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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.

Show MeSH

Related in: MedlinePlus

AmyloidMutants structure predictions match experimentally observed β-strand interactions of Aβ1-42 (a), HET-s (b), amylin (c), α-synuclein (d) and tau (e). (a) Diagram depicts Aβ1-42 β-strand in gray, residues in blue (with in/out orientation) and β-sheet/β-sheet packing as one β-strand above another, packed residues facing center. Predicted structure (green arrows) mirrors NMR structure (Lührs et al., 2005) (black arrows), including most packing orientations. Predicted kink occurs because schema does not account for known D23/K28 salt bridge. (b) Similar depiction of HET-s prediction (top, green arrows) compared with NMR model (Wasmer et al., 2008) (bottom, black arrows) shows near identical match, including residue orientations and kink location. (c) Top two amylin predictions (solid, striped green arrows) align well to NMR model (Luca et al., 2007) (black arrows). Predictions differ only by their inclusion of Phe23 (*) within β-sheet, a residue experimentally shown to form non-β-sheet interpeptide interactions not considered by schema. (d) Top two α-synuclein predictions (i,ii) agree very well with H/D exchange data (iii,iv) and NMR model (v) (Heise et al., 2005; Vilar et al., 2007). (e) Tau predictions identify 7/8 β-regions observed experimentally (Mukrasch et al., 2009). The highest AmyloidMutants scores (red boxes) specifically identify regions 274–279 and 305–310, positions believed crucial to fibril nucleation (von Bergan et al., 2000).
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Figure 4: AmyloidMutants structure predictions match experimentally observed β-strand interactions of Aβ1-42 (a), HET-s (b), amylin (c), α-synuclein (d) and tau (e). (a) Diagram depicts Aβ1-42 β-strand in gray, residues in blue (with in/out orientation) and β-sheet/β-sheet packing as one β-strand above another, packed residues facing center. Predicted structure (green arrows) mirrors NMR structure (Lührs et al., 2005) (black arrows), including most packing orientations. Predicted kink occurs because schema does not account for known D23/K28 salt bridge. (b) Similar depiction of HET-s prediction (top, green arrows) compared with NMR model (Wasmer et al., 2008) (bottom, black arrows) shows near identical match, including residue orientations and kink location. (c) Top two amylin predictions (solid, striped green arrows) align well to NMR model (Luca et al., 2007) (black arrows). Predictions differ only by their inclusion of Phe23 (*) within β-sheet, a residue experimentally shown to form non-β-sheet interpeptide interactions not considered by schema. (d) Top two α-synuclein predictions (i,ii) agree very well with H/D exchange data (iii,iv) and NMR model (v) (Heise et al., 2005; Vilar et al., 2007). (e) Tau predictions identify 7/8 β-regions observed experimentally (Mukrasch et al., 2009). The highest AmyloidMutants scores (red boxes) specifically identify regions 274–279 and 305–310, positions believed crucial to fibril nucleation (von Bergan et al., 2000).

Mentions: For these five proteins, AmyloidMutants correctly identifies experimentally observed β-sheet regions in 21 of 22 cases (Fig. 4, Table 1)—a per-residue secondary-structure classification sensitivity/specificity of 82%/95% and an average SOV score of 82 (Zelma et al., 1999). Using the same comparison, the best of the available full-length amyloid prediction tools (Bryan et al., 2009; Fernandez-Escamilla et al., 2004; Maurer-Stroh et al., 2010; Tartaglia and Vendruscolo et al., 2008; Trovato et al., 2007) produced a classification sensitivity/specificity of 42%/90% (Zyggregator) (Fig. 3). Per-residue β-sheet classification is used for this comparison since it can be inferred as a common output of all tools; however, AmyloidMutants can provide more rich predictions including super-secondary residue/residue interactions. Therefore, a detailed analysis of each protein's predictions is given to demonstrate these added benefits, along with a demonstration of how ensemble predictions can help identify alternate fibril conformations in agreement with published experimental data.Fig. 3.


