Structural bioinformatics

{{main|Protein structure}}

The structure of a protein is directly related to its function. The presence of certain chemical groups in specific locations allows proteins to act as enzymes, catalyzing several chemical reactions.{{Cite book|last1=Gu|first1=Jenny |last2=Bourne|first2=Philip E. | name-list-style = vanc |url=https://books.google.com/books?id=4H_ai7ivRIcC|title=Structural Bioinformatics|date=2009-03-16|publisher=John Wiley & Sons|isbn=978-0-470-18105-8|language=en}} 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{{cite journal | vauthors = Kocincová L, Jarešová M, Byška J, Parulek J, Hauser H, Kozlíková B | title = Comparative visualization of protein secondary structures | journal = BMC Bioinformatics | volume = 18 | issue = Suppl 2 | pages = 23 | date = February 2017 | pmid = 28251875 | pmc = 5333176 | doi = 10.1186/s12859-016-1449-z | doi-access = free }}). Also, weak interactions (such 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). Finally, the topological arrangement of interactions, whether strong or weak, and entanglements is being studied in the field of structural bioinformatics, utilizing frameworks such as circuit topology.

File:2LZM.png

Protein structure visualization is an important issue for structural bioinformatics.{{cite journal | vauthors = Shi M, Gao J, Zhang MQ | title = Web3DMol: interactive protein structure visualization based on WebGL | journal = Nucleic Acids Research | volume = 45 | issue = W1 | pages = W523–W527 | date = July 2017 | pmid = 28482028 | pmc = 5570197 | doi = 10.1093/nar/gkx383 }} It allows users to observe static or dynamic representations of the molecules, also allowing the detection of interactions that may be used to make inferences about molecular mechanisms. 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{{cite journal | vauthors = Stanfield RL, Wilson IA | title = Protein-peptide interactions | journal = Current Opinion in Structural Biology | volume = 5 | issue = 1 | pages = 103–13 | date = February 1995 | pmid = 7773739 | doi = 10.1016/0959-440X(95)80015-S }}, protein-ligand interactions (PLI){{cite book|title=Drug Design|vauthors=Klebe G|date=2015|publisher=Springer|isbn=978-3-642-17906-8|veditors=Scapin G, Patel D, Arnold E|series=NATO Science for Peace and Security Series A: Chemistry and Biology|location=Dordrecht|pages=83–92|chapter=Protein–Ligand Interactions as the Basis for Drug Action|doi=10.1007/978-3-642-17907-5_4}}, and protein-DNA interaction.File:Contacts between two amino acid residues- Q196-R200 (PDB ID- 2X1C).png

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.{{Cite book| vauthors = Martins PM, Mayrink VD, de Silveira S, da Silveira CH, de Lima LH, de Melo-Minardi RC |title=Proceedings of the 33rd Annual ACM Symposium on Applied Computing |chapter=How to compute protein residue contacts more accurately? |date=2018|chapter-url=http://dl.acm.org/citation.cfm?doid=3167132.3167136|language=en|location=Pau, France|publisher=ACM Press|pages=60–67|doi=10.1145/3167132.3167136|isbn=978-1-4503-5191-1|s2cid=49562347}}

Traditionally, computational methods have used threshold distance between atoms (also called cutoff) to detect possible interactions.{{cite journal | vauthors = da Silveira CH, Pires DE, Minardi RC, Ribeiro C, Veloso CJ, Lopes JC, Meira W, Neshich G, Ramos CH, Habesch R, Santoro MM | display-authors = 6 | title = Protein cutoff scanning: A comparative analysis of cutoff dependent and cutoff free methods for prospecting contacts in proteins | journal = Proteins | volume = 74 | issue = 3 | pages = 727–43 | date = February 2009 | pmid = 18704933 | doi = 10.1002/prot.22187 | s2cid = 1208256 | url = http://ainfo.cnptia.embrapa.br/digital/bitstream/item/147896/1/Proteins-Structure-Function-and-Bioinformatics.pdf }} 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.

{{main|Protein Data Bank}}

File:Number of structures of Protein Data Bank (1976-2020).png, NMR spectroscopy, and 3D electron microscopy experiments per year. Source: https://www.rcsb.org/stats/growth|alt=|335x335px]]

The Protein Data Bank (PDB) is a database of 3D structure data for large biological molecules, such as proteins, DNA, and RNA. PDB is managed by an international organization called the Worldwide Protein Data Bank (wwPDB), which is composed of several local organizations, as. PDBe, PDBj, RCSB, and BMRB. They are responsible for keeping copies of PDB data available on the internet at no charge. The number of structure data available at PDB has increased each year, being obtained typically by X-ray crystallography, NMR spectroscopy, or cryo-electron microscopy.

