Fragment-based lead discovery

Last updated

Fragment-based lead discovery (FBLD) also known as fragment-based drug discovery (FBDD) is a method used for finding lead compounds as part of the drug discovery process. Fragments are small organic molecules which are small in size and low in molecular weight. [1] It is based on identifying small chemical fragments, which may bind only weakly to the biological target, and then growing them or combining them to produce a lead with a higher affinity. FBLD can be compared with high-throughput screening (HTS). In HTS, libraries with up to millions of compounds, with molecular weights of around 500 Da, are screened, and nanomolar binding affinities are sought. In contrast, in the early phase of FBLD, libraries with a few thousand compounds with molecular weights of around 200 Da may be screened, and millimolar affinities can be considered useful. [2] FBLD is a technique being used in research for discovering novel potent inhibitors. [1] This methodology could help to design multitarget drugs for multiple diseases. The multitarget inhibitor approach is based on designing an inhibitor for the multiple targets. This type of drug design opens up new polypharmacological avenues for discovering innovative and effective therapies. Neurodegenerative diseases like Alzheimer’s (AD) and Parkinson’s, among others, also show rather complex etiopathologies. Multitarget inhibitors are more appropriate for addressing the complexity of AD and may provide new drugs for controlling the multifactorial nature of AD, stopping its progression. [3]

Contents

Library design

In analogy to the rule of five, it has been proposed that ideal fragments should follow the 'rule of three' (molecular weight < 300, ClogP < 3, the number of hydrogen bond donors and acceptors each should be < 3 and the number of rotatable bonds should be < 3). [4] Since the fragments have relatively low affinity for their targets, they must have high water solubility so that they can be screened at higher concentrations.

Library screening and quantification

In fragment-based drug discovery, the low binding affinities of the fragments pose significant challenges for screening. Many biophysical techniques have been applied to address this issue. In particular, ligand-observe nuclear magnetic resonance (NMR) methods such as water-ligand observed via gradient spectroscopy (waterLOGSY), saturation transfer difference spectroscopy (STD-NMR), 19F NMR spectroscopy and inter-ligand Overhauser effect (ILOE) spectroscopy, [5] [6] protein-observe NMR methods such as 1H-15N heteronuclear single quantum coherence (HSQC) that utilises isotopically-labelled proteins, [7] surface plasmon resonance (SPR), [8] isothermal titration calorimetry (ITC) [9] and Microscale Thermophoresis (MST) [10] are routinely-used for ligand screening and for the quantification of fragment binding affinity to the target protein. At modern X-ray crystallography synchrotron beamlines, several hundred data sets of protein-ligand complex crystal structures can be obtained within 24 hours. This technology makes crystallographic fragment screening possible, i.e. the use of X-ray crystallography directly for the fragment screening step. [11]

Once a fragment (or a combination of fragments) have been identified, protein X-ray crystallography is used to obtain structural models of the protein-fragment(s) complexes. [12] [13] Such information can then be used to guide organic synthesis for high-affinity protein ligands and enzyme inhibitors. [14]

Advantages over traditional libraries

Advantages of screening low molecular weight fragment based libraries over traditional higher molecular weight chemical libraries are several. [15] These include:

See also

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.

Within the fields of molecular biology and pharmacology, a small molecule or micromolecule is a low molecular weight organic compound that may regulate a biological process, with a size on the order of 1 nm. Many drugs are small molecules; the terms are equivalent in the literature. Larger structures such as nucleic acids and proteins, and many polysaccharides are not small molecules, although their constituent monomers are often considered small molecules. Small molecules may be used as research tools to probe biological function as well as leads in the development of new therapeutic agents. Some can inhibit a specific function of a protein or disrupt protein–protein interactions.

<span class="mw-page-title-main">Drug discovery</span> Process by which new candidate medications are discovered

In the fields of medicine, biotechnology and pharmacology, drug discovery is the process by which new candidate medications are discovered.

<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">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 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>

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.

<span class="mw-page-title-main">Enzyme inhibitor</span> Molecule that binds to an enzyme and decreases its activity

An enzyme inhibitor is a molecule that binds to an enzyme and blocks its activity. Enzymes are proteins that speed up chemical reactions necessary for life, in which substrate molecules are converted into products. An enzyme facilitates a specific chemical reaction by binding the substrate to its active site, a specialized area on the enzyme that accelerates the most difficult step of the reaction.

Protein–ligand docking is a molecular modelling technique. The goal of protein–ligand docking is to predict the position and orientation of a ligand when it is bound to a protein receptor or enzyme. Pharmaceutical research employs docking techniques for a variety of purposes, most notably in the virtual screening of large databases of available chemicals in order to select likely drug candidates. There has been rapid development in computational ability to determine protein structure with programs such as AlphaFold, and the demand for the corresponding protein-ligand docking predictions is driving implementation of software that can find accurate models. Once the protein folding can be predicted accurately along with how the ligands of various structures will bind to the protein, the ability for drug development to progress at a much faster rate becomes possible.

