scholarly journals Rosetta Protein Structure Prediction from Hydroxyl Radical Protein Footprinting Mass Spectrometry Data

2018 ◽  
Vol 90 (12) ◽  
pp. 7721-7729 ◽  
Author(s):  
Melanie L. Aprahamian ◽  
Emily E. Chea ◽  
Lisa M. Jones ◽  
Steffen Lindert
Keyword(s):  
Mass Spectrometry ◽  
Protein Structure ◽  
Hydroxyl Radical ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Mass Spectrometry Data ◽  
Protein Footprinting ◽  
Hydroxyl Radical Protein Footprinting

scholarly journals Accurate protein structure prediction with hydroxyl radical protein footprinting data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sarah E. Biehn ◽  
Steffen Lindert
Keyword(s):  
Protein Structure ◽  
Hydroxyl Radical ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Solvent Exposure ◽  
Protein Footprinting ◽  
Hydroxyl Radical Protein Footprinting ◽  
Predict Protein Structure ◽  
Ab Initio Models

AbstractHydroxyl radical protein footprinting (HRPF) in combination with mass spectrometry reveals the relative solvent exposure of labeled residues within a protein, thereby providing insight into protein tertiary structure. HRPF labels nineteen residues with varying degrees of reliability and reactivity. Here, we are presenting a dynamics-driven HRPF-guided algorithm for protein structure prediction. In a benchmark test of our algorithm, usage of the dynamics data in a score term resulted in notable improvement of the root-mean-square deviations of the lowest-scoring ab initio models and improved the funnel-like metric Pnear for all benchmark proteins. We identified models with accurate atomic detail for three of the four benchmark proteins. This work suggests that HRPF data along with side chain dynamics sampled by a Rosetta mover ensemble can be used to accurately predict protein structure.

Protein Structure Prediction with Mass Spectrometry Data

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Sarah E. Biehn ◽  
Steffen Lindert
Keyword(s):  
Mass Spectrometry ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Limited Proteolysis ◽  
Structural Features ◽  
Mass Spectrometry Data ◽  
Computational Techniques ◽  
Annual Review ◽  
Publication Date

Knowledge of protein structure is crucial to our understanding of biological function and is routinely used in drug discovery. High-resolution techniques to determine the three-dimensional atomic coordinates of proteins are available. However, such methods are frequently limited by experimental challenges such as sample quantity, target size, and efficiency. Structural mass spectrometry (MS) is a technique in which structural features of proteins are elucidated quickly and relatively easily. Computational techniques that convert sparse MS data into protein models that demonstrate agreement with the data are needed. This review features cutting-edge computational methods that predict protein structure from MS data such as chemical cross-linking, hydrogen–deuterium exchange, hydroxyl radical protein footprinting, limited proteolysis, ion mobility, and surface-induced dissociation. Additionally, we address future directions for protein structure prediction with sparse MS data. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 73 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Validated Determination of NRG1 Ig-like Domain Structure by Mass Spectrometry Coupled with Computational Modeling

2021 ◽  
Author(s):  
Niloofar Abolhasani Khaje ◽  
Alexander Eletsky ◽  
Sarah E. Biehn ◽  
Charles K. Mobley ◽  
Monique J. Rogals ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Structure ◽  
Computational Modeling ◽  
Solvent Exposure ◽  
Uncharacterized Protein ◽  
Single Experiment ◽  
Protein Footprinting ◽  
Hydroxyl Radical Protein Footprinting ◽  
Unknown Structure

High resolution hydroxyl radical protein footprinting (HR-HRPF) is a mass spectrometry-based method that measures the solvent exposure of multiple amino acids in a single experiment, offering constraints for experimentally-informed computational modeling. HR-HRPF-based modeling has previously been used to accurately model the structure of proteins of known structure, but the technique has never been used to determine the structure of a protein of unknown structure leaving questions of unintentional bias and applicability to unknown structures unresolved. Here, we present the use of HR-HRPF-based modeling to determine the structure of the Ig-like domain of NRG1, a protein with no close homolog of known structure. Independent determination of the protein structure by both HR-HRPF-based modeling and heteronuclear NMR was carried out, with results compared only after both processes were complete. The HR-HRPF-based model was highly similar to the lowest energy NMR model, with a backbone RMSD of 1.6 Å. To our knowledge, this is the first use of HR-HRPF-based modeling to determine a previously uncharacterized protein structure.

High-Resolution Hydroxyl Radical Protein Footprinting: Biophysics Tool for Drug Discovery

2018 ◽  
Vol 47 (1) ◽  
pp. 315-333 ◽  
Author(s):  
Janna Kiselar ◽  
Mark R. Chance
Keyword(s):  
Protein Structure ◽  
High Resolution ◽  
Hydroxyl Radical ◽  
Solvent Accessibility ◽  
Side Chains ◽  
Protein Footprinting ◽  
Hydroxyl Radical Protein Footprinting ◽  
Wide Range ◽  
Hydroxyl Radical Footprinting

Hydroxyl radical footprinting (HRF) of proteins with mass spectrometry (MS) is a widespread approach for assessing protein structure. Hydroxyl radicals react with a wide variety of protein side chains, and the ease with which radicals can be generated (by radiolysis or photolysis) has made the approach popular with many laboratories. As some side chains are less reactive and thus cannot be probed, additional specific and nonspecific labeling reagents have been introduced to extend the approach. At the same time, advances in liquid chromatography and MS approaches permit an examination of the labeling of individual residues, transforming the approach to high resolution. Lastly, advances in understanding of the chemistry of the approach have led to the determination of absolute protein topologies from HRF data. Overall, the technology can provide precise and accurate measures of side-chain solvent accessibility in a wide range of interesting and useful contexts for the study of protein structure and dynamics in both academia and industry.

