Structure-based screening for discovery of sweet compounds

Journal Pre-proofs Structure-based screening for discovery of sweet compounds Yaron Ben Shoshan-Galeczki, Masha Y. Niv PII: DOI: Reference:

S0308-8146(20)30134-5 https://doi.org/10.1016/j.foodchem.2020.126286 FOCH 126286

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Food Chemistry

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15 October 2019 10 January 2020 21 January 2020

Please cite this article as: Ben Shoshan-Galeczki, Y., Niv, M.Y., Structure-based screening for discovery of sweet compounds, Food Chemistry (2020), doi: https://doi.org/10.1016/j.foodchem.2020.126286

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Structure-based screening for discovery of sweet compounds

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Yaron Ben Shoshan-Galeczki and Masha Y Niv*

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The Institute of Biochemistry, Food and Nutrition, The Robert H Smith Faculty of

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Agriculture, Food and Environment, The Hebrew University, 76100 Rehovot and The

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Fritz Haber Center for Molecular Dynamics, The Hebrew University, Jerusalem, 91904,

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Israel.

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*correspondence to [email protected]

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Abstract

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Sweet taste is a cue for calorie-rich food and is innately attractive to animals, including humans. In the context of modern diets, attraction to sweetness presents a significant challenge to human health. Most known sugars and sweeteners bind to the Venus Fly Trap domain of T1R2 subunit of the sweet taste heterodimer. Because the sweet taste receptor structure has not been experimentally solved yet, a possible approach to finding sweet molecules is virtual screening using compatibility of candidate molecules to homology models of sugar-binding site. Here, the constructed structural models, docking and scoring schemes were validated by their ability to rank known sweet-tasting compounds higher than properties-matched random molecules. The best performing models were next used in virtual screening, retrieving recently patented sweeteners and providing novel predictions.

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Keywords: Sweet taste receptor, docking, modeling, sweeteners, drug discovery, GPCR,

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Tas1R2, T1R2

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Introduction

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Taste is one of the primary determinants of food preference and intake (Loper, La Sala,

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Dotson, & Steinle, 2015), and consequently has a major impact on health and well-

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being. The steady increase in the daily sugar consumption over recent decades has 1

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contributed to the obesity crisis, the early onset of type 2 diabetes and other chronic

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diseases (Lustig, Schmidt, & Brindis, 2012). Some studies present advantages associated

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with using non-caloric sweeteners for weight loss, like reduction of glucose intolerance

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and type 2 diabetes (Fitch & Keim, 2012). Others highlight safety issues and possible

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opposite outcomes, such as weight gain, increased risk of diabetes, modification of gut

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microbiota and even increased risk of neurodegenerative diseases (Pase, Himali, Beiser,

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Aparicio, Satizabal, Vasan, et al., 2017; Suez, Korem, Zeevi, Zilberman-Schapira, Thaiss,

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Maza, et al., 2014). Numerous low-calorie sweeteners have been identified in natural

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extracts or chemically synthesized (DuBois & Prakash, 2012). Notably, many non-sugar

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sweeteners elicit a bitter or metallic off-taste, or present a lingering after-taste (Di Pizio,

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Ben Shoshan-Galeczki, Hayes, & Niv, 2018). Thus, the quest for optimal low-calorie

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sweetener persists, with particular focus on natural or food-derived compounds.

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The major pathway of sweet taste recognition is mediated by T1R2/T1R3 heterodimer,

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while recognition of umami taste is mediated via T1R1/T1R3 heterodimer (Zhao, Zhang,

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Hoon, Chandrashekar, Erlenbach, Ryba, et al., 2003). Additional pathways for sweet

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taste recognition have also been suggested, involving glucose transporters and ATP-

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gated K+ channels (Damak, Rong, Yasumatsu, Kokrashvili, Varadarajan, Zou, et al., 2003;

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Yee, Sukumaran, Kotha, Gilbertson, & Margolskee, 2011).

