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One way to reduce the number of false positives is to recalculate the energy of the top scoring poses using (potentially) more accurate but computationally more intensive techniques such as [[Implicit solvation#Generalized Born|Generalized Born]] or [[Implicit solvation#Poisson-Boltzmann|Poisson-Boltzmann]] methods.<ref name="pmid14648625" />
One way to reduce the number of false positives is to recalculate the energy of the top scoring poses using (potentially) more accurate but computationally more intensive techniques such as [[Implicit solvation#Generalized Born|Generalized Born]] or [[Implicit solvation#Poisson-Boltzmann|Poisson-Boltzmann]] methods.<ref name="pmid14648625" />

== Evaluation ==

====== Retrospective ======
Docking screens are commonly evaluated by the enrichment of annotated ligands of known binders from among a large database of presumed non-binding, “[[decoy]]” molecules.<ref>{{Cite journal|title = Benchmarking Sets for Molecular Docking|url = http://dx.doi.org/10.1021/jm0608356|journal = Journal of Medicinal Chemistry|date = 2006-11-01|issn = 0022-2623|pmc = 3383317|pmid = 17154509|pages = 6789-6801|volume = 49|issue = 23|doi = 10.1021/jm0608356|first = Niu|last = Huang|first2 = Brian K.|last2 = Shoichet|first3 = John J.|last3 = Irwin}}</ref> The success of a docking screen is evaluated by its capacity to enrich the small number of known active compounds in the top ranks of a screen from among a much greater number of decoy molecules in the database. The area under the [[Receiver operating characteristic|receiver operating characteristic (ROC)]] curve is widely used to evaluate its performance.

====== Prospective ======
Resulting hits form docking screens are subjected to pharmacological validation (e.g. [[IC50|IC<sub>50</sub>]]<sub>,</sub>  [[Ligand (biochemistry)|affinity]] or [[Potency (pharmacology)|potency]] measurements). Only prospective studies constitute conclusive proof of the suitability of a technique for a particular target.<ref>{{Cite journal|title = Community benchmarks for virtual screening|url = http://link.springer.com/article/10.1007/s10822-008-9189-4|journal = Journal of Computer-Aided Molecular Design|date = 2008-02-14|issn = 0920-654X|pages = 193-199|volume = 22|issue = 3-4|doi = 10.1007/s10822-008-9189-4|language = en|first = John J.|last = Irwin}}</ref>


== Applications ==
== Applications ==

Revision as of 21:01, 8 December 2015

Docking glossary
Receptor or host or lock
The "receiving" molecule, most commonly a protein or other biopolymer.
Ligand or guest or key
The complementary partner molecule which binds to the receptor. Ligands are most often small molecules but could also be another biopolymer.
Docking
Computational simulation of a candidate ligand binding to a receptor.
Binding mode
The orientation of the ligand relative to the receptor as well as the conformation of the ligand and receptor when bound to each other.
Pose
A candidate binding mode.
Scoring
The process of evaluating a particular pose by counting the number of favorable intermolecular interactions such as hydrogen bonds and hydrophobic contacts.
Ranking
The process of classifying which ligands are most likely to interact favorably to a particular receptor based on the predicted free-energy of binding.
Docking assessment (DA)
Procedure to quantify the predictive capability of a docking protocol.
edit

In the field of molecular modeling, docking is a method which predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex.[1] Knowledge of the preferred orientation in turn may be used to predict the strength of association or binding affinity between two molecules using, for example, scoring functions.

The associations between biologically relevant molecules such as proteins, nucleic acids, carbohydrates, and lipids play a central role in signal transduction. Furthermore, the relative orientation of the two interacting partners may affect the type of signal produced (e.g., agonism vs antagonism). Therefore docking is useful for predicting both the strength and type of signal produced.