<span class="mw-page-title-main">Virtual screening</span>

Virtual screening (VS) is a computational technique used in drug discovery to search libraries of small molecules in order to identify those structures which are most likely to bind to a drug target, typically a protein receptor or enzyme.

Hit to lead (H2L) also known as lead generation is a stage in early drug discovery where small molecule hits from a high throughput screen (HTS) are evaluated and undergo limited optimization to identify promising lead compounds. These lead compounds undergo more extensive optimization in a subsequent step of drug discovery called lead optimization (LO). The drug discovery process generally follows the following path that includes a hit to lead stage:

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. Scoring functions have also been developed to predict the strength of intermolecular interactions between two proteins or between protein and DNA.

Inte:Ligand was founded in Maria Enzersdorf, Lower Austria (Niederösterreich) in 2003. They established the company headquarters on Mariahilferstrasse in Vienna, Austria that same year.

DNA-encoded chemical libraries (DEL) is a technology for the synthesis and screening on an unprecedented scale of collections of small molecule compounds. DEL is used in medicinal chemistry to bridge the fields of combinatorial chemistry and molecular biology. The aim of DEL technology is to accelerate the drug discovery process and in particular early phase discovery activities such as target validation and hit identification.

<span class="mw-page-title-main">Dynamic combinatorial chemistry</span>

Dynamic combinatorial chemistry (DCC); also known as constitutional dynamic chemistry (CDC) is a method to the generation of new molecules formed by reversible reaction of simple building blocks under thermodynamic control. The library of these reversibly interconverting building blocks is called a dynamic combinatorial library (DCL). All constituents in a DCL are in equilibrium, and their distribution is determined by their thermodynamic stability within the DCL. The interconversion of these building blocks may involve covalent or non-covalent interactions. When a DCL is exposed to an external influence, the equilibrium shifts and those components that interact with the external influence are stabilised and amplified, allowing more of the active compound to be formed.

CCR5 receptor antagonists are a class of small molecules that antagonize the CCR5 receptor. The C-C motif chemokine receptor CCR5 is involved in the process by which HIV, the virus that causes AIDS, enters cells. Hence antagonists of this receptor are entry inhibitors and have potential therapeutic applications in the treatment of HIV infections.

A thermal shift assay (TSA) measures changes in the thermal denaturation temperature and hence stability of a protein under varying conditions such as variations in drug concentration, buffer pH or ionic strength, redox potential, or sequence mutation. The most common method for measuring protein thermal shifts is differential scanning fluorimetry (DSF) or thermofluor, which utilizes specialized fluorogenic dyes.

Chemoproteomics entails a broad array of techniques used to identify and interrogate protein-small molecule interactions. Chemoproteomics complements phenotypic drug discovery, a paradigm that aims to discover lead compounds on the basis of alleviating a disease phenotype, as opposed to target-based drug discovery, in which lead compounds are designed to interact with predetermined disease-driving biological targets. As phenotypic drug discovery assays do not provide confirmation of a compound's mechanism of action, chemoproteomics provides valuable follow-up strategies to narrow down potential targets and eventually validate a molecule's mechanism of action. Chemoproteomics also attempts to address the inherent challenge of drug promiscuity in small molecule drug discovery by analyzing protein-small molecule interactions on a proteome-wide scale. A major goal of chemoproteomics is to characterize the interactome of drug candidates to gain insight into mechanisms of off-target toxicity and polypharmacology.

Molecular Operating Environment (MOE) is a drug discovery software platform that integrates visualization, modeling and simulations, as well as methodology development, in one package. MOE scientific applications are used by biologists, medicinal chemists and computational chemists in pharmaceutical, biotechnology and academic research. MOE runs on Windows, Linux, Unix, and macOS. Main application areas in MOE include structure-based design, fragment-based design, ligand-based design, pharmacophore discovery, medicinal chemistry applications, biologics applications, structural biology and bioinformatics, protein and antibody modeling, molecular modeling and simulations, virtual screening, cheminformatics & QSAR. The Scientific Vector Language (SVL) is the built-in command, scripting and application development language of MOE.

LeDock is a simple proprietary molecular docking software that can be used for docking of ligands with protein target. LeDock supports running on 64-bit Linux, macOS, and 32-bit and 64-bit Windows.

RNA-targeting small molecules represent a class of small molecules, organic compounds with traditional drug properties that can bind to RNA secondary or tertiary structures and alter translation patterns, localization, and degradation.