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The T1R2/T1R3 heterodimer consists of two Class C G Protein-Coupled Receptor (GPCR)

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subunits (Montmayeur, Liberles, Matsunami, & Buck, 2001). These receptors feature a

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Transmembrane Domain (TMD), a Cysteine Rich Domain (CRD) and an extracellular

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Venus Fly Trap (VFT) domain. The Class C GPCR group consists of approximately 20

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members, including Metabotropic Glutamate Receptors (mGluRs), Calcium Sensing

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Receptors (CaSRs) (Moller, Moreno-Delgado, Pin, & Kniazeff, 2017), and the sweet and

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umami taste receptors (Matsunami, Montmayeur, & Buck, 2000).

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A combination of experimental studies, in particular construction of chimeric receptors

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and site-directed mutagenesis (Maillet, Cui, Jiang, Mezei, Hecht, Quijada, et al., 2015;

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Zhang, Klebansky, Fine, Liu, Xu, Servant, et al., 2010), supported by in-silico modeling

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approaches (Temussi, 2011) led to the identification and characterization of the VFT 2

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domain of the T1R2 subunit as the main binding site for sweet compounds. Other

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binding sites were identified, as recently reviewed (Cheron, Golebiowski, Antonczak, &

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Fiorucci, 2017).

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Several machine learning methods were developed to predict sweetness of molecules

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(Cheron, Casciuc, Golebiowski, Antonczak, & Fiorucci, 2017; Zheng, Chang, Xu, Xu, & Lin,

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2019). These methods typically relied on physicochemical properties and fingerprints of

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molecules and do not include direct information regarding the binding site of the

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receptor. Acevedo et al. (Acevedo, Ramirez-Sarmiento, & Agosin, 2018) reported

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correlation between docking scores and experimental sweetness for selected

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sweeteners groups.

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Computational techniques that rely on homology modeling of the receptor and

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subsequent docking of ligands are useful for GPCRs in the absence of experimental

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structures (Lim, Du, Chen, & Fan, 2018), and were successfully applied to several

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chemosensory receptors, i.e. (Di Pizio, Waterloo, Brox, Lober, Weikert, Behrens, et al.,

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2019) (Spaggiari, Di Pizio, & Cozzini, 2020). However, to the best of our knowledge,

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structure-based methods have not yet been validated for discovery of sweet-tasting

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compounds.

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In the current study, we demonstrate the feasibility of structure-based virtual screening

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for sweet compounds using homology models of extracellular VFT domain of human

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hT1R2 receptor. We create several models of the orthosteric binding site of the sweet

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taste receptor in the hT1R2 VFT domain. The best model is chosen based on its ability to

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discriminate between known sweet compounds and decoys, quantified by ROC (receiver

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operating characteristic) curves (Irwin & Shoichet, 2016). Next, we apply it to the

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Generally Recognized As Safe (GRAS) data set, where success can be evaluated using

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reported taste of GRAS compounds. Finally, we screen FooDB (Wishart, D. S. "FooDB:

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the food database. FooDB version 1.0." (2014)) to predict sweet compounds from food

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sources.

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Methods 3

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Modeling:

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The sweet taste receptor sequence was obtained from Uniprot database (hT1R2 –ID:

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Q8TE23). The 3D structures of the VFT domain of the human monomer were modeled

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using several servers, including I-Tasser, Modeller, and Phyre2. I-Tasser was chosen for

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further analysis based on preliminary performance of the models for known ligands and

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on CASP competition results for template-based modeling (Yang, Zhang, He, Walker,

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Zhang, Govindarajoo, et al., 2016). I-Tasser server was used to create models of the

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hT1R2 VFT using default settings (multi-template) and by using specified templates –

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PDB 5X2M chains A and B. The sequence identities with the templates used by I-Tasser

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(April 2018), conservation analysis and binding site residues are listed in the

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Supplementary Information. The main model used hereafter is the 5X2MB-based model,

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also referred to as “fish-based model”. For analysis of compounds larger than 460

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g/mol, a VFT T1R2 open-form homology model was obtained via I-Tasser using open

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form class C GPCR. It was based on calcium sensing receptor (PDB ID: 5K5T) and on

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metabotropic glutamate receptor (PDB ID: 1EWT). The top model from each of the I-

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Tasser runs was minimized and prepared for docking with Protein Preparation Wizard

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tool in Maestro and Glide Grid Generation (Schrodinger tools 2017-2).