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.{{Cite web|url=http://mmcif.wwpdb.org/docs/faqs/pdbx-mmcif-faq-general.html|title=PDBx/mmCIF General FAQ|website=mmcif.wwpdb.org|access-date=2020-02-26}}

The PDBx/mmCIF (macromolecular Crystallographic Information File) is a standard text file format for representing crystallographic information.{{Cite web|url=https://www.wwpdb.org/documentation/file-formats-and-the-pdb|title=wwPDB: File Formats and the PDB|last=wwPDB.org|website=www.wwpdb.org|language=en|access-date=2020-02-26}} 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 to 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.{{Cite web|url=http://mmcif.wwpdb.org/docs/pubs/methods-enzymology-paper-1997.html|title=PDBx/mmCIF Dictionary Resources|website=mmcif.wwpdb.org|access-date=2020-02-26}}

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).{{Cite web|url=https://www.ncbi.nlm.nih.gov/Structure/MMDB/mmdb.shtml|title=Macromolecular Structures Resource Group|website=www.ncbi.nlm.nih.gov|access-date=2020-04-13}}
• Nucleic acid Data Base (NDB): Experimentally determined information about nucleic acids (DNA, RNA).{{Cite web|url=http://ndbserver.rutgers.edu/|title=Nucleic Acid Database (NDB)|website=ndbserver.rutgers.edu|access-date=2020-04-13}}
• Structural Classification of Proteins (SCOP): Comprehensive description of the structural and evolutionary relationships between structurally known proteins.{{Cite web|url=http://scop.mrc-lmb.cam.ac.uk/scop/|title=SCOP: Structural Classification of Proteins|date=2007-09-11|archive-url=https://web.archive.org/web/20070911012207/http://scop.mrc-lmb.cam.ac.uk/scop/|access-date=2020-04-13|archive-date=2007-09-11}}
• TOPOFIT-DB: Protein structural alignments based on the TOPOFIT method.{{cite journal | vauthors = Ilyin VA, Abyzov A, Leslin CM | title = Structural alignment of proteins by a novel TOPOFIT method, as a superimposition of common volumes at a topomax point | journal = Protein Science | volume = 13 | issue = 7 | pages = 1865–74 | date = July 2004 | pmid = 15215530 | pmc = 2279929 | doi = 10.1110/ps.04672604 }}
• Electron Density Server (EDS): Electron-density maps and statistics about the fit of crystal structures and their maps.{{Cite web|url=http://eds.bmc.uu.se/eds/|title=EDS - Uppsala Electron Density Server|website=eds.bmc.uu.se|access-date=2020-04-13}}
• CASP: Prediction Center Community-wide, worldwide experiment for protein structure prediction CASP.{{Cite web|url=http://www.predictioncenter.org/|title=Home - Prediction Center|website=www.predictioncenter.org|access-date=2020-04-13}}
• PISCES server for creating non-redundant lists of proteins: Generates PDB list by sequence identity and structural quality criteria.{{Cite web|url=http://dunbrack.fccc.edu/PISCES.php|title=:: Dunbrack Lab|website=dunbrack.fccc.edu|access-date=2020-04-13}}
• The Structural Biology Knowledgebase: Tools to aid in protein research design.{{Cite web|url=http://sbkb.org/|title=Structural Biology KnowlegebaseSBKB - SBKB|website=sbkb.org|access-date=2020-04-13}}
• ProtCID: The Protein Common Interface Database Database of similar protein-protein interfaces in crystal structures of homologous proteins.{{Cite web|url=http://dunbrack2.fccc.edu/protcid|title=Protein Common Interface Database|website=dunbrack2.fccc.edu|access-date=2020-04-13}}
• AlphaFold:AlphaFold - Protein Structure Database.{{Cite web|url=https://alphafold.ebi.ac.uk|title=AlphaFold}}

Structural alignment is a method for comparison between 3D structures based on their shape and conformation.{{Cite web|url=https://www.sciencedaily.com/terms/structural_alignment.htm|title=Structural alignment (genomics)|website=ScienceDaily|language=en|access-date=2020-02-26}} 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:

