1
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Caparotta M, Perez A. Advancing Molecular Dynamics: Toward Standardization, Integration, and Data Accessibility in Structural Biology. J Phys Chem B 2024; 128:2219-2227. [PMID: 38418288 DOI: 10.1021/acs.jpcb.3c04823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2024]
Abstract
Molecular dynamics (MD) simulations have become a valuable tool in structural biology, offering insights into complex biological systems that are difficult to obtain through experimental techniques alone. The lack of available data sets and structures in most published computational work has limited other researchers' use of these models. In recent years, the emergence of online sharing platforms and MD database initiatives favor the deposition of ensembles and structures to accompany publications, favoring reuse of the data sets. However, the lack of uniform metadata collection, formats, and what data are deposited limits the impact and its use by different communities that are not necessarily experts in MD. This Perspective highlights the need for standardization and better resource sharing for processing and interpreting MD simulation results, akin to efforts in other areas of structural biology. As the field moves forward, we will see an increase in popularity and benefits of MD-based integrative approaches combining experimental data and simulations through probabilistic reasoning, but these too are limited by uniformity in experimental data availability and choices on how the data are modeled that are not trivial to decipher from papers. Other fields have addressed similar challenges comprehensively by establishing task forces with different degrees of success. The large scope and number of communities to represent the breadth of types of MD simulations complicates a parallel approach that would fit all. Thus, each group typically decides what data and which format to upload on servers like Zenodo. Uploading data with FAIR (findable, accessible, interoperable, reusable) principles in mind including optimal metadata collection will make the data more accessible and actionable by the community. Such a wealth of simulation data will foster method development and infrastructure advancements, thus propelling the field forward.
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Affiliation(s)
- Marcelo Caparotta
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
| | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
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2
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Arvindekar S, Pathak AS, Majila K, Viswanath S. Optimizing representations for integrative structural modeling using Bayesian model selection. Bioinformatics 2024; 40:btae106. [PMID: 38391029 PMCID: PMC10924281 DOI: 10.1093/bioinformatics/btae106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 02/03/2024] [Accepted: 02/21/2024] [Indexed: 02/24/2024] Open
Abstract
MOTIVATION Integrative structural modeling combines data from experiments, physical principles, statistics of previous structures, and prior models to obtain structures of macromolecular assemblies that are challenging to characterize experimentally. The choice of model representation is a key decision in integrative modeling, as it dictates the accuracy of scoring, efficiency of sampling, and resolution of analysis. But currently, the choice is usually made ad hoc, manually. RESULTS Here, we report NestOR (Nested Sampling for Optimizing Representation), a fully automated, statistically rigorous method based on Bayesian model selection to identify the optimal coarse-grained representation for a given integrative modeling setup. Given an integrative modeling setup, it determines the optimal representations from given candidate representations based on their model evidence and sampling efficiency. The performance of NestOR was evaluated on a benchmark of four macromolecular assemblies. AVAILABILITY AND IMPLEMENTATION NestOR is implemented in the Integrative Modeling Platform (https://integrativemodeling.org) and is available at https://github.com/isblab/nestor. Data for the benchmark is at https://www.doi.org/10.5281/zenodo.10360718.
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Affiliation(s)
- Shreyas Arvindekar
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore 560065, India
| | - Aditi S Pathak
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore 560065, India
| | - Kartik Majila
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore 560065, India
| | - Shruthi Viswanath
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore 560065, India
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3
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Shor B, Schneidman-Duhovny D. CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2. Nat Methods 2024; 21:477-487. [PMID: 38326495 PMCID: PMC10927564 DOI: 10.1038/s41592-024-02174-0] [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: 05/17/2023] [Accepted: 01/09/2024] [Indexed: 02/09/2024]
Abstract
Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score >0.7) 72% of the complexes among the top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding Protein Data Bank entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold's high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins.
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Affiliation(s)
- Ben Shor
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dina Schneidman-Duhovny
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
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4
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Raveh B, Eliasian R, Rashkovits S, Russel D, Hayama R, Sparks SE, Singh D, Lim R, Villa E, Rout MP, Cowburn D, Sali A. Integrative spatiotemporal map of nucleocytoplasmic transport. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.31.573409. [PMID: 38260487 PMCID: PMC10802240 DOI: 10.1101/2023.12.31.573409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The Nuclear Pore Complex (NPC) facilitates rapid and selective nucleocytoplasmic transport of molecules as large as ribosomal subunits and viral capsids. It is not clear how key emergent properties of this transport arise from the system components and their interactions. To address this question, we constructed an integrative coarse-grained Brownian dynamics model of transport through a single NPC, followed by coupling it with a kinetic model of Ran-dependent transport in an entire cell. The microscopic model parameters were fitted to reflect experimental data and theoretical information regarding the transport, without making any assumptions about its emergent properties. The resulting reductionist model is validated by reproducing several features of transport not used for its construction, such as the morphology of the central transporter, rates of passive and facilitated diffusion as a function of size and valency, in situ radial distributions of pre-ribosomal subunits, and active transport rates for viral capsids. The model suggests that the NPC functions essentially as a virtual gate whose flexible phenylalanine-glycine (FG) repeat proteins raise an entropy barrier to diffusion through the pore. Importantly, this core functionality is greatly enhanced by several key design features, including 'fuzzy' and transient interactions, multivalency, redundancy in the copy number of FG nucleoporins, exponential coupling of transport kinetics and thermodynamics in accordance with the transition state theory, and coupling to the energy-reliant RanGTP concentration gradient. These design features result in the robust and resilient rate and selectivity of transport for a wide array of cargo ranging from a few kilodaltons to megadaltons in size. By dissecting these features, our model provides a quantitative starting point for rationally modulating the transport system and its artificial mimics.
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Arvindekar S, Pathak AS, Majila K, Viswanath S. Optimizing representations for integrative structural modeling using Bayesian model selection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.12.571227. [PMID: 38168172 PMCID: PMC10760022 DOI: 10.1101/2023.12.12.571227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Motivation Integrative structural modeling combines data from experiments, physical principles, statistics of previous structures, and prior models to obtain structures of macromolecular assemblies that are challenging to characterize experimentally. The choice of model representation is a key decision in integrative modeling, as it dictates the accuracy of scoring, efficiency of sampling, and resolution of analysis. But currently, the choice is usually made ad hoc, manually. Results Here, we report NestOR (Nested Sampling for Optimizing Representation), a fully automated, statistically rigorous method based on Bayesian model selection to identify the optimal coarse-grained representation for a given integrative modeling setup. Given an integrative modeling setup, it determines the optimal representations from given candidate representations based on their model evidence and sampling efficiency. The performance of NestOR was evaluated on a benchmark of four macromolecular assemblies. Availability NestOR is implemented in the Integrative Modeling Platform (https://integrativemodeling.org) and is available at https://github.com/isblab/nestor.
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Affiliation(s)
- Shreyas Arvindekar
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India 560065
| | - Aditi S. Pathak
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India 560065
| | - Kartik Majila
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India 560065
| | - Shruthi Viswanath
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India 560065
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6
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York DM. Modern Alchemical Free Energy Methods for Drug Discovery Explained. ACS PHYSICAL CHEMISTRY AU 2023; 3:478-491. [PMID: 38034038 PMCID: PMC10683484 DOI: 10.1021/acsphyschemau.3c00033] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 09/12/2023] [Accepted: 09/13/2023] [Indexed: 12/02/2023]
Abstract
This Perspective provides a contextual explanation of the current state-of-the-art alchemical free energy methods and their role in drug discovery as well as highlights select emerging technologies. The narrative attempts to answer basic questions about what goes on "under the hood" in free energy simulations and provide general guidelines for how to run simulations and analyze the results. It is the hope that this work will provide a valuable introduction to students and scientists in the field.
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Affiliation(s)
- Darrin M. York
- Laboratory for Biomolecular
Simulation Research, Institute for Quantitative Biomedicine, and Department
of Chemistry and Chemical Biology, Rutgers
University, Piscataway, New Jersey 08854, United States
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7
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Mondal A, Lenz S, MacCallum JL, Perez A. Hybrid computational methods combining experimental information with molecular dynamics. Curr Opin Struct Biol 2023; 81:102609. [PMID: 37224642 DOI: 10.1016/j.sbi.2023.102609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/12/2023] [Accepted: 04/23/2023] [Indexed: 05/26/2023]
Abstract
A goal of structural biology is to understand how macromolecules carry out their biological roles by identifying their metastable states, mechanisms of action, pathways leading to conformational changes, and the thermodynamic and kinetic relationships between those states. Integrative modeling brings structural insights into systems where traditional structure determination approaches cannot help. We focus on the synergies and challenges of integrative modeling combining experimental data with molecular dynamics simulations.
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Affiliation(s)
- Arup Mondal
- Quantum Theory Project, Department of Chemistry, University of Florida, Leigh, UK. https://twitter.com/@amondal_chem
| | - Stefan Lenz
- Department of Chemistry, University of Calgary, 2500 University Drive, Canada
| | - Justin L MacCallum
- Department of Chemistry, University of Calgary, 2500 University Drive, Canada. https://twitter.com/@jlmaccal
| | - Alberto Perez
- Quantum Theory Project, Department of Chemistry, University of Florida, Leigh, UK.
