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Strong nonadditivity as a key structure-activity relationship feature: distinguishing structural changes from assay artifacts.

Kramer C, Fuchs JE, Liedl KR - J Chem Inf Model (2015)

Bottom Line: At the same time, nonadditivity is a challenge for both basic SAR analysis as well as many ligand-based data analysis techniques such as Free-Wilson Analysis and Matched Molecular Pair analysis, since linear substituent contribution models inherently assume additivity and thus do not work in such cases.We find that all cases of strong nonadditivity (ΔΔpKi and ΔΔpIC50 > 2.0 log units) with sufficient structural information to generate reasonable hypothesis involve changes in binding mode.With the appropriate statistical basis, nonadditivity analysis offers a variety of new attempts for various areas in computer-aided drug design, including the validation of scoring functions and free energy perturbation approaches, binding pocket classification, and novel features in SAR analysis tools.

View Article: PubMed Central - PubMed

Affiliation: †Department of Theoretical Chemistry, Faculty for Chemistry and Pharmacy, Center for Molecular Biosciences Innsbruck (CMBI), Leopold-Franzens University Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria.

ABSTRACT
Nonadditivity in protein-ligand affinity data represents highly instructive structure-activity relationship (SAR) features that indicate structural changes and have the potential to guide rational drug design. At the same time, nonadditivity is a challenge for both basic SAR analysis as well as many ligand-based data analysis techniques such as Free-Wilson Analysis and Matched Molecular Pair analysis, since linear substituent contribution models inherently assume additivity and thus do not work in such cases. While structural causes for nonadditivity have been analyzed anecdotally, no systematic approaches to interpret and use nonadditivity prospectively have been developed yet. In this contribution, we lay the statistical framework for systematic analysis of nonadditivity in a SAR series. First, we develop a general metric to quantify nonadditivity. Then, we demonstrate the non-negligible impact of experimental uncertainty that creates apparent nonadditivity, and we introduce techniques to handle experimental uncertainty. Finally, we analyze public SAR data sets for strong nonadditivity and use recourse to the original publications and available X-ray structures to find structural explanations for the nonadditivity observed. We find that all cases of strong nonadditivity (ΔΔpKi and ΔΔpIC50 > 2.0 log units) with sufficient structural information to generate reasonable hypothesis involve changes in binding mode. With the appropriate statistical basis, nonadditivity analysis offers a variety of new attempts for various areas in computer-aided drug design, including the validation of scoring functions and free energy perturbation approaches, binding pocket classification, and novel features in SAR analysis tools.

No MeSH data available.


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Double transformation cycle for 1H-inden-1-onederivatives binding to the estrogen β receptor.43
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fig5: Double transformation cycle for 1H-inden-1-onederivatives binding to the estrogen β receptor.43

Mentions: We inspected the original publications and the PDB structuresforthe remaining 15 cycles to generate structural hypothesis that couldexplain the observed nonadditivity in SAR data. In five cases, theavailable X-ray structures showed that there can be alternative orientationsof the central scaffold. Such findings imply that the ligands cancompletely reorient, depending on the substitution patterns. A completereorientation of the scaffold can lead to strong nonadditivity, sincethe substituents then interact with completely different subpockets,yielding different contributions to the free energy of binding. Figure 5 shows an example for such a case: a set of estrogenreceptor β ligands with an indenone scaffold, published by Malamaset al.43


Strong nonadditivity as a key structure-activity relationship feature: distinguishing structural changes from assay artifacts.

Kramer C, Fuchs JE, Liedl KR - J Chem Inf Model (2015)

Double transformation cycle for 1H-inden-1-onederivatives binding to the estrogen β receptor.43
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4372821&req=5

fig5: Double transformation cycle for 1H-inden-1-onederivatives binding to the estrogen β receptor.43
Mentions: We inspected the original publications and the PDB structuresforthe remaining 15 cycles to generate structural hypothesis that couldexplain the observed nonadditivity in SAR data. In five cases, theavailable X-ray structures showed that there can be alternative orientationsof the central scaffold. Such findings imply that the ligands cancompletely reorient, depending on the substitution patterns. A completereorientation of the scaffold can lead to strong nonadditivity, sincethe substituents then interact with completely different subpockets,yielding different contributions to the free energy of binding. Figure 5 shows an example for such a case: a set of estrogenreceptor β ligands with an indenone scaffold, published by Malamaset al.43

Bottom Line: At the same time, nonadditivity is a challenge for both basic SAR analysis as well as many ligand-based data analysis techniques such as Free-Wilson Analysis and Matched Molecular Pair analysis, since linear substituent contribution models inherently assume additivity and thus do not work in such cases.We find that all cases of strong nonadditivity (ΔΔpKi and ΔΔpIC50 > 2.0 log units) with sufficient structural information to generate reasonable hypothesis involve changes in binding mode.With the appropriate statistical basis, nonadditivity analysis offers a variety of new attempts for various areas in computer-aided drug design, including the validation of scoring functions and free energy perturbation approaches, binding pocket classification, and novel features in SAR analysis tools.

View Article: PubMed Central - PubMed

Affiliation: †Department of Theoretical Chemistry, Faculty for Chemistry and Pharmacy, Center for Molecular Biosciences Innsbruck (CMBI), Leopold-Franzens University Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria.

ABSTRACT
Nonadditivity in protein-ligand affinity data represents highly instructive structure-activity relationship (SAR) features that indicate structural changes and have the potential to guide rational drug design. At the same time, nonadditivity is a challenge for both basic SAR analysis as well as many ligand-based data analysis techniques such as Free-Wilson Analysis and Matched Molecular Pair analysis, since linear substituent contribution models inherently assume additivity and thus do not work in such cases. While structural causes for nonadditivity have been analyzed anecdotally, no systematic approaches to interpret and use nonadditivity prospectively have been developed yet. In this contribution, we lay the statistical framework for systematic analysis of nonadditivity in a SAR series. First, we develop a general metric to quantify nonadditivity. Then, we demonstrate the non-negligible impact of experimental uncertainty that creates apparent nonadditivity, and we introduce techniques to handle experimental uncertainty. Finally, we analyze public SAR data sets for strong nonadditivity and use recourse to the original publications and available X-ray structures to find structural explanations for the nonadditivity observed. We find that all cases of strong nonadditivity (ΔΔpKi and ΔΔpIC50 > 2.0 log units) with sufficient structural information to generate reasonable hypothesis involve changes in binding mode. With the appropriate statistical basis, nonadditivity analysis offers a variety of new attempts for various areas in computer-aided drug design, including the validation of scoring functions and free energy perturbation approaches, binding pocket classification, and novel features in SAR analysis tools.

No MeSH data available.


Related in: MedlinePlus