Nucleic acid structure determinationExperimental approaches of determining the structure of nucleic acids, such as RNA and DNA, can be largely classified into biophysical and biochemical methods. Biophysical methods use the fundamental physical properties of molecules for structure determination, including X-ray crystallography, NMR and cryo-EM. Biochemical methods exploit the chemical properties of nucleic acids using specific reagents and conditions to assay the structure of nucleic acids.
Structural bioinformaticsStructural bioinformatics is the branch of bioinformatics that is related to the analysis and prediction of the three-dimensional structure of biological macromolecules such as proteins, RNA, and DNA. It deals with generalizations about macromolecular 3D structures such as comparisons of overall folds and local motifs, principles of molecular folding, evolution, binding interactions, and structure/function relationships, working both from experimentally solved structures and from computational models.
Lattice proteinLattice proteins are highly simplified models of protein-like heteropolymer chains on lattice conformational space which are used to investigate protein folding. Simplification in lattice proteins is twofold: each whole residue (amino acid) is modeled as a single "bead" or "point" of a finite set of types (usually only two), and each residue is restricted to be placed on vertices of a (usually cubic) lattice. To guarantee the connectivity of the protein chain, adjacent residues on the backbone must be placed on adjacent vertices of the lattice.
Chan's algorithmIn computational geometry, Chan's algorithm, named after Timothy M. Chan, is an optimal output-sensitive algorithm to compute the convex hull of a set of points, in 2- or 3-dimensional space. The algorithm takes time, where is the number of vertices of the output (the convex hull). In the planar case, the algorithm combines an algorithm (Graham scan, for example) with Jarvis march (), in order to obtain an optimal time. Chan's algorithm is notable because it is much simpler than the Kirkpatrick–Seidel algorithm, and it naturally extends to 3-dimensional space.