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8
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Bolinska A. Epistemic expression in the determination of biomolecular structure. STUDIES IN HISTORY AND PHILOSOPHY OF SCIENCE 2023; 100:107-115. [PMID: 37385143 DOI: 10.1016/j.shpsa.2023.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/15/2023] [Accepted: 05/29/2023] [Indexed: 07/01/2023]
Abstract
Scientific research is constrained by limited resources, so it is imperative that it be conducted efficiently. This paper introduces the notion of epistemic expression, a kind of representation that expedites the solution of research problems. Epistemic expressions are representations that (i) contain information in a way that enables more reliable information to place the most stringent constraints on possible solutions and (ii) make new information readily extractible by biasing the search through that space. I illustrate these conditions using historical and contemporary examples of biomolecular structure determination. Then, I argue that the notion of epistemic expression parts ways with pragmatic accounts of scientific representation and an understanding of models as artifacts, neither of which require models to accurately represent. Explicating epistemic expression thus fills a gap in our understanding of scientific practice, extending Morrison and Morgan's (1999) conception of models as investigative instruments.
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Affiliation(s)
- Agnes Bolinska
- Department of Philosophy, University of South Carolina, Columbia, SC 29208, USA.
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9
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Shor B, Schneidman-Duhovny D. Predicting structures of large protein assemblies using combinatorial assembly algorithm and AlphaFold2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.16.541003. [PMID: 37293053 PMCID: PMC10245790 DOI: 10.1101/2023.05.16.541003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score > 0.7) 72% of the complexes among the Top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding PDB entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold's high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins.
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Affiliation(s)
- Ben Shor
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Dina Schneidman-Duhovny
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
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10
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Cohen T, Halfon M, Carter L, Sharkey B, Jain T, Sivasubramanian A, Schneidman-Duhovny D. Multi-state modeling of antibody-antigen complexes with SAXS profiles and deep-learning models. Methods Enzymol 2022; 678:237-262. [PMID: 36641210 DOI: 10.1016/bs.mie.2022.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Antibodies are an established class of human therapeutics. Epitope characterization is an important part of therapeutic antibody discovery. However, structural characterization of antibody-antigen complexes remains challenging. On the one hand, X-ray crystallography or cryo-electron microscopy provide atomic resolution characterization of the epitope, but the data collection process is typically long and the success rate is low. On the other hand, computational methods for modeling antibody-antigen structures from the individual components frequently suffer from a high false positive rate, rarely resulting in a unique solution. Recent deep learning models for structure prediction are also successful in predicting protein-protein complexes. However, they do not perform well for antibody-antigen complexes. Small Angle X-ray Scattering (SAXS) is a reliable technique for rapid structural characterization of protein samples in solution albeit at low resolution. Here, we present an integrative approach for modeling antigen-antibody complexes using the antibody sequence, antigen structure, and experimentally determined SAXS profiles of the antibody, antigen, and the complex. The method models antibody structures using a novel deep-learning approach, NanoNet. The structures of the antibodies and antigens are represented using multiple 3D conformations to account for compositional and conformational heterogeneity of the protein samples that are used to collect the SAXS data. The complexes are predicted by integrating the SAXS profiles with scoring functions for protein-protein interfaces that are based on statistical potentials and antibody-specific deep-learning models. We validated the method via application to four Fab:EGFR and one Fab:PCSK9 antibody:antigen complexes with experimentally available SAXS datasets. The integrative approach returns accurate predictions (interface RMSD<4Å) in the top five predictions for four out of five complexes (respective interface RMSD values of 1.95, 2.18, 2.66 and 3.87Å), providing support for the utility of such a computational pipeline for epitope characterization during therapeutic antibody discovery.
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Affiliation(s)
- Tomer Cohen
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Matan Halfon
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Lester Carter
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA, United States
| | - Beth Sharkey
- High-Throughput Expression, Adimab LLC, Lebanon, NH, United States
| | - Tushar Jain
- Computational Biology, Adimab LLC, Palo Alto, CA, United States
| | | | - Dina Schneidman-Duhovny
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
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11
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Arvindekar S, Jackman MJ, Low JKK, Landsberg MJ, Mackay JP, Viswanath S. Molecular architecture of nucleosome remodeling and deacetylase sub-complexes by integrative structure determination. Protein Sci 2022; 31:e4387. [PMID: 36040254 PMCID: PMC9413472 DOI: 10.1002/pro.4387] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 05/18/2022] [Accepted: 06/19/2022] [Indexed: 11/11/2022]
Abstract
The nucleosome remodeling and deacetylase (NuRD) complex is a chromatin-modifying assembly that regulates gene expression and DNA damage repair. Despite its importance, limited structural information describing the complete NuRD complex is available and a detailed understanding of its mechanism is therefore lacking. Drawing on information from SEC-MALLS, DIA-MS, XLMS, negative-stain EM, X-ray crystallography, NMR spectroscopy, secondary structure predictions, and homology models, we applied Bayesian integrative structure determination to investigate the molecular architecture of three NuRD sub-complexes: MTA1-HDAC1-RBBP4, MTA1N -HDAC1-MBD3GATAD2CC , and MTA1-HDAC1-RBBP4-MBD3-GATAD2A [nucleosome deacetylase (NuDe)]. The integrative structures were corroborated by examining independent crosslinks, cryo-EM maps, biochemical assays, known cancer-associated mutations, and structure predictions from AlphaFold. The robustness of the models was assessed by jack-knifing. Localization of the full-length MBD3, which connects the deacetylase and chromatin remodeling modules in NuRD, has not previously been possible; our models indicate two different locations for MBD3, suggesting a mechanism by which MBD3 in the presence of GATAD2A asymmetrically bridges the two modules in NuRD. Further, our models uncovered three previously unrecognized subunit interfaces in NuDe: HDAC1C -MTA1BAH , MTA1BAH -MBD3MBD , and HDAC160-100 -MBD3MBD . Our approach also allowed us to localize regions of unknown structure, such as HDAC1C and MBD3IDR , thereby resulting in the most complete and robustly cross-validated structural characterization of these NuRD sub-complexes so far.
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Affiliation(s)
- Shreyas Arvindekar
- National Centre for Biological SciencesTata Institute of Fundamental ResearchBangaloreIndia
| | - Matthew J. Jackman
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia
| | - Jason K. K. Low
- School of Life and Environmental SciencesUniversity of SydneySydneyNew South WalesAustralia
| | - Michael J. Landsberg
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia
| | - Joel P. Mackay
- School of Life and Environmental SciencesUniversity of SydneySydneyNew South WalesAustralia
| | - Shruthi Viswanath
- National Centre for Biological SciencesTata Institute of Fundamental ResearchBangaloreIndia
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12
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Integrative modeling of the cell. Acta Biochim Biophys Sin (Shanghai) 2022; 54:1213-1221. [PMID: 36017893 PMCID: PMC9909318 DOI: 10.3724/abbs.2022115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
A whole-cell model represents certain aspects of the cell structure and/or function. Due to the high complexity of the cell, an integrative modeling approach is often taken to utilize all available information including experimental data, prior knowledge and prior models. In this review, we summarize an emerging workflow of whole-cell modeling into five steps: (i) gather information; (ii) represent the modeled system into modules; (iii) translate input information into scoring function; (iv) sample the whole-cell model; (v) validate and interpret the model. In particular, we propose the integrative modeling of the cell by combining available (whole-cell) models to maximize the accuracy, precision, and completeness. In addition, we list quantitative predictions of various aspects of cell biology from existing whole-cell models. Moreover, we discuss the remaining challenges and future directions, and highlight the opportunity to establish an integrative spatiotemporal multi-scale whole-cell model based on a community approach.
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13
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Ullanat V, Kasukurthi N, Viswanath S. PrISM: Precision for Integrative Structural Models. Bioinformatics 2022; 38:3837-3839. [PMID: 35723541 DOI: 10.1093/bioinformatics/btac400] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/04/2022] [Accepted: 06/16/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION A single precision value is currently reported for an integrative model. However, precision may vary for different regions of an integrative model owing to varying amounts of input information. RESULTS We develop PrISM (Precision for Integrative Structural Models), to efficiently identify high and low-precision regions for integrative models. AVAILABILITY PrISM is written in Python and available under the GNU General Public License v3.0 at https://github.com/isblab/prism; benchmark data used in this paper is available at doi:10.5281/zenodo.6241200. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Varun Ullanat
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
| | - Nikhil Kasukurthi
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
| | - Shruthi Viswanath
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
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14
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Topf M, Rosta E, Bowman GR, Bonomi M. Editorial: Experiments and Simulations: A Pas de Deux to Unravel Biological Function. Front Mol Biosci 2021; 8:799406. [PMID: 34912853 PMCID: PMC8667856 DOI: 10.3389/fmolb.2021.799406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 11/01/2021] [Indexed: 11/29/2022] Open
Affiliation(s)
- Maya Topf
- Center for Structural Systems Biology (CSSB), Leibniz-Institut für Experimentelle Virologie (HPI) and Universitätsklinikum Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Edina Rosta
- Department of Chemistry, King's College London, London, United Kingdom.,Department of Physics and Astronomy, University College London, London, United Kingdom
| | - Gregory R Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, United States
| | - Massimiliano Bonomi
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, CNRS UMR 3528, Institut Pasteur, Paris, France.,USR3756 Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Paris, France
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15
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Graziadei A, Rappsilber J. Leveraging crosslinking mass spectrometry in structural and cell biology. Structure 2021; 30:37-54. [PMID: 34895473 DOI: 10.1016/j.str.2021.11.007] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/11/2021] [Accepted: 11/17/2021] [Indexed: 12/18/2022]
Abstract
Crosslinking mass spectrometry (crosslinking-MS) is a versatile tool providing structural insights into protein conformation and protein-protein interactions. Its medium-resolution residue-residue distance restraints have been used to validate protein structures proposed by other methods and have helped derive models of protein complexes by integrative structural biology approaches. The use of crosslinking-MS in integrative approaches is underpinned by progress in estimating error rates in crosslinking-MS data and in combining these data with other information. The flexible and high-throughput nature of crosslinking-MS has allowed it to complement the ongoing resolution revolution in electron microscopy by providing system-wide residue-residue distance restraints, especially for flexible regions or systems. Here, we review how crosslinking-MS information has been leveraged in structural model validation and integrative modeling. Crosslinking-MS has also been a key technology for cell biology studies and structural systems biology where, in conjunction with cryoelectron tomography, it can provide structural and mechanistic insights directly in situ.
