Literature DB >> 31411026

Measuring Intrinsic Disorder and Tracking Conformational Transitions Using Rosetta ResidueDisorder.

Justin T Seffernick1, He Ren2, Stephanie S Kim1, Steffen Lindert1.   

Abstract

Many proteins contain regions of intrinsic disorder, not folding into unique, stable conformations. Numerous experimental methods have been developed to measure the disorder of all or select residues. In the absence of experimental data, computational methods are often utilized to identify these disordered regions and thus gain a better understanding of both structure and function. Many freely available computational methods have been developed to predict regions of intrinsic disorder from the primary sequence of a protein, including our recently developed Rosetta ResidueDisorder. While these methods are very useful, they are only designed to predict intrinsic disorder from the sequence. However, it would be useful to have a method that could also measure intrinsic disorder directly from structure. Such a method might also be used to identify changes in the structure of systems that can transition from folded to unfolded or vice versa, even systems that are not intrinsically disordered. Here we extended the capabilities of Rosetta ResidueDisorder to measure the intrinsic disorder from the coordinates of a single conformation of a protein. As a proof of principle, we show that ResidueDisorder can measure the intrinsic disorder from the coordinates with a higher accuracy (69.2%) than when predicted from sequence (65.4%) using a benchmark set of 229 proteins, showing that intrinsic disorder can be measured accurately from single structures over a large range of intrinsic disorder (0-100%). Additionally, we used ResidueDisorder to analyze unfolding trajectories of 12 fast-folding, nonintrinsically disordered proteins generated using molecular dynamics (MD), specifically steered MD (SMD), high-temperature MD, and accelerated MD (aMD) as well as long-time scale folding/unfolding trajectories. Using ResidueDisorder, a clear correlation between RMSD with respect to the native structure and measured fraction of denatured residues was observed. Finally, we introduced methods to predict folding/unfolding transitions as well as a native-like structure in the absence of a crystal structure from folding/unfolding MD trajectories. Rosetta ResidueDisorder is available as an application in the Rosetta software suite with the addition of new capabilities for directly identifying denatured regions and predicting events.

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Year:  2019        PMID: 31411026      PMCID: PMC6748046          DOI: 10.1021/acs.jpcb.9b04333

Source DB:  PubMed          Journal:  J Phys Chem B        ISSN: 1520-5207            Impact factor:   2.991


  44 in total

1.  Ab initio protein structure prediction of CASP III targets using ROSETTA.

Authors:  K T Simons; R Bonneau; I Ruczinski; D Baker
Journal:  Proteins       Date:  1999

2.  MaxSub: an automated measure for the assessment of protein structure prediction quality.

Authors:  N Siew; A Elofsson; L Rychlewski; D Fischer
Journal:  Bioinformatics       Date:  2000-09       Impact factor: 6.937

3.  Steered molecular dynamics investigations of protein function.

Authors:  B Isralewitz; J Baudry; J Gullingsrud; D Kosztin; K Schulten
Journal:  J Mol Graph Model       Date:  2001       Impact factor: 2.518

4.  Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules.

Authors:  Donald Hamelberg; John Mongan; J Andrew McCammon
Journal:  J Chem Phys       Date:  2004-06-22       Impact factor: 3.488

5.  The DISOPRED server for the prediction of protein disorder.

Authors:  Jonathan J Ward; Liam J McGuffin; Kevin Bryson; Bernard F Buxton; David T Jones
Journal:  Bioinformatics       Date:  2004-03-25       Impact factor: 6.937

6.  Scalable molecular dynamics with NAMD.

Authors:  James C Phillips; Rosemary Braun; Wei Wang; James Gumbart; Emad Tajkhorshid; Elizabeth Villa; Christophe Chipot; Robert D Skeel; Laxmikant Kalé; Klaus Schulten
Journal:  J Comput Chem       Date:  2005-12       Impact factor: 3.376

7.  IUPred: web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content.

Authors:  Zsuzsanna Dosztányi; Veronika Csizmok; Peter Tompa; István Simon
Journal:  Bioinformatics       Date:  2005-06-14       Impact factor: 6.937

8.  ROSETTALIGAND: protein-small molecule docking with full side-chain flexibility.

Authors:  Jens Meiler; David Baker
Journal:  Proteins       Date:  2006-11-15

9.  PrDOS: prediction of disordered protein regions from amino acid sequence.

Authors:  Takashi Ishida; Kengo Kinoshita
Journal:  Nucleic Acids Res       Date:  2007-06-12       Impact factor: 16.971

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  4 in total

1.  A Unified De Novo Approach for Predicting the Structures of Ordered and Disordered Proteins.

Authors:  John J Ferrie; E James Petersson
Journal:  J Phys Chem B       Date:  2020-06-11       Impact factor: 2.991

2.  Predicting Protein Conformational Disorder and Disordered Binding Sites.

Authors:  Ketty C Tamburrini; Giulia Pesce; Juliet Nilsson; Frank Gondelaud; Andrey V Kajava; Jean-Guy Berrin; Sonia Longhi
Journal:  Methods Mol Biol       Date:  2022

Review 3.  Computational Structure Prediction for Antibody-Antigen Complexes From Hydrogen-Deuterium Exchange Mass Spectrometry: Challenges and Outlook.

Authors:  Minh H Tran; Clara T Schoeder; Kevin L Schey; Jens Meiler
Journal:  Front Immunol       Date:  2022-05-26       Impact factor: 8.786

4.  Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction.

Authors:  S M Bargeen Alam Turzo; Justin T Seffernick; Amber D Rolland; Micah T Donor; Sten Heinze; James S Prell; Vicki H Wysocki; Steffen Lindert
Journal:  Nat Commun       Date:  2022-07-28       Impact factor: 17.694

  4 in total

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