Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
NanoAgents: Molecular Docking Using Multi-Agent Technology
M. Harindra R. Fernando, Asoka S. Karunananda, Roshan P. Perera
Pages - 37 - 51     |    Revised - 31-07-2016     |    Published - 31-08-2016
Volume - 7   Issue - 3    |    Publication Date - August 2016  Table of Contents
Molecular Docking, Multi-agent, Drug Discovery.
Traditional computer-based simulators for manual molecular docking for rational drug discovery have been very time consuming. In this research, a multi agent-based solution, named as NanoAgent, has been developed to automate the drug discovery process with little human intervention. In this solution, ligands and proteins are implemented as agents who pose the knowledge of permitted connections with other agents to form new molecules. The system also includes several other agents for surface determination, cavity finding and energy calculation. These agents autonomously activate and communicate with each other to come up with a most probable structure over the ligands and proteins, which are participating in deliberation. Domain ontology is maintained to store the common knowledge of molecular bindings, whereas specific rules pertaining to the behaviour of ligands and proteins are stored in their personal ontologies. Existing, Protein Data Bank (PDB) has also been used to calculate the space required by ligand to bond with the receptor. The drug discovery process of NanoAgent has exemplified exciting features of multi agent technology, including communication, coordination, negotiation, butterfly effect, self-organizing and emergent behaviour. Since agents consume fewer computing resources, NanoAgent has recorded optimal performance during the drug discovery process. NanoAgent has been tested for the discovery of the known drugs for the known protein targets. It has 80% accuracy by considering the prediction of the correct actual existence of the docked molecules using energy calculations. By comparing the time taken for the manual docking process with the time taken for the molecular docking by NanoAgent, there has been 95% efficiency.
CITED BY (0)  
1 Google Scholar
2 CiteSeerX
3 Scribd
4 SlideShare
5 PdfSR
1 D. T.-H. Chang, C.-H. Ke, J.-H. Lin, and J.-H. Chiang, “AutoBind: automatic extraction of protein–ligand-binding affinity data from biological literature,” Bioinformatics, vol. 28, no. 16, pp. 2162–2168, Aug. 2012.
2 M. Munetomo, K. Akama, and H. Maeda, “An automated ligand evolution system using Bayesian optimization algorithm,” WSEAS Trans. Inf. Sci. Appl., vol. 6, no. 5, pp. 788–797, 2009.
3 S. S. Azam and S. W. Abbasi, “Molecular docking studies for the identification of novel melatoninergic inhibitors for acetylserotonin-O-methyltransferase using different docking routines,” Theor. Biol. Med. Model., vol. 10, p. 63, Oct. 2013.
4 G. M. Morris and M. Lim-Wilby, “Molecular docking,” in Molecular Modeling of Proteins, Springer, 2008, pp. 365–382.
5 M. A. D. JE, “Virtual screening and lead optimisation to identify novel inhibitors for HDAC-8,” ArXiv Prepr. ArXiv12092793, 2012.
6 V. Lounnas, T. Ritschel, J. Kelder, R. McGuire, R. P. Bywater, and N. Foloppe, “CURRENT PROGRESS IN STRUCTURE-BASED RATIONAL DRUG DESIGN MARKS A NEW MINDSET IN DRUG DISCOVERY,” Comput. Struct. Biotechnol. J., vol. 5, no. 6, pp. 1–14, Feb. 2013.
7 “Molecular Docking to Ensembles of Protein Structures, Ronald M.A. Knegtel,Irwin D.Kuntz,Department of PharmaceuticalChemistry School of Pharmacy University of California, San Francisco, CAand, C.M.Oshiro, 1997.” .
8 Z. Zhou, A. K. Felts, R. A. Friesner, and R. M. Levy, “Comparative Performance of Several Flexible Docking Programs and Scoring Functions: Enrichment Studies for a Diverse Set of Pharmaceutically Relevant Targets,” J. Chem. Inf. Model., vol. 47, no. 4, pp. 1599–1608, 2007.
9 “AutoDock — AutoDock.” [Online]. Available: http://autodock.scripps.edu/. [Accessed: 15- Jul-2014].
10 “Home of David S. Goodsell.” [Online]. Available: http://mgl.scripps.edu/people/goodsell/. [Accessed: 15-Sep-2014].