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Affiliation(s)
- Andrea Graziadei
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany
| | - Juri Rappsilber
- Bioanalytics, Institute of Biotechnology, Technische Universität Berlin, 13355 Berlin, Germany; Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh EH9 3BF, UK.
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16
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Kaake RM, Echeverria I, Kim SJ, Von Dollen J, Chesarino NM, Feng Y, Yu C, Ta H, Chelico L, Huang L, Gross J, Sali A, Krogan NJ. Characterization of an A3G-Vif HIV-1-CRL5-CBFβ Structure Using a Cross-linking Mass Spectrometry Pipeline for Integrative Modeling of Host-Pathogen Complexes. Mol Cell Proteomics 2021; 20:100132. [PMID: 34389466 PMCID: PMC8459920 DOI: 10.1016/j.mcpro.2021.100132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 07/15/2021] [Accepted: 08/04/2021] [Indexed: 10/24/2022] Open
Abstract
Structural analysis of host-pathogen protein complexes remains challenging, largely due to their structural heterogeneity. Here, we describe a pipeline for the structural characterization of these complexes using integrative structure modeling based on chemical cross-links and residue-protein contacts inferred from mutagenesis studies. We used this approach on the HIV-1 Vif protein bound to restriction factor APOBEC3G (A3G), the Cullin-5 E3 ring ligase (CRL5), and the cellular transcription factor Core Binding Factor Beta (CBFβ) to determine the structure of the (A3G-Vif-CRL5-CBFβ) complex. Using the MS-cleavable DSSO cross-linker to obtain a set of 132 cross-links within this reconstituted complex along with the atomic structures of the subunits and mutagenesis data, we computed an integrative structure model of the heptameric A3G-Vif-CRL5-CBFβ complex. The structure, which was validated using a series of tests, reveals that A3G is bound to Vif mostly through its N-terminal domain. Moreover, the model ensemble quantifies the dynamic heterogeneity of the A3G C-terminal domain and Cul5 positions. Finally, the model was used to rationalize previous structural, mutagenesis and functional data not used for modeling, including information related to the A3G-bound and unbound structures as well as mapping functional mutations to the A3G-Vif interface. The experimental and computational approach described here is generally applicable to other challenging host-pathogen protein complexes.
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Affiliation(s)
- Robyn M Kaake
- Department of Cellular and Molecular Pharmacology, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, California, USA; Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, California, USA; Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, California, USA
| | - Ignacia Echeverria
- Department of Cellular and Molecular Pharmacology, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, California, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA
| | - Seung Joong Kim
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA
| | - John Von Dollen
- Department of Cellular and Molecular Pharmacology, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, California, USA; Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, California, USA
| | - Nicholas M Chesarino
- Divisions of Human Biology and Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Yuqing Feng
- Department of Biochemistry, Microbiology, Immunology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Clinton Yu
- Department of Physiology & Biophysics, University of California, Irvine, California, USA
| | - Hai Ta
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, USA
| | - Linda Chelico
- Department of Biochemistry, Microbiology, Immunology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Lan Huang
- Department of Physiology & Biophysics, University of California, Irvine, California, USA
| | - John Gross
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, California, USA; Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, USA
| | - Andrej Sali
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, California, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA; Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, USA.
| | - Nevan J Krogan
- Department of Cellular and Molecular Pharmacology, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, California, USA; Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, California, USA; Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, California, USA.
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17
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Appadurai R, Nagesh J, Srivastava A. High resolution ensemble description of metamorphic and intrinsically disordered proteins using an efficient hybrid parallel tempering scheme. Nat Commun 2021; 12:958. [PMID: 33574233 PMCID: PMC7878814 DOI: 10.1038/s41467-021-21105-7] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 01/08/2021] [Indexed: 12/26/2022] Open
Abstract
Mapping free energy landscapes of complex multi-funneled metamorphic proteins and weakly-funneled intrinsically disordered proteins (IDPs) remains challenging. While rare-event sampling molecular dynamics simulations can be useful, they often need to either impose restraints or reweigh the generated data to match experiments. Here, we present a parallel-tempering method that takes advantage of accelerated water dynamics and allows efficient and accurate conformational sampling across a wide variety of proteins. We demonstrate the improved sampling efficiency by benchmarking against standard model systems such as alanine di-peptide, TRP-cage and β-hairpin. The method successfully scales to large metamorphic proteins such as RFA-H and to highly disordered IDPs such as Histatin-5. Across the diverse proteins, the calculated ensemble averages match well with the NMR, SAXS and other biophysical experiments without the need to reweigh. By allowing accurate sampling across different landscapes, the method opens doors for sampling free energy landscape of complex uncharted proteins.
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Affiliation(s)
- Rajeswari Appadurai
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India
| | - Jayashree Nagesh
- Solid State & Structural Chemistry Unit, Indian Institute of Science, Bangalore, Karnataka, India
| | - Anand Srivastava
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India.
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18
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Hepburn M, Saltzberg DJ, Lee L, Fang S, Atkinson C, Strynadka NCJ, Sali A, Lees-Miller SP, Schriemer DC. The active DNA-PK holoenzyme occupies a tensed state in a staggered synaptic complex. Structure 2021; 29:467-478.e6. [PMID: 33412091 DOI: 10.1016/j.str.2020.12.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 11/14/2020] [Accepted: 12/09/2020] [Indexed: 01/06/2023]
Abstract
In the non-homologous end-joining (NHEJ) of a DNA double-strand break, DNA ends are bound and protected by DNA-PK, which synapses across the break to tether the broken ends and initiate repair. There is little clarity surrounding the nature of the synaptic complex and the mechanism governing the transition to repair. We report an integrative structure of the synaptic complex at a precision of 13.5 Å, revealing a symmetric head-to-head arrangement with a large offset in the DNA ends and an extensive end-protection mechanism involving a previously uncharacterized plug domain. Hydrogen/deuterium exchange mass spectrometry identifies an allosteric pathway connecting DNA end-binding with the kinase domain that places DNA-PK under tension in the kinase-active state. We present a model for the transition from end-protection to repair, where the synaptic complex supports hierarchical processing of the ends and scaffold assembly, requiring displacement of the catalytic subunit and tension release through kinase activity.
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Affiliation(s)
- Morgan Hepburn
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada
| | - Daniel J Saltzberg
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Sciences, and California Institute for Quantitative Biomedical Sciences, University of California, San Francisco, CA 94158, USA
| | - Linda Lee
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada
| | - Shujuan Fang
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada
| | - Claire Atkinson
- Department of Biochemistry and Molecular Biology and High-Resolution Macromolecular Electron Microscopy Facility, The University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
| | - Natalie C J Strynadka
- Department of Biochemistry and Molecular Biology and High-Resolution Macromolecular Electron Microscopy Facility, The University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Sciences, and California Institute for Quantitative Biomedical Sciences, University of California, San Francisco, CA 94158, USA
| | - Susan P Lees-Miller
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada; Robson DNA Science Centre, Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
| | - David C Schriemer
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada; Robson DNA Science Centre, Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada; Department of Chemistry, University of Calgary, Calgary, AB, Canada.
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19
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Sali A. From integrative structural biology to cell biology. J Biol Chem 2021; 296:100743. [PMID: 33957123 PMCID: PMC8203844 DOI: 10.1016/j.jbc.2021.100743] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 04/09/2021] [Accepted: 04/30/2021] [Indexed: 12/16/2022] Open
Abstract
Integrative modeling is an increasingly important tool in structural biology, providing structures by combining data from varied experimental methods and prior information. As a result, molecular architectures of large, heterogeneous, and dynamic systems, such as the ∼52-MDa Nuclear Pore Complex, can be mapped with useful accuracy, precision, and completeness. Key challenges in improving integrative modeling include expanding model representations, increasing the variety of input data and prior information, quantifying a match between input information and a model in a Bayesian fashion, inventing more efficient structural sampling, as well as developing better model validation, analysis, and visualization. In addition, two community-level challenges in integrative modeling are being addressed under the auspices of the Worldwide Protein Data Bank (wwPDB). First, the impact of integrative structures is maximized by PDB-Development, a prototype wwPDB repository for archiving, validating, visualizing, and disseminating integrative structures. Second, the scope of structural biology is expanded by linking the wwPDB resource for integrative structures with archives of data that have not been generally used for structure determination but are increasingly important for computing integrative structures, such as data from various types of mass spectrometry, spectroscopy, optical microscopy, proteomics, and genetics. To address the largest of modeling problems, a type of integrative modeling called metamodeling is being developed; metamodeling combines different types of input models as opposed to different types of data to compute an output model. Collectively, these developments will facilitate the structural biology mindset in cell biology and underpin spatiotemporal mapping of the entire cell.
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Affiliation(s)
- Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, the Department of Bioengineering and Therapeutic Sciences, the Quantitative Biosciences Institute (QBI), and the Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, USA.