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Ligand similarity

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Ligands similarity was calculated by Tanimoto scores:

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𝑇𝑎𝑛𝑖𝑚𝑜𝑡𝑜 =

𝐴∩𝐵 𝐴∪𝐵―𝐴∩𝐵

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MOLPRINT2D fingerprint was used for the similarity calculations, as described in

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previous work (Nissim, Dagan-Wiener, & Niv, 2017). Comparison of different

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fingerprints showed that MOLPRINT2D fingerprint had the best average enrichment

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across 11 targets, while being less sensitive to precise settings than other fingerprints

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(Sastry, Lowrie, Dixon, & Sherman, 2010). Commonly used similarity thresholds are

4

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between 0.75–0.85 regardless of the fingerprint used. (Ripphausen, Nisius, & Bajorath,

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2011). Here 0.75 threshold was used.

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Data sets and decoy preparation for evaluating models performance:

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The ligands were prepared for docking using LigPrep. Conformers, tautomers and

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protomers (different protonation states of ligands) were enumerated at pH 7.0 ± 1.0,

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retaining specified chiral centers. (Maestro Version 10.4.018, MMshare Version 3.2.018,

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Release 2017-2, Platform Windows-x64).

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Compounds reported as sweet by Rojas and coworkers (Rojas, Todeschini, Ballabio,

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Mauri, Consonni, Tripaldi, et al., 2017) comprised the true positive (TP) set, consisting of

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435 compounds. After ligand preparation there were 465 conformers, tautomers and

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protomers. Since we are focusing on the sugar-binding site in the T1R2 VFT domain, 8

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compounds that were reported to act via allosteric binding sites, and 5 compounds with

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at least 0.75 Tanimoto similarity to them, were removed from the set, leading to a final

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TP set of 452 compounds (conformers, tautomers and protomers). These 452

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compounds were divided to two groups: 404 compounds up to 460 g/mol and 48

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compounds over 460 g/mol.

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Decoy compounds were obtained from the ZINC12 and ZINC15 databases (Sterling &

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Irwin, 2015) according to physicochemical distribution properties of sweet compounds

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from Rojas et al (Rojas, et al., 2017). The following molecular properties were used to

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select decoy compounds: AlogP (–4.4 to –0.65), number of hydrogen bond acceptors (6–

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11), number of hydrogen bond donors (5–8), polarizability (15–32), number of rotatable

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bonds (1–5), number of chiral centers (3–10), and molecular weight; 180–460 g/mol or

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460–1100 g/mol. The ~7000 resulting compounds were prepared for docking with

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LigPrep, enumerating protomers at pH 7.0 ± 1.0 and generating up to 32 stereoisomers

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per compound. (Maestro Version 10.4.018, MMshare Version 3.2.018, Release 2017-2,

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Platform Windows-x64). Additional decoys were created using DUD-E (Mysinger,

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Carchia, Irwin, & Shoichet, 2012) web server (http://dude.docking.org/) based on the

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435 molecules in the TP dataset. For each true positive, up to 50 DUD-E decoys with 5

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similar physicochemical properties but dissimilar 2-D topology were generated, resulting

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in 2,619 DUD-E decoys. The final decoys set consisted of 22,125 entries within 180–460

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g/mol molecular weight range and 14,073 entries within 460–1100 g/mol molecular

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weight range (including conformers, tautomers and protomers).

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Datasets used for virtual screening:

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GRAS: A dataset of approved FDA Generally Recognized as Safe (GRAS) compounds

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downloaded on August 2016. The data set includes 1,877 compounds. Taste and odor

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descriptions of the GRAS compounds were obtained by data mining (annotations of

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taste thresholds of GRAS compounds by FEMA ID (https://www.femaflavor.org/flavor-

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library) of Fenaroli’s handbook of Flavor Ingredients (fifth edition, Burdock 2015).

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FooDB: (http://foodb.ca/) a data set which holds food constituent compounds. 24,399

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molecules extracted from the FooDB SQL version.

