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iModelTM

iORPDB

iInteraction

iOdorTools

Statistics

Examples

The architecture of iORbase.

It includes retrieving and analysis sub-systems with four modules of iORpdb, iInteraction, iModelTM and iOdorTool. They are integrated together for retrieving and visualization the predicted iORs structures and interactions with pheromones by iORpdb and iInteraction modules respectively, or predicting the homology models and interactions for user queried iOR sequences or pheromones by iModelTM and iOdorTool respectively.

Video introduction of iORbase

REFERENCES

The iORbase website citation:

1.Li, Q., Zhang, Y. F., Zhang, T. M., Wan, J. H., Zhang, Y. D., Yang, H., Huang, Y., Xu, C., Li, G. and Lu, H. M. (2023) iORbase: a database for the prediction of the structures and functions of insect olfactory receptors. Insect Science, doi: 10.1111/1744-7917.13162.

The following references are resources or toolkits for producing the iORbase data:

(Data resources)

1.Sayers, E.W., Beck, J., Bolton, E.E., Bourexis, D., Brister, J.R., Canese, K., Comeau, D.C., Funk, K., Kim, S., Klimke, W. et al. (2021) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res, 49, D10-D17.

2.Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B.A., Thiessen, P.A., Yu, B. et al. (2021) PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res, 49, D1388-D1395.

3.Mei, Y., Jing, D., Tang, S., Chen, X., Chen, H., Duanmu, H., Cong, Y., Chen, M., Ye, X., Zhou, H. et al. (2022) InsectBase 2.0: a comprehensive gene resource for insects. Nucleic Acids Res, 50, D1040-D1045. https://pubchem.ncbi.nlm.nih.gov

4.El-Sayed AM 2022. The Pherobase: Database of Pheromones and Semiochemicals. https://www.pherobase.com

5.Butterwick, J.A., Del Marmol, J., Kim, K.H., Kahlson, M.A., Rogow, J.A., Walz, T. and Ruta, V. (2018) Cryo-EM structure of the insect olfactory receptor Orco. Nature, 560, 447-452.

(Genome resources)

6.Burke, G.R., Walden, K.K., Whitfield, J.B., Robertson, H.M. and Strand, M.R. (2014) Widespread genome reorganization of an obligate virus mutualist. PLoS Genetics, 10, e1004660.

7.Chen, W., Hasegawa, D.K., Kaur, N., Kliot, A., Pinheiro, P.V., Luan, J., et al. (2016) The draft genome of whitefly Bemisia tabaci MEAM1, a global crop pest, provides novel insights into virus transmission, host adaptation, and insecticide resistance. BMC Biology, 14, 110.

8.Clark, A.G., Eisen, M.B., Smith, D.R., Bergman, C.M., Oliver, B., Markow, T.A., et al. (2007) Evolution of genes and genomes on the Drosophila phylogeny. Nature, 450, 203–218.

9.Fu, Y., Yang, Y., Zhang, H., Farley, G., Wang, J., Quarles, K.A., et al. (2018) The genome of the Hi5 germ cell line from Trichoplusia ni, an agricultural pest and novel model for small RNA biology. Elife, 7.

10.Gao, Q., Xiong, Z., Larsen, R.S., Zhou, L., Zhao, J., Ding, G., et al. (2020) High-quality chromosome-level genome assembly and full-length transcriptome analysis of the pharaoh ant Monomorium pharaonis. Gigascience, 9.

11.Harrison, M.C., Jongepier, E., Robertson, H.M., Arning, N., Bitard-Feildel, T., Chao, H., et al. (2018) Hemimetabolous genomes reveal molecular basis of termite eusociality. Nature Ecology & Evolution, 2, 557–566.

12.Karpe, S.D., Dhingra, S., Brockmann, A. and Sowdhamini, R. (2017) Computational genome-wide survey of odorant receptors from two solitary bees Dufourea novaeangliae (Hymenoptera: Halictidae) and Habropoda laboriosa (Hymenoptera: Apidae). Scientific Reports, 7, 10823.

