<i>Flexible-meccano:</i> a tool for the generation of explicit ensemble descriptions of intrinsically disordered proteins and their associated experimental observables

Ozenne, Valéry; Bauer, Frédéric; Salmon, Loïc; Huang, Jie Rong; Jensen, Malene Ringkjøbing; Segard, Stéphane; Bernadó, Pau; Charavay, Céline; Blackledge, Martin

doi:10.1093/bioinformatics/bts172

Cited by 338 publications

(404 citation statements)

References 57 publications

Supporting

Mentioning

398

Contrasting

Order By: Relevance

“…The computational advantage of this approach stems from the fact that structures are typically constructed by independently sampling individual residue phi-psi conformations for each residue in the protein. In this regard the potentials used are much simpler than molecular dynamics potentials and usually seek to reproduce coarse-grained behaviors, such as empirical backbone dihedral angle distributions for each residue from the Protein Data Bank (PDB) [83][84][85]. Like molecular dynamics potentials, however, the empirical potentials used in statistical coil-based approaches are usually trained on conformational propensities of natively folded proteins; e.g., the phi-psi angles of residues designated as coil (e.g., regions not in strand or helical conformations) in the PDB.…”

Section: Computational Methods For Describing Idp Ensemblesmentioning

confidence: 99%

“…Like molecular dynamics potentials, however, the empirical potentials used in statistical coil-based approaches are usually trained on conformational propensities of natively folded proteins; e.g., the phi-psi angles of residues designated as coil (e.g., regions not in strand or helical conformations) in the PDB. User-defined restraints can be included, such as done with the Flexible Mecanno tool [84], to adapt the potential to the particular protein in question.…”

Section: Computational Methods For Describing Idp Ensemblesmentioning

confidence: 99%

See 1 more Smart Citation

Intrinsically Disordered Proteins: Where Computation Meets Experiment

2014

View full text Add to dashboard Cite

Proteins are heteropolymers that play important roles in virtually every biological reaction. While many proteins have well-defined three-dimensional structures that are inextricably coupled to their function, intrinsically disordered proteins (IDPs) do not have a well-defined structure, and it is this lack of structure that facilitates their function. As many IDPs are involved in essential cellular processes, various diseases have been linked to their malfunction, thereby making them important drug targets. In this review we discuss methods for studying IDPs and provide examples of how computational methods can improve our understanding of IDPs. We focus on two intensely studied IDPs that have been implicated in very different pathologic pathways. The first, p53, has been linked to over 50% of human cancers, and the second, Amyloid-β (Aβ), forms neurotoxic aggregates in the brains of patients with Alzheimer's disease. We use these representative proteins to illustrate some of the challenges associated with studying IDPs and demonstrate how computational tools can be fruitfully applied to arrive at a more comprehensive understanding of these fascinating heteropolymers. OPEN ACCESSPolymers 2014, 6 2685

show abstract

Section: Computational Methods For Describing Idp Ensemblesmentioning

confidence: 99%

Section: Computational Methods For Describing Idp Ensemblesmentioning

confidence: 99%

Intrinsically Disordered Proteins: Where Computation Meets Experiment

2014

View full text Add to dashboard Cite

show abstract

“…This procedure assumes, in the absence of available crystallographic structures of the highly charged mutants, an identical, rigid core structure (b-barrel fold) for all models. A pool of 10,000 conformers with flexible tail conformations were generated for each of the three GFP mutants using an ensemble generation algorithm flexiblemeccano (53,59). Full details are provided in the Supporting Material.…”

Section: Generation Of Gfp Models and Ensemble Structuresmentioning

confidence: 99%

“…Care was taken to ensure sample quality in solution, especially with respect to monodispersity and ideality, which are paramount aspects of accurate SAXS/SANS data interpretation in terms of form factors of individual particles (50,51). Full-length GFP models including two flexible tails of a total of 14 residues (10 at the C-and 4 at the N-terminus (52)) were generated using the statistical coil algorithm flexible-meccano (53), and SAXS data were subsequently analyzed by several programs: CRYSOL (35), FOXS (39), and GENFIT (32). CRYSON (30) and GENFIT (32) were used to perform the SANS data analyses.…”

