Iterative Focused Screening with Biological Fingerprints Identifies Selective Asc-1 Inhibitors Distinct from Traditional High Throughput Screening

Kutchukian, Peter S.; Warren, Lee; Magliaro, Brian C.; Amoss, Adam; Cassaday, Jason; O'Donnell, Gregory; Squadroni, Brian; Zuck, Paul; Pascarella, Danette; Culberson, J. Chris; Cooke, Andrew; Hurzy, Danielle M.; Schlegel, Kelly-Ann S.; Thomson, Fiona; Johnson, Eric N.; Uebele, Victor N.; Hermes, Jeffrey D.; Parmentier-Batteur, Sophie; Finley, Michael

doi:10.1021/acschembio.6b00913

Cited by 18 publications

(30 citation statements)

References 21 publications

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“…We then used this model to select high-scoring molecules from our company’s compound collection for subsequent screening against the RNA targets. By including multiple expansion methods, additional chemotypes can be discovered; 27 thus, we also included compounds that were nearest neighbors based on chemical or biological similarity to the RNA binders. Comparison of the resulting set of ~3700 compounds, termed the “RNA-Focused Library,” with reference to the initial screening collection using Tanimoto similarity, revealed that our new set included similar compounds (high similarity) as well as distinct chemical moieties (low similarity), the latter largely introduced through biological fingerprint nearest-neighbor expansion and ECFP4 naïve Bayesian model expansion ( Suppl.…”

Section: Resultsmentioning

confidence: 99%

Targeting RNA with Small Molecules: Identification of Selective, RNA-Binding Small Molecules Occupying Drug-Like Chemical Space

et al. 2020

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Section: Resultsmentioning

confidence: 99%

Targeting RNA with Small Molecules: Identification of Selective, RNA-Binding Small Molecules Occupying Drug-Like Chemical Space

et al. 2020

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“…Another competitive Asc-1 inhibitor is S-Methyl-L-Cys (SMLC). Nevertheless ACPP, LuAE00527 and BMS-466442 block uptake and efflux of d-serine, while SMLC inhibits the uptake, but allows the efflux 8,16,18,19,21,46 . We performed the docking of SMLC at the binding site of our Asc-1 outward-open model.…”

Section: Potential Binding Modes Of Competitive Inhibitorsmentioning

confidence: 99%

Asc-1 Transporter (SLC7A10): Homology Models And Molecular Dynamics Insights Into The First Steps Of The Transport Mechanism

Mikou

Cabayé

Goupil

et al. 2020

Sci Rep

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The alanine-serine-cysteine transporter Asc-1 regulates the synaptic availability of d-serine and glycine (the two co-agonists of the NMDA receptor) and is regarded as an important drug target. To shuttle the substrate from the extracellular space to the cytoplasm, this transporter undergoes multiple distinct conformational states. In this work, homology modeling, substrate docking and molecular dynamics simulations were carried out to learn more about the transition between the "outwardopen" and "outward-open occluded" states. We identified a transition state involving the highlyconserved unwound TM6 region in which the Phe243 flips close to the d-serine substrate without major movements of TM6. This feature and those of other key residues are proposed to control the binding site and substrate translocation. Competitive inhibitors ACPP, LuAE00527 and SMLC were docked and their binding modes at the substrate binding site corroborated the key role played by Phe243 of TM6. For ACPP and LuAE00527, strong hydrophobic interactions with this residue hinder its mobility and prevent the uptake and the efflux of substrates. As for SMLC, the weaker interactions maintain the flexibility of Phe243 and the efflux process. Overall, we propose a molecular basis for the inhibition of substrate translocation of the Asc-1 transporter that should be valuable for rational drug design.The alanine-serine-cysteine transporter (referred to as SLC7A10 or Asc-1) is the light chain of the heterodimer amino acid transporter (HAT), and is a Na + -independent antiporter distributed throughout the central nervous system (CNS). Asc-1 is present at both astrocytes and neurons 1-3 , and displays high affinity for neutral amino acids and uses diverse substrates, such as l-serine, l-alanine, l-cysteine along with glycine and d-serine 4 . However, glycine and d-serine serve as two necessary and positive allosteric modulators of the N-methyl-d-aspartate (NMDA) subtype of glutamate receptors, and bind to the strychnine insensitive binding site (referred to as the 'glycine site') of those receptors (Fig. 1a) 5,6 . For these reasons, Asc-1 is a promising druggable target relevant for treating cognitive affections during normal or pathological aging but also in disorders like schizophrenia 7-12 . Indeed, Asc-1 primarily mediates the efflux of d-serine and glycine through the hetero-exchange with other neutral amino acids, and thus regulates glutamatergic neurotransmission and synaptic plasticity in the forebrain 13 . But, recent studies suggest that Asc-1 is also involved in the control of glycinergic transmission in the caudal brain and spinal cord 2,3,14 . SLC7a10-null mice display reduced levels of glycine, but not d-serine, and show rigidity and myoclonus characteristic of the clinical condition called hyperekplexia 2,14 .Given the important physiological function of Asc-1 in regulating d-serine and glycine levels, modulating Asc-1 function could provide therapeutical benefits for alleviating the symptoms caused notably by the hypofunction of NMDARs ...