13.Li, X., Fan, D., Zhang, W., Liu, G., Zhang, L., Zhao, L., et al. (2015) Outbred genome sequencing and CRISPR/Cas9 gene editing in butterflies. Nature Communications, 6, 8212.

14.McKenzie, S.K. and Kronauer, D.J.C. (2018) The genomic architecture and molecular evolution of ant odorant receptors. Genome Research, 28, 1757–1765.

15.Mohanty, S. and Khanna, R. (2017) Genome-wide comparative analysis of four Indian Drosophila species. Molecular Genetics and Genomics, 292, 1197–1208.

16.Nygaard, S., Zhang, G., Schiøtt, M., Li, C., Wurm, Y., Hu, H., et al. (2011) The genome of the leaf-cutting ant Acromyrmex echinatior suggests key adaptations to advanced social life and fungus farming. Genome Research, 21, 1339–1348.

17.Park, D., Jung, J.W., Choi, B.S., Jayakodi, M., Lee, J., Lim, J., et al. (2015) Uncovering the novel characteristics of Asian honey bee, Apis cerana, by whole genome sequencing. BMC Genomics, 16, 1.

18.Patalano, S., Vlasova, A., Wyatt, C., Ewels, P., Camara, F., Ferreira, P.G., et al. (2015) Molecular signatures of plastic phenotypes in two eusocial insect species with simple societies. Proceedings of the National Academy of Sciences of the United States of America, 112, 13970–13975.

19.Pearce, S.L., Clarke, D.F., East, P.D., Elfekih, S., Gordon, K.H.J., Jermiin, L.S., et al. (2017) Genomic innovations, transcriptional plasticity and gene loss underlying the evolution and divergence of two highly polyphagous and invasive Helicoverpa pest species. BMC Biology, 15, 63.

20.Ranz, J.M., Maurin, D., Chan, Y.S., von Grotthuss, M., Hillier, L.W., Roote, J., et al. (2007) Principles of genome evolution in the Drosophila melanogaster species group. PLoS Biology, 5, e152.

21.Richards, S., Gibbs, R.A., Weinstock, G.M., Brown, S.J., Denell, R., Beeman, R.W., et al. (2008) The genome of the model beetle and pest Tribolium castaneum. Nature, 452, 949–955.

22.Sadd, B.M., Barribeau, S.M., Bloch, G., de Graaf, D.C., Dearden, P., Elsik, C.G., et al. (2015) The genomes of two key bumblebee species with primitive eusocial organization. Genome Biology, 16, 76.

23.Sanchez-Flores, A., Peñaloza, F., Carpinteyro-Ponce, J., Nazario-Yepiz, N., Abreu-Goodger, C., Machado, C.A., et al. (2016) Genome evolution in three species of Cactophilic Drosophila. G3 (Bethesda), 6, 3097–3105.

24.Shields, E.J., Sheng, L., Weiner, A.K., Garcia, B.A. and Bonasio, R. (2018) High-Quality Genome Assemblies Reveal Long Non-coding RNAs Expressed in Ant Brains. Cell Reports, 23, 3078–3090.

25.Smith, C.D., Zimin, A., Holt, C., Abouheif, E., Benton, R., Cash, E., et al. (2011) Draft genome of the globally widespread and invasive Argentine ant (Linepithema humile). Proceedings of the National Academy of Sciences of the United States of America, 108, 5673–5678.

26.Standage, D.S., Berens, A.J., Glastad, K.M., Severin, A.J., Brendel, V.P. and Toth, A.L. (2016) Genome, transcriptome and methylome sequencing of a primitively eusocial wasp reveal a greatly reduced DNA methylation system in a social insect. Molecular Ecology, 25, 1769–1784.

