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AuPosSOM

 

Welcome!

AuPosSOM is a free virtual screening tool for the automatic analysis of docked structures.

The on-line version of AuPosSOM 2.1 is available !

The analysis of contacts takes into account hydrogen-bonds / Coulombic / hydrophobic / all contacts between drugs and protein.

A scoring function to identify putative active compounds in a tree has been developed. Filters are also available to remove non-specific contacts and different types of contacts can be fused in a same analysis.

Go here to create an account. Feel free to contact us

for any problems or suggestions.

NEWS

 
- Congrats to the 500th user who joined us on August 3, 2023.
 
- Version 2.0 (06 September 2012) is available on-line.

- AuPosSOM version 2.0 was presented for the first time by our colleague A. Mansyzov at the JOBIM congress at Pasteur Institute (Paris) in june 2011. Abstract: (pdf) JOBIM : site (in french).

- Tips and tricks: preAuPosSOM: a simple toolbox to make the complexes (Special thanks to A. Sakhteman)

References to cite

- Contact-based ligand-clustering approach for the identification of active compounds in virtual screening. Mantsyzov AB, Bouvier G, Evrard-Todeschi N and Bertho G*. (2012) Advances and Applications in Bioinformatics and Chemistry 5, 61-79
Abstract: ' Evaluation of docking results is one of the most important problems for virtual screening and in silico drug design. Modern approaches for the identification of active compounds in a large data set of docked molecules use energy scoring functions. One of the general and most significant limitations of these methods relates to inaccurate binding energy estimation, which results in false scoring of docked compounds. Automatic analysis of poses using self-organizing maps (AuPosSOM) represents an alternative approach for the evaluation of docking  results based on the clustering of compounds by the similarity of their contacts with the receptor. A scoring function was developed for the identification of the active compounds in the AuPosSOM clustered dataset. In addition, the AuPosSOM efficiency for the clustering of compounds and the identification of key contacts considered as important for its activity, were also improved. Benchmark tests for several targets revealed that together with the developed scoring function, AuPosSOM represents a good alternative to the energy-based scoring functions for the evaluation of docking results. '
- Automatic clustering of docking poses in virtual screening process using self-organizing map. Bouvier G, Evrard-Todeschi T, Girault JP, Bertho G*. (2010) Bioinformatics 26, 53-60

The AuPosSOM team:

Bouvier G

Mantsyzov AB

Melikian M

Girault JP

Bertho G*

Related publications

- Reviews

Protein-Ligand Docking in the Machine-Learning Era.

Yang C, Chen EA, Zhang Y.Molecules. 2022 Jul 18;27(14):4568. doi: 10.3390/molecules27144568. PMID: 35889440  Review.

'Based on the assumption that active compounds should have specific contacts with their target to display activity and also to tackle the inefficiency of traditional clustering of docking poses, Bouvier et al. have proposed the Automatic analysis of Poses using Self-Organizing Map (AuPosSOM) method for pose ranking with careful analysis of interatomic contacts between the docked ligand and the target. They have demonstrated that it is possible to differentiate active compounds from inactive ones using only mean protein contacts’ footprints calculated from the multiple conformations given by docking software.'

- Examples of applications

Synthesis of Four Steroidal Carbamates with Antitumor Activity against Mouse Colon Carcinoma CT26WT Cells: In Vitro and In Silico Evidence.

Pacheco DF, Alonso D, Ceballos LG, Castro AZ, Brown Roldán S, García Díaz M, Villa Testa A, Wagner SF, Piloto-Ferrer J, García YC, Olea AF, Espinoza L.Int J Mol Sci. 2022 Aug 7;23(15):8775. doi: 10.3390/ijms23158775. PMID: 35955909  

Understanding the Anti-Diarrhoeal Properties of Incomptines A and B: Antibacterial Activity against Vibrio cholerae and Its Enterotoxin Inhibition.

Calzada F, Bautista E, Hidalgo-Figueroa S, García-Hernández N, Velázquez C, Barbosa E, Valdes M, Solares-Pascasio JI.Pharmaceuticals (Basel). 2022 Feb 3;15(2):196. doi: 10.3390/ph15020196. PMID: 35215308

Discovery of Small Molecule NSC290956 as a Therapeutic Agent for KRas Mutant Non-Small-Cell Lung Cancer.

Zhang J, Liu Z, Zhao W, Yin X, Zheng X, Liu C, Wang J, Wang E.Front Pharmacol. 2022 Jan 5;12:797821. doi: 10.3389/fphar.2021.797821. eCollection 2021. PMID: 35069209

Machine Learning-Enabled Pipeline for Large-Scale Virtual Drug Screening.

Gupta A, Zhou HX.J Chem Inf Model. 2021 Sep 27;61(9):4236-4244. doi: 10.1021/acs.jcim.1c00710. Epub 2021 Aug 17. PMID: 34399578

Virtual Screening and In Vitro Evaluation of PD-1 Dimer Stabilizers for Uncoupling PD-1/PD-L1 Interaction from Natural Products.

