Structural bioinformatics

Structural 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. The term structural has the same meaning as in structural biology, and structural bioinformatics can be seen as a part of computational structural biology. The main objective of structural bioinformatics is the creation of new methods of analysing and manipulating biological macromolecular data in order to solve problems in biology and generate new knowledge.^[1]

Cartoon: this type of protein visualization highlights the secondary structure differences. In general, is represented as a type of screw, β-strands as arrows, and loops as lines.

α-helix

Lines: each amino acid residue is represented by thin lines, which allows a low cost for graphic rendering.

Surface: in this visualization, the external shape of the molecule is shown.

Sticks: each covalent bond between amino acid atoms is represented as a stick. This type of visualization is most used to visualize interactions between ...

amino acids

: Experimentally determined three-dimensional structures of biomolecules derived from Protein Data Bank (PDB).^[13]

MMDB

Nucleic acid Data Base (NDB): Experimentally determined information about nucleic acids (DNA, RNA).

[14]

: Comprehensive description of the structural and evolutionary relationships between structurally known proteins.^[15]

Structural Classification of Proteins (SCOP)

TOPOFIT-DB: Protein structural alignments based on the TOPOFIT method.

[16]

Electron Density Server (EDS): Electron-density maps and statistics about the fit of crystal structures and their maps.

[17]

: Prediction Center Community-wide, worldwide experiment for protein structure prediction CASP.^[18]

CASP

PISCES server for creating non-redundant lists of proteins: Generates PDB list by sequence identity and structural quality criteria.

[19]

The Structural Biology Knowledgebase: Tools to aid in protein research design.

[20]

: The Protein Common Interface Database Database of similar protein-protein interfaces in crystal structures of homologous proteins.^[21]

ProtCID

:AlphaFold - Protein Structure Database.^[22]

AlphaFold

Structure comparison[edit]

Structural alignment[edit]

Structural alignment is a method for comparison between 3D structures based on their shape and conformation.^[23] It could be used to infer the evolutionary relationship among a set of proteins even with low sequence similarity. Structural alignment implies superimposing a 3D structure over a second one, rotating and translating atoms in corresponding positions (in general, using the C_α atoms or even the backbone heavy atoms C, N, O, and C_α). Usually, the alignment quality is evaluated based on the root-mean-square deviation (RMSD) of atomic positions, i.e., the average distance between atoms after superimposition:

Selection of Target - Potential targets are identified by comparing them with databases of known structures and sequence. The importance of a target can be decided on the basis of published literature. Target can also be selected on the basis of its . Protein domains are building blocks that can be rearranged to form new proteins. They can be studied in isolation initially.

protein domain

Tracking trials - X-Ray crystallography can be used to reveal three-dimensional structure of a protein. But, in order to use X-ray for studying protein crystals, pure proteins crystals must be formed, which can take a lot of trials. This leads to a need for tracking the conditions and results of trials. Furthermore, supervised machine learning algorithms can be used on the stored data to identify conditions that might increase the yield of pure crystals.

X-ray crystallography

Analysis of X-Ray crystallographic data - The diffraction pattern obtained as a result of bombarding X-rays on electrons is of electron density distribution. There is a need for algorithms that can deconvolve Fourier transform with partial information ( due to missing phase information, as the detectors can only measure amplitude of diffracted X-rays, and not the phase shifts ). Extrapolation technique such as Multiwavelength anomalous dispersion can be used to generate electron density map, which uses the location of selenium atoms as a reference to determine rest of the structure. Standard Ball-and-stick model is generated from the electron density map.

Fourier transform

Analysis of NMR spectroscopy data - experiments produce two (or higher) dimensional data, with each peak corresponding to a chemical group within the sample. Optimization methods are used to convert spectra into three dimensional structures.

Nuclear magnetic resonance spectroscopy

Correlating Structural information with functional information - Structural studies can be used as probe for structural-functional relationship.