References

  1. 1 2 Price AJ, Howard S, Cons BD (November 2017). "Fragment-based drug discovery and its application to challenging drug targets". Essays in Biochemistry. 61 (5): 475–484. doi:10.1042/EBC20170029. PMID   29118094.
  2. ↑ Tounge BA, Parker MH (2011). "Designing a Diverse High-Quality Library for Crystallography-Based FBDD Screening". Fragment-Based Drug Design - Tools, Practical Approaches, and Examples. Methods in Enzymology. Vol. 493. pp. 3–20. doi:10.1016/B978-0-12-381274-2.00001-7. ISBN   9780123812742. PMID   21371585.
  3. ↑ Gharaghani S, Khayamian T, Ebrahimi M (October 2013). "Multitarget fragment-based design of novel inhibitors for AChE and SSAO/VAP-1 enzymes". Journal of Chemometrics. 27 (10): 297–305. doi:10.1002/cem.2556. S2CID   86409773.
  4. ↑ Congreve M, Carr R, Murray C, Jhoti H (October 2003). "A 'rule of three' for fragment-based lead discovery?". Drug Discov. Today. 8 (19): 876–7. doi:10.1016/S1359-6446(03)02831-9. PMID   14554012.
  5. ↑ Ma R, Wang P, Wu J, Ruan K (July 2016). "Process of Fragment-Based Lead Discovery-A Perspective from NMR". Molecules. 21 (7): 854. doi: 10.3390/molecules21070854 . PMC   6273320 . PMID   27438813.
  6. ↑ Norton RS, Leung EW, Chandrashekaran IR, MacRaild CA (July 2016). "Applications of (19)F-NMR in Fragment-Based Drug Discovery". Molecules. 21 (7): 860. doi: 10.3390/molecules21070860 . PMC   6273323 . PMID   27438818.
  7. ↑ Harner MJ, Frank AO, Fesik SW (June 2013). "Fragment-based drug discovery using NMR spectroscopy". Journal of Biomolecular NMR. 56 (2): 65–75. doi:10.1007/s10858-013-9740-z. PMC   3699969 . PMID   23686385.
  8. ↑ Neumann T, Junker HD, Schmidt K, Sekul R (Aug 2007). "SPR-based fragment screening: advantages and applications". Current Topics in Medicinal Chemistry. 7 (16): 1630–1642. doi:10.2174/156802607782341073. PMID   17979772. S2CID   17637118.
  9. ↑ Silvestre HL, Blundell TL, Abell C, Ciulli A (August 2013). "Integrated biophysical approach to fragment screening and validation for fragment-based lead discovery". Proceedings of the National Academy of Sciences of the United States of America. 110 (32): 12984–12989. Bibcode:2013PNAS..11012984S. doi: 10.1073/pnas.1304045110 . PMC   3740835 . PMID   23872845.
  10. ↑ Coletti A, Camponeschi F, Albini E, Greco FA, Maione V, Custodi C, et al. (December 2017). "Fragment-based approach to identify IDO1 inhibitor building blocks". European Journal of Medicinal Chemistry. 141: 169–177. doi: 10.1016/j.ejmech.2017.09.044 . PMID   29031064.
  11. ↑ Patel D, Bauman JD, Arnold E (Aug 2014). "Advantages of crystallographic fragment screening: functional and mechanistic insights from a powerful platform for efficient drug discovery". Progress in Biophysics and Molecular Biology. 116 (2–3): 92–100. doi: 10.1016/j.pbiomolbio.2014.08.004 . PMID   25117499.
  12. ↑ Caliandro R, Belviso DB, Aresta BM, de Candia M, Altomare CD (June 2013). "Protein crystallography and fragment-based drug design". Future Med. Chem. 5 (10): 1121–40. doi:10.4155/fmc.13.84. PMID   23795969.
  13. ↑ Chilingaryan Z, Yin Z, Oakley AJ (Oct 2012). "Fragment-based screening by protein crystallography: successes and pitfalls". Int. J. Mol. Sci. 13 (10): 12857–79. doi: 10.3390/ijms131012857 . PMC   3497300 . PMID   23202926.
  14. ↑ de Kloe GE, Bailey D, Leurs R, de Esch IJ (Jul 2009). "Transforming fragments into candidates: small becomes big in medicinal chemistry". Drug Discov. Today. 14 (13–14): 630–46. doi:10.1016/j.drudis.2009.03.009. PMID   19443265.
  15. ↑ Erlanson DA, McDowell RS, O'Brien T (July 2004). "Fragment-based drug discovery". J. Med. Chem. 47 (14): 3463–82. doi:10.1021/jm040031v. PMID   15214773. S2CID   15138472.

Further reading

This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.