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)

AmyloidMutants structure predictions match experimentally observed β-strand interactions of Aβ1-42 (a), HET-s (b), amylin (c), α-synuclein (d) and tau (e). (a) Diagram depicts Aβ1-42 β-strand in gray, residues in blue (with in/out orientation) and β-sheet/β-sheet packing as one β-strand above another, packed residues facing center. Predicted structure (green arrows) mirrors NMR structure (Lührs et al., 2005) (black arrows), including most packing orientations. Predicted kink occurs because schema does not account for known D23/K28 salt bridge. (b) Similar depiction of HET-s prediction (top, green arrows) compared with NMR model (Wasmer et al., 2008) (bottom, black arrows) shows near identical match, including residue orientations and kink location. (c) Top two amylin predictions (solid, striped green arrows) align well to NMR model (Luca et al., 2007) (black arrows). Predictions differ only by their inclusion of Phe23 (*) within β-sheet, a residue experimentally shown to form non-β-sheet interpeptide interactions not considered by schema. (d) Top two α-synuclein predictions (i,ii) agree very well with H/D exchange data (iii,iv) and NMR model (v) (Heise et al., 2005; Vilar et al., 2007). (e) Tau predictions identify 7/8 β-regions observed experimentally (Mukrasch et al., 2009). The highest AmyloidMutants scores (red boxes) specifically identify regions 274–279 and 305–310, positions believed crucial to fibril nucleation (von Bergan et al., 2000).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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Figure 4: AmyloidMutants structure predictions match experimentally observed β-strand interactions of Aβ1-42 (a), HET-s (b), amylin (c), α-synuclein (d) and tau (e). (a) Diagram depicts Aβ1-42 β-strand in gray, residues in blue (with in/out orientation) and β-sheet/β-sheet packing as one β-strand above another, packed residues facing center. Predicted structure (green arrows) mirrors NMR structure (Lührs et al., 2005) (black arrows), including most packing orientations. Predicted kink occurs because schema does not account for known D23/K28 salt bridge. (b) Similar depiction of HET-s prediction (top, green arrows) compared with NMR model (Wasmer et al., 2008) (bottom, black arrows) shows near identical match, including residue orientations and kink location. (c) Top two amylin predictions (solid, striped green arrows) align well to NMR model (Luca et al., 2007) (black arrows). Predictions differ only by their inclusion of Phe23 (*) within β-sheet, a residue experimentally shown to form non-β-sheet interpeptide interactions not considered by schema. (d) Top two α-synuclein predictions (i,ii) agree very well with H/D exchange data (iii,iv) and NMR model (v) (Heise et al., 2005; Vilar et al., 2007). (e) Tau predictions identify 7/8 β-regions observed experimentally (Mukrasch et al., 2009). The highest AmyloidMutants scores (red boxes) specifically identify regions 274–279 and 305–310, positions believed crucial to fibril nucleation (von Bergan et al., 2000).
Mentions: For these five proteins, AmyloidMutants correctly identifies experimentally observed β-sheet regions in 21 of 22 cases (Fig. 4, Table 1)—a per-residue secondary-structure classification sensitivity/specificity of 82%/95% and an average SOV score of 82 (Zelma et al., 1999). Using the same comparison, the best of the available full-length amyloid prediction tools (Bryan et al., 2009; Fernandez-Escamilla et al., 2004; Maurer-Stroh et al., 2010; Tartaglia and Vendruscolo et al., 2008; Trovato et al., 2007) produced a classification sensitivity/specificity of 42%/90% (Zyggregator) (Fig. 3). Per-residue β-sheet classification is used for this comparison since it can be inferred as a common output of all tools; however, AmyloidMutants can provide more rich predictions including super-secondary residue/residue interactions. Therefore, a detailed analysis of each protein's predictions is given to demonstrate these added benefits, along with a demonstration of how ensemble predictions can help identify alternate fibril conformations in agreement with published experimental data.Fig. 3.

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