Docking (molecular) From Wikipedia, the free encyclopedia Docking glossary • Receptor or host – The "receiving" molecule , most commonly a protein or other biopolymer . • Ligand or guest – 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. Schematic diagram illustrating the docking of a small molecule ligand (brown) to a protein receptor (green) to produce a complex.
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Docking (molecular)From Wikipedia, the free encyclopedia
Docking glossary
• Receptor or host – The "receiving" molecule, most commonly a protein or other biopolymer.
• Ligand or guest – 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 interactionssuch 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.
Schematic diagram illustrating the docking of a small molecule ligand (brown) to a protein receptor (green) to produce a complex.
of the marine flora and fauna of the Islands of Palau". Nat Prod Rep 21 (1):
50–76. doi:10.1039/b300664f. PMID 15039835.
[ ]Further reading
Gad, Shayne C. (2005). Drug discovery handbook. Hoboken, N.J: Wiley-
Interscience/J. Wiley. ISBN 0-471-21384-5.
Madsen, Ulf; Krogsgaard-Larsen, Povl; Liljefors, Tommy (2002). Textbook of
drug design and discovery. Washington, DC: Taylor & Francis.ISBN 0-415-
28288-8.
[ ]External links
Introduction to Drug Discovery - Combinatorial Chemistry Review
In Focus "Medical Research involving Minors: Medical, legal and ethical
aspects" (German Reference Centre for Ethics in the Life Sciences)
International Conference on Harmonisation of Technical Requirements for
Registration of Pharmaceuticals for Human Use (ICH)
Food and Drug Administration (FDA)
CDER Drug and Biologic Approval Reports
Pharmaceutical Research and Manufacturers of America (PhRMA)
European Medicines Agency (EMEA)
Pharmaceuticals and Medical Devices Agency (PMDA)
WHO Model List of Essential Medicines
Innovation and Stagnation: Challenge and Opportunity on the Critical Path to
New Medical Products - FDA
Priority Medicines for Europe and the World Project "A Public Health
Approach to Innovation" - WHO
International Union of Basic and Clinical Pharmacology
IUPHAR Committee on Receptor Nomenclature and Drug Classification
Advanced Cell Classifier program for high-content screen analysis (ETH
Zurich)
Drugdiscovery@home Early in silico drug discovery by volunteer computing.
Supercomputing Facility for Bioinformatics & Computational Biology, IIT Delhi
Sitemap | Biogrid | Tenders | Mail
Research
Software Tools
Publications
Services
Collaborations
Tutorials
Bioinformatics Links
Video
Photo Gallery
What is Drug Design ?
Drug discovery and development is an intense, lengthy and an interdisciplinary endeavor. Drug discovery is mostly portrayed as a linear, consecutive process that starts with target and lead discovery, followed by lead optimization and pre-clinical in vitro and in vivo studies to determine if such compounds satisfy a number of pre-set criteria for initiating clinical development. For the pharmaceutical industry, the number of years to bring a drug from discovery to market is approximately 12-14 years and costing upto $1.2 - $1.4 billion dollars. Traditionally, drugs were discovered by synthesizing compounds in a time-consuming multi-step processes against a battery of in vivo biological screens and further investigating the promising candidates for their pharmacokinetic properties, metabolism and potential toxicity. Such a development process has resulted in high attrition rates with failures attributed to poor pharmacokinetics (39%), lack of efficacy (30%), animal toxicity (11%), adverse effects in humans (10%) and various commercial and miscellaneous factors. Today, the process of drug discovery has been revolutionized with the advent of genomics, proteomics, bioinformatics and efficient technologies like, combinatorial chemistry, high throughput screening (HTS), virtual screening, de novo design, in vitro, in silicoADMET screening and structure-based drug design.
What is in-silico Drug Design ?
In silico methods can help in identifying drug targets via bioinformatics tools.They can also be used to analyze the target structures for possible binding/ active sites, generate candidate molecules, check for their drug likeness , dock these molecules with the target , rank them according to their binding affinites , further optimize the molecules to improve binding characteristics
The use of computers and computational methods permeates all aspects of drug discovery today and forms the core of structure-based drug design. High-performance computing, data management software and internet are facilitating the access of huge amount of data generated and transforming the massive complex biological data into workable knowledge in modern day drug discovery process. The use of complementary experimental and informatics techniques increases the chance of success in many stages of the discovery process, from the identification of novel targets and elucidation of their functions to the discovery and development of lead compounds with desired properties. Computational tools offer the advantage of delivering new drug candidates more quickly and at a lower cost. Major roles of computation in drug discovery are; (1) Virtual screening & de novo design, (2) in silicoADME/T prediction and (3) Advanced methods for determining protein-ligand binding
Combinatorial Chemistry Review
Home
Introduction
Solid Phase Synthesis
Resins for Solid Phase
Linkers for Solid Phase
Solution Phase Synthesis
Analytical Techniques
Combichem Glossary
Drug Discovery
Articles
News & Events
Useful Links
About Site
Link to Combichem Review
Info for Organic Chemists
Privacy Policy
Introduction to Drug DiscoveryDrug discovery and development is an expensive process due to the high costs of R&D and human clinical tests. The average total cost per drug development varies from US$ 897 million to US$ 1.9 billion. The typical development time is 10-15 years.