Lung J, Hung MS, Lin YC, Hung CH, Chen CC, Lee KD, Tsai YH.Molecules. 2020 Nov 13;25(22):5293. doi: 10.3390/molecules25225293. PMID: 33202823

Prospects of Indole derivatives as methyl transfer inhibitors: antimicrobial resistance managers.

Tha S, Shakya S, Malla R, Aryal P.BMC Pharmacol Toxicol. 2020 May 4;21(1):33. doi: 10.1186/s40360-020-00402-9. PMID: 32366298 

Repositioning FDA Drugs as Potential Cruzain Inhibitors from Trypanosoma cruzi: Virtual Screening, In Vitro and In Vivo Studies.

Palos I, Lara-Ramirez EE, Lopez-Cedillo JC, Garcia-Perez C, Kashif M, Bocanegra-Garcia V, Nogueda-Torres B, Rivera G.Molecules. 2017 Jun 18;22(6):1015. doi: 10.3390/molecules22061015. PMID: 28629155

Interaction of a small molecule Natura-α and STAT3-SH2 domain to block Y705 phosphorylation and inhibit lupus nephritis Chiao JW, Melikian M, Xue C, Tsao A, Wang L, Mencher SK, Fallon J, Solangi K, Bertho G*, Wang LG* Biochem Pharmacol. (2015) Nov 28. pii: S0006-2952(15)00746-7. doi: 10.1016/j.bcp.2015.11.018

Binding mode of triazole derivatives as aromatase inhibitors based on docking, protein ligand interaction fingerprinting, and molecular dynamics simulation studies. Mojaddami A, Sakhteman A, Fereidoonnezhad M, Faghih Z, Najdian A, Khabnadideh S, Sadeghpour H, Rezaei Z. Res Pharm Sci. (2017) 1, 21-30

A Comparative QSAR Analysis, Molecular Docking and PLIF Studies of Some N-arylphenyl-2, 2-Dichloroacetamide Analogues as Anticancer Agents. Fereidoonnezhad M, Faghih Z, Mojaddami A, Rezaei Z, Sakhteman A. Iran J Pharm Res. (2017) 16(3):981-998

  • In silico-based identification of human α-enolase inhibitors to block cancer cell growth metabolically. Lung J, Chen KL, Hung CH, Chen CC, Hung MS, Lin YC, Wu CY, Lee KD, Shih NY, Tsai YH. Drug Des Devel Ther. (2017) 11:3281-3290
  • Leishmania infantum 5'-Methylthioadenosine Phosphorylase presents relevant structural divergence to constitute a potential drug target. Abid H, Harigua-Souiai E, Mejri T, Barhoumi M, Guizani I. BMC Struct Biol. 2017 Dec 19;17(1):9. doi: 10.1186/s12900-017-0079-7.

  • Discovery of Novel Haloalkane Dehalogenase Inhibitors. Buryska T, Daniel L, Kunka A, Brezovsky J, Damborsky J, Prokop Z. Appl Environ Microbiol. 2016 Jan 15;82(6):1958-1965.

  • Identification of binding sites and favorable ligand binding moieties by virtual screening and self-organizing map analysis. Harigua-Souiai E, Cortes-Ciriano I, Desdouits N, Malliavin TE, Guizani I, Nilges M, Blondel A, Bouvier G. BMC Bioinformatics. 2015 Mar 21;16:93.