Molecular docking is one of the most frequently used methods in structure-based drug design, due to its ability to predict the binding-conformation of small molecule ligands to the appropriate target binding site. Characterisation of the binding behaviour plays an important role in rational design of drugs as well as to elucidate fundamental biochemical processes.[2]

Definition of problem

One can think of molecular docking as a problem of “lock-and-key”, in which one wants to find the correct relative orientation of the “key” which will open up the “lock” (where on the surface of the lock is the key hole, which direction to turn the key after it is inserted, etc.). Here, the protein can be thought of as the “lock” and the ligand can be thought of as a “key”. Molecular docking may be defined as an optimization problem, which would describe the “best-fit” orientation of a ligand that binds to a particular protein of interest. However, since both the ligand and the protein are flexible, a “hand-in-glove” analogy is more appropriate than “lock-and-key”.[3] During the course of the docking process, the ligand and the protein adjust their conformation to achieve an overall "best-fit" and this kind of conformational adjustment resulting in the overall binding is referred to as "induced-fit".[4]

Molecular docking research focusses on computationally simulating the molecular recognition process. It aims to achieve an optimized conformation for both the protein and ligand and relative orientation between protein and ligand such that the free energy of the overall system is minimized.

Docking approaches

Two approaches are particularly popular within the molecular docking community. One approach uses a matching technique that describes the protein and the ligand as complementary surfaces.[5][6][7] The second approach simulates the actual docking process in which the ligand-protein pairwise interaction energies are calculated.[8] Both approaches have significant advantages as well as some limitations. These are outlined below.

Schematic illustration of docking a small molecule ligand (green) to a protein target (black) producing a stable complex.

Shape complementarity

Small molecule docked to a protein.

Geometric matching/ shape complementarity methods describe the protein and ligand as a set of features that make them dockable.[9] These features may include molecular surface / complementary surface descriptors. In this case, the receptor’s molecular surface is described in terms of its solvent-accessible surface area and the ligand’s molecular surface is described in terms of its matching surface description. The complementarity between the two surfaces amounts to the shape matching description that may help finding the complementary pose of docking the target and the ligand molecules. Another approach is to describe the hydrophobic features of the protein using turns in the main-chain atoms. Yet another approach is to use a Fourier shape descriptor technique.[10][11][12] Whereas the shape complementarity based approaches are typically fast and robust, they cannot usually model the movements or dynamic changes in the ligand/ protein conformations accurately, although recent developments allow these methods to investigate ligand flexibility. Shape complementarity methods can quickly scan through several thousand ligands in a matter of seconds and actually figure out whether they can bind at the protein’s active site, and are usually scalable to even protein-protein interactions. They are also much more amenable to pharmacophore based approaches, since they use geometric descriptions of the ligands to find optimal binding.

Simulation

Simulating the docking process as such is much more complicated. In this approach, the protein and the ligand are separated by some physical distance, and the ligand finds its position into the protein’s active site after a certain number of “moves” in its conformational space. The moves incorporate rigid body transformations such as translations and rotations, as well as internal changes to the ligand’s structure including torsion angle rotations. Each of these moves in the conformation space of the ligand induces a total energetic cost of the system. Hence, the system's total energy is calculated after every move.

The obvious advantage of docking simulation is that ligand flexibility is easily incorporated, whereas shape complementarity techniques must use ingenious methods to incorporate flexibility in ligands. Also, it more accurately models reality, whereas shape complimentary techniques are more of an abstraction.

Clearly, simulation is computationally expensive, having to explore a large energy landscape. Grid-based techniques, optimization methods, and increased computer speed have made docking simulation more realistic.

Mechanics of docking

To perform a docking screen, the first requirement is a structure of the protein of interest. Usually the structure has been determined using a biophysical technique such as x-ray crystallography, or NMR spectroscopy. This protein structure and a database of potential ligands serve as inputs to a docking program. The success of a docking program depends on two components: the search algorithm and the scoring function.

Search algorithm

Main article: Searching the conformational space for docking

The search space in theory consists of all possible orientations and conformations of the protein paired with the ligand. However in practice with current computational resources, it is impossible to exhaustively explore the search space—this would involve enumerating all possible distortions of each molecule (molecules are dynamic and exist in an ensemble of conformational states) and all possible rotational and translational orientations of the ligand relative to the protein at a given level of granularity. Most docking programs in use account for a flexible ligand, and several attempt to model a flexible protein receptor. Each "snapshot" of the pair is referred to as a pose.

