OdoriFy: A conglomerate of artificial intelligence–driven prediction engines for olfactory decoding
2021; Elsevier BV; Volume: 297; Issue: 2 Linguagem: Inglês
10.1016/j.jbc.2021.100956
ISSN1083-351X
AutoresRia Gupta, Aayushi Mittal, V.P. Agrawal, Sushant Gupta, Krishan Gupta, Rishi Raj Jain, Prakriti Garg, Sanjay Kumar Mohanty, Riya Sogani, Harshit Singh Chhabra, Vishakha Gautam, Tripti Mishra, Debarka Sengupta, Gaurav Ahuja,
Tópico(s)Insect Pheromone Research and Control
ResumoThe molecular mechanisms of olfaction, or the sense of smell, are relatively underexplored compared with other sensory systems, primarily because of its underlying molecular complexity and the limited availability of dedicated predictive computational tools. Odorant receptors (ORs) allow the detection and discrimination of a myriad of odorant molecules and therefore mediate the first step of the olfactory signaling cascade. To date, odorant (or agonist) information for the majority of these receptors is still unknown, limiting our understanding of their functional relevance in odor-induced behavioral responses. In this study, we introduce OdoriFy, a Web server featuring powerful deep neural network–based prediction engines. OdoriFy enables (1) identification of odorant molecules for wildtype or mutant human ORs (Odor Finder); (2) classification of user-provided chemicals as odorants/nonodorants (Odorant Predictor); (3) identification of responsive ORs for a query odorant (OR Finder); and (4) interaction validation using Odorant–OR Pair Analysis. In addition, OdoriFy provides the rationale behind every prediction it makes by leveraging explainable artificial intelligence. This module highlights the basis of the prediction of odorants/nonodorants at atomic resolution and for the ORs at amino acid levels. A key distinguishing feature of OdoriFy is that it is built on a comprehensive repertoire of manually curated information of human ORs with their known agonists and nonagonists, making it a highly interactive and resource-enriched Web server. Moreover, comparative analysis of OdoriFy predictions with an alternative structure-based ligand interaction method revealed comparable results. OdoriFy is available freely as a web service at https://odorify.ahujalab.iiitd.edu.in/olfy/. The molecular mechanisms of olfaction, or the sense of smell, are relatively underexplored compared with other sensory systems, primarily because of its underlying molecular complexity and the limited availability of dedicated predictive computational tools. Odorant receptors (ORs) allow the detection and discrimination of a myriad of odorant molecules and therefore mediate the first step of the olfactory signaling cascade. To date, odorant (or agonist) information for the majority of these receptors is still unknown, limiting our understanding of their functional relevance in odor-induced behavioral responses. In this study, we introduce OdoriFy, a Web server featuring powerful deep neural network–based prediction engines. OdoriFy enables (1) identification of odorant molecules for wildtype or mutant human ORs (Odor Finder); (2) classification of user-provided chemicals as odorants/nonodorants (Odorant Predictor); (3) identification of responsive ORs for a query odorant (OR Finder); and (4) interaction validation using Odorant–OR Pair Analysis. In addition, OdoriFy provides the rationale behind every prediction it makes by leveraging explainable artificial intelligence. This module highlights the basis of the prediction of odorants/nonodorants at atomic resolution and for the ORs at amino acid levels. A key distinguishing feature of OdoriFy is that it is built on a comprehensive repertoire of manually curated information of human ORs with their known agonists and nonagonists, making it a highly interactive and resource-enriched Web server. Moreover, comparative analysis of OdoriFy predictions with an alternative structure-based ligand interaction method revealed comparable results. OdoriFy is available freely as a web service at https://odorify.ahujalab.iiitd.edu.in/olfy/. The sense of smell allows an organism to sense its surroundings by recognizing and processing the information from diverse chemical clues present within the environment. These clues are largely composed of thousands of structurally diverse odorant molecules that mediate vital behavioral responses, such as social communication, identification and quality assessment of food (1Boesveldt S. Parma V. The importance of the olfactory system in human well-being, through nutrition and social behavior.Cell Tissue Res. 2021; 383: 559-567Crossref PubMed Scopus (9) Google Scholar), and the recognition of prey and predators (2Hughes N.K. Price C.J. Banks P.B. Predators are attracted to the olfactory signals of prey.PLoS One. 2010; 5e13114Crossref PubMed Scopus (68) Google Scholar, 3Hussain A. Saraiva L.R. Ferrero D.M. Ahuja G. Krishna V.S. Liberles S.D. Korsching S.I. 