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“…[18][19][20][21] Furthermore, Kutchukian et al reported a comparison of HTSFPs versus structural descriptors in the exercise of hit expansion during an active iterative screening campaign which lead to the identification of a novel Asc-1 inhibitor. 22 In a more recent article, Cabrera and Petrone, proposed a desirability function for selecting HTS assays with optimal informative content for HTSFP definitions. 23 Even though most HTS data is in the industry, and is not publicly accessible, efforts have also been put into the evaluation of publicly accessible HTSFPs build upon 243 HTS assays (biochemical and cell-based) deposited in PubChem for hit expansion.…”

Section: Introductionmentioning

confidence: 99%

Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models

Sturm

Sun

Vandriessche³

et al. 2018

J. Chem. Inf. Model.

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The volume of high throughput screening data has considerably increased since the begin of the automated biochemical and cell-based assays era. This information rich data source provides tremendous repurposing opportunities for data mining. It was recently shown that biochemical or cell-based assay results can be compiled into so called high-throughput fingerprints (HTSFPs) as a new type of descriptor describing molecular bioactivity profiles which can be applied in virtual screening, iterative screening, and target deconvolution. However, so far studies around HTSFPs and machine learning have mainly focused on predicting the outcome of molecules in single highthroughput assays and no one has reported the modelling of compounds' biochemical assay activities towards a panel of target proteins. Therefore, there is a need for a detailed analysis of the performance of predictive models built with HTSFPs with respect to models built with more widely used structural descriptors both in terms of hit identification and of scaffold hopping potentials. In this article, in-house HTSFPs were built and combined with multi-task deep learning and support vector machine methods to build compound activity predictive models. Performances of HTSFP models were compared to the performances of models built with the conventional structural descriptors ECFPs. Moreover, we investigated the effect of high throughput screening false positives and negatives on the performance of deep learning models. Our results showed that the two fingerprints yielded in similar performances and in diverse hits with very little overlap, thus demonstrating the orthogonality of bioactivity profile based descriptors with structural descriptors. Therefore, modelling compound activity data using ECFP together with HTSFPs increases the scaffold potential of the predictive models.

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Iterative Focused Screening with Biological Fingerprints Identifies Selective Asc-1 Inhibitors Distinct from Traditional High Throughput Screening

Cited by 18 publications

References 21 publications

Targeting RNA with Small Molecules: Identification of Selective, RNA-Binding Small Molecules Occupying Drug-Like Chemical Space

Targeting RNA with Small Molecules: Identification of Selective, RNA-Binding Small Molecules Occupying Drug-Like Chemical Space

Asc-1 Transporter (SLC7A10): Homology Models And Molecular Dynamics Insights Into The First Steps Of The Transport Mechanism

Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models

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