Prediction of the Secondary Structure of Proteins from their Amino Acid Sequence

Chou, Peter Y.; Fasman, Gerald D.

doi:10.1002/9780470122921.ch2

Cited by 1,538 publications

(578 citation statements)

References 245 publications

Supporting

Mentioning

545

Contrasting

Unclassified

Order By: Relevance

“…The second axis is found to correlate well with average non-bonded energies [25], which is related to size. Interestingly, the third axis is found to correlate with computed alphahelix propensities [18], as well as with statistics on turns in proteins [5]. representing amino acids as 3D vectors improves the decoding of their properties.…”

Section: Blv62: Information In Each Dimensionmentioning

confidence: 90%

A Geometric Representation of Protein Sequences

Wang

et al. 2007

2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007)

View full text Add to dashboard Cite

The amino acid sequence of a protein is the key to understanding its structure and ultimately its function in the cell. This paper addresses the fundamental issue of encoding amino acids in ways that the visualization of protein sequences facilitates the decoding of its information content. We show that a feature-based representation in a three-dimensional (3D) space derived from substitution matrices provides an adequate representation from which the domain content of a protein can be predicted. In addition, we show that each dimension of the feature space can be related to a physical property of the amino acids.

show abstract

Section: Blv62: Information In Each Dimensionmentioning

confidence: 90%

A Geometric Representation of Protein Sequences

Wang

et al. 2007

2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007)

View full text Add to dashboard Cite

show abstract

“…We also found little correlation (r = 0.3) between the data and a statistical β-sheet propensity scale by Chou and Fasman (data not shown). 23,24 This little dependence of the ΔΔG values with β-sheet propensity is evident from the observation that residues of the same amino-acid type (i.e. four Ser, four Glu, four Lys, and three Thr) exhibit significantly different ΔΔG values (Fig.…”

Section: Contribution Of β-Sheet Propensitymentioning

confidence: 97%

Hydrophobic Surface Burial Is the Major Stability Determinant of a Flat, Single-layer β-Sheet

Yan¹,

Gawlak²,

Makabe³

et al. 2007

Journal of Molecular Biology

View full text Add to dashboard Cite

Formation of a flat β-sheet is a fundamental event in β-sheet-mediated protein self-assembly. To investigate contributions of various factors to the stability of flat β-sheets, we performed extensive alanine-scanning mutagenesis experiments on the single-layer β-sheet segment of Borrelia outer surface protein A (OspA). This β-sheet segment consists of β-strands with highly regular geometries that can serve as a building block for self-assembly. Our Ala-scanning approach is distinct from the conventional host-guest method in that it introduces only conservative, truncation mutations that should minimize structural perturbation. Our results showed very weak correlation with experimental β-sheet propensity scales, statistical β-sheet propensity scales, or cross-strand pairwise correlations. In contrast, our data showed strong positive correlation with the change in buried nonpolar surface area. Polar interactions including prominent Glu-Lys cross-strand pairs marginally contribute to the β-sheet stability. These results were corroborated by results from additional non-Ala mutations. Taken together, these results demonstrate the dominant contribution of nonpolar surface burial to flat β-sheet stability even at solvent-exposed positions. The OspA single-layer β-sheet achieves efficient hydrophobic surface burial without forming a hydrophobic core by a strategic placement of a variety of side chains. These findings further suggest the importance of hydrophobic interactions within a β-sheet layer in peptide self-assembly.

show abstract

“…The three terms of the proposed speed function are required to be weighted properly to guide the evolving curve under different image conditions. We have used the measure proposed by [26], to numerically evaluate the segmentation results obtained using various of the parameters. The overall accuracy (ACC) of the segmentation result can be estimated using the following equation;…”

Section: Property Analysis Of Parameter Valuesmentioning

confidence: 99%

Medical Image Segmentation Using Level Set Method with a New Hybrid Speed Function Based on Boundary and Region Segmentation

Park

Cho

2012

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYThis paper presents a new hybrid speed function needed to perform image segmentation within the level-set framework. The proposed speed function uses both the boundary and region information of objects to achieve robust and accurate segmentation results. This speed function provides a general form that incorporates the robust alignment term as a part of the driving force for the proper edge direction of an active contour, an active region term derived from the region partition scheme, and the smoothing term for regularization. First, we use an external force for active contours as the Gradient Vector Flow field. This is computed as the diffusion of gradient vectors of a gray level edge map derived from an image. Second, we partition the image domain by progressively fitting statistical models to the intensity of each region. Here we adopt two Gaussian distributions to model the intensity distribution of the inside and outside of the evolving curve partitioning the image domain. Third, we use the active contour model that has the computation of geodesics or minimal distance curves, which allows stable boundary detection when the model's gradients suffer from large variations including gaps or noise. Finally, we test the accuracy and robustness of the proposed method for various medical images. Experimental results show that our method can properly segment low contrast, complex images. key words: image segmentation, geometric deformable model, level set method, gradient vector flow

show abstract

Prediction of the Secondary Structure of Proteins from their Amino Acid Sequence

Cited by 1,538 publications

References 245 publications

A Geometric Representation of Protein Sequences

A Geometric Representation of Protein Sequences

Hydrophobic Surface Burial Is the Major Stability Determinant of a Flat, Single-layer β-Sheet

Medical Image Segmentation Using Level Set Method with a New Hybrid Speed Function Based on Boundary and Region Segmentation

Contact Info

Product

Resources

About