Hydrophobic cluster analysis: An efficient new way to compare and analyse amino acid sequences

Gaboriaud, Christine; Bissery, V.; Benchetrit, T.; Mornon, Jean‐Paul

doi:10.1016/0014-5793(87)80439-8

Cited by 588 publications

(468 citation statements)

References 22 publications

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“…Hydrophobic and polarity gradients within the loops were measured as described by Kyte and Doolittle [18] and of Grantham [19], respectively. Hydrophobic cluster analysis [20] on the amino acid sequence of the first external loop (M1-M2) was carried out using the internet site bioserv.rpbs.jussieu.fr. The modeling software used in this study are either programed for single letter or three letter amino acid codes.…”

Section: Peptide Topology Analysismentioning

confidence: 99%

“…After 5 turns, residues I 109 and P 126 have similar positions parallel to the axis of the cylinder. Gaboriaud et al [20] have shown that if the cylinder is cut parallel to its axis and unrolled, sets of adjacent hydrophobic residues can be encircled and termed hydrophobic clusters (see Figure 6). As some adjacent amino acids (e.g.…”

Section: Hydrophobicity and Polarity Gradients Within The First Extermentioning

confidence: 99%

“…hydrophobic only in a hydrophobic environment. Consistent with the method of Gaboriaud et al [20], different symbols are used for proline (star), glycines (square), which are often present in loops, and cysteines (C) which may be involved in disulfide bond formation. As shown in the lower portion of Figure 6, a large hydrophobic cluster exists in the N-terminal region of the first external loop.…”

Section: Hydrophobicity and Polarity Gradients Within The First Extermentioning

confidence: 99%

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Progesterone binding to the α1-subunit of the Na/K-ATPase on the cell surface: Insights from computational modeling

2008

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Progesterone triggers the resumption of meiosis in the amphibian oocyte through a signaling system at the plasma membrane. Analysis of [ 3 H]ouabain and [ 3 H]progesterone binding to the plasma membrane of the Rana pipiens oocyte indicates that progesterone competes with ouabain for a low affinity ouabain binding site on a 112 kDa α1-subunit of the membrane Na/K-ATPase. Published amino acid sequences from both low and high affinity ouabain binding α1-subunits are compared, together with published site-directed mutagenesis studies of ouabain binding. We propose that the progesterone binding site is located in the external loop (23 amino acids) between the M1-M2 transmembrane helices. Analysis of loop topology and the countercurrent hydrophobicity/polarity gradients within the M1-M2 loop further suggest that the polar β and hydrophobic α surfaces of the planar progesterone molecule interact with opposite sides of the amino acid loop. The 19-angular methyl group of progesterone is essential for activity; it could bind to the C-terminal region of the M1-M2 loop. Maximum biological activity requires formation of hydrogen-bond networks between the 3-keto group of progesterone and Arg 118 , Asp 129 and possibly Glu 122-124 in the C-terminal region of the loop. The 20-keto group hydrogen may in turn hydrogen bond to Cys 111 near the M1 helix. Peptide flexibility undergoes a maximal transition near the midway point in the M1-M2 loop, suggesting that folding occurs within the loop, which further stabilizes progesterone binding.

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Section: Peptide Topology Analysismentioning

confidence: 99%

Section: Hydrophobicity and Polarity Gradients Within The First Extermentioning

confidence: 99%

Section: Hydrophobicity and Polarity Gradients Within The First Extermentioning

confidence: 99%

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Progesterone binding to the α1-subunit of the Na/K-ATPase on the cell surface: Insights from computational modeling

2008

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“…Hydrophobic cluster analysis (HCA) [23] was performed to predict the structural features of IPI-O along the deduced amino acid sequence. HCA plots were generated using the program HCA-Plot2 (Doriane, Paris).…”

Section: Amino Acid Sequencing Of Recombinant Ipi-o Protein and Hydromentioning

confidence: 99%

High affinity recognition of a Phytophthora protein by Arabidopsis via an RGD motif

Senchou

Weide

Carrasco

et al. 2004

Cellular and Molecular Life Sciences (CMLS)

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“…The new encoding scheme can be derived in the 3-, 2-, or 1-dimensional form [19]. Finally, hydrophobic cluster analysis (HCA) analysis constitutes another well-known technique for the 2D representation of protein sequences [20][21][22][23]. Randić et al ultimately approached protein representations by using 2D schemes based on nucleotide triplet codons or virtual genetic code [24,25].…”

Section: Introductionmentioning

confidence: 99%

Novel 2D maps and coupling numbers for protein sequences. The first QSAR study of polygalacturonases; isolation and prediction of a novel sequence from Psidium guajava L.

et al. 2006

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The development of 2D graph-theoretic representations for DNA sequences was very important for qualitative and quantitative comparison of sequences. Calculation of numeric features for these representations is useful for DNA-QSAR studies. Most of all graph-theoretic representations identify each one of the four bases with a unitary walk in one axe direction in the 2D space. In the case of proteins, twenty amino acids instead of four bases have to be considered. This fact has limited the introduction of useful 2D Cartesian representations and the corresponding sequences descriptors to encode protein sequence information. In this study, we overcome this problem grouping amino acids into four groups: acid, basic, polar and non-polar amino acids. The identification of each group with one of the four axis directions determines a novel 2D representation and numeric descriptors for proteins sequences. Afterwards, a Markov model has been used to calculate new numeric descriptors of the protein sequence. These descriptors are called herein the sequence 2D coupling numbers (f k ). In this work, we calculated the f k values for 108 sequences of different polygalacturonases (PGs) and for 100 sequences of other proteins. A Linear Discriminant Analysis model derived here (PG = 5.36 AE f 1 À 3.98 AE f 3 À 42.21) successfully discriminates between PGs and other proteins. The model correctly classified 100% of a subset of 81 PGs and 75 non-PG proteins sequences used to train the model. The model also correctly classified 51 out of 52 (98.07%) of proteins sequences used as external validation series. The uses of different group of amino acids and/or axes orientation give different results, so it is suggested to be explored for other databases. Finally, to illustrates the use of the model we report the isolation and prediction of the PG action for a novel sequence (AY908988) isolated by our group from Psidium guajava L. This prediction coincides very well with sequence alignment results found by the BLAST methodology. These findings illustrate the possibilities of the sequence descriptors derived for this novel 2D sequence representation in proteins sequence QSAR studies.

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Hydrophobic cluster analysis: An efficient new way to compare and analyse amino acid sequences

Cited by 588 publications

References 22 publications

Progesterone binding to the α1-subunit of the Na/K-ATPase on the cell surface: Insights from computational modeling

Progesterone binding to the α1-subunit of the Na/K-ATPase on the cell surface: Insights from computational modeling

High affinity recognition of a Phytophthora protein by Arabidopsis via an RGD motif

Novel 2D maps and coupling numbers for protein sequences. The first QSAR study of polygalacturonases; isolation and prediction of a novel sequence from Psidium guajava L.

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