A variety of conformational search strategies have been applied to the ligand and to the receptor. These include:

Ligand flexibility

Conformations of the ligand may be generated in the absence of the receptor and subsequently docked[13] or conformations may be generated on-the-fly in the presence of the receptor binding cavity,[14] or with full rotational flexibility of every dihedral angle using fragment based docking.[15] Force field energy evaluation are most often used to select energetically reasonable conformations,[16] but knowledge-based methods have also been used.[17]

Receptor flexibility

Computational capacity has increased dramatically over the last decade making possible the use of more sophisticated and computationally intensive methods in computer-assisted drug design. However, dealing with receptor flexibility in docking methodologies is still a thorny issue. The main reason behind this difficulty is the large number of degrees of freedom that have to be considered in this kind of calculations. Neglecting it, however, leads to poor docking results in terms of binding pose prediction.[18]

Multiple static structures experimentally determined for the same protein in different conformations are often used to emulate receptor flexibility.[19] Alternatively rotamer libraries of amino acid side chains that surround the binding cavity may be searched to generate alternate but energetically reasonable protein conformations.[20][21]

Scoring function

Main article: Scoring functions for docking

The scoring function takes a pose as input and returns a number indicating the likelihood that the pose represents a favorable binding interaction.

Most scoring functions are physics-based molecular mechanics force fields that estimate the energy of the pose; a low (negative) energy indicates a stable system and thus a likely binding interaction. An alternative approach is to derive a statistical potential for interactions from a large database of protein-ligand complexes, such as the Protein Data Bank, and evaluate the fit of the pose according to this inferred potential.

There are a large number of structures from X-ray crystallography for complexes between proteins and high affinity ligands, but comparatively fewer for low affinity ligands as the later complexes tend to be less stable and therefore more difficult to crystallize. Scoring functions trained with this data can dock high affinity ligands correctly, but they will also give plausible docked conformations for ligands that do not bind. This gives a large number of false positive hits, i.e., ligands predicted to bind to the protein that actually don't when placed together in a test tube.

One way to reduce the number of false positives is to recalculate the energy of the top scoring poses using (potentially) more accurate but computationally more intensive techniques such as Generalized Born or Poisson-Boltzmann methods.[8]

Evaluation

Retrospective

Docking screens are commonly evaluated by the enrichment of annotated ligands of known binders from among a large database of presumed non-binding, “decoy” molecules.[22] The success of a docking screen is evaluated by its capacity to enrich the small number of known active compounds in the top ranks of a screen from among a much greater number of decoy molecules in the database. The area under the receiver operating characteristic (ROC) curve is widely used to evaluate its performance.

Prospective

Resulting hits form docking screens are subjected to pharmacological validation (e.g. IC50,  affinity or potency measurements). Only prospective studies constitute conclusive proof of the suitability of a technique for a particular target.[23]

Applications

A binding interaction between a small molecule ligand and an enzyme protein may result in activation or inhibition of the enzyme. If the protein is a receptor, ligand binding may result in agonism or antagonism. Docking is most commonly used in the field of drug design — most drugs are small organic molecules, and docking may be applied to:

  • hit identification – docking combined with a scoring function can be used to quickly screen large databases of potential drugs in silico to identify molecules that are likely to bind to protein target of interest (see virtual screening).
  • lead optimization – docking can be used to predict in where and in which relative orientation a ligand binds to a protein (also referred to as the binding mode or pose). This information may in turn be used to design more potent and selective analogs.
  • Bioremediation – Protein ligand docking can also be used to predict pollutants that can be degraded by enzymes.[24]

List of Protein-Ligand Docking Software

The number of docking programs currently available is high and has been steadily increasing over the last decades. The following list presents an overview of the most common protein-ligand docking programs, listed alphabetically,[25] with indication of the corresponding year of publication and country of origin. This list is comprehensive but not complete.

