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...

Schizophrenia is a highly debilitating mental disorder which afflicts approximately 1% of the global population. Cognitive and negative deficits account for the lifelong disability associated with schizophrenia, whose symptoms are not effectively addressed by current treatments. New medicines are needed to treat these aspects of the disease. Neurodevelopmental, neuropathological, genetic, and behavioral pharmacological data indicate that schizophrenia stems from a dysfunction of glutamate synaptic transmission, particularly in frontal cortical networks. A number of novel pre- and postsynaptic mechanisms affecting glutamatergic synaptic transmission have emerged as viable targets for schizophrenia. While developing orthosteric glutamatergic agents for these targets has proven extremely difficult, targeting allosteric sites of these targets has emerged as a promising alternative. From a medicinal chemistry perspective, allosteric sites provide an opportunity of finding agents with better drug-like properties and greater target specificity. Furthermore, allosteric modulators are better suited to maintaining the highly precise temporal and spatial aspects of glutamatergic synaptic transmission. Herein, we review neuropathological and genomic/genetic evidence underscoring the importance of glutamate synaptic dysfunction in the etiology of schizophrenia and make a case for allosteric targets for therapeutic intervention. We review progress in identifying allosteric modulators of AMPA receptors, NMDA receptors, and metabotropic glutamate receptors, all with the aim of restoring physiological glutamatergic synaptic transmission. Challenges remain given the complexity of schizophrenia and the difficulty in studying cognition in animals and humans. Nonetheless, important compounds have emerged from these efforts and promising preclinical and variable clinical validation has been achieved.

Summary NMDA receptors mediate excitatory synaptic transmission and regulate synaptic plasticity in the central nervous system, but their dysregulation is also implicated in numerous brain disorders. Here, we describe GluN2A-selective negative allosteric modulators (NAMs) that inhibit NMDA receptors by stabilizing the apo-state of the GluN1 ligand binding domain (LBD), which is incapable of triggering channel gating. We describe structural determinants of NAM binding in crystal structures of the GluN1/2A LBD heterodimer, and analyses of NAM-bound LBD structures corresponding to active and inhibited receptor states reveal a molecular switch in the modulatory binding site that mediate the allosteric inhibition. NAM binding causes displacement of a valine in GluN2A and the resulting steric effects can be mitigated by the transition from glycine-bound to apo-state of the GluN1 LBD. This work provides mechanistic insight to allosteric NMDA receptor inhibition, thereby facilitating the development of novel classes NMDA receptor modulators as therapeutic agents.

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

Stem cell biology offers advantages to investigators seeking to identify new therapeutic molecules. Specifically, stem cells are genetically stable, scalable for molecular screening, and function in cellular assays for drug efficacy and safety. A key hurdle for drug discoverers of central nervous system disease is a lack of high quality neuronal cells. In the central nervous system, ␣-amino-3-hydroxyl-5-methyl-4-isoxazolepropionate (AMPA) subtype glutamate receptors mediate the vast majority of excitatory neurotransmissions. Embryonic stem (ES) cell protocols were developed to differentiate into neuronal subtypes that express AMPA receptors and were pharmacologically responsive to standard compounds for AMPA potentiation. Therefore, we hypothesized that stem cell-derived neurons should be predictive in high-throughput screens (HTSs). Here, we describe a murine ES cell-based HTS of a 2.4 ؋ 10 6 compound library, the identification of novel chemical "hits" for AMPA potentiation, structure function relationship of compounds and receptors, and validation of chemical leads in secondary assays using human ES cell-derived neurons. This reporting of murine ES cell derivatives being formatted to deliver HTS of greater than 10 6 compounds for a specific drug target conclusively demonstrates a new application for stem cells in drug discovery. In the future new molecular entities may be screened directly in human ES or induced pluripotent stem cell derivatives.Cognitive impairment is a fundamental trademark of many CNS 2 diseases such as Alzheimer disease, schizophrenia, and attention deficit hyperactivity disorder. Unfortunately few pharmaceutical agents have proven to be efficacious in treating cognitive impairment. As such, the medical need for new therapeutic approaches for improving cognitive deficits has emerged as an important frontier in treating central nervous system diseases. Glutamate mediates the vast majority of excitatory neurotransmission in the CNS with post-synaptic signaling regulated by either ligand-gated cation channels (ionotropic) or G-protein-coupled receptors (metabotropic). Ionotropic ␣-amino-3-hydroxy-5-methylisoxazole-4-propionic acid (AMPA) receptors are tetrameric aggregates composed of combinations of one or more of four subunits (GluR1-4) that can each bind a glutamate neurotransmitter molecule. Upon binding of glutamate, the associated ion channel pore opens, allowing cations to cross the membrane and depolarize the postsynaptic cell. Termination of the postsynaptic signal is driven primarily by the removal of glutamate from the synaptic cleft, reducing the occupancy of the glutamate binding site and subsequent channel closure (deactivation). Additionally, the conduction pathway will close in the continued presence of bound agonist as a result of conformational changes secondary to glutamate binding (desensitization).AMPA receptor pharmacology has identified agonists, competitive and non-competitive antagonists, and positive allosteric modulators (3). This latter class of pharmacologic agen...

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.