Efficient DNA-ligand interaction framework using fuzzy C-means clustering based glowworm swarm optimization (FCMGSO) method

Abstract Assessment of DNA and ligand interaction is a great challenge to the medical researchers and drug industries since the accurate mapping of DNA and ligand plays an important role in associating drugs for suitable diseases. The primary objective of this research work is to develop an efficient model for predicting the best DNA and Ligand mapping. In this research work, 500 instances of DNA and drugs used for cancer and non-cancer diseases from the National Centre for Biotechnology Information (NCBI) were considered for analysis. Binding energy is one of the important measures to predict and finalize the best DNA and ligand interaction. Existing methods used for the docking process such as Simulated Annealing (SA), Lamarckian Genetic Algorithm (LGA), Genetic Clustering (GC), Fuzzy C-means clustering (FCM), and Genetic Clustering with Multi swarm Optimization (GCMSO) were applied for all 500 instances. These algorithms failed to produce better binding energy due to a lack of optimization in the existing approaches. Optimization methods play a major role in predicting accurate DNA ligand docking. Hence, this research proposes an efficient architecture using Fuzzy C-Means Clustering with Glowworm Swarm (FCMGSO) optimization method for accurate analysis of the DNA-ligand docking process. Results are proving that the proposed FCMGSO algorithm shows less binding energy than other existing methods in all instances of samples considered from the NCBI dataset. Communicated by Ramaswamy H. Sarma

[1]  Charles W Christoffer,et al.  Computational structure modeling for diverse categories of macromolecular interactions. , 2020, Current opinion in structural biology.

[2]  Thomas G. Dietterich.  What is machine learning? , 2015, Archives of Disease in Childhood.

[3]  B. Shapiro,et al.  Modeling ligand docking to RNA in the design of RNA-based nanostructures. , 2019, Current opinion in biotechnology.

[4]  P. Fong,et al.  Evaluation of Scoring Function Performance on DNA-ligand Complexes , 2019, The Open Medicinal Chemistry Journal.

[5]  D. Prazeres,et al.  Multimodal chromatography of supercoiled minicircles: A closer look into DNA-ligand interactions , 2019, Separation and Purification Technology.

[6]  James M. Keller,et al.  Fuzzy Clustering: A Historical Perspective , 2019, IEEE Computational Intelligence Magazine.

[7]  Charles W Christoffer,et al.  Kinetic and structural parameters governing Fic-mediated adenylylation/AMPylation of the Hsp70 chaperone, BiP/GRP78 , 2018, Cell Stress and Chaperones.

[8]  A. Kudrev.  The Evidence of Cooperative Binding of a Ligand to G4 DNA , 2017, Journal of analytical methods in chemistry.

[9]  Dima Kozakov,et al.  The ClusPro web server for protein–protein docking , 2017, Nature Protocols.

[10]  Mohamed A. Khamis,et al.  Deep learning is competing random forest in computational docking , 2016, ArXiv.

[11]  Azlan Mohd Zain,et al.  Glowworm swarm optimization (GSO) for optimization of machining parameters , 2016, J. Intell. Manuf..

[12]  Victor Guallar,et al.  New Monte Carlo Based Technique To Study DNA-Ligand Interactions. , 2015, Journal of chemical theory and computation.

[13]  Mohamed A. Khamis,et al.  Comparative assessment of machine-learning scoring functions on PDBbind 2013 , 2015, Eng. Appl. Artif. Intell..

[14]  Simone A. Ludwig.  MapReduce-based fuzzy c-means clustering algorithm: implementation and scalability , 2015, Int. J. Mach. Learn. Cybern..

[15]  Simone A. Ludwig.  MapReduce-based fuzzy c-means clustering algorithm: implementation and scalability , 2015, International Journal of Machine Learning and Cybernetics.

[16]  Walid Gomaa,et al.  Machine learning in computational docking , 2015, Artif. Intell. Medicine.

[17]  Hanoch Senderowitz,et al.  Docking Studies on DNA Intercalators , 2014, J. Chem. Inf. Model..

[18]  Yves Pommier,et al.  Development of purely structure-based pharmacophores for the topoisomerase I-DNA-ligand binding pocket , 2013, Journal of Computer-Aided Molecular Design.

[19]  Martin Zacharias,et al.  Flexible docking and refinement with a coarse‐grained protein model using ATTRACT , 2013, Proteins.

