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

Abstract

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 (ETs) from PyRosetta to separate native and mirror models. To automate the procedure for differentiating these models, the k-means clustering algorithm was applied. Using total 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 ETs. Finally, applying two most differentiating ETs 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 ETs for each class were considered. Finally, the k-means clustering algorithm used three common ETs: probability of amino acid assuming certain values of dihedral angles Φ and Ψ, 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.

Detection of native and mirror protein structures based on Ramachandran plot analysis by interpretable machine learning models

“…Class C shows the best of all investigated accuracies. This result concurs with the findings in [28]. Among the protein classes which were not categorized due to their secondary structure, the multi-domain class E by far shows the best accuracy with 91.8%, whereas classes F and G do not exceed 80%.…”

“…The accuracy for this class is 86.57% (see Tab. 3 for all accuracies). Interestingly, our model achieves an accuracy of 92.56% for class B, which obviously collides with the statement of those native and mirror models being indistinguishable [28,55] and, furthermore, even exceeds the accuracy of class A.…”

“…Further, for visual inspection and evaluation, we generated a summarized R-plot, which is collecting all pairs (Φ, Ψ) of dihedral angles for all samples of the considered task in a single R-plot but separately for native and mirror samples. Thus, these R-plots can be seen as estimated dihedral angles densities in the (Φ, Ψ)-plane for mirror and native As previous publications suggest [28,55], native and mirror conformations of proteins rich in helices (belonging to class A), can be distinguished based on chirality derived information, or in more detail: right-handed α-helices are favoured in native conformations [40]. We can confirm this finding, since the features corresponding to the left-and right-handed α-helices predominantly contribute to the class discrimination of mirror and native (see S3 Fig and Tab.…”

“…Kurczynska and Kotulska tried in their work [28] to distinguish between native and mirror models in an automated way (Automated Mirror-Native-Discrimination -AMND). For this purpose, they used the energy terms obtained by PyRosetta, a Python-based interface to Rosetta for molecular modeling [9].…”

“…We used exactly the same data set as given in [28], which was generated and analyzed there to detect native and mirror structures. Therefore, we give only a short summary of the data generation and preprocessing, for a more detailed description it is referred to [28]:…”