Program Country of Origin Year Published
AADS India 2011
ADAM Japan 1994
AutoDock USA 1990
AutoDock Vina USA 2010
BetaDock South Korea 2011
DARWIN USA 2000
DIVALI USA 1995
DOCK USA 1988
DockVision Canada 1992
EADock Switzerland 2007
eHiTS UK 2006
EUDOC USA 2001
FDS UK 2003
FlexE Germany 2001
FlexX Germany 1996
FLIPDock USA 2007
FLOG USA 1994
FRED UK 2003
FTDOCK UK 1997
GEMDOCK Taiwan 2004
Glide USA 2004
GOLD UK 1995
Hammerhead USA 1996
ICM-Dock USA 1997
Lead finder Canada 2008
LigandFit USA 2003
LigDockCSA South Korea 2011
LIGIN Germany 1996
LUDI Germany 1992
MADAMM Portugal 2009
MCDOCK USA 1999
MDock USA 2007
MolDock Denmark 2006
MS-DOCK France 2008
ParDOCK India 2007
PhDOCK USA 2003
PLANTS Germany 2006
PRO_LEADS UK 1998
PRODOCK USA 1999
ProPose Germany 2004
PSI-DOCK China 2006
PSO@AUTODOCK Germany 2007
PythDock South Korea 2011
Q-Dock USA 2008
QXP USA 1997
rDock UK 2013
SANDOCK UK 1998
SFDOCK China 1999
SODOCK Taiwan 2007
SOFTDocking USA 1991
Surflex USA 2003
SYSDOC USA 1994
VoteDock Poland 2011
YUCCA USA 2005

See also

References

  1. ^ Lengauer T, Rarey M (1996). "Computational methods for biomolecular docking". Curr. Opin. Struct. Biol. 6 (3): 402–6. doi:10.1016/S0959-440X(96)80061-3. PMID 8804827.
  2. ^ Kitchen DB, Decornez H, Furr JR, Bajorath J (2004). "Docking and scoring in virtual screening for drug discovery: methods and applications". Nature reviews. Drug discovery. 3 (11): 935–49. doi:10.1038/nrd1549. PMID 15520816.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  3. ^ Jorgensen WL (1991). "Rusting of the lock and key model for protein-ligand binding". Science. 254 (5034): 954–5. doi:10.1126/science.1719636. PMID 1719636.
  4. ^ Wei BQ, Weaver LH, Ferrari AM, Matthews BW, Shoichet BK (2004). "Testing a flexible-receptor docking algorithm in a model binding site". J. Mol. Biol. 337 (5): 1161–82. doi:10.1016/j.jmb.2004.02.015. PMID 15046985.{{cite journal}}: CS1 maint: multiple names: authors list (link)
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  18. ^ Cerqueira NM, Bras NF, Fernandes PA, Ramos MJ (January 2009). "MADAMM: a multistaged docking with an automated molecular modeling protocol". Proteins. 74 (1): 192–206. doi:10.1002/prot.22146. PMID 18618708.{{cite journal}}: CS1 maint: multiple names: authors list (link)
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  20. ^ Hartmann C, Antes I, Lengauer T (February 2009). "Docking and scoring with alternative side-chain conformations". Proteins. 74 (3): 712–26. doi:10.1002/prot.22189. PMID 18704939.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  21. ^ Taylor RD, Jewsbury PJ, Essex JW (October 2003). "FDS: flexible ligand and receptor docking with a continuum solvent model and soft-core energy function". J Comput Chem. 24 (13): 1637–56. doi:10.1002/jcc.10295. PMID 12926007.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  22. ^ Huang, Niu; Shoichet, Brian K.; Irwin, John J. (2006-11-01). "Benchmarking Sets for Molecular Docking". Journal of Medicinal Chemistry. 49 (23): 6789–6801. doi:10.1021/jm0608356. ISSN 0022-2623. PMC 3383317. PMID 17154509.
  23. ^ Irwin, John J. (2008-02-14). "Community benchmarks for virtual screening". Journal of Computer-Aided Molecular Design. 22 (3–4): 193–199. doi:10.1007/s10822-008-9189-4. ISSN 0920-654X.
  24. ^ Suresh PS, Kumar A, Kumar R, Singh VP (January 2008). "An in silico [correction of insilico] approach to bioremediation: laccase as a case study". J. Mol. Graph. Model. 26 (5): 845–9. doi:10.1016/j.jmgm.2007.05.005. PMID 17606396.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  25. ^ Sousa SF, Ribeiro AJ, Coimbra JTS, Neves RPP, Martins SA, Moorthy HNS, Fernandes PA, Ramos MJ (January 2013). "Protein-ligand docking in the new millennium - A Retrospective of 10 years in the field". Curr. Med. Chem. 20 (5): 2296–314. doi:10.2174/0929867311320180002. PMID 23531220.{{cite journal}}: CS1 maint: multiple names: authors list (link)

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