3d structure prediction software mc-sym  (Moderna)

Journal: Nucleic Acids Research

Article Title: Computational identification of RNA functional determinants by three-dimensional quantitative structure–activity relationships

doi: 10.1093/nar/gku816

Figure Lengend Snippet: MC-3DQSAR steps. A set of 3D models is built for each sequence of the learning set by using atomic superimposition of substituted bases in a template of a reference high-resolution structure, or by 3D modeling. The models constitute the training set. The exposed area of each atomic group is computed using pymol and a probe water of radius 1.4 Å. The solvent accessible groups are considered potential determinants if they are present in all positive examples of the training set and absent in at least one negative example. Determining the activity of a new sequence variant consists in building its 3D structure, computing the solvent accessibility of its chemical groups and comparing its profile to the activity profile.

Article Snippet: Note that this step can alternatively be achieved by using 3D structure prediction software, such as MC-Sym ( ) or modeRNA , for instance.

Techniques: Sequencing, Solvent, Activity Assay, Variant Assay

Journal: Nucleic Acids Research

Article Title: Computational identification of RNA functional determinants by three-dimensional quantitative structure–activity relationships

doi: 10.1093/nar/gku816

Figure Lengend Snippet: E. coli 23S rRNA SRL. ( A ) Stereo view of the SRL 3D structure (PDB ID 2AWB). The base and phosphodiester linkage (cylinder) of each nucleotide are shown in dark gray. Activity determinants identified by MC-3DQSAR are shown with spheres, where the donor groups are shown in black and bold labels and acceptors in light gray and regular labels. Neutral determinants are not shown. ( B ) Secondary structure and NCMs. The NCMs are numbered 1–7. The backbone is shown using bold lines; the base pairing interactions are shown using the Leontis and Westhof nomenclature; and the base stacking interactions are shown using the Major and Thibault nomenclature. The activity determinants identified by MC-3DQSAR are shown in boxes. The identity of the atomic groups from the seed sequence is shown in bold for donor, italic for neutral and regular for acceptor determinants.

Article Snippet: Note that this step can alternatively be achieved by using 3D structure prediction software, such as MC-Sym ( ) or modeRNA , for instance.

Techniques: Activity Assay, Sequencing

Journal: Nucleic Acids Research

Article Title: Computational identification of RNA functional determinants by three-dimensional quantitative structure–activity relationships

doi: 10.1093/nar/gku816

Figure Lengend Snippet: Leadzyme structure. ( A ) Stereo view of the leadzyme 3D structure (PDB ID 1NUJ). Graphical details as in Figure . ( B ) Secondary structure and NCMs (numbered 1–8). Base pairing and stacking nomenclature, and activity determinants are shown as in Figure .

Article Snippet: Note that this step can alternatively be achieved by using 3D structure prediction software, such as MC-Sym ( ) or modeRNA , for instance.

Techniques: Activity Assay

Journal: Nucleic Acids Research

Article Title: Computational identification of RNA functional determinants by three-dimensional quantitative structure–activity relationships

doi: 10.1093/nar/gku816

Figure Lengend Snippet: Hammerhead ribozyme structure. ( A ) Stereo view of the hammerhead ribozyme 3D structure (PDB ID 2OEU). Graphical details as in Figure . ( B ) Secondary structure and NCMs (numbered 1–7). Base pairing and stacking nomenclature, and activity determinants are shown as in Figure .

Article Snippet: Note that this step can alternatively be achieved by using 3D structure prediction software, such as MC-Sym ( ) or modeRNA , for instance.

Techniques: Activity Assay