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Binding site analysis

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Binding pockets of the two models were analyzed with SiteMap (Schrödinger, LLC, New

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York, NY, 2017): the binding site was defined as the region within 6 Å from the center of

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mass of a docked D-glucose ligand, which turned out to be large enough for all the

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docked ligands.

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Docking protocol:

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The binding site was defined as a 12 Å grid around the L-glutamate binding site in the

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class C GPCR mGluR1 (PDB ID: 1EWK). Overlap of the models to the crystal structure was

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used to define the binding grid in the models. Two docking protocols were applied

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(Glide Standard Precision (SP) and Glide Extra Precision (XP)) Flexible and Rigid sampling

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options.

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Initial testing (see Supplementary Figure 1) indicated that the screening protocol, which

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obtained the best combination of sensitivity and specificity was Maestro Schrodinger

6

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2017-2, Glide Extra Precision mode (XP), flexible ligand sampling, and Glide XP docking

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scores (Supplementary Figure 2). These settings were used in the rest of the study.

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Enrichment:

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ROC curves were prepared with Maestro Schrodinger 2017-2 using the enrichment

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calculator, and evaluated using the ROC AUC (Truchon & Bayly, 2007). The AUC value

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represents the total area below the ROC curve and can span values between 0

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(minimum possible enrichment) and 1 (maximum possible enrichment). The ROC curve

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horizontal axis (100-specificity, also called the false positive rate) shows the number of

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false positives identified during the screen from all the decoys available in the set. The

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ROC curve vertical axis (sensitivity, also called the true positive rate) indicates how many

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true positives are retrieved during the screen.

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Sensitivity and specificity measures are defined in the following way:

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Sensitivity = True Positives Rate =

𝑇𝑟𝑢𝑒 𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠

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Specificity = True Negatives Rate =

𝑇𝑟𝑢𝑒 𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠

𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠

𝑁𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠

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Enrichment Factors (EFs) are the ratios of the true positives in a sample size to the

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amount of true positives in the entire dataset (Huang, Shoichet, & Irwin, 2006).

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Enrichment factors provide additional information on the success of the scoring or

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ranking function in a selected subset size. 𝑆𝑢𝑏𝑠𝑒𝑡 𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠

Enrichment factor =

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𝑆𝑢𝑏𝑠𝑒𝑡 𝑐𝑜𝑚𝑝𝑜𝑢𝑛𝑑𝑠 𝑇𝑜𝑡𝑎𝑙𝑇𝑟𝑢𝑒𝑃𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠 𝑇𝑜𝑡𝑎𝑙𝐶𝑜𝑚𝑝𝑜𝑢𝑛𝑑𝑠

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Data Availability

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The prospective predictions on FooDB and the datasets in the current study are

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available on the supplementary download section in the Niv-lab website

7

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(https://biochem-food-nutrition.agri.huji.ac.il/mashaniv). Requests for predictions on

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additional datasets can be submitted to the authors via email request.

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Results

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Currently 452 known sweet compounds constitute the TP set, consisting of 404

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molecules with molecular weight up to 460 g/mol and 48 molecules over 460 g/mol

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(Figure 1). Decoys for these molecules were filtered from ZINC based on pre-determined

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properties based on properties of TPs, or generated for each TP by DUD-E. The ZINC and

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DUD-E decoys datasets were used for validation. GRAS was used for retrospective

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screening of annotated compounds, and FooDB dataset was screened for providing

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prospective predictions.

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“Default” I-Tasser (Yang, et al., 2016) model was mainly based on human mGluR

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templates (PDB IDs: 2E4X and 2E4U). Additional model was created based on Medaka

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fish crystal structure (PDB ID 5X2M, chain B) and termed “fish-based model”. Model

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based on chain A was evaluated but did not perform as well, see Supplementary Figures

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3 and 4. The models were evaluated for their ability to discriminate between true

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positives and decoys using the Glide XP docking protocol, which was chosen after

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preliminary testing (Supplementary Figures 1 and 2).