27.Wallberg, A., Bunikis, I., Pettersson, O.V., Mosbech, M.B., Childers, A.K., Evans, J.D., et al. (2019) A hybrid de novo genome assembly of the honeybee, Apis mellifera, with chromosome-length scaffolds. BMC Genomics, 20, 275.

28.Xiao, J.H., Yue, Z., Jia, L.Y., Yang, X.H., Niu, L.H., Wang, Z., et al. (2013) Obligate mutualism within a host drives the extreme specialization of a fig wasp genome. Genome Biology, 14, R141.

(Protein structures prediction)

29.Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Zidek, A., Potapenko, A. et al. (2021) Highly accurate protein structure prediction with AlphaFold. Nature, 596, 583-589.

30.Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G.R., Wang, J., Cong, Q., Kinch, L.N., Schaeffer, R.D. et al. (2021) Accurate prediction of protein structures and interactions using a three-track neural network. Science, 373, 871-876.

31.Pereira, J., Simpkin, A.J., Hartmann, M.D., Rigden, D.J., Keegan, R.M. and Lupas, A.N. (2021) High-accuracy protein structure prediction in CASP14. Proteins-Structure Function and Bioinformatics, 89, 1687-1699.

32.Marti-Renom, M.A., Stuart, A.C., Fiser, A., Sanchez, R., Melo, F. and Sali, A. (2000) Comparative protein structure modeling of genes and genomes. Annu Rev Biophys Biomol Struct, 29, 291-325.

(Pheromone molecules analysis and simulation)

33.Trott, O. and Olson, A.J. (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem, 31, 455-461.

34.T, S., C, R., L, M., L, Q. and Z, Y. (2021) Accelerating AutoDock VINA with GPUs. ChemRxiv.

35.O'Boyle, N.M., Banck, M., James, C.A., Morley, C., Vandermeersch, T. and Hutchison, G.R. (2011) Open Babel: An open chemical toolbox. J. Cheminf., 3, 33.

36.Haddad, R., Khan, R., Takahashi, Y.K., Mori, K., Harel, D. and Sobel, N. (2008) A metric for odorant comparison. Nat. Meth., 5, 425-429.

37.Lovell, S.C., Davis, I.W., Arendall, W.B., 3rd, de Bakker, P.I., Word, J.M., Prisant, M.G., Richardson, J.S. and Richardson, D.C. (2003) Structure validation by Calpha geometry: phi,psi and Cbeta deviation. Proteins, 50, 437-450.

38.Bouysset, C. and Fiorucci, S. (2021) ProLIF: a library to encode molecular interactions as fingerprints. J. Cheminf., 13.

39.Naughton, F.B., Alibay, I., Barnoud, J., Barreto-Ojeda, E., Beckstein, O., Bouysset, C., Cohen, O., Gowers, R.J., MacDermott-Opeskin, H., Matta, M. et al. (2022) MDAnalysis 2.0 and beyond: fast and interoperable, community driven simulation analysis. Biophys. J., 121, 272a-273a.

(Gene annotation)

40.Simão, F.A., Waterhouse, R.M., Ioannidis, P., Kriventseva, E.V. and Zdobnov, E.M. (2015) BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics, 31, 3210-3212.

41.Finn, R.D., Clements, J. and Eddy, S.R. (2011) HMMER web server: interactive sequence similarity searching. Nucleic Acids Res, 39, W29-37.

42.She, R., Chu, J.S., Uyar, B., Wang, J., Wang, K. and Chen, N. (2011) genBlastG: using BLAST searches to build homologous gene models. Bioinformatics, 27, 2141-2143.

43.She, R., Chu, J.S., Wang, K., Pei, J. and Chen, N. (2009) GenBlastA: enabling BLAST to identify homologous gene sequences. Genome Res, 19, 143-149.

44.Pertea, G. and Pertea, M. (2020) GFF Utilities: GffRead and GffCompare. F1000Res, 9.