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20
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Braberg H, Echeverria I, Bohn S, Cimermancic P, Shiver A, Alexander R, Xu J, Shales M, Dronamraju R, Jiang S, Dwivedi G, Bogdanoff D, Chaung KK, Hüttenhain R, Wang S, Mavor D, Pellarin R, Schneidman D, Bader JS, Fraser JS, Morris J, Haber JE, Strahl BD, Gross CA, Dai J, Boeke JD, Sali A, Krogan NJ. Genetic interaction mapping informs integrative structure determination of protein complexes. Science 2020; 370:eaaz4910. [PMID: 33303586 PMCID: PMC7946025 DOI: 10.1126/science.aaz4910] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 07/23/2020] [Accepted: 10/23/2020] [Indexed: 12/17/2022]
Abstract
Determining structures of protein complexes is crucial for understanding cellular functions. Here, we describe an integrative structure determination approach that relies on in vivo measurements of genetic interactions. We construct phenotypic profiles for point mutations crossed against gene deletions or exposed to environmental perturbations, followed by converting similarities between two profiles into an upper bound on the distance between the mutated residues. We determine the structure of the yeast histone H3-H4 complex based on ~500,000 genetic interactions of 350 mutants. We then apply the method to subunits Rpb1-Rpb2 of yeast RNA polymerase II and subunits RpoB-RpoC of bacterial RNA polymerase. The accuracy is comparable to that based on chemical cross-links; using restraints from both genetic interactions and cross-links further improves model accuracy and precision. The approach provides an efficient means to augment integrative structure determination with in vivo observations.
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Affiliation(s)
- Hannes Braberg
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Ignacia Echeverria
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Stefan Bohn
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Gladstone Institutes, San Francisco, CA 94158, USA
| | - Peter Cimermancic
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Anthony Shiver
- Graduate Group in Biophysics, University of California San Francisco, San Francisco, CA 94158, USA
| | - Richard Alexander
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Jiewei Xu
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Gladstone Institutes, San Francisco, CA 94158, USA
| | - Michael Shales
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Raghuvar Dronamraju
- Department of Biochemistry and Biophysics, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Shuangying Jiang
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Gajendradhar Dwivedi
- Department of Biology and Rosenstiel Basic Medical Sciences Research Center, Brandeis University, Waltham, MA 02454, USA
| | - Derek Bogdanoff
- Center for Advanced Technology, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Kaitlin K Chaung
- Center for Advanced Technology, Department of Biophysics and Biochemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Ruth Hüttenhain
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Gladstone Institutes, San Francisco, CA 94158, USA
| | - Shuyi Wang
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - David Mavor
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Riccardo Pellarin
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Dina Schneidman
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Joel S Bader
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - James S Fraser
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - John Morris
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - James E Haber
- Department of Biology and Rosenstiel Basic Medical Sciences Research Center, Brandeis University, Waltham, MA 02454, USA
| | - Brian D Strahl
- Department of Biochemistry and Biophysics, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Carol A Gross
- Department of Microbiology and Immunology and Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Junbiao Dai
- CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics and Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jef D Boeke
- NYU Langone Health, New York, NY 10016, USA.
- High Throughput Biology Center and Department of Molecular Biology & Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU Langone Health, New York, NY 10016, USA
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY 11201, USA
| | - Andrej Sali
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA.
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Nevan J Krogan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94158, USA
- Gladstone Institutes, San Francisco, CA 94158, USA
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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21
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Saltzberg DJ, Viswanath S, Echeverria I, Chemmama IE, Webb B, Sali A. Using Integrative Modeling Platform to compute, validate, and archive a model of a protein complex structure. Protein Sci 2020; 30:250-261. [PMID: 33166013 DOI: 10.1002/pro.3995] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 11/06/2020] [Accepted: 11/06/2020] [Indexed: 12/18/2022]
Abstract
Biology is advanced by producing structural models of biological systems, such as protein complexes. Some systems are recalcitrant to traditional structure determination methods. In such cases, it may still be possible to produce useful models by integrative structure determination that depends on simultaneous use of multiple types of data. An ensemble of models that are sufficiently consistent with the data is produced by a structural sampling method guided by a data-dependent scoring function. The variation in the ensemble of models quantified the uncertainty of the structure, generally resulting from the uncertainty in the input information and actual structural heterogeneity in the samples used to produce the data. Here, we describe how to generate, assess, and interpret ensembles of integrative structural models using our open source Integrative Modeling Platform program (https://integrativemodeling.org).
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Affiliation(s)
- Daniel J Saltzberg
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California, San Francisco, California, USA
| | - Shruthi Viswanath
- National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India
| | - Ignacia Echeverria
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California, San Francisco, California, USA.,Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA
| | - Ilan E Chemmama
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California, San Francisco, California, USA
| | - Ben Webb
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California, San Francisco, California, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California, San Francisco, California, USA
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22
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Medeiros Selegato D, Bracco C, Giannelli C, Parigi G, Luchinat C, Sgheri L, Ravera E. Comparison of Different Reweighting Approaches for the Calculation of Conformational Variability of Macromolecules from Molecular Simulations. Chemphyschem 2020; 22:127-138. [DOI: 10.1002/cphc.202000714] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 09/14/2020] [Indexed: 11/07/2022]
Affiliation(s)
- Denise Medeiros Selegato
- Magnetic Resonance Center (CERM) and Interuniversity Consortium for Magnetic Resonance of Metallo Proteins (CIRMMP) Via L. Sacconi 6 50019 Sesto Fiorentino Italy
- Dipartimento di Chimica “Ugo Schiff” Università degli Studi di Firenze Via della Lastruccia 3 50019 Sesto Fiorentino Italy
- Present address: Fundación MEDINA, Centro de Excelentia en Investigación de Medicamentos Innovadores and Andalucía MSD España Granada Spain
| | - Cesare Bracco
- Dipartimento di Matematica e Informatica “U. Dini” Università degli Studi di Firenze Viale Morgagni 67/a 50134 Florence Italy
| | - Carlotta Giannelli
- Dipartimento di Matematica e Informatica “U. Dini” Università degli Studi di Firenze Viale Morgagni 67/a 50134 Florence Italy
| | - Giacomo Parigi
- Magnetic Resonance Center (CERM) and Interuniversity Consortium for Magnetic Resonance of Metallo Proteins (CIRMMP) Via L. Sacconi 6 50019 Sesto Fiorentino Italy
- Dipartimento di Chimica “Ugo Schiff” Università degli Studi di Firenze Via della Lastruccia 3 50019 Sesto Fiorentino Italy
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM) and Interuniversity Consortium for Magnetic Resonance of Metallo Proteins (CIRMMP) Via L. Sacconi 6 50019 Sesto Fiorentino Italy
- Dipartimento di Chimica “Ugo Schiff” Università degli Studi di Firenze Via della Lastruccia 3 50019 Sesto Fiorentino Italy
| | - Luca Sgheri
- Istituto per le Applicazioni del Calcolo (CNR) sede di Firenze via Madonna del Piano 10 50019 Sesto Fiorentino Italy
| | - Enrico Ravera
- Magnetic Resonance Center (CERM) and Interuniversity Consortium for Magnetic Resonance of Metallo Proteins (CIRMMP) Via L. Sacconi 6 50019 Sesto Fiorentino Italy
- Dipartimento di Chimica “Ugo Schiff” Università degli Studi di Firenze Via della Lastruccia 3 50019 Sesto Fiorentino Italy
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23
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Kwon Y, Kaake RM, Echeverria I, Suarez M, Karimian Shamsabadi M, Stoneham C, Ramirez PW, Kress J, Singh R, Sali A, Krogan N, Guatelli J, Jia X. Structural basis of CD4 downregulation by HIV-1 Nef. Nat Struct Mol Biol 2020; 27:822-828. [PMID: 32719457 PMCID: PMC7483821 DOI: 10.1038/s41594-020-0463-z] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 06/16/2020] [Indexed: 02/06/2023]
Abstract
The HIV-1 Nef protein suppresses multiple immune surveillance mechanisms to promote viral pathogenesis and is an attractive target for the development of novel therapeutics. A key function of Nef is to remove the CD4 receptor from the cell surface by hijacking clathrin- and adaptor protein complex 2 (AP2)-dependent endocytosis. However, exactly how Nef does this has been elusive. Here, we describe the underlying mechanism as revealed by a 3.0-Å crystal structure of a fusion protein comprising Nef and the cytoplasmic domain of CD4 bound to the tetrameric AP2 complex. An intricate combination of conformational changes occurs in both Nef and AP2 to enable CD4 binding and downregulation. A pocket on Nef previously identified as crucial for recruiting class I MHC is also responsible for recruiting CD4, revealing a potential approach to inhibit two of Nef's activities and sensitize the virus to immune clearance.
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Affiliation(s)
- Yonghwa Kwon
- Department of Chemistry and Biochemistry, University of Massachusetts Dartmouth, Dartmouth, MA, USA
| | - Robyn M Kaake
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Gladstone Institutes, San Francisco, CA, USA
| | - Ignacia Echeverria
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
| | | | | | - Charlotte Stoneham
- The VA San Diego Healthcare System, San Diego, CA, USA
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Peter W Ramirez
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Jacob Kress
- Department of Chemistry and Biochemistry, University of Massachusetts Dartmouth, Dartmouth, MA, USA
| | - Rajendra Singh
- The VA San Diego Healthcare System, San Diego, CA, USA
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Pharmaceutical Chemistry and Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Nevan Krogan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
- Gladstone Institutes, San Francisco, CA, USA
| | - John Guatelli
- The VA San Diego Healthcare System, San Diego, CA, USA
- Department of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Xiaofei Jia
- Department of Chemistry and Biochemistry, University of Massachusetts Dartmouth, Dartmouth, MA, USA.
