Literature DB >> 28949136

Iterative Molecular Dynamics-Rosetta Membrane Protein Structure Refinement Guided by Cryo-EM Densities.

Sumudu P Leelananda1, Steffen Lindert1.   

Abstract

Knowing atomistic details of proteins is essential not only for the understanding of protein function but also for the development of drugs. Experimental methods such as X-ray crystallography, NMR, and cryo-electron microscopy (cryo-EM) are the preferred forms of protein structure determination and have achieved great success over the most recent decades. Computational methods may be an alternative when experimental techniques fail. However, computational methods are severely limited when it comes to predicting larger macromolecule structures with little sequence similarity to known structures. The incorporation of experimental restraints in computational methods is becoming increasingly important to more reliably predict protein structure. One such experimental input used in structure prediction and refinement is cryo-EM densities. Recent advances in cryo-EM have arguably revolutionized the field of structural biology. Our previously developed cryo-EM-guided Rosetta-MD protocol has shown great promise in the refinement of soluble protein structures. In this study, we extended cryo-EM density-guided iterative Rosetta-MD to membrane proteins. We also improved the methodology in general by picking models based on a combination of their score and fit-to-density during the Rosetta model selection. By doing so, we have been able to pick models superior to those with the previous selection based on Rosetta score only and we have been able to further improve our previously refined models of soluble proteins. The method was tested with five membrane spanning protein structures. By applying density-guided Rosetta-MD iteratively we were able to refine the predicted structures of these membrane proteins to atomic resolutions. We also showed that the resolution of the density maps determines the improvement and quality of the refined models. By incorporating high-resolution density maps (∼4 Å), we were able to more significantly improve the quality of the models than when medium-resolution maps (6.9 Å) were used. Beginning from an average starting structure root mean square deviation (RMSD) to native of 4.66 Å, our protocol was able to refine the structures to bring the average refined structure RMSD to 1.66 Å when 4 Å density maps were used. The protocol also successfully refined the HIV-1 CTD guided by an experimental 5 Å density map.

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Year:  2017        PMID: 28949136      PMCID: PMC5642286          DOI: 10.1021/acs.jctc.7b00464

Source DB:  PubMed          Journal:  J Chem Theory Comput        ISSN: 1549-9618            Impact factor:   6.006


  90 in total

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Authors:  Helen M Berman; Gerard J Kleywegt; Haruki Nakamura; John L Markley
Journal:  Structure       Date:  2012-03-07       Impact factor: 5.006

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Journal:  Nature       Date:  2015-09-10       Impact factor: 49.962

4.  Toward high-resolution de novo structure prediction for small proteins.

Authors:  Philip Bradley; Kira M S Misura; David Baker
Journal:  Science       Date:  2005-09-16       Impact factor: 47.728

5.  Multipass membrane protein structure prediction using Rosetta.

Authors:  Vladimir Yarov-Yarovoy; Jack Schonbrun; David Baker
Journal:  Proteins       Date:  2006-03-01

6.  De novo high-resolution protein structure determination from sparse spin-labeling EPR data.

Authors:  Nathan Alexander; Marco Bortolus; Ahmad Al-Mestarihi; Hassane Mchaourab; Jens Meiler
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7.  Modeling of proteins and their assemblies with the integrative modeling platform.

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Journal:  Methods Mol Biol       Date:  2011

8.  Determining protein structures by combining semireliable data with atomistic physical models by Bayesian inference.

Authors:  Justin L MacCallum; Alberto Perez; Ken A Dill
Journal:  Proc Natl Acad Sci U S A       Date:  2015-05-18       Impact factor: 11.205

9.  BCL::EM-Fit: rigid body fitting of atomic structures into density maps using geometric hashing and real space refinement.

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Review 10.  Single-Particle Cryo-EM at Crystallographic Resolution.

Authors:  Yifan Cheng
Journal:  Cell       Date:  2015-04-23       Impact factor: 41.582

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  17 in total

1.  Using NMR Chemical Shifts and Cryo-EM Density Restraints in Iterative Rosetta-MD Protein Structure Refinement.

Authors:  Sumudu P Leelananda; Steffen Lindert
Journal:  J Chem Inf Model       Date:  2019-12-24       Impact factor: 4.956

2.  Cryo_fit: Democratization of flexible fitting for cryo-EM.

Authors:  Doo Nam Kim; Nigel W Moriarty; Serdal Kirmizialtin; Pavel V Afonine; Billy Poon; Oleg V Sobolev; Paul D Adams; Karissa Sanbonmatsu
Journal:  J Struct Biol       Date:  2019-07-03       Impact factor: 2.867

Review 3.  Hybrid methods for combined experimental and computational determination of protein structure.

Authors:  Justin T Seffernick; Steffen Lindert
Journal:  J Chem Phys       Date:  2020-12-28       Impact factor: 3.488

4.  Improving the Efficiency of Ligand-Binding Protein Design with Molecular Dynamics Simulations.

Authors:  Emilia P Barros; Jamie M Schiffer; Anastassia Vorobieva; Jiayi Dou; David Baker; Rommie E Amaro
Journal:  J Chem Theory Comput       Date:  2019-09-10       Impact factor: 6.006

5.  Practical Considerations for Atomistic Structure Modeling with Cryo-EM Maps.

Authors:  Doo Nam Kim; Dominik Gront; Karissa Y Sanbonmatsu
Journal:  J Chem Inf Model       Date:  2020-05-18       Impact factor: 4.956

6.  Accurately Predicting Disordered Regions of Proteins Using Rosetta ResidueDisorder Application.

Authors:  Stephanie S Kim; Justin T Seffernick; Steffen Lindert
Journal:  J Phys Chem B       Date:  2018-03-29       Impact factor: 2.991

7.  Rosetta Protein Structure Prediction from Hydroxyl Radical Protein Footprinting Mass Spectrometry Data.

Authors:  Melanie L Aprahamian; Emily E Chea; Lisa M Jones; Steffen Lindert
Journal:  Anal Chem       Date:  2018-06-06       Impact factor: 6.986

8.  Structure refinement of membrane proteins via molecular dynamics simulations.

Authors:  Bercem Dutagaci; Lim Heo; Michael Feig
Journal:  Proteins       Date:  2018-05-06

9.  Measuring Intrinsic Disorder and Tracking Conformational Transitions Using Rosetta ResidueDisorder.

Authors:  Justin T Seffernick; He Ren; Stephanie S Kim; Steffen Lindert
Journal:  J Phys Chem B       Date:  2019-08-14       Impact factor: 2.991

Review 10.  In Silico Modeling of the α7 Nicotinic Acetylcholine Receptor: New Pharmacological Challenges Associated with Multiple Modes of Signaling.

Authors:  Alican Gulsevin; Roger L Papke; Nicole Horenstein
Journal:  Mini Rev Med Chem       Date:  2020       Impact factor: 3.862

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