45.Arai, M., Mitsuke, H., Ikeda, M., Xia, J.-X., Kikuchi, T., Satake, M. and Shimizu, T. (2004) ConPred II: a consensus prediction method for obtaining transmembrane topology models with high reliability. Nucleic acids research, 32, W390-W393.

46.Tusnady, G.E. and Simon, I. (2001) The HMMTOP transmembrane topology prediction server. Bioinformatics, 17, 849-850.

47.Käll, L., Krogh, A. and Sonnhammer, E.L. (2007) Advantages of combined transmembrane topology and signal peptide prediction—the Phobius web server. Nucleic acids research, 35, W429-W432.

48.Krogh, A., Larsson, B., Von Heijne, G. and Sonnhammer, E.L. (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol., 305, 567-580.

(Data statistcs and web constructions)

49.Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K. and Madden, T.L. (2009) BLAST+: architecture and applications. BMC Bioinformatics, 10, 421.

50.Cao, Y., Charisi, A., Cheng, L.C., Jiang, T. and Girke, T. (2008) ChemmineR: a compound mining framework for R. Bioinformatics, 24, 1733-1734.

51.Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V. et al. (2011) Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.

52.Rego, N. and Koes, D. (2015) 3Dmol.js: molecular visualization with WebGL. Bioinformatics, 31, 1322-1324.

53.Ruiz-Moreno, A.J., Reyes-Romero, A., Domling, A. and Velasco-Velazquez, M.A. (2021) In Silico Design and Selection of New Tetrahydroisoquinoline-Based CD44 Antagonist Candidates. Molecules, 26.

54.Django HTTP server (v.3.2.5). https://www.djangoproject.com

55.Axios library. https://axios-http.com

56.MySQL database service. https://www.mysql.com

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Step 1.Enter the fasta protein sequence you need to model by iModelTM

Step 2.Or upload fasta files

Step 3.Run an example

1.Template and similarity information used for modelling

2.Download the result pdb file by iModelTM

3.Blast information

4.Modeling results visualization by 3Dmol

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Step 1.Select the species you are interested in

Step 2.Selected species information

Step 3.Olfactory receptors contained in the selected species

Step 4.Select a protein for more detailed information

1.Details of the selected protein

2.Select a different display model

3.Download

4.The Ramachandran Plot of selected pretein

5.The VOCs sensitive to the selected iOR

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  The iInteraction module is divided into three parts according to the source of iORs and pheromones

figure 1. iInteraction module main page

  If the iOR and pheromone you interested are both present in iORbase, you can use the iDocking Retrieve module (figure2 A). If the pheromone you interested does not exist in iORbase, you can use the iDocking Ligand module (figure2 B). If neither iOR nor pheromone exists in iORbase, you can use the iDocking interaction module (figure2 C).

figure 2. three parts flow charts. A: iDocking Retrieve module; B: iDocking Ligand module

iDocking Retrieve module Tutorial

  This module can only select iORs and pheromones in iORbase.

figure 3. iDocking Retrieve module usage instructions

  Step 1, you can select the iOR you interested in iORbase.

figure 4. Step 1 Detailed instructions. Search Module: Directly search for iORsin iORbase (Note: the input format must be the same as the name in iORbase; eg: CiLe_7); Select Module: You can select the iOR you interested in the list on the left; 1: different orders; 2: different species;3: select a specific iOR; 4: Show the selected iOR.

  Step 2, you can select the pheromone you interested in iORbase.

figure 5. Step 2 Detailed instructions. Search Module: Directly search for pheromone in iORbase (Note: the input format must be the CID number, if you don’t know the CID number of the pheromone, you can search it on Pubchem); Select Module: You can choose the pheromone you follow in the list on the left. The specific steps are similar to the selection of iOR

  Step 3, Click the “Docking the chosen iOR and pheromone!” button.