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24
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Allison TM, Barran P, Cianférani S, Degiacomi MT, Gabelica V, Grandori R, Marklund EG, Menneteau T, Migas LG, Politis A, Sharon M, Sobott F, Thalassinos K, Benesch JLP. Computational Strategies and Challenges for Using Native Ion Mobility Mass Spectrometry in Biophysics and Structural Biology. Anal Chem 2020; 92:10872-10880. [DOI: 10.1021/acs.analchem.9b05791] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Timothy M. Allison
- School of Physical and Chemical Sciences, Biomolecular Interaction Centre, University of Canterbury, Christchurch 8140, New Zealand
| | - Perdita Barran
- Michael Barber Centre for Collaborative Mass Spectrometry, Manchester Institute of Biotechnology, School of Chemistry, University of Manchester, Manchester M1 7DN, United Kingdom
| | - Sarah Cianférani
- Laboratoire de Spectrométrie de Masse BioOrganique (LSMBO), Université de Strasbourg, CNRS, IPHC UMR 7178, 67000 Strasbourg, France
| | - Matteo T. Degiacomi
- Department of Physics, Durham University, South Road, Durham, DH1 3LE, United Kingdom
| | - Valérie Gabelica
- University of Bordeaux, INSERM and CNRS, ARNA Laboratory, IECB site, 2 Rue Robert Escarpit, 33600 Pessac, France
| | - Rita Grandori
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, 20126, Milan, Italy
| | - Erik G. Marklund
- Department of Chemistry - BMC, Uppsala University, Box 576, 75123, Uppsala, Sweden
| | - Thomas Menneteau
- Division of Biosciences, Institute of Structural and Molecular Biology, University College of London, Gower Street, London WC1E 6BT, United Kingdom
| | - Lukasz G. Migas
- Michael Barber Centre for Collaborative Mass Spectrometry, Manchester Institute of Biotechnology, School of Chemistry, University of Manchester, Manchester M1 7DN, United Kingdom
| | - Argyris Politis
- Department of Chemistry, King’s College London, 7 Trinity Street, London SE1 1DB, United Kingdom
| | - Michal Sharon
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Frank Sobott
- Biomolecular & Analytical Mass Spectrometry, Department of Chemistry, University of Antwerp, 2020 Antwerp, Belgium
- School of Molecular and Cellular Biology, University of Leeds, Leeds LS2 9JT, United Kingdom
- Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds LS2 9JT, United Kingdom
| | - Konstantinos Thalassinos
- Department of Chemistry, King’s College London, 7 Trinity Street, London SE1 1DB, United Kingdom
- Department of Biological Sciences, Institute of Structural and Molecular Biology, Birkbeck, Malet Street, London WC1E 7HX, United Kingdom
| | - Justin L. P. Benesch
- Department of Chemistry, Chemistry Research Laboratory, University of Oxford, South Parks Road, Oxford OX1 3TA, United Kingdom
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25
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Ganesan SJ, Feyder MJ, Chemmama IE, Fang F, Rout MP, Chait BT, Shi Y, Munson M, Sali A. Integrative structure and function of the yeast exocyst complex. Protein Sci 2020; 29:1486-1501. [PMID: 32239688 DOI: 10.1002/pro.3863] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 03/25/2020] [Accepted: 03/27/2020] [Indexed: 12/13/2022]
Abstract
Exocyst is an evolutionarily conserved hetero-octameric tethering complex that plays a variety of roles in membrane trafficking, including exocytosis, endocytosis, autophagy, cell polarization, cytokinesis, pathogen invasion, and metastasis. Exocyst serves as a platform for interactions between the Rab, Rho, and Ral small GTPases, SNARE proteins, and Sec1/Munc18 regulators that coordinate spatial and temporal fidelity of membrane fusion. However, its mechanism is poorly described at the molecular level. Here, we determine the molecular architecture of the yeast exocyst complex by an integrative approach, based on a 3D density map from negative-stain electron microscopy (EM) at ~16 Å resolution, 434 disuccinimidyl suberate and 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride cross-links from chemical-crosslinking mass spectrometry, and partial atomic models of the eight subunits. The integrative structure is validated by a previously determined cryo-EM structure, cross-links, and distances from in vivo fluorescence microscopy. Our subunit configuration is consistent with the cryo-EM structure, except for Sec5. While not observed in the cryo-EM map, the integrative model localizes the N-terminal half of Sec3 near the Sec6 subunit. Limited proteolysis experiments suggest that the conformation of Exo70 is dynamic, which may have functional implications for SNARE and membrane interactions. This study illustrates how integrative modeling based on varied low-resolution structural data can inform biologically relevant hypotheses, even in the absence of high-resolution data.
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Affiliation(s)
- Sai J Ganesan
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA.,Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, USA
| | - Michael J Feyder
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Ilan E Chemmama
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA.,Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, USA
| | - Fei Fang
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Michael P Rout
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, New York, USA
| | - Brian T Chait
- Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, New York, USA
| | - Yi Shi
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Mary Munson
- Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA.,Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, USA
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26
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Rout MP, Sali A. Principles for Integrative Structural Biology Studies. Cell 2020; 177:1384-1403. [PMID: 31150619 DOI: 10.1016/j.cell.2019.05.016] [Citation(s) in RCA: 165] [Impact Index Per Article: 41.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 04/24/2019] [Accepted: 05/06/2019] [Indexed: 12/22/2022]
Abstract
Integrative structure determination is a powerful approach to modeling the structures of biological systems based on data produced by multiple experimental and theoretical methods, with implications for our understanding of cellular biology and drug discovery. This Primer introduces the theory and methods of integrative approaches, emphasizing the kinds of data that can be effectively included in developing models and using the nuclear pore complex as an example to illustrate the practice and challenges involved. These guidelines are intended to aid the researcher in understanding and applying integrative structural methods to systems of their interest and thus take advantage of this rapidly evolving field.
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Affiliation(s)
- Michael P Rout
- Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY 10065, USA.
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, Byers Hall, 1700 4th Street, Suite 503B, University of California, San Francisco, San Francisco, CA 94158, USA.
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27
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Löhr T, Camilloni C, Bonomi M, Vendruscolo M. A Practical Guide to the Simultaneous Determination of Protein Structure and Dynamics Using Metainference. Methods Mol Biol 2020; 2022:313-340. [PMID: 31396909 DOI: 10.1007/978-1-4939-9608-7_13] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Accurate protein structural ensembles can be determined with metainference, a Bayesian inference method that integrates experimental information with prior knowledge of the system and deals with all sources of uncertainty and errors as well as with system heterogeneity. Furthermore, metainference can be implemented using the metadynamics approach, which enables the computational study of complex biological systems requiring extensive conformational sampling. In this chapter, we provide a step-by-step guide to perform and analyse metadynamic metainference simulations using the ISDB module of the open-source PLUMED library, as well as a series of practical tips to avoid common mistakes. Specifically, we will guide the reader in the process of learning how to model the structural ensemble of a small disordered peptide by combining state-of-the-art molecular mechanics force fields with nuclear magnetic resonance data, including chemical shifts, scalar couplings and residual dipolar couplings.
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Affiliation(s)
- Thomas Löhr
- Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Carlo Camilloni
- Dipartimento di Bioscienze, Università degli Studi di Milano, Milano, Italy
| | - Massimiliano Bonomi
- Structural Bioinformatics Unit, Institut Pasteur, CNRS UMR 3528, Paris, France
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28
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Structural proteomics, electron cryo-microscopy and structural modeling approaches in bacteria-human protein interactions. Med Microbiol Immunol 2020; 209:265-275. [PMID: 32072248 PMCID: PMC7223518 DOI: 10.1007/s00430-020-00663-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 01/30/2020] [Indexed: 01/01/2023]
Abstract
A central challenge in infection medicine is to determine the structure and function of host-pathogen protein-protein interactions to understand how these interactions facilitate bacterial adhesion, dissemination and survival. In this review, we focus on proteomics, electron cryo-microscopy and structural modeling to showcase instances where affinity-purification (AP) and cross-linking (XL) mass spectrometry (MS) has advanced our understanding of host-pathogen interactions. We highlight cases where XL-MS in combination with structural modeling has provided insight into the quaternary structure of interspecies protein complexes. We further exemplify how electron cryo-tomography has been used to visualize bacterial-human interactions during attachment and infection. Lastly, we discuss how AP-MS, XL-MS and electron cryo-microscopy and -tomography together with structural modeling approaches can be used in future studies to broaden our knowledge regarding the function, dynamics and evolution of such interactions. This knowledge will be of relevance for future drug and vaccine development programs.
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29
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Structural dynamics of the human COP9 signalosome revealed by cross-linking mass spectrometry and integrative modeling. Proc Natl Acad Sci U S A 2020; 117:4088-4098. [PMID: 32034103 DOI: 10.1073/pnas.1915542117] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The COP9 signalosome (CSN) is an evolutionarily conserved eight-subunit (CSN1-8) protein complex that controls protein ubiquitination by deneddylating Cullin-RING E3 ligases (CRLs). The activation and function of CSN hinges on its structural dynamics, which has been challenging to decipher by conventional tools. Here, we have developed a multichemistry cross-linking mass spectrometry approach enabled by three mass spectometry-cleavable cross-linkers to generate highly reliable cross-link data. We applied this approach with integrative structure modeling to determine the interaction and structural dynamics of CSN with the recently discovered ninth subunit, CSN9, in solution. Our results determined the localization of CSN9 binding sites and revealed CSN9-dependent structural changes of CSN. Together with biochemical analysis, we propose a structural model in which CSN9 binding triggers CSN to adopt a configuration that facilitates CSN-CRL interactions, thereby augmenting CSN deneddylase activity. Our integrative structure analysis workflow can be generalized to define in-solution architectures of dynamic protein complexes that remain inaccessible to other approaches.