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Only compounds below 460 g/mol could dock into either of these models. Docking of

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larger compounds is discussed in the following section. Fish-based hT1R2 VFT model

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(Figure 2) had slightly higher enrichment than the default model, see Supplementary

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Table 2. The overall Area Under the Curve (AUC) of the fish-based model (0.83) was

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better than the default model (0.75). Additionally, the fish-based model had overall

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more docked compounds compared to the default model with 97% and 73% compounds

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from the TPs dataset, respectively. The fish-based model had somewhat higher

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sensitivity in top results of screening, compared to the default model (Supplementary

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table 2).

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The docking scores did not correlate with experimentally measured sweetness intensity.

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This may be due to shortcomings of the model and the simplistic scoring function, as 8

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well as to the complexity of the sensory system that depends on multiple factors,

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including: genetic variation in the taste receptors, number of receptors expressed in the

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cells, temperature, salivary proteins, age and mood of the human subjects reporting the

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perceived sweetness.

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To understand the reasons for improved performance of the fish-based model, we

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compared the binding cavities of the fish-based model to the default model. The default

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model is narrower than the fish-based model (Table 1), possibly due to the side chain

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orientations of R383 and D142. In the fish-based model, these residues are facing away

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from the binding site, contrary to the default model, in which both of these residues

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face inwards toward the proposed binding site, in a manner likely to interfere with

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ligand binding (Figure 3). In the default template model, the D142 and R383 side-chains

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face towards the D-glucose ligand and are located 3.2 Å from the glucose 3-position

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hydroxyl.

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Area (Å2)

Default based Fish-based model model binding site binding site

H-Bond acceptor

103.1

102.6

H-bond donor

127.6

276.5

Hydrophilic

253.8

395

Hydrophobic

117.2

194.9

Table 1 – Comparison of the binding site of the default and fish-based models, within 6 angstrom radius of docked glucose.

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Notably, larger ligands require more space and may clash with these residues. The

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importance and orientation of D142 and R383 had been suggested previously (Kumari,

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Choudhary, Arora, & Sharma, 2016). D142A mutation led to a loss of sucrose or

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sucralose activity (Zhang, et al., 2010). Mutations of R383, namely R383A, R383D,

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R383Q, R383L, R383F, R383K and R383H, led to weak or no response to aspartame. Any

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charge-changing mutations led to weak or diminished activation by all tested ligands.

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Mutations that kept the positive charge (R383H and R383K) had similar activation by 9

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sucralose and D-tryptophan as the WT. These results can be explained by R383 that is

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not directly involved in binding the ligand, but rather stabilized the VFT conformation

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through interaction with D449. This is in agreement with fish-based template model

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(see figure 4). Other mutagenesis data also support the involvement of binding site

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residues: I67, L71, Y103, D142, S165, E302, S303, W304, D307 and V384 (Cheron,

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Golebiowski, Antonczak, & Fiorucci, 2017).

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The good performance of the fish-based model in prioritizing true positives indicates

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that the model and the docking protocol are suitable for virtual screening of molecules

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below 460 g/mol MW.

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Ligands in the 460–1100 g/mol range: Since some of the true positives, such as stevia

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glycosides (see Figure 1) are bigger than what the binding site was able to

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accommodate, an additional model was created based on an open conformation of a

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calcium receptor (human calcium-sensing receptor extracellular domain, 5K5T, (Geng,

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Mosyak, Kurinov, Zuo, Sturchler, Cheng, et al., 2016)).

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Although the open conformation is considered inactive, the open model of sweet taste

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receptor does accommodate larger agonists that do not fit to the closed conformation.

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We hypothesized that there are several active conformations that fit to the size of the

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ligands, and that the open conformation may be used as an approximate model for the

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larger compounds-induced active conformation. 48 known sweeteners with MW above

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460 g/mol and ~14,000 property-matched decoys were docked using the protocol

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described in Methods. For these compounds, the open form model performs well, with

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an AUC of 0.85, EF2 of 4.17, and EF5 of 5. In this screening, all of 48 TPs compounds in

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this range docked successfully.

