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A method for probing the mutational landscape of amyloid structure.

O'Donnell CW, Waldispühl J, Lis M, Halfmann R, Devadas S, Lindquist S, Berger B - Bioinformatics (2011)

Bottom Line: Based on the premise of protein mutational landscapes, AmyloidMutants energetically quantifies the effects of sequence mutation on fibril conformation and stability.Predictions on mutant, yeast-toxic strains of HET-s suggest similar alternate folds.We confirm this finding by conducting mutagenesis experiments.

View Article: PubMed Central - PubMed

Affiliation: Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA.

ABSTRACT

Motivation: Proteins of all kinds can self-assemble into highly ordered β-sheet aggregates known as amyloid fibrils, important both biologically and clinically. However, the specific molecular structure of a fibril can vary dramatically depending on sequence and environmental conditions, and mutations can drastically alter amyloid function and pathogenicity. Experimental structure determination has proven extremely difficult with only a handful of NMR-based models proposed, suggesting a need for computational methods.

Results: We present AmyloidMutants, a statistical mechanics approach for de novo prediction and analysis of wild-type and mutant amyloid structures. Based on the premise of protein mutational landscapes, AmyloidMutants energetically quantifies the effects of sequence mutation on fibril conformation and stability. Tested on non-mutant, full-length amyloid structures with known chemical shift data, AmyloidMutants offers roughly 2-fold improvement in prediction accuracy over existing tools. Moreover, AmyloidMutants is the only method to predict complete super-secondary structures, enabling accurate discrimination of topologically dissimilar amyloid conformations that correspond to the same sequence locations. Applied to mutant prediction, AmyloidMutants identifies a global conformational switch between Aβ and its highly-toxic 'Iowa' mutant in agreement with a recent experimental model based on partial chemical shift data. Predictions on mutant, yeast-toxic strains of HET-s suggest similar alternate folds. When applied to HET-s and a HET-s mutant with core asparagines replaced by glutamines (both highly amyloidogenic chemically similar residues abundant in many amyloids), AmyloidMutants surprisingly predicts a greatly reduced capacity of the glutamine mutant to form amyloid. We confirm this finding by conducting mutagenesis experiments.

Availability: Our tool is publically available on the web at http://amyloid.csail.mit.edu/.

Contact: lindquist_admin@wi.mit.edu; bab@csail.mit.edu.

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Related in: MedlinePlus

Amyloid fibril schemas used for analysis. Amyloid fibril schemas, diagrammed from side and top perspectives. Red indicates a single fibril peptide flanked by two gray adjacent peptides along the fibril axis. (a) Schema 𝒫, a 2-sheet β-solenoid with unrestricted number of rungs per peptide and parallel intra- and interchain interactions. (b) Schema 𝒜, identical to 𝒫 except with antiparallel interchain interactions. (c) Schema 𝒮, a serpentine cross-β structure with unrestricted number of packed intrachain β-sheets. All β-strand hydrogen bonds formed interchain.
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Figure 1: Amyloid fibril schemas used for analysis. Amyloid fibril schemas, diagrammed from side and top perspectives. Red indicates a single fibril peptide flanked by two gray adjacent peptides along the fibril axis. (a) Schema 𝒫, a 2-sheet β-solenoid with unrestricted number of rungs per peptide and parallel intra- and interchain interactions. (b) Schema 𝒜, identical to 𝒫 except with antiparallel interchain interactions. (c) Schema 𝒮, a serpentine cross-β structure with unrestricted number of packed intrachain β-sheets. All β-strand hydrogen bonds formed interchain.

Mentions: We introduce ‘schemas’ as an algorithmic construct to solve this by partitioning fibrillar from non-fibrillar conformations, enforcing steric consistency and enabling energetic calculations over all amyloid fibril sequence/structure states. For efficiency and usability purposes, putative amyloid fibril states are separated into three largely distinct topology families: schemas 𝒫, 𝒜 and 𝒮, which to our knowledge, together subsume the variation found in most published experimental and hypothetical amyloid fibril structure models (Fig. 1). These schemas also account for sequence variation through a simple user specification of the mutational possibilities that should be explored: e.g. ‘all Val can mutate to Ala, Leu, or Ile’. For example, schema 𝒫 and 𝒜 describes an abstract ‘β-solenoid” encompassing millions of structures with unique residue/residue interactions and varying numbers of β-strands, β-rungs, β-sheet width, coil location, residue orientation and residue packing neighbors (for example, HET-s 𝒜 predictions in Section 4 calculate the energy of ~4 billions states). Specific 2-, 3- and 4-sheet β-helix-like structures are accounted for by the introduction of ‘kinks’ (Fig. 2). Similarly, schema 𝒮 represents millions of possible full-length peptide ‘serpentine’ conformations, putatively containing multiple steric zipper interfaces.Fig. 1.