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

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.{{cite journal | vauthors = Pires DE, de Melo-Minardi RC, dos Santos MA, da Silveira CH, Santoro MM, Meira W | title = Cutoff Scanning Matrix (CSM): structural classification and function prediction by protein inter-residue distance patterns | journal = BMC Genomics | volume = 12 Suppl 4 | issue = S4 | pages = S12 | date = December 2011 | pmid = 22369665 | pmc = 3287581 | doi = 10.1186/1471-2164-12-S4-S12 | doi-access = free }} Furthermore, linear algebra and machine learning can be used for clustering protein signatures, detecting protein-ligand interactions, predicting ΔΔG, and proposing mutations based on Euclidean distance.{{cite journal | vauthors = Mariano DC, Santos LH, Machado KD, Werhli AV, de Lima LH, de Melo-Minardi RC | title = A Computational Method to Propose Mutations in Enzymes Based on Structural Signature Variation (SSV) | journal = International Journal of Molecular Sciences | volume = 20 | issue = 2 | pages = 333 | date = January 2019 | pmid = 30650542 | pmc = 6359350 | doi = 10.3390/ijms20020333 | doi-access = free }}

Image:1axc PCNA ProCheck Rama.jpg (PDB ID 1AXC). The red, brown, and yellow regions represent the favored, allowed, and "generously allowed" regions as defined by ProCheck. This plot can be used to verify incorrectly modeled amino acids.]]The atomic structures of molecules can be obtained by several methods, such as X-ray crystallography (XRC), NMR spectroscopy, and 3D electron microscopy; however, these processes can present high costs and sometimes some structures can be hardly established, such as membrane proteins. Hence, it is necessary to use computational approaches for determining 3D structures of macromolecules. The structure prediction methods are classified into comparative modeling and de novo modeling.

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.{{Cite journal|last1=Kaczanowski|first1=Szymon|last2=Zielenkiewicz|first2=Piotr | name-list-style = vanc |date=March 2010|title=Why similar protein sequences encode similar three-dimensional structures?|journal=Theoretical Chemistry Accounts|language=en|volume=125|issue=3–6|pages=643–650|doi=10.1007/s00214-009-0656-3|s2cid=95593331|issn=1432-881X|url=http://eprints.ibb.waw.pl/21/1/publikcja1.pdf}} In addition, sequences of distantly related proteins with identity lower than 20% can present different folds.{{cite journal | vauthors = Chothia C, Lesk AM | title = The relation between the divergence of sequence and structure in proteins | journal = The EMBO Journal | volume = 5 | issue = 4 | pages = 823–6 | date = April 1986 | pmid = 3709526 | pmc = 1166865 | doi = 10.1002/j.1460-2075.1986.tb04288.x }}

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.{{cite journal | title = So much more to know | journal = Science | volume = 309 | issue = 5731 | pages = 78–102 | date = July 2005 | pmid = 15994524 | doi = 10.1126/science.309.5731.78b | doi-access = free }}

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.{{cite journal | vauthors = Webb B, Sali A | title = Comparative Protein Structure Modeling Using MODELLER | journal = Current Protocols in Bioinformatics | volume = 47 | issue = 1 | pages = 5.6.1–32 | date = September 2014 | pmid = 25199792 | doi = 10.1002/0471250953.bi0506s47 | pmc = 4186674 }}

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.

File:Docking representation 2.png

Molecular docking (also referred to only as docking) is a method used to predict the orientation coordinates of a molecule (ligand) when bound to another one (receptor or target). The binding may be mostly through non-covalent interactions while covalently linked binding can also be studied. Molecular docking aims to predict possible poses (binding modes) of the ligand when it interacts with specific regions on the receptor. Docking tools use force fields to estimate a score for ranking best poses that favored better interactions between the two molecules.

In general, docking protocols are used to predict the interactions between small molecules and proteins. However, docking also can be used to detect associations and binding modes among proteins, peptides, DNA or RNA molecules, carbohydrates, and other macromolecules.

Virtual screening (VS) is a computational approach used for 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 molecular 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.{{cite book |last1=Dhasmana|first1=Anupam |last2=Raza |first2=Sana |last3=Jahan |first3=Roshan |last4=Lohani |first4=Mohtashim |last5=Arif |first5=Jamal M.| name-list-style = vanc |chapter=Chapter 19 - High-Throughput Virtual Screening (HTVS) of Natural Compounds and Exploration of Their Biomolecular Mechanisms: An In Silico Approach |date=2019-01-01 | title =New Look to Phytomedicine|pages=523–548|editor-last=Ahmad Khan|editor-first=Mohd Sajjad |editor2-last=Ahmad|editor2-first=Iqbal|editor3-last=Chattopadhyay|editor3-first=Debprasad|publisher=Academic Press |doi=10.1016/b978-0-12-814619-4.00020-3 |isbn=978-0-12-814619-4 |s2cid=69534557 }}{{cite book | vauthors = Wermuth CG, Villoutreix B, Grisoni S, Olivier A, Rocher JP | chapter = Strategies in the search for new lead compounds or original working hypotheses. | veditors = Wermuth CG, Aldous D, Raboisson P, Rognan D | title = The practice of Medicinal Chemistry | date = January 2015 | pages = 73–99 | publisher = Academic Press | doi = 10.1016/B978-0-12-417205-0.00004-3 | isbn = 978-0-12-417205-0 }}