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Similarity of structure-based hits to known ligands

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To ensure that the structure-based method does not simply return results that could be

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trivially found using simple ligand-based similarity searches, the docking results

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similarity to any of the sweet compounds used in the TPs dataset was evaluated by 2D

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fingerprints similarity (MOLPRINT2D). For the smaller compounds (screened using the 10

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fish-based model), only 8 compounds had a Tanimoto similarity score equal to or higher

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than 0.75. For the larger compounds (screened using the open-form model), 163

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compounds had a Tanimoto similarity score above 0.75. Hence, most structure-based

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screening results are chemically different from known true-positives. Interestingly,

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among these compounds we find Tubercidin (ZINC03873956) which was patented in

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2006 as part of the application for “Fast dissolving composition with prolonged sweet

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taste” (US7122198B1).

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GRAS data set

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The 1877 GRAS compounds are Generally Recognized As Safe for use in humans. The

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GRAS dataset is relevant for food products usage, has annotations of taste and is fit for

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validations of predictions with the fish-based model: all except 7 GRAS compounds are

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below 460 g/mol. Top 5% docked compounds resulted in 100 compounds, out of which

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49 compounds are annotated with sweet taste. For comparison, there are 37

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compounds in GRAS that are within 0.75 Tanimoto similarity with any of the molecules

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used in the true positives set; 13 of these had sweet taste annotation, 9 of these

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appeared in the top docking results.

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The percentage of sweet-tasting molecules in the docked subset (49%) increased

281

compared to the entire GRAS dataset (16%). As control, we examined the percentage of

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sweet-smelling compounds, that act via odorant receptors and therefore should not be

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affected by the screen. Indeed, sweet-smelling compounds were 7% in the original set

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and remained at a similar 8% within the 5% top scoring compounds in the virtual screen.

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These results lend further support to the virtual screening protocol for sweet-tasting

286

compounds.

287

FooDB data set

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The docking protocol was next applied to FooDB dataset, the majority of which is not

289

yet annotated in terms of sensory properties. The FooDB data subset of MW under 460

290

g/mol was docked to the fish-based model, while molecules above this threshold were 11

291

docked to the open model. Compounds with Tanimoto similarity above 0.75

292

(MOLPRINT2D fingerprints) to any compound in the true positive set, were considered

293

“sweet-like” compounds. The full FooDB dataset contained 14,384 compounds under

294

460 g/mol (figure 5), with 177 sweet-like compounds (0.007%). The docking yielded

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10,897 scored compounds, 117 of which were sweet-like compounds (1%). In the top

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scoring 5% (545 compounds), 47 sweet-like compounds (8.6%) were present. The top

297

200 (top 1.3% ranked) compounds were subjected to manual inspection for existing

298

sensory information. After filtering out 20 (10%) sweet-like compounds, the IUPAC

299

names and SMILES strings of the remaining 180 compounds were submitted to searches

300

in literature and patent databases via Google Scholar and SciFinder-N (https://scifinder-

301

n.cas.org/). Three of the hits turned out to be recently patented sweeteners:

302

protocatechuic

303

(US20180132516A1) and galloyl glucose (EP3571933A1). An additional four compounds

304

turned out to be sweet compounds: sakebiose (also known as nigerose), turanose,

305

melibiitol, and inulobiose; none were found in the true positives dataset (see chemical

306

structures in Supplementary figure 6).

307

Since the 5X2MA-based model had high EF2 value in the initial screening (see

308

Supplemental data), it was applied as well. In the top 200 results of FooDB screening, 62

309

compounds overlapped with compounds already found with the hits retrieved by the

310

5X2MB model. These 62 overlapping compounds contained sakebiose, turanose and

311

melibiitol. in the remaining top hits was an additional patented compound, isopropyl

312

apiosylglucoside (WO2012107207A1).

313

Out of ~9500 FooDB compounds within the range of 460
314

docked to the open model. This portion of the dataset contained 104 sweet-like

315

molecules, 8 in the top 5% of the structure-based screen. 5 sweet or sweet-like

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molecules were in the top 2% of the structure-based screen, including rebaudioside A

317

and rebaudioside C. In the top 200 ranked compounds (~2%), four additional patented

318

compounds were found: mannan (US9012520B2), proanthocyanidin B2 3,3'-digallate

319

(US9247758B2), maltotetraose (US20020025366A1) and narirutin (US9247758B2).

acid

4-glucoside

(WO2013121264A1),

morachalcone

A

12

320

To the best of our knowledge, the remaining compounds have no reported sensory data

321

and are therefore novel potential sweeteners candidates.

