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...
The venom of the funnel-web spider Agelenopsis aperta contains several peptides that paralyze prey by blocking voltage-sensitive calcium channels. Two peptides, omega-Aga-IVB (IVB) and omega-Aga-IVC (IVC), have identical amino acid sequences, yet have opposite absolute configurations at serine 46. These toxins had similar selectivities for blocking voltage-sensitive calcium channel subtypes but different potencies for blocking P-type voltage-sensitive calcium channels in rat cerebellar Purkinje cells as well as calcium-45 influx into rat brain synaptosomes. An enzyme purified from venom converts IVC to IVB by isomerizing serine 46, which is present in the carboxyl-terminal tail, from the L to the D configuration. Unlike the carboxyl terminus of IVC, that of IVB was resistant to the major venom protease. These results show enzymatic activities in A. aperta venom being used in an unprecedented strategy for coproduction of necessary neurotoxins that possess enhanced stability and potency.
The high failure rate of experimental medicines in clinical trials accentuates inefficiencies of current drug discovery processes caused by a lack of tools for translating the information exchange between protein and organ system networks. Recently, we reported that biological activity spectra (biospectra), derived from in vitro protein binding assays, provide a mechanism for assessing a molecule's capacity to modulate the function of protein-network components. Herein we describe the translation of adverse effect data derived from 1,045 prescription drug labels into effect spectra and show their utility for diagnosing drug-induced effects of medicines. In addition, notwithstanding the limitation imposed by the quality of drug label information, we show that biospectrum analysis, in concert with effect spectrum analysis, provides an alignment between preclinical and clinical drug-induced effects. The identification of this alignment provides a mechanism for forecasting clinical effect profiles of medicines.
Since ribosomally mediated protein biosynthesis is confined to the L-amino acid pool, the presence of D-amino acids in peptides was considered for many years to be restricted to proteins of prokaryotic origin. Unicellular
Establishing quantitative relationships between molecular structure and broad biological effects has been a long-standing goal in drug discovery. Evaluation of the capacity of molecules to modulate protein functions is a prerequisite for understanding the relationship between molecular structure and in vivo biological response. A particular challenge in these investigations is to derive quantitative measurements of a molecule's functional activity pattern across different proteins. Herein we describe an operationally simple probabilistic structure-activity relationship (SAR) approach, termed biospectra analysis, for identifying agonist and antagonist effect profiles of medicinal agents by using pattern similarity between biological activity spectra (biospectra) of molecules as the determinant. Accordingly, in vitro binding data (percent inhibition values of molecules determined at single high drug concentration in a battery of assays representing a cross section of the proteome) are useful for identifying functional effect profile similarity between medicinal agents. To illustrate this finding, the relationship between biospectra similarity of 24 molecules, identified by hierarchical clustering of a 1567 molecule dataset as being most closely aligned with the neurotransmitter dopamine, and their agonist or antagonist properties was probed. Distinguishing the results described in this study from those obtained with affinity-based methods, the observed association between biospectra and biological response profile similarity remains intact even upon removal of putative drug targets from the dataset (four dopaminergic [D 1 /D 2 /D 3 /D 4 ] and two adrenergic [R 1 and R 2 ] receptors). These findings indicate that biospectra analysis provides an unbiased new tool for forecasting structure-response relationships and for translating broad biological effect information into chemical structure design.
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