File:Molecular dynamics of a glucose-tolerant β-Glucosidase.gif

Molecular dynamics (MD) is a computational method for simulating interactions between molecules and their atoms during a given period of time.{{Cite journal| vauthors = Alder BJ, Wainwright TE |date=August 1959|title=Studies in Molecular Dynamics. I. General Method|journal=The Journal of Chemical Physics|language=en|volume=31|issue=2|pages=459–466|doi=10.1063/1.1730376|bibcode=1959JChPh..31..459A|issn=0021-9606}} This method allows the observation of the behavior of molecules and their interactions, considering the system as a whole. To calculate the behavior of the systems and, thus, determine the trajectories, an MD can use Newton's equation of motion, in addition to using molecular mechanics methods to estimate the forces that occur between particles (force fields).{{Cite journal|last=Yousif|first=Ragheed Hussam|date=2020|title=Exploring the Molecular Interactions between Neoculin and the Human Sweet Taste Receptors through Computational Approaches|url=http://www.ukm.my/jsm/pdf_files/SM-PDF-49-3-2020/ARTIKEL_6.pdf|journal=Sains Malaysiana|volume=49|issue=3|pages=517–525|doi=10.17576/jsm-2020-4903-06|doi-access=free}}

Informatics approaches used in structural bioinformatics are:

• 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 domain. Protein domains are building blocks that can be rearranged to form new proteins. They can be studied in isolation initially.
• Tracking X-ray crystallography 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.
• Analysis of X-Ray crystallographic data - The diffraction pattern obtained as a result of bombarding X-rays on electrons is Fourier transform 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.
• Analysis of NMR spectroscopy data - Nuclear magnetic resonance spectroscopy 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.
• Correlating Structural information with functional information - Structural studies can be used as probe for structural-functional relationship.

{{columns-list|colwidth=30em|

• MMDB
• Protein Data Bank
• Structural Classification of Proteins database
• STING
• Molecular modelling
• List of software for molecular mechanics modeling
• Protein structure prediction
• CASP

}}

{{Reflist|32em}}

{{refbegin|32em}}

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• {{cite book | vauthors = Bourne PE, Weissig H | date = 2003 | title = Structural Bioinformatics | publisher = Wiley | isbn = 0-471-20199-5 }}
• {{cite book | last = Leach | first = Andrew | name-list-style = vanc | date = 2001 | title = Molecular Modelling: Principles and Applications | edition = 2nd | publisher = Prentice Hall | isbn = 978-0-582-38210-7 }}
• {{cite book | vauthors = Peitsch MC, Schwede T | date = 2008 | title = Computational Structural Biology: Methods and Applications | publisher = World Scientific | isbn = 978-9812778772 }}
• {{cite journal | vauthors = Leontis NB, Westhof E | title = Geometric nomenclature and classification of RNA base pairs | journal = RNA | volume = 7 | issue = 4 | pages = 499–512 | date = April 2001 | pmid = 11345429 | pmc = 1370104 | doi = 10.1017/S1355838201002515 }}
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• {{cite book | vauthors = Ramachandran GN, Sasisekharan V | title = Advances in Protein Chemistry Volume 23 | chapter = Conformation of polypeptides and proteins | volume = 23 | pages = 283–438 | year = 1968 | pmid = 4882249 | doi = 10.1016/S0065-3233(08)60402-7 | isbn = 978-0-12-034223-5 | author-link = Gopalasamudram Narayana Iyer Ramachandran }}
• {{cite journal | vauthors = Ramachandran GN, Ramakrishnan C, Sasisekharan V | title = Stereochemistry of polypeptide chain configurations | journal = Journal of Molecular Biology | volume = 7 | pages = 95–9 | date = July 1963 | pmid = 13990617 | doi = 10.1016/S0022-2836(63)80023-6 }}

{{refend|32em}}

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