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30
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Geraets JA, Pothula KR, Schröder GF. Integrating cryo-EM and NMR data. Curr Opin Struct Biol 2020; 61:173-181. [PMID: 32028106 DOI: 10.1016/j.sbi.2020.01.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/13/2020] [Accepted: 01/14/2020] [Indexed: 01/06/2023]
Abstract
Single-particle cryo-electron microscopy (cryo-EM) is increasingly used as a technique to determine the atomic structure of challenging biological systems. Recent advances in microscope engineering, electron detection, and image processing have allowed the structural determination of bigger and more flexible targets than possible with the complementary techniques X-ray crystallography and NMR spectroscopy. However, there exist many biological targets for which atomic resolution cannot be currently achieved with cryo-EM, making unambiguous determination of the protein structure impossible. Although determining the structure of large biological systems using solely NMR is often difficult, highly complementary experimental atomic-level data for each molecule can be derived from the spectra, and used in combination with cryo-EM data. We review here strategies with which both techniques can be synergistically combined, in order to reach detail and understanding unattainable by each technique acting alone; and the types of biological systems for which such an approach would be desirable.
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Affiliation(s)
- James A Geraets
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry) and JuStruct, Jülich Center for Structural Biology, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Karunakar R Pothula
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry) and JuStruct, Jülich Center for Structural Biology, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Gunnar F Schröder
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry) and JuStruct, Jülich Center for Structural Biology, Forschungszentrum Jülich, 52425 Jülich, Germany; Physics Department, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany.
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31
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Abstract
Bayesian and Maximum Entropy approaches allow for a statistically sound and systematic fitting of experimental and computational data. Unfortunately, assessing the relative confidence in these two types of data remains difficult as several steps add unknown error. Here we propose the use of a validation-set method to determine the balance, and thus the amount of fitting. We apply the method to synthetic NMR chemical shift data of an intrinsically disordered protein. We show that the method gives consistent results even when other methods to assess the amount of fitting cannot be applied. Finally, we also describe how the errors in the chemical shift predictor can lead to an incorrect fitting and how using secondary chemical shifts could alleviate this problem.
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32
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Dülfer J, Kadek A, Kopicki JD, Krichel B, Uetrecht C. Structural mass spectrometry goes viral. Adv Virus Res 2019; 105:189-238. [PMID: 31522705 DOI: 10.1016/bs.aivir.2019.07.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Over the last 20 years, mass spectrometry (MS), with its ability to analyze small sample amounts with high speed and sensitivity, has more and more entered the field of structural virology, aiming to investigate the structure and dynamics of viral proteins as close to their native environment as possible. The use of non-perturbing labels in hydrogen-deuterium exchange MS allows for the analysis of interactions between viral proteins and host cell factors as well as their dynamic responses to the environment. Cross-linking MS, on the other hand, can analyze interactions in viral protein complexes and identify virus-host interactions in cells. Native MS allows transferring viral proteins, complexes and capsids into the gas phase and has broken boundaries to overcome size limitations, so that now even the analysis of intact virions is possible. Different MS approaches not only inform about size, stability, interactions and dynamics of virus assemblies, but also bridge the gap to other biophysical techniques, providing valuable constraints for integrative structural modeling of viral complex assemblies that are often inaccessible by single technique approaches. In this review, recent advances are highlighted, clearly showing that structural MS approaches in virology are moving towards systems biology and ever more experiments are performed on cellular level.
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Affiliation(s)
- Jasmin Dülfer
- Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany
| | - Alan Kadek
- Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany; European XFEL GmbH, Schenefeld, Germany
| | - Janine-Denise Kopicki
- Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany
| | - Boris Krichel
- Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany
| | - Charlotte Uetrecht
- Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germany; European XFEL GmbH, Schenefeld, Germany.
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33
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Bonomi M, Vendruscolo M. Determination of protein structural ensembles using cryo-electron microscopy. Curr Opin Struct Biol 2019; 56:37-45. [DOI: 10.1016/j.sbi.2018.10.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 10/24/2018] [Accepted: 10/26/2018] [Indexed: 10/27/2022]
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34
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Trnka MJ, Pellarin R, Robinson PJ. Role of integrative structural biology in understanding transcriptional initiation. Methods 2019; 159-160:4-22. [PMID: 30890443 PMCID: PMC6617507 DOI: 10.1016/j.ymeth.2019.03.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 03/14/2019] [Accepted: 03/15/2019] [Indexed: 12/12/2022] Open
Abstract
Integrative structural biology combines data from multiple experimental techniques to generate complete structural models for the biological system of interest. Most commonly cross-linking data sets are employed alongside electron microscopy maps, crystallographic structures, and other data by computational methods that integrate all known information and produce structural models at a level of resolution that is appropriate to the input data. The precision of these modelled solutions is limited by the sparseness of cross-links observed, the length of the cross-linking reagent, the ambiguity arisen from the presence of multiple copies of the same protein, and structural and compositional heterogeneity. In recent years integrative structural biology approaches have been successfully applied to a range of RNA polymerase II complexes. Here we will provide a general background to integrative structural biology, a description of how it should be practically implemented and how it has furthered our understanding of the biology of large transcriptional assemblies. Finally, in the context of recent breakthroughs in microscope and direct electron detector technology, where increasingly EM is capable of resolving structural features directly without the aid of other structural techniques, we will discuss the future role of integrative structural techniques.
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Affiliation(s)
- Michael J Trnka
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
| | - Riccardo Pellarin
- Institut Pasteur, Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, CNRS UMR 3528, C3BI USR 3756 CNRS & IP, Paris, France
| | - Philip J Robinson
- Department of Biological Sciences, Birkbeck University of London, Institute of Structural and Molecular Biology, London, United Kingdom.
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35
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Simultaneous Determination of Protein Structure and Dynamics Using Cryo-Electron Microscopy. Biophys J 2019; 114:1604-1613. [PMID: 29642030 PMCID: PMC5954442 DOI: 10.1016/j.bpj.2018.02.028] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Revised: 02/05/2018] [Accepted: 02/20/2018] [Indexed: 11/21/2022] Open
Abstract
Cryo-electron microscopy is rapidly emerging as a powerful technique to determine the structures of complex macromolecular systems elusive to other techniques. Because many of these systems are highly dynamical, characterizing their movements is also a crucial step to unravel their biological functions. To achieve this goal, we report an integrative modeling approach to simultaneously determine structure and dynamics of macromolecular systems from cryo-electron microscopy density maps. By quantifying the level of noise in the data and dealing with their ensemble-averaged nature, this approach enables the integration of multiple sources of information to model ensembles of structures and infer their populations. We illustrate the method by characterizing structure and dynamics of the integral membrane receptor STRA6, thus providing insights into the mechanisms by which it interacts with retinol binding protein and translocates retinol across the membrane.
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36
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Su X, Wang K, Liu N, Chen J, Li Y, Duan M. All‐atom structure ensembles of islet amyloid polypeptides determined by enhanced sampling and experiment data restraints. Proteins 2019; 87:541-550. [DOI: 10.1002/prot.25677] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 12/22/2018] [Accepted: 02/17/2019] [Indexed: 01/28/2023]
Affiliation(s)
- Xinyue Su
- CAS Key Laboratory of magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in WuhanWuhan Institute of Physics and Mathematics, Chinese Academy of Sciences Wuhan China
- Department of PhysicsCentral China Normal University Wuhan China
| | - Ke Wang
- CAS Key Laboratory of magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in WuhanWuhan Institute of Physics and Mathematics, Chinese Academy of Sciences Wuhan China
- Department of ChemistryUniversity of Chinese Academy of Sciences Beijing China
| | - Na Liu
- CAS Key Laboratory of magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in WuhanWuhan Institute of Physics and Mathematics, Chinese Academy of Sciences Wuhan China
- Department of ChemistryUniversity of Chinese Academy of Sciences Beijing China
| | - Jiawen Chen
- CAS Key Laboratory of magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in WuhanWuhan Institute of Physics and Mathematics, Chinese Academy of Sciences Wuhan China
| | - Yong Li
- Department of PhysicsCentral China Normal University Wuhan China
| | - Mojie Duan
- CAS Key Laboratory of magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in WuhanWuhan Institute of Physics and Mathematics, Chinese Academy of Sciences Wuhan China
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37
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Marklund EG, Benesch JL. Weighing-up protein dynamics: the combination of native mass spectrometry and molecular dynamics simulations. Curr Opin Struct Biol 2019; 54:50-58. [PMID: 30743182 DOI: 10.1016/j.sbi.2018.12.011] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 12/22/2018] [Accepted: 12/27/2018] [Indexed: 12/21/2022]
Abstract
Structural dynamics underpin biological function at the molecular level, yet many biophysical and structural biology approaches give only a static or averaged view of proteins. Native mass spectrometry yields spectra of the many states and interactions in the structural ensemble, but its spatial resolution is limited. Conversely, molecular dynamics simulations are innately high-resolution, but have a limited capacity for exploring all structural possibilities. The two techniques hence differ fundamentally in the information they provide, returning data that reflect different length scales and time scales, making them natural bedfellows. Here we discuss how the combination of native mass spectrometry with molecular dynamics simulations is enabling unprecedented insights into a range of biological questions by interrogating the motions of proteins, their assemblies, and interactions.
