Abstract:Modern drug discovery usually involves the rapid screening of large numbers of compounds, either individually or in resolvable mixtures. These compounds may be complex and lead-like or may be small fragments representing optimal scaffolds. Several methods are suitable for detecting binding interactions based on a wide range of different physical platforms. However, the use of thermodynamic measurements has a role to play both in the high-throughput identification of binders and also in the fundamental understa… Show more
“…This has therefore led to concepts such as “drug-likeness” (e.g. Empfield and Leeson 2010; Hay et al 2014; Kell 2013; Kola 2008; Kola and Landis 2004; van der Greef and McBurney 2005), “lead-likeness” (Gozalbes and Pineda-Lucena 2011; Holdgate 2007; Oprea et al 2007, 2001; Wunberg et al 2006), and “ligand efficiency” (Hopkins et al 2014) by which the potentially desirable properties of such molecules have been assessed.…”
Section: Introductionmentioning
confidence: 99%
“…We recognise that any molecule bioactive in human cells (whether as a drug or for purposes of chemical genomics) must cross at least one membrane, that nutrients necessarily do so, that natural products remain a major source of successful (marketed) pharmaceutical drugs (Gozalbes and Pineda-Lucena 2011; Holdgate 2007; Oprea et al 2007, 2001; van Deursen et al 2011; Wunberg et al 2006), and that successful drugs require or at least use membrane transporters (Dobson et al 2009; Dobson and Kell 2008; Giacomini and Huang 2013; Giacomini et al 2010; Kell 2013; Kell and Dobson 2009; Kell et al 2013, 2011; Kell and Goodacre 2014; Lanthaler et al 2011) that normally are used for the transport of intermediary metabolites (Herrgård et al 2008; Swainston et al 2013; Thiele et al 2013). Given the natural role for these transporters as transporters of intermediary metabolites, we and others have thus suggested (hypothesised) that successful drugs are in fact much more like metabolites (we use this term to mean the natural intermediary metabolites of human metabolism, and do not consider metabolites of the drugs) than are the typical structures found in drug discovery libraries (e.g.…”
We exploit the recent availability of a community reconstruction of the human metabolic network (‘Recon2’) to study how close in structural terms are marketed drugs to the nearest known metabolite(s) that Recon2 contains. While other encodings using different kinds of chemical fingerprints give greater differences, we find using the 166 Public MDL Molecular Access (MACCS) keys that 90 % of marketed drugs have a Tanimoto similarity of more than 0.5 to the (structurally) ‘nearest’ human metabolite. This suggests a ‘rule of 0.5’ mnemonic for assessing the metabolite-like properties that characterise successful, marketed drugs. Multiobjective clustering leads to a similar conclusion, while artificial (synthetic) structures are seen to be less human-metabolite-like. This ‘rule of 0.5’ may have considerable predictive value in chemical biology and drug discovery, and may represent a powerful filter for decision making processes.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-014-0733-z) contains supplementary material, which is available to authorized users.