Views the docking result

  The docking results in iORbase are mainly divided into five parts (the results of different methods are not exactly same), including the information of the selected iOR, the information of the selected pheromone, the docking score of the selected iOR and the pheromone, the spatial docking posture of the selected iOR and the pheromone, and the interaction relationship between the pheromone and the residues of the iOR.

figure 6. Schematic diagram of docking results. 1: Information on selected iOR; 2: Information on selected insect pheromone; 3: AffinityScore of vina; 4: Vina result of iOR and insect pheromone; 5: schematic diagrams of protein-ligand interactions.

  On the docking results page, you can view iOR or VOC details by clicking on the iOR or VOC name (figure 7). The iOR and VOC that can view the details are both existing in iORbase

figure 7. Docking result expending information

iDocking Ligand module Tutorial

  In this module, you can upload pheromone molecules to docking with iOR in iORbase, and analyze the docking results. The operation process is divided into four parts: (1) select the iOR present in iORbase; (2) click the “iDOcking” button (figure 8); (3) upload the VOC molecular structure (Note: it must be SDF format file); (4) run iDocking (figure 9). The docking result page of this module is basically similar to the iDocking Retrieve module.

figure 8. iDocking Ligand module usage instructions

figure 9. iDocking Ligand module usage instructions expansion

iDocking Interaction module Tutorial

  If you have an iOR PDB file and a VOC SDF file, and want to analyze the interaction between iOR and VOC through molecular docking but do not know how to perform molecular docking, you can use this module. The operation steps are very simple, just upload the iOR PDB file and the VOC SDF file. Due to the difference in the size of different iOR binding pockets, we provide different sizes of docking boxes to choose from, and finally click the RUN iDocking button (figure 10).

figure 10. iDocking Interaction module usage instructions

  In the docking results page of this module, you can download the PDB file of the receptor-ligand complex (figure 11).

figure 11. Download receptor-ligand complex

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1.Mole providing a display of insect pheromones

2.Pheromone selection by pheromone type and functional group type

3.Select a pheromone to view details

1.Molecular visualisation of pheromones by 3Dmol

2.Details of the selected pheromone molecule

3.Download the pheromone sdf file

4.Jump to Pubchem for more detailed information

5.The iORs sensitive to the selected VOC

1.Search the iORbase for the most similar insect pheromone to the molecule uploaded by the user

2.Enter smile text format

3.Upload molecular files, such as sdf files

4.Run an example

1.Visual comparison of the input results with the query results, the green part has a high degree of structural similarity

2.Information on the molecule with the highest molecular similarity

3.Results for pheromones most similar to the query molecule in the spatial distribution of 2077 insect pheromones

4.Results for pheromones most similar to the query molecule in a planar distribution of 2077 insect pheromones

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Showing some statistical information on species, olfactory receptors and insect pheromones

Introduction

The iORbase website (v.1.2.1) has greatly upgraded the online molecular docking function of iInteraction. The following is the basic operation process for users' reference only.

The iModelTM module of iORbase website can realize the rapid modeling of insect OR structure, and the spatial conformation of the modeled protein is closer to the prediction result of AlphaFold 2 (AF2) than the commonly used SwissModel software, but it is more time-saving and convenient. Therefore, it is recommended that users refer to the content in this section and first use the iModelTM module to model the insect OR structure and check the structural rationality, so as to avoid the serious impact of non-functional OR sequences caused by gene sequencing, assembly and annotation errors on subsequent studies and experiments.

After rapid modeling and evaluation of iModelTM qualified functional insect OR, it can be used as a target protein receptor for molecular docking studies. At this point, establish user use currently recognized as the most accurate protein structure mode to build software AF2 OR its new AlphaFold3 (https://deepmind.google/technologies/alphafold) software to the user by insects OR sequence for precise mould, and get the PDB file of the exact model of the target OR.

Finally, refer to the procedure in Section 2 of this manual to complete the molecular docking of the target protein insect OR with the small molecular ligand to obtain the Affinity Scores, the spatial conformation of the complex, and the residues of the interaction.