R&D of a new drug involves the identification of a target (e.g. protein) and the discovery of some suitable drug candidates that can block or activate the target. Clinical testing is the most extensive and expensive phase in drug development and is done in order to obtain the necessary governmental approvals. In the US drugs must be approved by the Food and Drug Administration (FDA).
R&D – Finding the Drug
One of the most successful ways to find promising drug candidates is to investigate how the target protein interacts with randomly chosen compounds, which are usually a part of compound libraries. This testing is often done in so called high-thoughput screening (HTS) facilities. Compound libraries are commercially available in sizes of up to several millions of compounds. The most promising compounds obtained from the screening are called hits – these are the compounds that show binding activity towards the target.
Some of these hits are then promoted to lead compounds – candidate structures which are further refined and modified in order to achieve more favorable interactions and less side-effects.
Drug Discovery Methods
The following are methods for finding a drug candidate, along with their pros and cons:1. Virtual screening (VS) based on the computationally inferred or simulated real screening;The main advantages of this method compared to laboratory experiments are:-low costs, no compounds have to be purchased externally or synthesized by a chemist;-it is possible to investigate compounds that have not been synthesized yet;-conducting HTS experiments is expensive and VS can be used to reduce the initial number of compounds before using HTS methods;-huge amount of chemicals to search from. The number of possible virtual molecules available for VS is exceedingly higher than the number
of compounds presently available for HTS;The disadvantage of virtual screening is that it can not substitute the real screening.2. The real screening, such as high-throughput screening (HTS), can experimentally test the activity of hundreds of thousands of compounds against the target a day. This method provides real results that are used for drug discovery. However, it is highly expensive.
Virtual Screening in Drug Discovery
Computational methods can be used to predict or simulate how a particular compound interacts with a given protein target. They can be used to assist in building hypotheses about desirable chemical properties when designing the drug and, moreover, they can be used to refine and modify drug candidates. The following three virtual screening or computational methods are used in the modern drug discovery process: Molecular Docking, Quantitative Structure-Activity Relationships (QSAR) and Pharmacopoeia Mapping.
Molecular Docking
When the structure of the target is available, usually from X-ray crystallography, the most commonly used virtual screening method is molecular docking. Molecular docking can also be used to test possible hypotheses before conducting costly laboratory experiments. Molecular docking programs predict how a drug candidate binds to a protein target. This software consists of two core components:
1. A search algorithm, sometimes called an optimisation algorithm. The search algorithm is responsible for finding the best conformations of the ligand, a small drug-like molecule and protein system. A conformation is the position and orientation of the ligand relative to the protein. In flexible docking the conformation also contains information about the internal flexible structure of the ligand – and in some cases about the internal flexible structure of the protein. Since the number of possible conformations is extremely large, it is not possible to test all of them. Therefore, sophisticated search techniques have to be applied. Examples of some commonly used methods are Genetic Algorithms and Monte Carlo simulations.
2. An evaluation function, sometimes called a score function. This is a function providing a measure of how strongly a given ligand will interact with a particular protein. Energy force fields are often used as evaluation functions. These force fields calculate the energy contribution from different terms such as the known electrostatic forces between the atoms in the ligand and in the protein, forces arising from deformation of the ligand, pure electron-shell repulsion between atoms and effect from the solvent in which the interaction takes place.
It is not possible to guarantee that the search algorithm will find the same solution as the true natural process, but more efficient search algorithms will be more likely to find the true solution if the evaluation functions properly reflect the natural processes.
Metaphorically, the active site of the protein can be viewed as a lock, and the ligand can be thought of as a key. Molecular docking is the process of testing whether a given key fits a particular lock. This description is slightly oversimplified due to the fact that neither the
ligand nor the proteins are completely rigid structures. Their shapes are somewhat flexible and may adapt to each other.
As mentioned in the previous paragraph it is necessary to know the geometrical structure of both the ligand and the target protein in order to use molecular docking methods. QSAR (Quantitative Structure-Activity Relationships) is an example of a method which can be applied regardless of whether the structure is known or not.
QSAR formalizes what is experimentally known about how a given protein interacts with some tested compounds. As an example, it may be known from previous experiments that the protein under investigation shows signs of activity against one group of compounds, but not against another group.
In terms of the lock and key metaphor, we do not know what the lock looks like, but we do know which keys work, and which do not. In order to build a QSAR model for deciding why some compounds show sign of activity and others do not, a set of descriptors are chosen. These are assumed to influence whether a given compound will succeed or fail in binding to a given target. Typical descriptors are parameters such as molecular weight, molecular volume, and electrical and thermodynamical properties. QSAR models are used for virtual screening of compounds to investigate their appropriate drug candidates descriptors for the target.
Pharmacopoeia Mapping
Where QSAR focused on a set of descriptors like electrostatic and thermodynamic properties, Pharmacopoeia Mapping is a geometrical approach. A pharmacophore can be thought of as a 3D model of characteristic features of the binding site of the investigated protein (target). It may describe properties like: "In this region of the target a positive charge is needed, in this region there is a hydrogen donor, that region may not be occupied" and so on. On a pharmacophore model the spheres indicate regions where a certain feature (e.g. a cation or an anion) is required. The pharmacophores are also used to define the essential features of one or more molecules with the same biological activity.
Like QSAR models, pharmacophores can be built without knowing the structure of the target. This can be done by extracting features from compounds which are known experimentally to interact with the target in question. Afterwards, the derived pharmacophore model can be used to search compound databases (libraries) thus screening for potential drug candidates that may be of interest.