Scoring functions for docking

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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.
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In the fields of computational chemistry and molecular modelling, scoring functions are mathematical functions used to approximately predict the binding affinity between two molecules after they have been docked. Most commonly one of the molecules is a small organic compound such as a drug and the second is the drug's biological target such as a protein receptor. [1] Scoring functions have also been developed to predict the strength of intermolecular interactions between two proteins [2] or between protein and DNA. [3]

Contents

Utility

Scoring functions are widely used in drug discovery and other molecular modelling applications. These include: [4]

A potentially more reliable but much more computationally demanding alternative to scoring functions are free energy perturbation calculations. [8]

Prerequisites

Scoring functions are normally parameterized (or trained) against a data set consisting of experimentally determined binding affinities between molecular species similar to the species that one wishes to predict.

For currently used methods aiming to predict affinities of ligands for proteins the following must first be known or predicted:

The above information yields the three-dimensional structure of the complex. Based on this structure, the scoring function can then estimate the strength of the association between the two molecules in the complex using one of the methods outlined below. Finally the scoring function itself may be used to help predict both the binding mode and the active conformation of the small molecule in the complex, or alternatively a simpler and computationally faster function may be utilized within the docking run.

Classes

There are four general classes of scoring functions: [9] [10] [11]

The first three types, force-field, empirical and knowledge-based, are commonly referred to as classical scoring functions and are characterized by assuming their contributions to binding are linearly combined. Due to this constraint, classical scoring functions are unable to take advantage of large amounts of training data. [35]

Refinement

Since different scoring functions are relatively co-linear, consensus scoring functions may not improve accuracy significantly. [36] This claim went somewhat against the prevailing view in the field, since previous studies had suggested that consensus scoring was beneficial. [37]

A perfect scoring function would be able to predict the binding free energy between the ligand and its target. But in reality both the computational methods and the computational resources put restraints to this goal. So most often methods are selected that minimize the number of false positive and false negative ligands. In cases where an experimental training set of data of binding constants and structures are available a simple method has been developed to refine the scoring function used in molecular docking. [38]

Related Research Articles

<span class="mw-page-title-main">Structural bioinformatics</span> Bioinformatics subfield

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.

<span class="mw-page-title-main">Binding site</span> Molecule-specific coordinate bonding area in biological systems

In biochemistry and molecular biology, a binding site is a region on a macromolecule such as a protein that binds to another molecule with specificity. The binding partner of the macromolecule is often referred to as a ligand. Ligands may include other proteins, enzyme substrates, second messengers, hormones, or allosteric modulators. The binding event is often, but not always, accompanied by a conformational change that alters the protein's function. Binding to protein binding sites is most often reversible, but can also be covalent reversible or irreversible.

<span class="mw-page-title-main">Drug design</span> Inventive process of finding new medications based on the knowledge of a biological target

Drug design, often referred to as rational drug design or simply rational design, is the inventive process of finding new medications based on the knowledge of a biological target. The drug is most commonly an organic small molecule that activates or inhibits the function of a biomolecule such as a protein, which in turn results in a therapeutic benefit to the patient. In the most basic sense, drug design involves the design of molecules that are complementary in shape and charge to the biomolecular target with which they interact and therefore will bind to it. Drug design frequently but not necessarily relies on computer modeling techniques. This type of modeling is sometimes referred to as computer-aided drug design. Finally, drug design that relies on the knowledge of the three-dimensional structure of the biomolecular target is known as structure-based drug design. In addition to small molecules, biopharmaceuticals including peptides and especially therapeutic antibodies are an increasingly important class of drugs and computational methods for improving the affinity, selectivity, and stability of these protein-based therapeutics have also been developed.

<span class="mw-page-title-main">Protein–protein interaction</span> Physical interactions and constructions between multiple proteins

Protein–protein interactions (PPIs) are physical contacts of high specificity established between two or more protein molecules as a result of biochemical events steered by interactions that include electrostatic forces, hydrogen bonding and the hydrophobic effect. Many are physical contacts with molecular associations between chains that occur in a cell or in a living organism in a specific biomolecular context.

<span class="mw-page-title-main">Ligand (biochemistry)</span> Substance that forms a complex with a biomolecule

In biochemistry and pharmacology, a ligand is a substance that forms a complex with a biomolecule to serve a biological purpose. The etymology stems from Latin ligare, which means 'to bind'. In protein-ligand binding, the ligand is usually a molecule which produces a signal by binding to a site on a target protein. The binding typically results in a change of conformational isomerism (conformation) of the target protein. In DNA-ligand binding studies, the ligand can be a small molecule, ion, or protein which binds to the DNA double helix. The relationship between ligand and binding partner is a function of charge, hydrophobicity, and molecular structure.

<span class="mw-page-title-main">Docking (molecular)</span> Prediction method in molecular modeling

In the field of molecular modeling, docking is a method which predicts the preferred orientation of one molecule to a second when a ligand and a target are bound to each other to form a stable complex. 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.

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Lead Finder is a computational chemistry tool designed for modeling protein-ligand interactions. This application is useful for conducting molecular docking studies and quantitatively assessing ligand binding and biological activity. Lead Finder offers free access to individual users, especially those in non-commercial and academic settings.

Jeffrey Skolnick is an American computational biologist. He is currently a Georgia Institute of Technology School of Biology Professor, the Director of the Center for the Study of Systems Biology, the Mary and Maisie Gibson Chair, the Georgia Research Alliance Eminent Scholar in Computational Systems Biology, the Director of the Integrative BioSystems Institute, and was previously the Scientific Advisor at Intellimedix.

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References

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