scholarly journals Blind testing cross-linking/mass spectrometry under the auspices of the 11th critical assessment of methods of protein structure prediction (CASP11)

2016 ◽  
Vol 1 ◽  
pp. 24 ◽  
Author(s):  
Adam Belsom ◽  
Michael Schneider ◽  
Lutz Fischer ◽  
Mahmoud Mabrouk ◽  
Kolja Stahl ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Structure ◽  
Crystal Structures ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Hybrid Approach ◽  
Critical Assessment ◽  
Cross Linking ◽  
Blind Test ◽  
Contact Data

Determining the structure of a protein by any method requires various contributions from experimental and computational sides. In a recent study, high-density cross-linking/mass spectrometry (HD-CLMS) data in combination with ab initio structure prediction determined the structure of human serum albumin (HSA) domains, with an RMSD to X-ray structure of up to 2.5 Å, or 3.4 Å in the context of blood serum. This paper reports the blind test on the readiness of this technology through the help of Critical Assessment of protein Structure Prediction (CASP). We identified between 201-381 unique residue pairs at an estimated 5% FDR (at link level albeit with missing site assignment precision evaluation), for four target proteins. HD-CLMS proved reliable once crystal structures were released. However, improvements in structure prediction using cross-link data were slight. We identified two reasons for this. Spread of cross-links along the protein sequence and the tightness of the spatial constraints must be improved. However, for the selected targets even ideal contact data derived from crystal structures did not allow modellers to arrive at the observed structure. Consequently, the progress of HD-CLMS in conjunction with computational modeling methods as a structure determination method, depends on advances on both arms of this hybrid approach.

scholarly journals Blind testing cross-linking/mass spectrometry under the auspices of the 11th critical assessment of methods of protein structure prediction (CASP11)

2016 ◽  
Author(s):  
Adam Belsom ◽  
Michael Schneider ◽  
Lutz Fischer ◽  
Oliver Brock ◽  
Juri Rappsilber
Keyword(s):  
Mass Spectrometry ◽  
Protein Structure ◽  
Human Serum Albumin ◽  
Serum Albumin ◽  
Human Serum ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Critical Assessment ◽  
Conformational Space ◽  
Cross Linking

SummaryDetermining the structure of a protein by any method requires varies contributions from experimental and computational sides. In a recent study, high-density cross-linking/mass spectrometry data in combination with ab initio structure prediction by conformational space search determined the structure of human serum albumin (HSA) domains, with an RMSD to X-ray structure of up to 2.53 Å, or 3.38 Å in the context of blood serum. This paper reports the blind test on the readiness of this technology through the help of Critical Assessment of protein Structure Prediction (CASP). We identified between 201-381 unique residue pairs at an estimated 5% FDR (at link level albeit with missing site assignment precision evaluation), for the four proteins that we provided data for. This equates to between 0.63-1.20 proximal residues per residue, which is comparable to that obtained in the HSA study (0.85 links per residue at 5% FDR). Nevertheless, initial results of CASP11 have suggested that improvements in structure prediction using cross-link data are slight. Most significantly, however, CASP11 revealed to us some of the current limitations of cross-linking, spelling out areas in which the method must develop in future: links spread unevenly over sequence and beta sheets both lacked links and suffered from weak definition of observed links over structure. With CASP12 taking place this year and biannually in the future, blind testing low-resolution structure analysis tools is a worthwhile and feasible undertaking. Data are available via ProteomeXchange with identifier PXD003643.The abbreviations used areCLMScross-linking/mass spectrometry;NHSN-hydroxysuccinimide;NMRnuclear magnetic resonance;sulfo-SDAsulfo-NHSdiazirine, sulfosuccinimidyl 4,4’-azipentanoate;FDRfalse discovery rate;MBSmodel-based search;HSAhuman serum albumin;RMSDroot-mean-square deviation;CASPCritical Assessment of protein Structure Prediction;Tristris(hydroxymethyl)aminomethane;PESpolyethersulphone;IAAiodoacetamide;LTQlinear trap quadrupole;MS2tandem MS scan;LC-MSliquid chromatography mass spectrometry;FMfree modelling.

Sequence Specific Dihedral Angle Distribution: Application in Protein Structure Prediction and Evaluation

1970 ◽  
Vol 19 (2) ◽  
pp. 217-226
Author(s):  
S. M. Minhaz Ud-Dean ◽  
Mahdi Muhammad Moosa
Keyword(s):  
Protein Structure ◽  
Dihedral Angle ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Protein Structures ◽  
Angle Distribution ◽  
Ramachandran Plot ◽  
Specific Data ◽  
Specific Distribution ◽  
Structure Evaluation

Protein structure prediction and evaluation is one of the major fields of computational biology. Estimation of dihedral angle can provide information about the acceptability of both theoretically predicted and experimentally determined structures. Here we report on the sequence specific dihedral angle distribution of high resolution protein structures available in PDB and have developed Sasichandran, a tool for sequence specific dihedral angle prediction and structure evaluation. This tool will allow evaluation of a protein structure in pdb format from the sequence specific distribution of Ramachandran angles. Additionally, it will allow retrieval of the most probable Ramachandran angles for a given sequence along with the sequence specific data. Key words: Torsion angle, φ-ψ distribution, sequence specific ramachandran plot, Ramasekharan, protein structure appraisal D.O.I. 10.3329/ptcb.v19i2.5439 Plant Tissue Cult. & Biotech. 19(2): 217-226, 2009 (December)

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