Structural bioinformatics

The protein structure is directly related to its function. The presence of some chemical groups in specific locations allows proteins to act as enzymes, catalyzing several chemical reactions.[2] In general, protein structures are classified into four levels: primary (sequences), secondary (local conformation of the polypeptide chain), tertiary (three-dimensional structure of the protein fold), and quaternary (association of multiple polypeptide structures). Structural bioinformatics mainly addresses interactions among structures taking into consideration their space coordinates. Thus, the primary structure is better analyzed in traditional branches of bioinformatics. However, the sequence implies restrictions that allow the formation of conserved local conformations of the polypeptide chain, such as alpha-helix, beta-sheets, and loops (secondary structure[3]). Also, weak interactions (as hydrogen bonds) stabilize the protein fold. Interactions could be intrachain, i.e., when occurring between parts of the same protein monomer (tertiary structure), or interchain, i.e., when occurring between different structures (quaternary structure).

Structural visualization of BACTERIOPHAGE T4 LYSOZYME (PDB ID: 2LZM). (A) Cartoon; (B) Lines; (C) Surface; (D) Sticks.

Protein structure visualization is an important issue for structural bioinformatics.[4] It allows users to observe static or dynamic representations of the molecules, also allowing the detection of interactions that could be used to infer about molecular mechanisms studied. The most common types of visualization are:

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

The classic DNA duplexes structure was initially described by Watson and Crick (and contributions of Rosalind Franklin). The DNA molecule is composed of three substances: a phosphate group, a pentose, and a nitrogen base (adenine, thymine, cytosine, or guanine). The DNA double helix structure is stabilized by hydrogen bonds formed between base pairs: adenine with thymine (A-T) and cytosine with guanine (C-G). Many structural bioinformatics studies have focused on understanding interactions between DNA and small molecules, which has been the target of several drug design studies.

Interactions are contacts established between parts of molecules at different levels. They are responsible for stabilizing protein structures and perform a varied range of activities. In biochemistry, interactions are characterized by the proximity of atom groups or molecules regions that present an effect upon one another, such as electrostatic forces, hydrogen bonding, and hydrophobic effect. Proteins can perform several types of interactions, such as protein-protein interactions (PPI), protein-peptide interactions[5], protein-ligand interactions (PLI)[6], and protein-DNA interaction.

Contacts between two amino acid residues: Q196-R200 (PDB ID- 2X1C) [7]

Calculating contacts is an important task in structural bioinformatics, being important for the correct prediction of protein structure and folding, thermodynamic stability, protein-protein and protein-ligand interactions, docking and molecular dynamics analyses, and so on.[8]

Traditionally, computational methods have used threshold distance between atoms (also called cutoff) to detect possible interactions.[9] This detection is performed based on Euclidean distance and angles between atoms of determined types. However, most of the methods based on simple Euclidean distance cannot detect occluded contacts. Hence, cutoff free methods, such as Delaunay triangulation, have gained prominence in recent years. In addition, the combination of a set of criteria, for example, physicochemical properties, distance, geometry, and angles, have been used to improve the contact determination.[8]

The PDB format (.pdb) is the legacy textual file format used to store information of three-dimensional structures of macromolecules used by the Protein Data Bank. Due to restrictions in the format structure conception, the PDB format does not allow large structures containing more than 62 chains or 99999 atom records.[10]

The PDBx/mmCIF (macromolecular Crystallographic Information File) is a standard text file format for representing crystallographic information.[11] Since 2014, the PDB format was substituted as the standard PDB archive distribution by the PDBx/mmCIF file format (.cif). While PDB format contains a set of records identified by a keyword of up six characters, the PDBx/mmCIF format uses a structure based on key and value, where the key is a name that identifies some feature and the value is the variable information.[12]

In addition to the Protein Data Bank (PDB), there are several databases of protein structures and other macromolecules. Examples include:

• MMDB: Experimentally determined three-dimensional structures of biomolecules derived from Protein Data Bank (PDB).[13]
• Nucleic acid Data Base (NDB): Experimentally determined information about nucleic acids (DNA, RNA).[14]
• Structural Classification of Proteins (SCOP): Comprehensive description of the structural and evolutionary relationships between structurally known proteins.[15]
• 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]
• CASP: Prediction Center Community-wide, worldwide experiment for protein structure prediction CASP.[18]
• 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]
• ProtCID: The Protein Common Interface Database Database of similar protein-protein interfaces in crystal structures of homologous proteins.[21]

Structural alignment is a method for comparison between 3D structures based on their shape and conformation.[22] It could be used to infer the evolutionary relationship among a set of proteins even with low sequence similarity. Structural alignment implies in superimpose a 3D structure under 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:

${\displaystyle \mathrm {RMSD} ={\sqrt {{\frac {1}{N}}\sum _{i=1}^{N}\delta _{i}^{2}}}}$

where δ_i is the distance between atom i and either a reference atom corresponding in the other structure or the mean coordinate of the N equivalent atoms. In general, the RMSD outcome is measured in Ångström (Å) unit, which is equivalent to 10⁻¹⁰ m. The nearer to zero the RMSD value, the more similar are the structures.

Structural signatures, also called fingerprints, are macromolecule pattern representations that can be used to infer similarities and differences. Comparisons among a large set of proteins using RMSD still is a challenge due to the high computational cost of structural alignments. Structural signatures based on graph distance patterns among atom pairs have been used to determine protein identifying vectors and to detect non-trivial information.[23] Furthermore, algebra linear and machine learning can be used for clustering protein signatures, detecting protein-ligand interactions, predicting ΔΔG, and proposing mutations based on Euclidean distance.[24]

Comparative modeling, also known as homology modeling, corresponds to the methodology to construct three-dimensional structures from an amino acid sequence of a target protein and a template with known structure. The literature has described that evolutionarily related proteins tend to present a conserved three-dimensional structure.[25] In addition, sequences of distantly related proteins with identity lower than 20% can present different folds.[26]

In structural bioinformatics, de novo modeling, also known as ab initio modeling, refers to approaches for obtaining three-dimensional structures from sequences without the necessity of a homologous known 3D structure. Despite the new algorithms and methods proposed in the last years, de novo protein structure prediction is still considered one of the remain outstanding issues in modern science.[27]

After structure modeling, an additional step of structure validation is necessary since many of both comparative and 'de novo' modeling algorithms and tools use heuristics to try assembly the 3D structure, which can generate many errors. Some validation strategies consist of calculating energy scores and comparing them with experimentally determined structures. For example, the DOPE score is an energy score used by the MODELLER tool for determining the best model.[28]

Another validation strategy is calculating φ and ψ backbone dihedral angles of all residues and construct a Ramachandran plot. The side-chain of amino acids and the nature of interactions in the backbone restrict these two angles, and thus, the visualization of allowed conformations could be performed based on the Ramachandran plot. A high quantity of amino acids allocated in no permissive positions of the chart is an indication of a low-quality modeling.

A list with commonly used software tools for protein structure prediction, including comparative modeling, protein threading, de novo protein structure prediction, and secondary structure prediction is available in the list of protein structure prediction software.

Virtual screening (VS) is a computational approach used to fast screening of large compound libraries for drug discovery. Usually, virtual screening uses docking algorithms to rank small molecules with the highest affinity to a target receptor.

In recent times, several tools have been used to evaluate the use of virtual screening in the process of discovering new drugs. However, problems such as missing information, inaccurate understanding of drug-like molecules properties, weak scoring functions, or insufficient docking strategies hinder the docking process. Hence, the literature has described that it is still not considered a mature technology.[29][30]