1. Use the iModelTM module of this website to model OR structure and check the structural rationality

1.1. Rapid modeling of OR protein structure

Using the iModelTM module, user input in the page OR sequence, click run, and wait for the 3D model to build the results.

1.2. Obtain the display information of user OR's spatial structure iModelTM modeling results

1.3. Observe the modeling result page and evaluate the rationality of OR structure.

Figure (A) shows the Sequence Similarity with Template between the user OR and the iORbase database. If the similarity is less than 30%, it should check whether the OR sequence submitted by the user belongs to the insect OR protein family.

Figure (B) shows the protein spatial structure of the modeled user OR. Click and rotate the 3D structure with the left mouse button to make its viewing Angle close to the OR reference structure in Figure (C).

According to the OR reference structure features provided in (C) of the figure, judge whether the structure submitted OR generated by the user is reasonable. Focus on evaluating whether the user OR structure contains a complete 7-pass transmembrane helix (7TM, S1-S7); Whether the Anchor domain is included; The N-terminal domain (S7b) of the 7th transmembrane helix of the ion channel bottleneck that contains insects OR heterotetramers.

If the above conditions are met, it means that the OR submitted by the user is reasonable in structure, and it is necessary to further predict and analyze its function; If the above conditions are not met OR partially met, it is recommended that the user check the rationality of the obtained OR sequence, such as whether the bioinformatics analysis work such as genome sequencing, splicing, annotation needs to be improved and other possible problems.

If it is found that there are no errors in the sequencing and annotation of the gene, but it does not have reasonable structural characteristics to realize the function of the protein, it is suggested that the gene may be a pseudogene.

1.4. Download the iModelTM modeling structure PDB file.

For the structure evaluated by OR structure rationality, you can click (D) "Download the PDB file:" in the figure to download the PDB file of user OR's iModelTM model structure. This file can be used for the next step of molecular docking.

2. Use iInteraction module for molecular docking of OR and ligand

The iInteraction module provides three kinds of OR molecular docking functions, which can be used by clicking the corresponding button:

（1）iDocking Retrieve：

Retrieving docking information between iORs and VOCs in our database.

（2）iDocking Ligand：

Docking your uploaded VOCs (Ligands) with iORs in our database.

（3）iDocking Interaction：

Docking both of your uploaded iORs and VOCs to gain their interaction information.

2.1. iDocking Retrieve.

This feature can retrieve molecular docking data from the iORbase database for nearly 6000 OR about 12 million matching pairs between 2077 VOC.

2.2. iDocking Ligand.

This function enables online molecular docking of nearly 6000 OR's included in iORbase database with user-submitted ligand molecular structure files and the acquisition of molecular docking data.

2.3. iDocking Interaction.

This function can make online molecular docking between the insect OR structure file (PDB file) submitted by users and the ligand small molecule structure file (SDF file) submitted by users, and obtain molecular docking data.

The insect OR structure file (PDB file) can be selected according to the prediction purpose and accuracy requirements, using the PDB file previously generated and downloaded in the iModelTM module of the iORbase database, or using the PDB file generated by AlphaFold2 or other protein structure modeling software. And the PDB file after conformational optimization through molecular dynamics and other sources.

Among them, the ligand small molecule structure file (SDF file) can select the small molecule spatial structure file queried and downloaded by PubChem or other databases.

2.4. Molecular docking results

The following figure shows the molecular docking results and related information.

(e.g., https://www.iorbase.com/3Dinteraction?change_name=SiFl_9&cids=11174599)

Figure (A) shows the position of the small molecule in the active pocket of the OR protein, which can be viewed by adjusting the Angle of the mouse.

Figure (B) shows the molecular docking affinity of OR with small molecules.

Figure (C) shows the amino acid residues that OR interacts with small molecules, their AA type (different color blocks of AA), and the type of interaction force (color of connecting line segments).