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Affiliation(s)
- Erik G Marklund
- Department of Chemistry - BMC, Uppsala University, Box 576, 75 123, Uppsala, Sweden.
| | - Justin Lp Benesch
- Department of Chemistry, Chemistry Research Laboratory, University of Oxford, Mansfield Road, Oxford, OX1 3TA, United Kingdom.
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38
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Optimizing model representation for integrative structure determination of macromolecular assemblies. Proc Natl Acad Sci U S A 2018; 116:540-545. [PMID: 30587581 DOI: 10.1073/pnas.1814649116] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Integrative structure determination of macromolecular assemblies requires specifying the representation of the modeled structure, a scoring function for ranking alternative models based on diverse types of data, and a sampling method for generating these models. Structures are often represented at atomic resolution, although ad hoc simplified representations based on generic guidelines and/or trial and error are also used. In contrast, we introduce here the concept of optimizing representation. To illustrate this concept, the optimal representation is selected from a set of candidate representations based on an objective criterion that depends on varying amounts of information available for different parts of the structure. Specifically, an optimal representation is defined as the highest-resolution representation for which sampling is exhaustive at a precision commensurate with the precision of the representation. Thus, the method does not require an input structure and is applicable to any input information. We consider a space of representations in which a representation is a set of nonoverlapping, variable-length segments (i.e., coarse-grained beads) for each component protein sequence. We also implement a method for efficiently finding an optimal representation in our open-source Integrative Modeling Platform (IMP) software (https://integrativemodeling.org/). The approach is illustrated by application to three complexes of two subunits and a large assembly of 10 subunits. The optimized representation facilitates exhaustive sampling and thus can produce a more accurate model and a more accurate estimate of its uncertainty for larger structures than were possible previously.
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39
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Rangan R, Bonomi M, Heller GT, Cesari A, Bussi G, Vendruscolo M. Determination of Structural Ensembles of Proteins: Restraining vs Reweighting. J Chem Theory Comput 2018; 14:6632-6641. [DOI: 10.1021/acs.jctc.8b00738] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ramya Rangan
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Massimiliano Bonomi
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Gabriella T. Heller
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Andrea Cesari
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), 34136 Trieste, Italy
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), 34136 Trieste, Italy
| | - Michele Vendruscolo
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
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40
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Bonomi M, Hanot S, Greenberg CH, Sali A, Nilges M, Vendruscolo M, Pellarin R. Bayesian Weighing of Electron Cryo-Microscopy Data for Integrative Structural Modeling. Structure 2018; 27:175-188.e6. [PMID: 30393052 DOI: 10.1016/j.str.2018.09.011] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 08/07/2018] [Accepted: 09/19/2018] [Indexed: 10/28/2022]
Abstract
Cryo-electron microscopy (cryo-EM) has become a mainstream technique for determining the structures of complex biological systems. However, accurate integrative structural modeling has been hampered by the challenges in objectively weighing cryo-EM data against other sources of information due to the presence of random and systematic errors, as well as correlations, in the data. To address these challenges, we introduce a Bayesian scoring function that efficiently and accurately ranks alternative structural models of a macromolecular system based on their consistency with a cryo-EM density map as well as other experimental and prior information. The accuracy of this approach is benchmarked using complexes of known structure and illustrated in three applications: the structural determination of the GroEL/GroES, RNA polymerase II, and exosome complexes. The approach is implemented in the open-source Integrative Modeling Platform (http://integrativemodeling.org), thus enabling integrative structure determination by combining cryo-EM data with other sources of information.
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Affiliation(s)
| | - Samuel Hanot
- Institut Pasteur, Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, CNRS UMR 3528, C3BI USR 3756 CNRS & IP, Paris, France
| | - Charles H Greenberg
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Sciences, and California Institute for Quantitative Biomedical Sciences, University of California, San Francisco, CA 94158, USA
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Sciences, and California Institute for Quantitative Biomedical Sciences, University of California, San Francisco, CA 94158, USA
| | - Michael Nilges
- Institut Pasteur, Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, CNRS UMR 3528, C3BI USR 3756 CNRS & IP, Paris, France
| | | | - Riccardo Pellarin
- Institut Pasteur, Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, CNRS UMR 3528, C3BI USR 3756 CNRS & IP, Paris, France.
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41
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Andrałojć W, Ravera E. Treating Biomacromolecular Conformational Variability. PARAMAGNETISM IN EXPERIMENTAL BIOMOLECULAR NMR 2018. [DOI: 10.1039/9781788013291-00107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The function of a biomacromolecule is related not only to its structure but also to the different conformations that its structural elements can sample. It is therefore important to determine the extent of the structural fluctuations and to identify the states that are actually populated as a result of the rearrangement. However, this accomplishment is undermined by an intrinsic limitation: the amount of experimental data is by and large inferior to the number of the states that a biomacromolecule can actually sample. This means that additional, a priori information must be applied in order to derive the most from the available experimental data but not to run into overinterpretation. In this chapter we will give a summary of the experimental observables that can be used towards the reconstruction of structural ensembles, how the data can be profitably combined and to what extent the data are affected by error; finally we will give an overview of the computational methods that have been developed to model structural ensembles, highlighting their difference and similarities, advantages and disadvantages.
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Affiliation(s)
- Witold Andrałojć
- Polish Academy of Sciences, Institute of Bioorganic Chemistry Noskowskiego 12/14 Poznan 61-704 Poland
| | - Enrico Ravera
- University of Florence, Department of Chemistry and Magnetic Resonance Center Via L. Sacconi 6 50019 Sesto Fiorentino (FI) Italy
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42
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Cassidy CK, Himes BA, Luthey-Schulten Z, Zhang P. CryoEM-based hybrid modeling approaches for structure determination. Curr Opin Microbiol 2018; 43:14-23. [PMID: 29107896 PMCID: PMC5934336 DOI: 10.1016/j.mib.2017.10.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 10/04/2017] [Accepted: 10/09/2017] [Indexed: 12/21/2022]
Abstract
Recent advances in cryo-electron microscopy (cryoEM) have dramatically improved the resolutions at which vitrified biological specimens can be studied, revealing new structural and mechanistic insights over a broad range of spatial scales. Bolstered by these advances, much effort has been directed toward the development of hybrid modeling methodologies for the construction and refinement of high-fidelity atomistic models from cryoEM data. In this brief review, we will survey the key elements of cryoEM-based hybrid modeling, providing an overview of available computational tools and strategies as well as several recent applications.
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Affiliation(s)
- C Keith Cassidy
- Department of Physics, Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Benjamin A Himes
- Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Zaida Luthey-Schulten
- Department of Chemistry, Center for the Physics of Living Cells, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Peijun Zhang
- Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Division of Structural Biology, Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK; Electron Bio-Imaging Centre, Diamond Light Sources, Harwell Science and Innovation Campus, Didcot OX11 0DE, UK.
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Bottaro S, Bussi G, Kennedy SD, Turner DH, Lindorff-Larsen K. Conformational ensembles of RNA oligonucleotides from integrating NMR and molecular simulations. SCIENCE ADVANCES 2018; 4:eaar8521. [PMID: 29795785 PMCID: PMC5959319 DOI: 10.1126/sciadv.aar8521] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2017] [Accepted: 04/05/2018] [Indexed: 05/08/2023]
Abstract
RNA molecules are key players in numerous cellular processes and are characterized by a complex relationship between structure, dynamics, and function. Despite their apparent simplicity, RNA oligonucleotides are very flexible molecules, and understanding their internal dynamics is particularly challenging using experimental data alone. We show how to reconstruct the conformational ensemble of four RNA tetranucleotides by combining atomistic molecular dynamics simulations with nuclear magnetic resonance spectroscopy data. The goal is achieved by reweighting simulations using a maximum entropy/Bayesian approach. In this way, we overcome problems of current simulation methods, as well as in interpreting ensemble- and time-averaged experimental data. We determine the populations of different conformational states by considering several nuclear magnetic resonance parameters and point toward properties that are not captured by state-of-the-art molecular force fields. Although our approach is applied on a set of model systems, it is fully general and may be used to study the conformational dynamics of flexible biomolecules and to detect inaccuracies in molecular dynamics force fields.
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Affiliation(s)
- Sandro Bottaro
- Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark
- Corresponding author. (S.B.); (K.L.-L.)
| | - Giovanni Bussi
- International School for Advanced Studies, Trieste, Italy
| | - Scott D. Kennedy
- Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, NY 14642, USA
| | - Douglas H. Turner
- Department of Chemistry, University of Rochester, Rochester, NY 14627, USA
| | - Kresten Lindorff-Larsen
- Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark
- Corresponding author. (S.B.); (K.L.-L.)
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44
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Interpreting solution X-ray scattering data using molecular simulations. Curr Opin Struct Biol 2018; 49:18-26. [DOI: 10.1016/j.sbi.2017.11.002] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Revised: 10/20/2017] [Accepted: 11/04/2017] [Indexed: 01/23/2023]
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45
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Singla J, McClary KM, White KL, Alber F, Sali A, Stevens RC. Opportunities and Challenges in Building a Spatiotemporal Multi-scale Model of the Human Pancreatic β Cell. Cell 2018; 173:11-19. [PMID: 29570991 PMCID: PMC6014618 DOI: 10.1016/j.cell.2018.03.014] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 11/25/2017] [Accepted: 03/06/2018] [Indexed: 12/25/2022]
Abstract
The construction of a predictive model of an entire eukaryotic cell that describes its dynamic structure from atomic to cellular scales is a grand challenge at the intersection of biology, chemistry, physics, and computer science. Having such a model will open new dimensions in biological research and accelerate healthcare advancements. Developing the necessary experimental and modeling methods presents abundant opportunities for a community effort to realize this goal. Here, we present a vision for creation of a spatiotemporal multi-scale model of the pancreatic β-cell, a relevant target for understanding and modulating the pathogenesis of diabetes.