“…This has therefore led to concepts such as “drug-likeness” (e.g. Empfield and Leeson 2010; Hay et al 2014; Kell 2013; Kola 2008; Kola and Landis 2004; van der Greef and McBurney 2005), “lead-likeness” (Gozalbes and Pineda-Lucena 2011; Holdgate 2007; Oprea et al 2007, 2001; Wunberg et al 2006), and “ligand efficiency” (Hopkins et al 2014) by which the potentially desirable properties of such molecules have been assessed.…”
Section: Introductionmentioning
confidence: 99%
“…We recognise that any molecule bioactive in human cells (whether as a drug or for purposes of chemical genomics) must cross at least one membrane, that nutrients necessarily do so, that natural products remain a major source of successful (marketed) pharmaceutical drugs (Gozalbes and Pineda-Lucena 2011; Holdgate 2007; Oprea et al 2007, 2001; van Deursen et al 2011; Wunberg et al 2006), and that successful drugs require or at least use membrane transporters (Dobson et al 2009; Dobson and Kell 2008; Giacomini and Huang 2013; Giacomini et al 2010; Kell 2013; Kell and Dobson 2009; Kell et al 2013, 2011; Kell and Goodacre 2014; Lanthaler et al 2011) that normally are used for the transport of intermediary metabolites (Herrgård et al 2008; Swainston et al 2013; Thiele et al 2013). Given the natural role for these transporters as transporters of intermediary metabolites, we and others have thus suggested (hypothesised) that successful drugs are in fact much more like metabolites (we use this term to mean the natural intermediary metabolites of human metabolism, and do not consider metabolites of the drugs) than are the typical structures found in drug discovery libraries (e.g.…”
We exploit the recent availability of a community reconstruction of the human metabolic network (‘Recon2’) to study how close in structural terms are marketed drugs to the nearest known metabolite(s) that Recon2 contains. While other encodings using different kinds of chemical fingerprints give greater differences, we find using the 166 Public MDL Molecular Access (MACCS) keys that 90 % of marketed drugs have a Tanimoto similarity of more than 0.5 to the (structurally) ‘nearest’ human metabolite. This suggests a ‘rule of 0.5’ mnemonic for assessing the metabolite-like properties that characterise successful, marketed drugs. Multiobjective clustering leads to a similar conclusion, while artificial (synthetic) structures are seen to be less human-metabolite-like. This ‘rule of 0.5’ may have considerable predictive value in chemical biology and drug discovery, and may represent a powerful filter for decision making processes.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-014-0733-z) contains supplementary material, which is available to authorized users.
“…The thermodynamic profiles of CIB1 binding to the various α-integrin CT peptides suggest that the CIB1-integrin interaction is mainly driven by hydrophobic interactions (Fig. 4A) (41). Overall, CIB1 bound to α-integrin CTs with similar stoichiometry, affinity, free energy, and entropy.…”
The short cytoplasmic tails of the α and β chains of integrin adhesion receptors regulate integrin activation and cell signaling. Significantly less is known about proteins that bind to α-integrin cytoplasmic tails (CTs) than β-CTs to regulate integrins. CIB1 was previously identified as an αIIb binding partner that inhibits agonist-induced activation of the platelet-specific integrin, αIIbβ3. A sequence alignment of all α-integrin CTs revealed that key residues in the CIB1 binding site on αIIb are well-conserved, and was used to delineate a consensus binding site (I/L-x-x-x-L/M-W/Y-K-x-G-F-F). Because the CIB1 binding site on αIIb is conserved in all α-integrins, and CIB1 expression is ubiquitous, we asked if CIB1 could interact with other α-integrin CTs. We predicted that multiple α-integrin CTs were capable of binding to the same hydrophobic binding pocket on CIB1 with docking models generated by all-atom replica exchange discrete molecular dynamics. After demonstrating novel in vivo interactions between CIB1 and other whole integrin complexes with co-immunopreceipitations, we validated the modeled predictions with solid-phase competitive binding assays showing that other α-integrin CTs compete with the αIIb CT for binding to CIB1 in vitro. Isothermal titration calorimetry measurements indicated that this binding is driven by hydrophobic interactions and depends on residues in the CIB1 consensus binding site. These new mechanistic details of CIB1-integrin binding imply that CIB1 could bind to all integrin complexes and act as a broad regulator of integrin function.
“…Understanding the thermodynamics of a molecular interaction is key in drug discovery, as it allows modifications to be made to test compounds in more meaningful way. Thermodynamic measurements are fundamental in trying to understand molecular interaction, and in applying that learning in the pursuit of compounds, not only with higher affinity, but with the appropriate thermodynamic and kinetic profiles for their biological function (Holdgate, 2007). The binding affinity of a test compound is related to the free energy of the interaction, which is dependent upon the enthalpic and entropic components.…”
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