  • Functional motions modulating VanA ligand binding unraveled by self-organizing maps. Bouvier, G., Duclert-Savatier, N., Desdouits, N., Meziane-Cherif, D., Blondel, A., Courvalin, P., ... & Malliavin, T. E. (2014). Journal of chemical information and modeling, 54(1), 289-301
  • Kumar, D. T. (2014). Computational Approaches and Resources in Single Amino Acid Substitutions Analysis Toward Clinical Research. Advances in protein chemistry and structural biology, 94, 365.
  • Stabilization of the integrase-DNA complex by Mg2+ ions and prediction of key residues for binding HIV-1 integrase inhibitors 
    Miri, L., Bouvier, G., Kettani, A., (...), Nilges, M., Malliavin, T.E. 2014 Proteins: Structure, Function and Bioinformatics
  • Navigating traditional Chinese medicine network pharmacology and computational tools. Yang, M., Chen, J.-L., Xu, L.-W., Ji, G. 2013. Evidence-based Complementary and Alternative Medicine
  • Rational drug design: the search for Ras protein hydrolysis intermediate conformation Inhibitors with both affinity and specificity. Zheng, X., Liu, Z., Li, D., Wang, E., & Wang, J. (2013). Current pharmaceutical design, 19(12), 2246-2258. http://www.ingentaconnect.com/content/ben/cpd/2013/00000019/00000012/art00012
  • Artificial Neural Networks for Efficient Clustering of Conformational Ensembles and their Potential for Medicinal Chemistry. Pandini, A., Fraccalvieri, D., & Bonati, L. (2013). Current topics in medicinal chemistry, 13(5), 642-651. http://www.eurekaselect.com/109236/article
  • Drug repositioning by structure-based virtual screening. Ma, D. L., Chan, D. S. H., & Leung, C. H. (2013). Chemical Society Reviews 42, 2130-2141. http://pubs.rsc.org/en/Content/ArticleLanding/2013/CS/c2cs35357a
  • Latest developments in molecular docking: 2010–2011 in review. Yuriev, E., & Ramsland, P. A. (2013). Journal of Molecular Recognition, 26, 215-239. http://onlinelibrary.wiley.com/doi/10.1002/jmr.2266/abstract
  • A combined 3D-QSAR and docking studies for the In-silico prediction of HIV-protease inhibitors. Ul-Haq, Z., Usmani, S., Shamshad, H., Mahmood, U., & Halim, S. A. (2013). Chemistry Central Journal, 7, 1-12. http://link.springer.com/article/10.1186/1752-153X-7-88#page-1
  • Exploring c‐Met kinase flexibility by sampling and clustering its conformational space. Asses, Y., Venkatraman, V., Leroux, V., Ritchie, D. W., & Maigret, B. (2012). Proteins: Structure, Function, and Bioinformatics, 80, 1227-1238. http://onlinelibrary.wiley.com/doi/10.1002/prot.24021/full
  • A scalable and accurate method for classifying protein–ligand binding geometries using a MapReduce approach. Estrada, T., Zhang, B., Cicotti, P., Armen, R. S., & Taufer, M. (2012). Computers in Biology and Medicine, 42, 758–771. http://www.sciencedirect.com/science/article/pii/S0010482512000807
  • Recent Trends and Applications in 3D Virtual Screening. Ghemtio, L., I Perez-Nueno, V., Leroux, V., Asses, Y., Souchet, M., Mavridis, L., ... & W Ritchie, D. (2012). Combinatorial Chemistry & High Throughput Screening, 15, 749-769. http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6332193&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6332193
  • Prospects of Modulating Protein–Protein Interactions, in Protein-Ligand Interactions, Zhong, S., Oashi, T., Yu, W., Shapiro, P. and MacKerell, A. D. (2012) First Edition (ed H. Gohlke), Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany. http://onlinelibrary.wiley.com/doi/10.1002/9783527645947.ch15/summary
  • Identification of Novel beta3-Adrenoceptor Agonists Using Energetic Analysis, Structure Based Pharmacophores and Virtual Screening. Tewatia, P., Malik, B. K., & Sahi, S. (2012). Combinatorial Chemistry & High Throughput Screening, 15(8), 623-640. http://www.ingentaconnect.com/content/ben/cchts/2012/00000015/00000008/art00004
  • Identification of Potential Inhibitors of Haloalkane Dehalogenases by Virtual Screening L. Daniel, J. Damborský, and J. Brezovský. (2012). Materials Structure 12, 13.
  • Self-Organizing Maps for In Silico Screening and Data Visualization Digles D, Ecker GF. (2011). Molecular Informatics, 30, p838-846
  • Conformational and functional analysis of molecular dynamics trajectories by Self-Organising MapsFraccalvieri D, Pandini A, Stella F, Bonati L. (2011). BMC Bioinformatics, 12, Article number: 158
  • Automatic selection of near-native protein-ligand conformations using a hierarchical clustering and volunteer computing. Estrada, T., Armen, R., & Taufer, M. (2010, August). In Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology (pp. 204-213). ACM. http://dl.acm.org/citation.cfm?id=1854807

    'Docking simulations are commonly used to understand drug binding and require the search of a large space of proteinligand conformations. Cloud and volunteer computing enable computationally expensive docking simulations at a rate never seen before but at the same time require scientists to deal with larger datasets. When analysing these datasets, a common practice is to reduce the resulting number of candidates up to 10 to 100 conformations based on energy values and then leave the scientists with the tedious task of subjectively selecting a possible near-native ligand. Scientists normally perform this task manually by using visual tools. Not only the manual process still depends on inaccurate energy scoring but also can be highly error-prone.'

 

PhD thesis:

-Fraccalvieri, D. (2011). Comparison of protein dynamics: a new methodology based on self-organizing maps. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2011). http://boa.unimib.it/handle/10281/19615#.Ucv1FtcyLAE

-Bouvier, G. (2010). Etude d'inhibiteurs de l'intégrase du VIH-1 par RMN et modélisation moléculaire: développement et validation d'un outil de criblage virtuel (Doctoral dissertation Université Pierre & Marie Curie, 2010).

 

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