A method for probing the mutational landscape of amyloid structure.

O'Donnell CW, Waldispühl J, Lis M, Halfmann R, Devadas S, Lindquist S, Berger B - Bioinformatics (2011)

Amyloid fibril schemas used for analysis. Amyloid fibril schemas, diagrammed from side and top perspectives. Red indicates a single fibril peptide flanked by two gray adjacent peptides along the fibril axis. (a) Schema 𝒫, a 2-sheet β-solenoid with unrestricted number of rungs per peptide and parallel intra- and interchain interactions. (b) Schema 𝒜, identical to 𝒫 except with antiparallel interchain interactions. (c) Schema 𝒮, a serpentine cross-β structure with unrestricted number of packed intrachain β-sheets. All β-strand hydrogen bonds formed interchain.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117379&req=5

Figure 1: Amyloid fibril schemas used for analysis. Amyloid fibril schemas, diagrammed from side and top perspectives. Red indicates a single fibril peptide flanked by two gray adjacent peptides along the fibril axis. (a) Schema 𝒫, a 2-sheet β-solenoid with unrestricted number of rungs per peptide and parallel intra- and interchain interactions. (b) Schema 𝒜, identical to 𝒫 except with antiparallel interchain interactions. (c) Schema 𝒮, a serpentine cross-β structure with unrestricted number of packed intrachain β-sheets. All β-strand hydrogen bonds formed interchain.
Mentions: We introduce ‘schemas’ as an algorithmic construct to solve this by partitioning fibrillar from non-fibrillar conformations, enforcing steric consistency and enabling energetic calculations over all amyloid fibril sequence/structure states. For efficiency and usability purposes, putative amyloid fibril states are separated into three largely distinct topology families: schemas 𝒫, 𝒜 and 𝒮, which to our knowledge, together subsume the variation found in most published experimental and hypothetical amyloid fibril structure models (Fig. 1). These schemas also account for sequence variation through a simple user specification of the mutational possibilities that should be explored: e.g. ‘all Val can mutate to Ala, Leu, or Ile’. For example, schema 𝒫 and 𝒜 describes an abstract ‘β-solenoid” encompassing millions of structures with unique residue/residue interactions and varying numbers of β-strands, β-rungs, β-sheet width, coil location, residue orientation and residue packing neighbors (for example, HET-s 𝒜 predictions in Section 4 calculate the energy of ~4 billions states). Specific 2-, 3- and 4-sheet β-helix-like structures are accounted for by the introduction of ‘kinks’ (Fig. 2). Similarly, schema 𝒮 represents millions of possible full-length peptide ‘serpentine’ conformations, putatively containing multiple steric zipper interfaces.Fig. 1.

Bottom Line: Based on the premise of protein mutational landscapes, AmyloidMutants energetically quantifies the effects of sequence mutation on fibril conformation and stability.Predictions on mutant, yeast-toxic strains of HET-s suggest similar alternate folds.We confirm this finding by conducting mutagenesis experiments.

View Article: PubMed Central - PubMed

Affiliation: Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139, USA.

ABSTRACT

Motivation: Proteins of all kinds can self-assemble into highly ordered β-sheet aggregates known as amyloid fibrils, important both biologically and clinically. However, the specific molecular structure of a fibril can vary dramatically depending on sequence and environmental conditions, and mutations can drastically alter amyloid function and pathogenicity. Experimental structure determination has proven extremely difficult with only a handful of NMR-based models proposed, suggesting a need for computational methods.

Results: We present AmyloidMutants, a statistical mechanics approach for de novo prediction and analysis of wild-type and mutant amyloid structures. Based on the premise of protein mutational landscapes, AmyloidMutants energetically quantifies the effects of sequence mutation on fibril conformation and stability. Tested on non-mutant, full-length amyloid structures with known chemical shift data, AmyloidMutants offers roughly 2-fold improvement in prediction accuracy over existing tools. Moreover, AmyloidMutants is the only method to predict complete super-secondary structures, enabling accurate discrimination of topologically dissimilar amyloid conformations that correspond to the same sequence locations. Applied to mutant prediction, AmyloidMutants identifies a global conformational switch between Aβ and its highly-toxic 'Iowa' mutant in agreement with a recent experimental model based on partial chemical shift data. Predictions on mutant, yeast-toxic strains of HET-s suggest similar alternate folds. When applied to HET-s and a HET-s mutant with core asparagines replaced by glutamines (both highly amyloidogenic chemically similar residues abundant in many amyloids), AmyloidMutants surprisingly predicts a greatly reduced capacity of the glutamine mutant to form amyloid. We confirm this finding by conducting mutagenesis experiments.

Availability: Our tool is publically available on the web at http://amyloid.csail.mit.edu/.

Contact: lindquist_admin@wi.mit.edu; bab@csail.mit.edu.

Show MeSH
Related in: MedlinePlus