11 M. Lape, C. Elam, and S. Paula, “Comparison of current docking tools for the simulation of inhibitor binding by the transmembrane domain of the sarco/endoplasmic reticulum calcium ATPase,” Biophys. Chem., vol. 150, no. 1–3, pp. 88–97, Aug. 2010.
12 “MGLTools Website - Welcome — MGLTools.” [Online]. Available: http://mgltools.scripps.edu/. [Accessed: 15-Sep-2014].
13 M. Olšák, J. Filipovic, and M. Prokop, “FastGrid–The Accelerated AutoGrid Potential Maps Generation for Molecular Docking,” Comput. Inform., vol. 29, no. 6+, pp. 1325–1336, 2012.
14 “Laboratory for Molecular Design and Drug Discovery.” [Online]. Available: http://www.rosalindfranklin.edu/cop/Home/LaboratoryforMolecularDesignandDrugDiscovery. aspx. [Accessed: 25-Sep-2014].
15 C. Ma, R. Kotaria, J. A. Mayor, S. Remani, D. E. Walters, and R. S. Kaplan, “The Yeast Mitochondrial Citrate Transport Protein CHARACTERIZATION OF TRANSMEMBRANE DOMAIN III RESIDUE INVOLVEMENT IN SUBSTRATE TRANSLOCATION,” J. Biol. Chem., vol. 280, no. 3, pp. 2331–2340, Jan. 2005.
16 C. A. Nicolaou and N. Brown, “Multi-objective optimization methods in drug design,” Drug Discov. Today Technol., vol. 10, no. 3, pp. e427–e435, Sep. 2013.
17 P. H. Palestro, L. Gavernet, G. L. Estiu, and L. E. Bruno Blanch, “Docking Applied to the Prediction of the Affinity of Compounds to P-Glycoprotein,” BioMed Res. Int., vol. 2014, p. e358425, May 2014.
18 W. Duch, K. Swaminathan, and J. Meller, “Artificial intelligence approaches for rational drug design and discovery,” Curr. Pharm. Des., vol. 13, no. 14, pp. 1497–1508, 2007.
19 E. Yu, “Agent-oriented modelling: software versus the world,” in Agent-Oriented Software Engineering II, Springer, 2002, pp. 206–225.
20 G. D. Crnkovic, “The Cybersemiotics and Info-Computationalist Research Programmes as Platforms for Knowledge Production in Organisms and Machines,” Entropy, vol. 12, no. 4, pp. 878–901, Apr. 2010.
21 B. Chandrasekaran, J. R. Josephson, and V. R. Benjamins, “What are ontologies, and why do we need them?,” IEEE Intell. Syst., vol. 14, no. 1, pp. 20–26, 1999.
22 A. H. Andre´ Filipe de Moraes Batista, Emerson Aguiar Noronha, Fa´bio Araga~o da Silva, Maria das Grac¸as Bruno Marietto, Robson dos Santos Franc¸a Terry Lima Ruas, Modeling Artificial Life Through Multi-Agent Based Simulation. INTECH Open Access Publisher, 2011.
23 M. University of Southampton Luck and AgentLink, Agent technology: computing as interaction?; a roadmap for agent based computing. Southampton: University of Southampton on behalf of AgentLink III, 2005.
24 “‘Agent Technology?: Computing as Interaction,A Roadmap for Agent Based Computing’.[Online]. Available: http://www.das.ufsc.br/~jomi/mas/leituras/agentlink- roadmap.pdf [Accessed: 16-Sep-2014].” .
25 D. J. Cook, “Multi-agent smart environments,” J. Ambient Intell. Smart Environ., vol. 1, no. 1, pp. 51–55, 2009.
26 Q. Guo and M. Zhang, “Research on intelligent manufacturing system based on multi- agent,” in Intelligent Robotics and Applications, Springer, 2008, pp. 829–838.
27 Z. Li, C. H. Sim, and M. Y. H. Low, “A survey of emergent behavior and its impacts in agent- based systems,” in Industrial Informatics, 2006 IEEE International Conference on, 2006, pp. 1295–1300.
28 “Wooldridge M. and Jennings N. ‘Intelligent Agents: Theory and Practice’.1995.” .
29 G. Weiss, Multiagent systems a modern approach to distributed artificial intelligence. Cambridge, Mass.: MIT Press, 1999.
30 “An Ontology for Engineering Mathematics.” [Online]. Available: http://www- ksl.stanford.edu/knowledge-sharing/papers/engmath.html. [Accessed: 03-Aug-2015].
