OdoriFy: A conglomerate of artificial intelligence-driven prediction engines for olfactory decoding

被引:15
|
作者
Gupta, Ria [1 ]
Mittal, Aayushi [1 ]
Agrawal, Vishesh [1 ]
Gupta, Sushant [1 ]
Gupta, Krishan [2 ]
Jain, Rishi Raj [3 ]
Garg, Prakriti [1 ]
Mohanty, Sanjay Kumar [1 ]
Sogani, Riya [1 ]
Chhabra, Harshit Singh [2 ]
Gautam, Vishakha [1 ]
Mishra, Tripti [4 ]
Sengupta, Debarka [1 ,2 ,5 ,6 ]
Ahuja, Gaurav [1 ]
机构
[1] Indraprastha Inst Informat Technol Delhi IIIT Del, Dept Computat Biol, New Delhi, India
[2] Indraprastha Inst Informat Technol Delhi IIIT Del, Dept Comp Sci & Engn, New Delhi, India
[3] Indraprastha Inst Informat Technol Delhi IIIT Del, Dept Comp Sci & Design, New Delhi, India
[4] Pathfinder Res & Training Fdn, Greater Noida, Uttar Pradesh, India
[5] Indraprastha Inst Informat Technol, Ctr Artificial Intelligence, New Delhi, India
[6] Queensland Univ Technol, Inst Hlth & Biomed Innovat, Brisbane, Qld, Australia
关键词
ODORANT RECEPTORS; EVOLUTION; PROTEIN; HUMANS; GENES;
D O I
10.1016/j.jbc.2021.100956
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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/.
引用
收藏
页数:14
相关论文
共 50 条
  • [41] Artificial intelligence-driven surgical innovation: A catalyst for medical equity
    Chiu, Si-Wai Vivian
    Liu, Chung-Feng
    Liao, Kuang-Ming
    Chiu, Chong-Chi
    ANNALS OF GASTROENTEROLOGICAL SURGERY, 2024, 8 (05): : 952 - 953
  • [42] ARTIFICIAL INTELLIGENCE-DRIVEN PSYCHIATRY INTERVENTIONS: FROM CHATBOTS TO TOYS
    Wong, Dustin
    JOURNAL OF THE AMERICAN ACADEMY OF CHILD AND ADOLESCENT PSYCHIATRY, 2024, 63 (10): : S103 - S104
  • [43] A Surgeon's Guide to Artificial Intelligence-Driven Predictive Models
    Hassan, Abbas M.
    Rajesh, Aashish
    Asaad, Malke
    Jonas, Nelson A.
    Coert, J. Henk
    Mehrara, Babak J.
    Butler, Charles E.
    AMERICAN SURGEON, 2023, 89 (01) : 11 - 19
  • [44] Artificial intelligence-driven phenotyping of zebrafish psychoactive drug responses
    Bozhko, Dmitrii, V
    Myrov, Vladislav O.
    Kolchanova, Sofia M.
    Polovian, Aleksandr, I
    Galumov, Georgii K.
    Demin, Konstantin A.
    Zabegalov, Konstantin N.
    Strekalova, Tatiana
    de Abreu, Murilo S.
    Petersen, Elena, V
    Kalueff, Allan, V
    PROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY, 2022, 112
  • [45] Efficiency and transparency of artificial intelligence-driven visual Chatbot: Comment
    Daungsupawong, Hinpetch
    Wiwanitkit, Viroj
    SKIN RESEARCH AND TECHNOLOGY, 2024, 30 (05)
  • [46] New Generation Artificial Intelligence-driven Intelligent Manufacturing (NGAIIM)
    Li, Bohu
    Chai, Xudong
    Hou, Baocun
    Zhang, Lin
    Zhou, Jiehan
    Liu, Yang
    2018 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTING, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI), 2018, : 1864 - 1869
  • [47] Artificial Intelligence-Driven Mammography-Based Future Breast Cancer Risk Prediction: A Systematic Review
    Schopf, Cody M.
    Ramwala, Ojas A.
    Lowry, Kathryn P.
    Hofvind, Solveig
    Marinovich, M. Luke
    Houssami, Nehmat
    Elmore, Joann G.
    Dontchos, Brian N.
    Lee, Janie M.
    Lee, Christoph I.
    JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY, 2024, 21 (02) : 319 - 328
  • [48] Artificial Intelligence-Driven Prediction Revealed CFTR Associated with Therapy Outcome of Breast Cancer: A Feasibility Study
    Kovacova, Maria
    Hlavac, Viktor
    Kozevnikovova, Renata
    Raus, Karel
    Gatek, Jiri
    Soucek, Pavel
    ONCOLOGY, 2024, 102 (12) : 1029 - 1040
  • [49] Artificial intelligence-driven pan-cancer analysis reveals miRNA signatures for cancer stage prediction
    Sathipati, Srinivasulu Yerukala
    Tsai, Ming-Ju
    Shukla, Sanjay K.
    Ho, Shinn-Ying
    HUMAN GENETICS AND GENOMICS ADVANCES, 2023, 4 (03):
  • [50] Artificial Intelligence-Driven Algorithm for Drug Effect Prediction on Atrial Fibrillation: An in silico Population of Models Approach
    Sanchez de la Nava, Ana Maria
    Arenal, Angel
    Fernandez-Aviles, Francisco
    Atienza, Felipe
    FRONTIERS IN PHYSIOLOGY, 2021, 12