Section: Introductionmentioning

confidence: 99%

SAXS/SANS on Supercharged Proteins Reveals Residue-Specific Modifications of the Hydration Shell

Kim

Martel

Girard

et al. 2016

Biophysical Journal

View full text Add to dashboard Cite

Water molecules in the immediate vicinity of biomacromolecules, including proteins, constitute a hydration layer characterized by physicochemical properties different from those of bulk water and play a vital role in the activity and stability of these structures, as well as in intermolecular interactions. Previous studies using solution scattering, crystallography, and molecular dynamics simulations have provided valuable information about the properties of these hydration shells, including modifications in density and ionic concentration. Small-angle scattering of x-rays (SAXS) and neutrons (SANS) are particularly useful and complementary techniques to study biomacromolecular hydration shells due to their sensitivity to electronic and nuclear scattering-length density fluctuations, respectively. Although several sophisticated SAXS/SANS programs have been developed recently, the impact of physicochemical surface properties on the hydration layer remains controversial, and systematic experimental data from individual biomacromolecular systems are scarce. Here, we address the impact of physicochemical surface properties on the hydration shell by a systematic SAXS/SANS study using three mutants of a single protein, green fluorescent protein (GFP), with highly variable net charge (þ36, À6, and À29). The combined analysis of our data shows that the hydration shell is locally denser in the vicinity of acidic surface residues, whereas basic and hydrophilic/hydrophobic residues only mildly modify its density. Moreover, the data demonstrate that the density modifications result from the combined effect of residue-specific recruitment of ions from the bulk in combination with water structural rearrangements in their vicinity. Finally, we find that the specific surface-charge distributions of the different GFP mutants modulate the conformational space of flexible parts of the protein.

show abstract

“…Here, we assume that the structures generated by the MD simulation can represent the diverse conformational space of the IDP. In principle, other softwares, such as Flexible-Meccano (Ozenne et al, 2012) and TraDES Hogue, 2000, 2002), can also be used to increase the efficiency of conformational sampling.…”

Section: Constructing the Initial Structure Poolmentioning

confidence: 99%

Constructing Structure Ensembles of Intrinsically Disordered Proteins from Chemical Shift Data

Gong

Zhang

Wang³

et al. 2015

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Modeling the structural ensemble of intrinsically disordered proteins (IDPs), which lack fixed structures, is essential in understanding their cellular functions and revealing their regulation mechanisms in signaling pathways of related diseases (e.g., cancers and neurodegenerative disorders). Though the ensemble concept has been widely believed to be the most accurate way to depict 3D structures of IDPs, few of the traditional ensemble-based approaches effectively address the degeneracy problem that occurs when multiple solutions are consistent with experimental data and is the main challenge in the IDP ensemble construction task. In this article, based on a predefined conformational library, we formalize the structure ensemble construction problem into a least squares framework, which provides the optimal solution when the data constraints outnumber unknown variables. To deal with the degeneracy problem, we further propose a regularized regression approach based on the elastic net technique with the assumption that the weights to be estimated for individual structures in the ensemble are sparse. We have validated our methods through a reference ensemble approach as well as by testing the real biological data of three proteins, including alpha-synuclein, the translocation domain of Colocin N, and the K18 domain of Tau protein.

show abstract

Flexible-meccano: a tool for the generation of explicit ensemble descriptions of intrinsically disordered proteins and their associated experimental observables

Abstract: martin.blackledge@ibs.fr.

Cited by 338 publications

References 57 publications

Intrinsically Disordered Proteins: Where Computation Meets Experiment

Intrinsically Disordered Proteins: Where Computation Meets Experiment

SAXS/SANS on Supercharged Proteins Reveals Residue-Specific Modifications of the Hydration Shell

Constructing Structure Ensembles of Intrinsically Disordered Proteins from Chemical Shift Data

Contact Info

Product

Resources

About