322

Summary and Discussion

323

We found that structure-based methods are applicable for identifying sweet-tasting

324

compounds. This study emphasizes the importance of the template used for homology

325

modeling of the sweet taste receptor and the necessity to validate the resulting models.

326

A model built via I-Tasser, using fish monomer (Nuemket, Yasui, Kusakabe, Nomura,

327

Atsumi, Akiyama, et al., 2017) as a template, performed better than a model using

328

default I-Tasser settings, which chose the mGluR2 structures as templates (PDB ID: 2E4X

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and 2E4U). We tested the effect of different Glide protocols, sampling and scoring

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functions and found that the Glide XP docking protocol with flexible ligand sampling

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provides better ROC curves for virtual screening against the validation dataset.

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Additionally, XP docking ranks known sweet compounds better than SP or XP rigid, and

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is able to dock more true positive compounds. Despite heavier computational resources

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required by XP docking, in this system it was the most successful protocol, as shown by

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the AUC (Figure 1). Using this virtual screening experiment, the model was able to

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detect sweeteners among both the known true positives and among the decoy

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compounds: interestingly, a recently patented sweetener compound found among the

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decoys, was ranked better than some of the TP compounds (top 2%).

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In comparing the binding site of the two models, a major difference in the orientation of

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R383 sidechain was observed. R383 faces outward in the binding site of the fish-based

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model, but inward in the default template model. Analysis of previously reported

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mutagenesis data (Maillet, et al., 2015; Zhang, et al., 2010) supported the suggested

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orientations of D142 and R383 in the selected model and is in agreement with potential

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interaction between R383 and D449. These differences between the models led to the

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more restricted area of binding site in the default template model, which contributed to

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its poorer performance in retrieving true positives.

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The selected fish-based model and docking protocol were applied to the GRAS dataset.

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The top 5% of docking results had a greater ratio of sweeteners to non-sweet

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compounds (~50%) compared to the entire compounds list (16%), and, as expected, did

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not affect the percentage of sweet-smelling compounds. The docking campaign was

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more effective than a simple 2D similarity screening campaign: the top 5% of docking

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hits identified 49 compounds of the sweet molecules in GRAS, while 2D similarity

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identified only 13 compounds.

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Interestingly, the template that provided the best results was based on the VFT of T1R3

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monomer (5X2MB in pdb). The fish-T1R2 (5X2MA) based model resulted in lower EF2

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than fish-T1R3 or the default model (Supplementary Figure 3). Additionally, GRAS top

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5% screening results were not significantly enriched with sweet compounds

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(supplementary Figure 4) for the fish-T1R2 based model.

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The fish taste receptor Medaka fish heterodimer recognizes L-amino acids (Gln, Ala, Arg,

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Glu and Gly) but not sugars or artificial sweeteners (Nuemket, et al., 2017), and the

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amino acids were shown to bind to T1R2 and (with lower affinity) to T1R3. Chickens do

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not recognize sweet taste, and when VFT of chicken T1R3 was introduced into T1R3 of

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hummingbird (a bird that does recognize sugars) the heterodimeric receptor was

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activated in-vitro by amino acids rather than sugars. Reintroducing 109 amino acids of

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hummingbird T1R3 into the chicken T1R3 VFT restored sucrose responses (Baldwin,

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Toda, Nakagita, O'Connell, Klasing, Misaka, et al., 2014). This suggests that T1R3 might

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harbor a generalist binding site that can mutate into specialist recognition, and our

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results indicate that it can serve as a successful template for human T1R2 modeling.

