AuPosSOM
Welcome!
AuPosSOM is a 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.
We have developed a scoring function to identify putative active compounds in a tree. Filters are also available to remove non-specific contacts. Different kind of contacts can be fused in a same analysis.
Go here to create an account. Feel free to contact usfor any problems or suggestions.
NEWS
- 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 (Thanks to A. Sakhteman)
References
The AuPosSOM team:
Bouvier G
Mantsyzov AB
Melikian M
Girault JP
Bertho G*
Related publications
- Review
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.
- Structure-Based Virtual Screening for Drug Discovery: a Problem-Centric Review Cheng, T., Li, Q., Zhou, Z., Wang, Y., & Bryant, S. H. (2012). The AAPS journal, 14, 133-141.
- Examples of applications
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
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
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
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
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
- In silico identification and evaluation of new Trypanosoma cruzi trypanothione reductase (TcTR) inhibitors obtained from natural products database of the Bahia semi-arid region (NatProDB). da Paixão VG, Pita SSDR. Comput Biol Chem. (2019) Apr;79:36-47. doi: 10.1016/j.compbiolchem.2019.01.009
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. Iran J Pharm Res. (2017) 16(3):981-998
- Integrative computational protocol for the discovery of inhibitors of the Helicobacter pylori nickel response regulator (NikR) Segura-Cabrera A, Guo X, Rojo-Domínguez A, Rodríguez-Pérez MA. (2011) J Mol Model. Mar 1. [Epub ahead of print]
- Discovery of novel anti-leishmanial agents targeting LdLip3 lipase. Parameswaran, S., Saudagar, P., Dubey, V.K., Patra, S. 2014. Journal of Molecular Graphics and Modelling
- Screening natural products database for identification of potential antileishmanial chemotherapeutic agents Venkatesan S. K., Saudagar P., Shukla A. K., Dubey V. K. (2011) Interdiscip Sci. 3, 217-31
- Footprinting of Inhibitor Interactions of in silico identified inhibitors of trypanothione reductase of Leishmania parasite Venkatesan, S. K., & Dubey, V. K. (2012). The Scientific World Journal, Epub 2011 Nov 2
- In silico-based identification of human α-enolase inhibitors to block cancer cell growth metabolically. 11:3281-3290 Drug Des Devel Ther. (2017)
- 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.
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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.
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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
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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
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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
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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
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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
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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
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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
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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
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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).
-Who is using AuPosSOM ?