A closer look into the α-helix basin

Haimov, Boris; Srebnik, Simcha

doi:10.1038/srep38341

Cited by 29 publications

(37 citation statements)

References 46 publications

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“…The conformational pair ðq; rÞ was calculated using the following estimated linear transformation that was developed elsewhere (16): q ¼ ðj À 4 þ 11:4Þ=2:76 and r ¼ À ðj þ 4Þ=29:5, where q, f, and j are in degrees and the units of r are [residues/turn]. q is the angle of backbone carbonyls relative to the helix direction vector, and r is the number of residues per single a-helix turn.…”

Section: Calculation Of ðQ; Rþ From ðF; Cþ Dihedralsmentioning

confidence: 99%

“…Every transition from one AA to another along the protein backbone had a prominent ð4; jÞ peak within the a-helix basin (16), which corresponds to a ðq; rÞ peak representing the mean a-helical conformation of the given transition, totaling 400 AA-pair transitions (presented in Tables S1 and S2). The prominent ðq; rÞ peak values are used in this study as estimations of the a-helical conformation for the given transition.…”

Section: Estimation Of A-helical Conformationmentioning

confidence: 99%

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The Relation between α-Helical Conformation and Amyloidogenicity

Haimov

Srebnik

2018

Biophysical Journal

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Amyloid fibrils are stable aggregates of misfolded proteins and polypeptides that are insoluble and resistant to protease activity. Abnormal formation of amyloid fibrils in vivo may lead to neurodegenerative disorders and other systemic amyloidosis, such as Alzheimer's, Parkinson's, and atherosclerosis. Because of their clinical importance, amyloids are under intense scientific research. It is believed that short polypeptide segments within proteins are responsible for the transformation of correctly folded proteins into parts of larger amyloid fibrils and that this transition is modulated by environmental factors, such as pH, salt concentration, interaction with the cell membrane, and interaction with metal ions. Most studies on amyloids focus on the amyloidogenic sequences. The focus of this study is on the structure of the amyloidogenic α-helical segments because the α-helical secondary structure has been recognized to be a key player in different stages of the amyloidogenesis process. We have previously shown that the α-helical conformation may be expressed by two parameters (θ and ρ) that form orthogonal coordinates based on the Ramachandran dihedrals (φ and ψ) and provide an illuminating interpretation of the α-helical conformation. By performing statistical analysis on α-helical conformations found in the Protein Data Bank, an apparent relation between α-helical conformation, as expressed by θ and ρ, and amyloidogenicity is revealed. Remarkably, random amino acid sequences, whose helical structures were obtained from the most probable dihedral angles, revealed the same dependency of amyloidogenicity, suggesting the importance of α-helical structure as opposed to sequence.

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Section: Calculation Of ðQ; Rþ From ðF; Cþ Dihedralsmentioning

confidence: 99%

Section: Estimation Of A-helical Conformationmentioning

confidence: 99%

The Relation between α-Helical Conformation and Amyloidogenicity

Haimov

Srebnik

2018

Biophysical Journal

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“…Proteins display a limited number of secondary structure types, being alpha helix and beta strand the most abundant [34] [35]. To determine whether secondary structure affects QUILLO values, the percentage of residues in beta strand of each protein was calculated.…”

Section: Independence From Different Factorsmentioning

confidence: 99%

A Constant Proportion in Protein Structure

Lobo-Cabrera

2018

Preprint

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The principles governing protein structure are largely unknown. Here, a structural proportion universal (R² = 0.978) among proteins is reported. The model variance is shown to be independent from protein size, secondary structure composition, compactness or relative surface area. The structural characteristic under study --named here QUILLO--quantifies residue-type spatial clustering. In this way, polar, hydrophobic, acidic and basic residues are evaluated individually and their values added up. For the analysis, all X-Ray currently determined structures deposited in the Protein Data Bank were studied. The QUILLO proportion offers for the first time an a priori protein prediction quality-check. Indeed, 1 predictions with unexpected proportion values correspond to low ranks in the CASP12 experiment. The reason behind a specific, constant rule for protein folding remains unknown.

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“…where 0 ≤ S ≤ 1 is the alignment score between every amide hydrogen and carbonyl oxygen along the polypeptide backbone, introduced elsewhere 40 . For the calculation of residue-residue (RR) interaction energy, we use a 20X20 contact energy cost matrix (CECM) which defines the interaction energy of the 20 AAs.…”

Section: Displacementmentioning

confidence: 99%

Assessment of hydrophobicity scales for protein stability and folding using energy and RMSD criteria

Srebnik

Haimov

2017

Preprint

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De novo prediction of protein folding is an open scientific challenge. Many folding models and force fields have been developed, yet all face difficulties converging to native conformations. Hydrophobicity scales (HSs) play a crucial role in such simulations as they define the energetic interactions between protein residues, thus determining the energetically favorable conformation. While many HSs have been developed over the years using various methods, it is surprising that the scales show very weak consensus in their assignment of hydrophobicity indexes to the various residues. In this work, several HSs are systematically assessed via atomistic Monte Carlo simulation of folding of small proteins, by converting the HSs of interest into residue-residue contact energy matrices. HSs that poorly preserve native structures of proteins were tuned by applying a linear transformation. Subsequently, folding simulations were used to examine the ability of the HSs to correctly fold the proteins from a random initial conformation. Root mean square deviation (RMSD) and energy of the proteins during folding were sampled and used to define an ER-score, as the correlation between the 2-dimensional energy-RMSD (ER) histogram with 50% lowest energy conformations and the ER histogram with 50% lowest RMSD conformations. Thus, we were able to compare the ability of the different HSs to predict de novo protein folding quantitatively.

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A closer look into the α-helix basin

Cited by 29 publications

References 46 publications

The Relation between α-Helical Conformation and Amyloidogenicity

The Relation between α-Helical Conformation and Amyloidogenicity

A Constant Proportion in Protein Structure

Assessment of hydrophobicity scales for protein stability and folding using energy and RMSD criteria

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