Accuracy of ΔΔG prediction on a per protein basis after leave-one-protein-out cross-validation for the 24 proteins with more than 10 mutants available based on the standard error of prediction.

<p>Proteins are arranged left to right in order from the low to high mean experimental ΔΔG value. The mean standard error across the set increases from 1.11 kcal/mol to 1.33 kcal/mol if the tested protein is left out during training.</p

An illustration of the interface residue types onto the surface shown from the growth hormone-receptor complex structure (PDB ID: 1A22).

<p>The monomer structure of one of the chains is shown on top with the complex structure on bottom. ‘Core’ residues (blue) are exposed in the monomeric structure but buried in the complex; ‘Support’ residues (green) are partly buried in the monomeric structure and fully buried in the complex; ‘Rim’ residues (orange) are fully exposed in the monomeric structure and partly buried in the complex; ‘Interior’ residues (sky blue) are fully buried in the monomer, while surface residues (red) are fully exposed in both the monomeric and complex structures.</p

Breakdown of the performance of the interface profile score compared to other potentials for different types of interface residues.

<p>See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004494#pcbi.1004494.g006" target="_blank">Fig 6</a> for the definition of the interface residue types.</p

Pipeline of BindProf for predicting protein-binding affinity using features derived from interface structural profiles, wild type (WT) and mutant sequences, and physics based scoring of the structures of the WT and mutant complexes.

<p>(<b>1</b>) Interface profile scores and Interface profile scores features are derived by profile scoring structural alignment of structurally similar interface using an interface similarity cutoff to define the aligned sequences that are used to build the profile. (<b>2</b>) Physics based scores are formed at the residue or atomic level formed by modeling the mutant monomeric protein and complex and evaluating the difference in energy. (<b>3</b>) Sequence features are formed by the difference between the WT and mutant sequences in the number of hydrophobic (V, I, L, M, F, W, or C), aromatic (Y, F, or W), charged (R, K, D, or E), hydrogen bond acceptors (D, E, N, H, Q, S, T, or Y), and hydrogen bond donating residues (R, K, W, N, Q, H, S, T, or Y) along with the difference in amino acid volume calculated from the sequence.</p

Prediction of ΔΔG value by different combinations of the interface profile scores.

<p>(A) Interface profile only; (B) Interface profile and residue level potentials; (C) Interface potential, residue level potentials, and atomic level potentials. In each picture, the right panel shows the overall correlation between predicted and experimental ΔΔG values; the right penal shows different features from random forest model as sorted by their effect on the residual error (right) or the node purity (a measure of the efficiency of splitting on feature during the construction of the decision tree) (left). Correlation values are for 10 fold cross-validation repeated three times.</p

Breakdown of the performance of the interface profile score compared to other potentials for different classes of mutations.

<p>Favorable: ΔΔG ≤ 0 kcal/mol, Strongly Favorable ≤ -1 kcal/mol, Unfavorable: ΔΔG ≥ 0 kcal/mol, Strongly Unfavorable: ΔΔG ≥ 0 kcal/mol, Neutral ΔΔG ≤ 1 kcal/mol and ≥ 1 kcal/mol. See text for a description of each potential.</p

Median and interquartile ranges of experimental ΔΔG values by interface classification.

<p>Full distributions can be found in the Supporting Information as <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004494#pcbi.1004494.s001" target="_blank">S1 Fig</a>.</p

Median and interquartile ranges of the RMSD of the alignment at the mutation site at low (Iscore = 0.19) (A) and high (Iscore = 0.25) (B) interface similarity.

<p>Median and interquartile ranges of the RMSD of the alignment at the mutation site at low (Iscore = 0.19) (A) and high (Iscore = 0.25) (B) interface similarity.</p

Phosphatidylethanolamine Enhances Amyloid Fiber-Dependent Membrane Fragmentation

The toxicity of amyloid-forming peptides has been hypothesized to reside in the ability of protein oligomers to interact with and disrupt the cell membrane. Much of the evidence for this hypothesis comes from in vitro experiments using model membranes. However, the accuracy of this approach depends on the ability of the model membrane to accurately mimic the cell membrane. The effect of membrane composition has been overlooked in many studies of amyloid toxicity in model systems. By combining measurements of membrane binding, membrane permeabilization, and fiber formation, we show that lipids with the phosphatidylethanolamine (PE) headgroup strongly modulate the membrane disruption induced by IAPP (islet amyloid polypeptide protein), an amyloidogenic protein involved in type II diabetes. Our results suggest that PE lipids hamper the interaction of prefibrillar IAPP with membranes but enhance the membrane disruption correlated with the growth of fibers on the membrane surface via a detergent-like mechanism. These findings provide insights into the mechanism of membrane disruption induced by IAPP, suggesting a possible role of PE and other amyloids involved in other pathologies

Side-Chain Dynamics Reveals Transient Association of Aβ_1–40 Monomers with Amyloid Fibers

Low-lying excited states that correspond to rare conformations or transiently bound species have been hypothesized to play an important role for amyloid nucleation. Despite their hypothesized importance in amyloid formation, transiently occupied states have proved difficult to detect directly. To experimentally characterize these invisible states, we performed a series of Carr–Purcell–Meiboom–Gill (CPMG)-based relaxation dispersion NMR experiments for the amyloidogenic Aβ_1–40 peptide implicated in Alzheimer’s disease. Significant relaxation dispersion of the resonances corresponding to the side-chain amides of Q15 and N27 was detected before the onset of aggregation. The resonances corresponding to the peptide backbone did not show detectable relaxation dispersion, suggesting an exchange rate that is not within the practical limit of detection. This finding is consistent with the proposed “dock and lock” mechanism based on molecular dynamics simulations in which the Aβ_1–40 monomer transiently binds to the Aβ_1–40 oligomer by non-native contacts with the side chains before being incorporated into the fiber through native contacts with the peptide backbone