The distributions of the lengths of the <i>α</i>-helices from our algorithm, dssp and stride (a), and an example of 4-residue <i>α</i>-helix by dssp(b).

<p>The x-axis in (a) is the helix length while the y-axis is the number of the helices with that particular length. The two arrows point to the most frequently appeared helices assigned by dssp and by both our algorithm and stride. The right figure (b) depicts a dssp-assigned 4-residue <i>α</i>-helix in a protein (pdbid 1CC5) that is not assigned to a helix by our algorithm.</p

The clusters of <i>α</i>–helices by our algorithm, dssp and p-sea.

<p>The sets of helices in the left have a length of 12 residues while the sets in the right 24 residues. The 12-residue set (11,756 helices) and 24-residue set (1,211 helices) by our algorithm are classified respectively into 12 and 17 clusters. The dssp assigned 12-residue set (12,631 helices) and 24-residue set (1,285 helices) are classified respectively into 21 and 35 clusters while the p-sea assigned 12-residue set (5,306 helices) and 24-residue set (574 helices) are classified respectively into 10 and 24 clusters. The clusters are produced using our geometric clustering algorithm [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129674#pone.0129674.ref022" target="_blank">22</a>]. The RMSD threshold for clustering is 1.5Å.</p

Helix assignment on a residue and a helix basis by our algorithm, dssp and stride.

<p>The third column for each helix type presents respectively the range in helix length and the length of the most frequently appeared helices.</p

Amino Acids in Nine Ligand-Prefer Ramachandran Regions

Several secondary structures, such as π-helix and left-handed helix, have been frequently identified at protein ligand-binding sites. A secondary structure is considered to be constrained to a specific region of dihedral angles. However, a comprehensive analysis of the correlation between main chain dihedral angles and ligand-binding sites has not been performed. We undertook an extensive analysis of the relationship between dihedral angles in proteins and their distance to ligand-binding sites, frequency of occurrence, molecular potential energy, amino acid composition, van der Waals contacts, and hydrogen bonds with ligands. The results showed that the values of dihedral angles have a strong preference for ligand-binding sites at certain regions in the Ramachandran plot. We discovered that amino acids preceding the ligand-prefer ϕ/ψ box residues are exposed more to solvents, whereas amino acids following ligand-prefer ϕ/ψ box residues form more hydrogen bonds and van der Waals contacts with ligands. Our method exhibited a similar performance compared with the program Ligsite-csc for both ligand-bound structures and ligand-free structures when just one ligand-binding site was predicted. These results should be useful for the prediction of protein ligand-binding sites and for analysing the relationship between structure and function

The assignments on

<p>The assignments are made for a set of 100 x-ray structures with different resolutions. The first row is the total number of residues. All the other rows are the agreement between a pair of programs in percentage. The percentage is computed as </p><p></p><p></p><p></p><p><mi>n</mi></p><p></p><p><mi>n</mi><mn>1</mn></p><mo>+</mo><mi>n</mi><mo>+</mo><p><mi>n</mi><mn>2</mn></p><p></p><p></p><p></p><p></p><p></p> where <i>n</i> is the number of residues assigned by both programs while <i>n</i>₁ and <i>n</i>₂ are respectively the numbers of residues assigned only by the first and second programs.<p></p

A New Secondary Structure Assignment Algorithm Using Cα Backbone Fragments

The assignment of secondary structure elements in proteins is a key step in the analysis of their structures and functions. We have developed an algorithm, SACF (secondary structure assignment based on Cα fragments), for secondary structure element (SSE) assignment based on the alignment of Cα backbone fragments with central poses derived by clustering known SSE fragments. The assignment algorithm consists of three steps: First, the outlier fragments on known SSEs are detected. Next, the remaining fragments are clustered to obtain the central fragments for each cluster. Finally, the central fragments are used as a template to make assignments. Following a large-scale comparison of 11 secondary structure assignment methods, SACF, KAKSI and PROSS are found to have similar agreement with DSSP, while PCASSO agrees with DSSP best. SACF and PCASSO show preference to reducing residues in N and C cap regions, whereas KAKSI, P-SEA and SEGNO tend to add residues to the terminals when DSSP assignment is taken as standard. Moreover, our algorithm is able to assign subtle helices (310-helix, π-helix and left-handed helix) and make uniform assignments, as well as to detect rare SSEs in β-sheets or long helices as outlier fragments from other programs. The structural uniformity should be useful for protein structure classification and prediction, while outlier fragments underlie the structure–function relationship

The averaging process for helix model computation.

<p>In this example the first helical curve <b>H</b>₁₄ is computed using the first quadruple of backbone atoms {<b>a</b>₁,<b>a</b>₂,<b>a</b>₃,<b>a</b>₄}, the second curve <b>H</b>₂₅ the next quadruple of atoms {<b>a</b>₂,<b>a</b>₃,<b>a</b>₄,<b>a</b>₅} and so on. For a pair of two consecutive interior atoms up to three slightly different curves could be computed. The final model curve for the segment between a pair of consecutive atoms is their average (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0129653#pone.0129653.e005" target="_blank">Eq 3</a>).</p