chemistry, Australian National University, Canberra, ACT 2601, Australia
| | - Nicholas K. Sauter
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Paul D. Adams
- Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Department of Bioengineering, University of California Berkeley, Berkeley, CA 94720, USA
| | - Alexandre Urzhumtsev
- Centre for Integrative Biology, Institut de Génétique et de Biologie Moléculaire et Cellulaire, CNRS–INSERM–UdS, 1 Rue Laurent Fries, BP 10142, 67404 Illkirch, France
- Faculté des Sciences et Technologies, Université de Lorraine, BP 239, 54506 Vandoeuvre-les-Nancy, France
| | - James S. Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
| |
Collapse
|
5
|
Zhou L, Liu Q. Aligning experimental and theoretical anisotropic B-factors: water models, normal-mode analysis methods, and metrics. J Phys Chem B 2014; 118:4069-79. [PMID: 24673391 PMCID: PMC4397101 DOI: 10.1021/jp4124327] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The strength of X-ray crystallography in providing the information for protein dynamics has been under appreciated. The anisotropic B-factors (ADPs) from high-resolution structures are invaluable in studying the relationship among structure, dynamics, and function. Here, starting from an in-depth evaluation of the metrics used for comparing the overlap between two ellipsoids, we applied normal-mode analysis (NMA) to predict the theoretical ADPs and then align them with experimental results. Adding an extra layer of explicitly treated water on protein surface significantly improved the energy minimization results and better reproduced the anisotropy of experimental ADPs. In comparing experimental and theoretical ADPs, we focused on the overlap in shape, the alignment of dominant directions, and the similarity in magnitude. The choices of water molecules, NMA methods, and the metrics for evaluating the overlap of ADPs determined final results. This study provides useful information for exploring the physical basis and the application potential of experimental ADPs.
Collapse
Affiliation(s)
- Lei Zhou
- Department of Physiology and Biophysics, School of Medicine, Virginia Commonwealth University , Richmond, Virginia 23298, United States
| | | |
Collapse
|