Biological spectra analysis: Linking biological activity profiles to molecular structure

Abstract

Establishing quantitative relationships between molecular structure and broad biological effects has been a longstanding challenge in science. Currently, no method exists for forecasting broad biological activity profiles of medicinal agents even within narrow boundaries of structurally similar molecules. Starting from the premise that biological activity results from the capacity of small organic molecules to modulate the activity of the proteome, we set out to investigate whether descriptor sets could be developed for measuring and quantifying this molecular property. Using a 1,567-compound database, we show that percent inhibition values, determined at single high drug concentration in a battery of in vitro assays representing a cross section of the proteome, provide precise molecular property descriptors that identify the structure of molecules. When broad biological activity of molecules is represented in spectra form, organic molecules can be sorted by quantifying differences between biological spectra. Unlike traditional structure-activity relationship methods, sorting of molecules by using biospectra comparisons does not require knowledge of a molecule's putative drug targets. To illustrate this finding, we selected as starting point the biological activity spectra of clotrimazole and tioconazole because their putative target, lanosterol demethylase (CYP51), was not included in the bioassay array. Spectra similarity obtained through profile similarity measurements and hierarchical clustering provided an unbiased means for establishing quantitative relationships between chemical structures and biological activity spectra. This methodology, which we have termed biological spectra analysis, provides the capability not only of sorting molecules on the basis of biospectra similarity but also of predicting simultaneous interactions of new molecules with multiple proteins. biospectra ͉ proteome ͉ structure-function relationships O rganic molecules have the intrinsic capacity of both storing and transmitting information. This ability provides the link between chemical scaffold design and biological activity. Identification of structure features that allow differentiation between effect and side-effect profiles of medicinal agents is currently rate limiting in drug discovery (1). Current understanding of structure-activity relationship (SAR) components evolved from ''lock and key'' models of protein-ligand interactions (2, 3). Each protein family has its own sets of rules, which depend on dynamic and structural aspects of ligand and ligand-binding site, for identifying molecular properties that provide specific interactions with proteins (4, 5). Current drug discovery methods estimate biological response of potential medicinal agents by constructing independent and linear models. Although these models provide a link between specific biological targets and therapeutic effects, the properties of natural signals are too complex to expect that an independent set of descriptors would be capable of forecasting broad bi...

Biospectra Analysis: Model Proteome Characterizations for Linking Molecular Structure and Biological Response

“…3 The Y axis dendrogram of the complete linkage map (1567 compounds) identified 33 clusters containing 109 structures exhibiting CCS values g0.90 and 73 clusters containing 317 structures exhibiting a CCS score of g0.80. 3 Figure 1 highlights a section of this dendrogram containing dopamine 21. Shown are molecules sharing biospectra similarity with dopamine having CCS scores of greater than 0.608.…”

“…7 Biospectra were generated as reported earlier using percent inhibition values determined at single high ligand concentration as described and listed in CEREP's BioPrint database. 3 Ligand and in vitro binding assay conditions were conducted by the same laboratory and were the same for all 1567 molecules, and hence percent inhibition values and biospectra are directly comparable. Molecule selection was essentially random and based only on availability of complete sets of percent inhibition values (complete biospectra with no missing values) determined at 10 µM drug concentration in 92 different bioassays.…”

“…3 Biospectra similarity between these molecules was measured using confidence in cluster similarity (CCS) values. 3 The Y axis dendrogram of the complete linkage map (1567 compounds) identified 33 clusters containing 109 structures exhibiting CCS values g0.90 and 73 clusters containing 317 structures exhibiting a CCS score of g0.80. 3 Figure 1 highlights a section of this dendrogram containing dopamine 21.…”

“…This strategy provides a single descriptor set that integrates information on structure and dynamic properties of both drugs and the model proteome. 2,3 Accordingly, determination of biospectra similarity, for example, via hierarchical clustering, provides information on molecular properties that govern the information exchange between druglike molecules and the model proteome. What distinguishes probabilistic SAR concepts from traditional affinity-based studies is the use of broad biological effect patterns for forecasting structure-property associations (probabilistic SAR).…”

“…Biospectra are obtained by screening druglike molecules at single high concentrations in a battery of bioassays representing a cross section of the drugable proteome. 3 The string of percent inhibition values (biospectra) generated in such a bioassay array describes complimentary properties: the interaction capacity of a druglike molecule with the model proteome and the capacity of the model proteome to differentiate between druglike molecules. By using for all drugs the same radioligands and 10 µM as screening concentration, one can take advantage of the modular design of the proteome by capturing properties of protein families and protein networks.…”