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When the docking screen was applied to FooDB dataset, the abundance of sweet-like

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compounds in the final output (10% for the compounds up to 460 g/mol and 2.5% for

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compounds larger than 460 g/mol) increased from the initial dataset (0.007% for the

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smaller compounds and 0.004% for the larger compounds). Overall, the screen found 7

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newly patented and 4 known-to-be-sweet compounds in the top hits, all of which had

14

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less than 0.75 similarity with the true positives used in this work, suggesting that

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additional novel sweeteners may be found among the rest of the top scoring molecules.

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The low sensitivity of this structure-based screen means that the molecules that are not

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highly scored by this protocol cannot be claimed to be non-sweet. Potential parameters

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for improving sensitivity may be considered in the future, such as inclusion of water

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molecules in the binding site and ligand-induced conformational changes in the

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receptor. Molecular Dynamics simulations may help to obtain enhanced sampling of the

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receptor that will mimic ligand-induced conformational changes.

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Importantly, this work focused on the sugar-binding site in the T1R2 VFT domain. Other

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sites may be of importance: sweet proteins bind in the CRD between the two subunits –

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T1R2 and T1R3, in a wedge model (Temussi, 2011). NHDC (Winnig, Bufe, Kratochwil,

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Slack, & Meyerhof, 2007) and cyclamate (Jiang, Cui, Zhao, Snyder, Benard, Osman, et al.,

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2005) bind the CRD of T1R3. Thus, some sweet compounds cannot be found with the

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suggested protocol. Compounds that interact with other sites can be modeled by QSAR

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approaches or machine learning techniques (Rojas, et al., 2017; Zheng, Chang, Xu, Xu, &

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Lin, 2019).

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The recently published ligand-based or machine-learning methods, together with the

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structural screening presented in the current paper can work in conjunction, to

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maximize the diversity of novel sweeteners.

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Conflict of interests

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The authors declare no conflict of interests.

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Acknowledgements

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The authors thank Dr. Tamir Dingjan, Dr. Tali Yarnitzky and Mr. Ido Nissim for critical

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reading of the manuscript and Dr. Hillary Voet for helpful discussions. Funding from ISF

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grants #2463/16 and #1129/19 and from UHJ-France and the Foundation Scopus, is

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gratefully acknowledged. MYN is a member of COST actions Mu.Ta.Lig (CA15135) and

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ERNEST (CA18133). 15

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Figure 1 – Example of varying MW compounds from the true positives data set that were used for evaluation of enrichment and preparation of decoys. 18

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Figure 2.A. ROC curves for hT1R2 models, for compounds with MW up to 460 g/mol, using fishbased model (green curve) and mGluR class-C GPCR template (blue curve) models. Red dotted line indicates random enrichment performance. B. ROC curve for hT1R2 open-form model, for compounds with MW of 460-1100 g/mol, the model is colored in magenta. Red dotted line indicates random enrichment performance

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Figure 3- Ribbon representation of superimposed hT1R2 models, fish-based (5XDMB) model residues are colored in green, default model in blue, glucose ligand in purple.

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Figure 4 - A. Ribbon representation of hT1R2, fish-based model in green. B. Fish-based model 2D binding site with docked glucose and residues within 5A from the docked glucose.

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Figure 5 – Virtual screening of FooDB dataset against the fish-based model.

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Structure-based screening for discovery of sweet compounds

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Yaron Ben Shoshan-Galeczki and Masha Y Niv*

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The Institute of Biochemistry, Food and Nutrition, The Robert H Smith Faculty of

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Agriculture, Food and Environment, The Hebrew University, 76100 Rehovot and The

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Fritz Haber Center for Molecular Dynamics, The Hebrew University, Jerusalem, 91904,

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Israel.

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*correspondence to [email protected]

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Yaron Ben Shoshan - Galeczki: Methodology, Data Curation, Formal Analysis, Resources, Writing, Visualization, Editing

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Masha Y Niv: Conceptualization, Writing, Review and Editing, Supervision, Funding acquisition

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Highlights

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

Docking to homology models of VFT domain of human T1R2 was evaluated

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

Medaka fish-based model performed well for compounds below 460 g/mol

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

Model based on open form experimental structures was useful for larger compounds

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

Screening of FooDB retrieved recently patented sweeteners and provides novel candidates

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26