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Mouse trp2, the homologue of the human trpc2 pseudogene, encodes mTrp2, a store depletion-activated capacitative Ca2+ entry channel.Proc. Natl. Acad. Sci. U. S. A. 1999; 96: 2060-2064Crossref PubMed Scopus (233) Google Scholar). A recent transcriptomics study of human olfactory mucosa revealed the expression of guanylyl cyclases (GUCY2D/GC-D+ or GUCY1B2) as well as membrane-spanning 4-pass A transcripts (26Saraiva L.R. Riveros-McKay F. Mezzavilla M. Abou-Moussa E.H. Arayata C.J. Makhlouf M. Trimmer C. Ibarra-Soria X. Khan M. Van Gerven L. Jorissen M. Gibbs M. O'Flynn C. McGrane S. Mombaerts P. et al.A transcriptomic atlas of mammalian olfactory mucosae reveals an evolutionary influence on food odor detection in humans.Sci. Adv. 2019; 5eaax0396Crossref PubMed Scopus (28) Google Scholar). However, the study lacks companion immunohistological data, leaving uncertainty about the synthesis of associated proteins. Numerous studies suggest that olfactory receptors adopt a combinatorial coding strategy to enable the identification of a myriad of structurally distinct odorant molecules (3Hussain A. Saraiva L.R. Ferrero D.M. Ahuja G. Krishna V.S. Liberles S.D. Korsching S.I. High-affinity olfactory receptor for the death-associated odor cadaverine.Proc. Natl. Acad. Sci. U. S. A. 2013; 110: 19579-19584Crossref PubMed Scopus (118) Google Scholar, 4Sarafoleanu C. Mella C. Georgescu M. Perederco C. The importance of the olfactory sense in the human behavior and evolution.J. Med. Life. 2009; 2: 196-198PubMed Google Scholar, 5Raman B. Ito I. Stopfer M. Bilateral olfaction: Two is better than one for navigation.Genome Biol. 2008; 9: 212Crossref PubMed Scopus (9) Google Scholar, 6Gaillard I. Rouquier S. Giorgi D. Olfactory receptors.Cell. Mol. Life Sci. 2004; 61: 456-469Crossref PubMed Scopus (107) Google Scholar, 7Ahuja G. Korsching S. Zebrafish olfactory receptor ORA1 recognizes a putative reproductive pheromone.Commun. Integr. Biol. 2014; 7e970501Crossref Scopus (7) Google Scholar). In contrast to other gene families, because of its large number of functional genes, the family of ORs presents a strong case of machine learning–based exploration and identification of their agonists and nonagonists (27Malnic B. Godfrey P.A. Buck L.B. The human olfactory receptor gene family.Proc. Natl. Acad. Sci. U. S. A. 2004; 101: 2584-2589Crossref PubMed Scopus (371) Google Scholar). Notably, we use the term agonists for chemicals that are known to interact and activate an OR. Similarly, nonagonists refer to those chemicals that fail to elicit a functional activation response in the receptor–ligand interaction assays. In case, the given chemical activates an OR and possesses an odor percept, we use the term odorants for those chemicals. Similar to other chemoreceptors, ORs primarily reside on the cilia of the olfactory sensory neurons of the olfactory epithelium. Olfactory signal transduction initiates on the cilia upon odorant or agonist binding to its cognate receptor. Mechanistically, the interaction between an odorant molecule at the binding pocket of an OR triggers conformational changes, resulting in the dissociation of GDP from the Gα subunit (28Pace U. Lancet D. Olfactory GTP-binding protein: Signal-transducing polypeptide of vertebrate chemosensory neurons.Proc. Natl. Acad. Sci. U. S. A. 1986; 83: 4947-4951Crossref PubMed Scopus (78) Google Scholar). These biochemical reactions subsequently lead to the binding of GTP, which further activates adenylyl cyclase III (29Pace U. Hanski E. Salomon Y. Lancet D. Odorant-sensitive adenylate cyclase may mediate olfactory reception.Nature. 1985; 316: 255-258Crossref PubMed Scopus (392) Google Scholar). 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However, because of their enormous chemical diversity, efforts are still ongoing to understand the chemical basis of odorant molecules (50Goh G.B. Hodas N.O. Siegel C. Vishnu A. SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties. arXiv:1712.02034 [stat.ML].2017Google Scholar). Initial understanding has been that odorants possessing similar functional groups harbor similar perception properties, for example, esters are associated with a fruity smell or a floral smell, whereas thiols induce a rotten smell (51Poivet E. Tahirova N. Peterlin Z. Xu L. Zou D.-J. Acree T. Firestein S. Functional odor classification through a medicinal chemistry approach.Sci. Adv. 2018; 4eaao6086Crossref PubMed Scopus (23) Google Scholar). However, with the increasing number of odorant molecules, it has become apparent that the underlying olfactory mechanisms are more complex than previously anticipated. In recent years, machine learning–based approaches have been implemented in the field of chemosensory research, particularly in predicting the perception response of an odorant or tastant (41Gupta A. Choudhary M. Mohanty S.K. Mittal A. Gupta K. Arya A. Kumar S. Katyayan N. Dixit N.K. Kalra S. Goel M. Sahni M. Singhal V. Mishra T. Sengupta D. et al.Machine-OlF-action: A unified framework for developing and interpreting machine-learning models for chemosensory research.Bioinformatics. 2021; https://doi.org/10.1093/bioinformatics/btaa1104Crossref Scopus (1) Google Scholar, 52Huang W. Shen Q. Su X. Ji M. Liu X. Chen Y. Lu S. Zhuang H. Zhang J. BitterX: A tool for understanding bitter taste in humans.Sci. Rep. 2016; 6: 23450Crossref PubMed Scopus (29) Google Scholar, 53Dagan-Wiener A. Nissim I. Ben Abu N. Borgonovo G. Bassoli A. Niv M.Y. Bitter or not? BitterPredict, a tool for predicting taste from chemical structure.Sci. 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OdoriFy is built on a comprehensive repertoire of manually curated olfactory information, constituting 5003 odorants, 857 nonodorants, and 6153 interaction pairs (agonist receptor: 679; nonagonist receptor: 5474), making it one of the largest curated data resources to date. In total, OdoriFy contains four prediction engines, that is, Odorant Predictor, Odor Finder, OR Finder, and Odorant–OR Pair Analysis that collectively allow prediction of odorant status, identification of responsive ORs for the given odorant(s), and prediction of putative odorants for user supplemented wildtype or mutant OR protein sequences. In addition to these, OdoriFy also contains modules of explainable artificial intelligence that enable the highlighting of key decision-making structural elements of the predicted odorants or ORs at the atomic or amino acid levels, respectively. OdoriFy is an open-source Web server with deep neural network–based prediction models cou
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