31 M. N. Huhns and M. P. Singh, “Ontologies for agents,” Internet Comput. IEEE, vol. 1, no. 6, pp. 81–83, 1997.
32 F. Zini and L. Sterling, “(1) DISI-Universit a di Genova via Dodecaneso 35, 16146 Genova, Italy zini@ disi. unige. it (2) Department of Computer Science & Software Engineering.”
33 P. Maret, J. Calmet, and others, “Agent-based knowledge communities,” Int. J. Comput. Sci. Appl., vol. 6, no. 2, pp. 1–18, 2009.
34 “Molecular_Docking_Tutorial.pdf.” .
35 “Machado_et_al_LNCS_4643_1-11_2007.pdf.” .
36 “Carboxypeptidase.” [Online]. Available: http://www.chemistry.wustl.edu/~edudev/LabTutorials/Carboxypeptidase/carboxypeptidase.h tml. [Accessed: 17-Sep-2014].
37 “Potential Energy Function.” [Online]. Available: http://www.ch.embnet.org/MD_tutorial/pages/MD.Part2.html. [Accessed: 17-Sep-2014].
38 “Electric energy and potential.” [Online]. Available: http://physics.bu.edu/~duffy/PY106/Potential.html. [Accessed: 25-Oct-2014].
39 P. Leeson, “Drug discovery: Chemical beauty contest,” Nature, vol. 481, no. 7382, pp. 455– 456, Jan. 2012.
40 J. W. Ponder and D. A. Case, “Force fields for protein simulations,” Adv. Protein Chem., vol. 66, pp. 27–85, 2003.
41 “Jade Site | Java Agent DEvelopment Framework.” [Online]. Available: http://jade.tilab.com/. [Accessed: 17-Sep-2014].
42 “FIPA ACL Message Structure Specification.” [Online]. Available: http://www.fipa.org/specs/fipa00061/SC00061G.html. [Accessed: 18-Jul-2014].
43 “Apache Jena - Home.” [Online]. Available: https://jena.apache.org/. [Accessed: 09-Sep- 2014].
44 “RDF Current Status - W3C.” [Online]. Available: http://www.w3.org/standards/techs/rdf#w3c_all. [Accessed: 29-Jul-2014].
45 Y. Yan, C. Wang, A. Zhou, W. Qian, L. Ma, and Y. Pan, “Efficiently querying rdf data in triple stores,” in Proceedings of the 17th international conference on World Wide Web, 2008, pp. 1053–1054.
46 J. C. Arpírez, A. Gómez-Pérez, A. Lozano-Tello, and H. S. A. N. Pinto, “Reference ontology and (ONTO) 2 agent: the ontology yellow pages,” Knowl. Inf. Syst., vol. 2, no. 4, pp. 387– 412, 2000.
47 “SPARQL Query Language for RDF.” [Online]. Available: http://www.w3.org/TR/rdf-sparql- query/. [Accessed: 25-Jul-2014].
48 “Jmol: an open-source Java viewer for chemical structures in 3D.” [Online]. Available: http://jmol.sourceforge.net/. [Accessed: 17-Sep-2014].
49 D. Rudenko and A. Borisov, “An overview of blackboard architecture application for real tasks,” in Scientific Proceedings Of Riga Technical University, Ser, 2007, vol. 5, pp. 50–57.
50 T. J. Ewing and I. D. Kuntz, “Critical evaluation of search algorithms for automated molecular docking and database screening,” J. Comput. Chem., vol. 18, no. 9, pp. 1175– 1189, 1997.
51 “Locating Binding Sites in Protein Structures.” [Online]. Available: http://www.chemcomp.com/journal/sitefind.htm. [Accessed: 08-Sep-2014].
52 L. Jirkovsk?, S. M. Manák, and I. Kolingerová, “Finding Cavities in a Molecule”.
53 “Latest pairings | IUPHAR/BPS Guide to PHARMACOLOGY.” [Online]. Available: http://www.guidetopharmacology.org/latestPairings.jsp. [Accessed: 17-Sep-2014].
Mr. M. Harindra R. Fernando
University of Moratuwa - Sri Lanka
Professor Asoka S. Karunananda
General Sir John Kotelawala Defence University, Sri Lanka - Sri Lanka
Professor Roshan P. Perera
General Sir John Kotelawala Defence University, Sri Lanka - Sri Lanka