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Affiliation(s)
- Jitin Singla
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Kyle M McClary
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Kate L White
- Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Frank Alber
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
| | - Andrej Sali
- California Institute for Quantitative Biosciences, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Raymond C Stevens
- Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
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Abstract
Despite the central role of Nuclear Pore Complexes (NPCs) as gatekeepers of RNA and protein transport between the cytoplasm and nucleoplasm, their large size and dynamic nature have impeded a full structural and functional elucidation. Here, we have determined a subnanometer precision structure for the entire 552-protein yeast NPC by satisfying diverse data including stoichiometry, a cryo-electron tomography map, and chemical cross-links. The structure reveals the NPC’s functional elements in unprecedented detail. The NPC is built of sturdy diagonal columns to which are attached connector cables, imbuing both strength and flexibility, while tying together all other elements of the NPC, including membrane-interacting regions and RNA processing platforms. Inwardly-directed anchors create a high density of transport factor-docking Phe-Gly repeats in the central channel, organized in distinct functional units. Taken together, this integrative structure allows us to rationalize the architecture, transport mechanism, and evolutionary origins of the NPC.
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47
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Joo K, Heo S, Joung I, Hong SH, Lee SJ, Lee J. Data-assisted protein structure modeling by global optimization in CASP12. Proteins 2018; 86 Suppl 1:240-246. [PMID: 29341255 DOI: 10.1002/prot.25457] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 12/29/2017] [Accepted: 01/08/2018] [Indexed: 12/26/2022]
Abstract
In CASP12, 2 types of data-assisted protein structure modeling were experimented. Either SAXS experimental data or cross-linking experimental data was provided for a selected number of CASP12 targets that the CASP12 predictor could utilize for better protein structure modeling. We devised 2 separate energy terms for SAXS data and cross-linking data to drive the model structures into more native-like structures that satisfied the given experimental data as much as possible. In CASP11, we successfully performed protein structure modeling using simulated sparse and ambiguously assigned NOE data and/or correct residue-residue contact information, where the only energy term that folded the protein into its native structure was the term which was originated from the given experimental data. However, the 2 types of experimental data provided in CASP12 were far from being sufficient enough to fold the target protein into its native structure because SAXS data provides only the overall shape of the molecule and the cross-linking contact information provides only very low-resolution distance information. For this reason, we combined the SAXS or cross-linking energy term with our regular modeling energy function that includes both the template energy term and the de novo energy terms. By optimizing the newly formulated energy function, we obtained protein models that fit better with provided SAXS data than the X-ray structure of the target. However, the improvement of the model relative to the 1 modeled without the SAXS data, was not significant. Consistent structural improvement was achieved by incorporating cross-linking data into the protein structure modeling.
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Affiliation(s)
- Keehyoung Joo
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, 02455, South Korea.,Center for Advanced Computation, Korea Institute for Advanced Study, Seoul, 02455, South Korea
| | - Seungryong Heo
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul, 02455, South Korea
| | - InSuk Joung
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, 02455, South Korea
| | - Seung Hwan Hong
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul, 02455, South Korea
| | - Sung Jong Lee
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, 02455, South Korea.,The Research Institute for Basic Sciences, Changwon National University, Changwon-Si, Gyeongsangnam-do, South Korea
| | - Jooyoung Lee
- Center for In Silico Protein Science, Korea Institute for Advanced Study, Seoul, 02455, South Korea.,Center for Advanced Computation, Korea Institute for Advanced Study, Seoul, 02455, South Korea.,School of Computational Sciences, Korea Institute for Advanced Study, Seoul, 02455, South Korea
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48
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Harrer N, Schindler CEM, Bruetzel LK, Forné I, Ludwigsen J, Imhof A, Zacharias M, Lipfert J, Mueller-Planitz F. Structural Architecture of the Nucleosome Remodeler ISWI Determined from Cross-Linking, Mass Spectrometry, SAXS, and Modeling. Structure 2018; 26:282-294.e6. [PMID: 29395785 DOI: 10.1016/j.str.2017.12.015] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Revised: 10/25/2017] [Accepted: 12/27/2017] [Indexed: 11/17/2022]
Abstract
Chromatin remodeling factors assume critical roles by regulating access to nucleosomal DNA. To determine the architecture of the Drosophila ISWI remodeling enzyme, we developed an integrative structural approach that combines protein cross-linking, mass spectrometry, small-angle X-ray scattering, and computational modeling. The resulting structural model shows the ATPase module in a resting state with both ATPase lobes twisted against each other, providing support for a conformation that was recently trapped by crystallography. The autoinhibiting NegC region does not protrude from the ATPase module as suggested previously. The regulatory NTR domain is located near both ATPase lobes. The full-length enzyme is flexible and can adopt a compact structure in solution with the C-terminal HSS domain packing against the ATPase module. Our data imply a series of conformational changes upon activation of the enzyme and illustrate how the NTR, NegC, and HSS domains contribute to regulation of the ATPase module.
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Affiliation(s)
- Nadine Harrer
- Molecular Biology, Biomedical Center, Faculty of Medicine, LMU Munich, 82152 Martinsried, Germany
| | - Christina E M Schindler
- Physics Department (T38), Technical University of Munich, 85748 Garching, Germany; Center for Integrated Protein Science Munich, 81377 Munich, Germany
| | - Linda K Bruetzel
- Department of Physics, Nanosystems Initiative Munich, and Center for Nanoscience, LMU Munich, 80799 Munich, Germany
| | - Ignasi Forné
- Molecular Biology, Biomedical Center, Faculty of Medicine, LMU Munich, 82152 Martinsried, Germany
| | - Johanna Ludwigsen
- Molecular Biology, Biomedical Center, Faculty of Medicine, LMU Munich, 82152 Martinsried, Germany
| | - Axel Imhof
- Molecular Biology, Biomedical Center, Faculty of Medicine, LMU Munich, 82152 Martinsried, Germany
| | - Martin Zacharias
- Physics Department (T38), Technical University of Munich, 85748 Garching, Germany; Center for Integrated Protein Science Munich, 81377 Munich, Germany
| | - Jan Lipfert
- Department of Physics, Nanosystems Initiative Munich, and Center for Nanoscience, LMU Munich, 80799 Munich, Germany.
| | - Felix Mueller-Planitz
- Molecular Biology, Biomedical Center, Faculty of Medicine, LMU Munich, 82152 Martinsried, Germany.
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49
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Assessing Exhaustiveness of Stochastic Sampling for Integrative Modeling of Macromolecular Structures. Biophys J 2018; 113:2344-2353. [PMID: 29211988 DOI: 10.1016/j.bpj.2017.10.005] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 09/22/2017] [Accepted: 10/02/2017] [Indexed: 12/22/2022] Open
Abstract
Modeling of macromolecular structures involves structural sampling guided by a scoring function, resulting in an ensemble of good-scoring models. By necessity, the sampling is often stochastic, and must be exhaustive at a precision sufficient for accurate modeling and assessment of model uncertainty. Therefore, the very first step in analyzing the ensemble is an estimation of the highest precision at which the sampling is exhaustive. Here, we present an objective and automated method for this task. As a proxy for sampling exhaustiveness, we evaluate whether two independently and stochastically generated sets of models are sufficiently similar. The protocol includes testing 1) convergence of the model score, 2) whether model scores for the two samples were drawn from the same parent distribution, 3) whether each structural cluster includes models from each sample proportionally to its size, and 4) whether there is sufficient structural similarity between the two model samples in each cluster. The evaluation also provides the sampling precision, defined as the smallest clustering threshold that satisfies the third, most stringent test. We validate the protocol with the aid of enumerated good-scoring models for five illustrative cases of binary protein complexes. Passing the proposed four tests is necessary, but not sufficient for thorough sampling. The protocol is general in nature and can be applied to the stochastic sampling of any set of models, not just structural models. In addition, the tests can be used to stop stochastic sampling as soon as exhaustiveness at desired precision is reached, thereby improving sampling efficiency; they may also help in selecting a model representation that is sufficiently detailed to be informative, yet also sufficiently coarse for sampling to be exhaustive.
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50
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Trewhella J. Small Angle Scattering and Structural Biology: Data Quality and Model Validation. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1105:77-100. [PMID: 30617825 DOI: 10.1007/978-981-13-2200-6_7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
This chapter provides a brief review of the current state-of-the-art in small-angle scattering (SAS) from biomolecules in solution in regard to: (1) sample preparation and instrumentation, (2) data reduction and analysis, and (3) three-dimensional structural modelling and validation. In this context, areas of ongoing research in regard to the interpretation of SAS data will be discussed with a particular focus on structural modelling using computational methods and data from different experimental techniques, including SAS (hybrid methods). Finally, progress made in establishing community accepted publication guidelines and a standard reporting framework that includes SAS data deposition in a public data bank will be described. Importantly, SAS data with associated meta-data can now be held in a format that supports exchange between data archives and seamless interoperability with the world-wide Protein Data Bank (wwPDB). Biomolecular SAS is thus well positioned to contribute to an envisioned federation of data archives in support of hybrid structural biology.
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Affiliation(s)
- Jill Trewhella
- School of Life and Environmental Sciences, The University of Sydney, NSW, Australia. .,Department of Chemistry, University of Utah, Salt Lake City, UT, USA.
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