The mean RMSD of models of each class.

<p>The mean RMSD of models of each class.</p

Automated method to differentiate between native and mirror protein models obtained from contact maps - Fig 3

<p><b>Mean differences in Ramachandran plots between natively oriented (blue bars) and mirror models (red bars) of domains in A, B, C and D classes.</b> Histograms of mean percentages of residues in favored (left), allowed (middle) and outlier (right) regions.</p

Automated method to differentiate between native and mirror protein models obtained from contact maps - Fig 1

<p><b>Exemplary models of the domains: a) A class d1tx4a_, b) B class d1osya_.</b> The yellow structures are natively oriented models (left), the blue structures are SCOP structures (middle), and the green structures are mirror models (right). Their Ramachandran plots are presented below the structures. Red area is the general favored region, yellow area is the allowed region, and black points denote residues.</p

Automated method to differentiate between native and mirror protein models obtained from contact maps

<div><p>Mirror protein structures are often considered as artifacts in modeling protein structures. However, they may soon become a new branch of biochemistry. Moreover, methods of protein structure reconstruction, based on their residue-residue contact maps, need methodology to differentiate between models of native and mirror orientation, especially regarding the reconstructed backbones. We analyzed 130 500 structural protein models obtained from contact maps of 1 305 SCOP domains belonging to all 7 structural classes. On average, the same numbers of native and mirror models were obtained among 100 models generated for each domain. Since their structural features are often not sufficient for differentiating between the two types of model orientations, we proposed to apply various energy terms (<i>ETs</i>) from PyRosetta to separate native and mirror models. To automate the procedure for differentiating these models, the k-means clustering algorithm was applied. Using <i>total</i> energy did not allow to obtain appropriate clusters–the accuracy of the clustering for class A (all helices) was no more than 0.52. Therefore, we tested a series of different k-means clusterings based on various combinations of <i>ETs</i>. Finally, applying two most differentiating <i>ETs</i> for each class allowed to obtain satisfying results. To unify the method for differentiating between native and mirror models, independent of their structural class, the two best <i>ETs</i> for each class were considered. Finally, the k-means clustering algorithm used three common <i>ETs</i>: probability of amino acid assuming certain values of dihedral angles <i>Φ</i> and <i>Ψ</i>, Ramachandran preferences and Coulomb interactions. The accuracies of clustering with these ETs were in the range between 0.68 and 0.76, with sensitivity and selectivity in the range between 0.68 and 0.87, depending on the structural class. The method can be applied to all fully-automated tools for protein structure reconstruction based on contact maps, especially those analyzing big sets of models.</p></div

Automated method to differentiate between native and mirror protein models obtained from contact maps - Fig 2

<p>Histograms of RMSD models to the SCOP structures demonstrating structural differences between native and mirror models: a) different (domain d1hx1b_), b) similar (domain d1boua_) and c) very similar (domain d1a9xa1).</p

Description of the energy terms from PyRosetta used in the analysis.

<p>Description of the energy terms from PyRosetta used in the analysis.</p

Descriptions of data set.

<p>Descriptions of data set.</p

Mean accuracy of the k-means clustering using energy terms.

<p>Mean accuracy of the k-means clustering using energy terms.</p

Bar graph showing the ratio of domains for which the ET was significantly different in the groups of natively oriented and mirror models.

<p>Graph includes also <i>Φ</i>^<i>+</i> <i>ratio</i>.</p

Scatter plot of differentiating ETs to all ETs vs. protein length, mean RMSD of native models (mean by domains) and RMSD between SCOP structure and its mirror: Red is class A, blue is class B.

<p>Scatter plot of differentiating ETs to all ETs vs. protein length, mean RMSD of native models (mean by domains) and RMSD between SCOP structure and its mirror: Red is class A, blue is class B.</p