[20]  Mieczyslaw Torchala,et al.  SwarmDock: a server for flexible protein-protein docking , 2013, Bioinform..

[21]  Yifeng D. Yang,et al.  Multi‐LZerD: Multiple protein docking for asymmetric complexes , 2012, Proteins.

[22]  Bin Wu,et al.  The improvement of glowworm swarm optimization for continuous optimization problems , 2012, Expert Syst. Appl..

[23]  C. P. Chandran,et al.  Genetic clustering with Bee Colony Optimization for flexible protein-ligand docking , 2012, International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012).

[24]  F. J. Luque,et al.  Frontiers in molecular dynamics simulations of DNA. , 2012, Accounts of chemical research.

[25]  David W. Ritchie,et al.  Ultra-fast FFT protein docking on graphics processors , 2010, Bioinform..

[26]  Dirk Neumann,et al.  A new Lamarckian genetic algorithm for flexible ligand‐receptor docking , 2010, J. Comput. Chem..

[27]  Daisuke Kihara,et al.  Protein-protein docking using region-based 3D Zernike descriptors , 2009, BMC Bioinformatics.

[28]  David S. Goodsell,et al.  AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility , 2009, J. Comput. Chem..

[29]  Supa Hannongbua,et al.  A detailed binding free energy study of 2:1 ligand-DNA complex formation by experiment and simulation. , 2009, Physical chemistry chemical physics : PCCP.

[30]  Paulo A. Netz,et al.  Docking Studies on DNA-Ligand Interactions: Building and Application of a Protocol To Identify the Binding Mode , 2009, J. Chem. Inf. Model..

[31]  Debasish Ghose,et al.  Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions , 2009, Swarm Intelligence.

[32]  Yi Liang,et al.  Design and Implementation of Parallel Lamarckian Genetic Algorithm for Automated Docking of Molecules , 2008, 2008 10th IEEE International Conference on High Performance Computing and Communications.

[33]  Sergey Lyskov,et al.  The RosettaDock server for local protein–protein docking , 2008, Nucleic Acids Res..

[34]  Z. Weng,et al.  Integrating statistical pair potentials into protein complex prediction , 2007, Proteins.

[35]  Stephen J. Wright,et al.  Global optimization in protein docking using clustering, underestimation and semidefinite programming , 2007, Optim. Methods Softw..

[36]  Armin Madadkar Sobhani,et al.  A theory of mode of action of azolylalkylquinolines as DNA binding agents using automated flexible ligand docking. , 2006, Journal of molecular graphics & modelling.

[37]  Pedro Alexandrino Fernandes,et al.  Protein–ligand docking: Current status and future challenges , 2006, Proteins.

[38]  Thomas Stützle,et al.  PLANTS: Application of Ant Colony Optimization to Structure-Based Drug Design , 2006, ANTS Workshop.

[39]  Dima Kozakov,et al.  Optimal clustering for detecting near-native conformations in protein docking. , 2005, Biophysical journal.

[40]  Ruth Nussinov,et al.  PatchDock and SymmDock: servers for rigid and symmetric docking , 2005, Nucleic Acids Res..

[41]  Jürgen Branke,et al.  Multi-swarm Optimization in Dynamic Environments , 2004, EvoWorkshops.

[42]  Dao-Qiang Zhang,et al.  Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm , 2003, Neural Processing Letters.

[43]  C. Dominguez,et al.  HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. , 2003, Journal of the American Chemical Society.

[44]  Sebastián Lozano,et al.  Parallel Fuzzy c-Means Clustering for Large Data Sets , 2002, Euro-Par.

[45]  Lawrence W. Lan,et al.  Genetic clustering algorithms , 2001, Eur. J. Oper. Res..

[46]  David S. Goodsell,et al.  Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function , 1998, J. Comput. Chem..

[47]  K. Sharp,et al.  Electrostatic contributions to heat capacity changes of DNA-ligand binding. , 1998, Biophysical journal.

[48]  A. D. Hunter.  ACD/ChemSketch 1.0 (freeware); ACD/ChemSketch 2.0 and its Tautomers, Dictionary, and 3D Plug-ins; ACD/HNMR 2.0; ACD/CNMR 2.0 , 1997 .

[49]  G Gauglitz,et al.  Label-free monitoring of DNA-ligand interactions. , 1997, Analytical biochemistry.

[50]  W. Pohle,et al.  Infrared spectroscopy as a tool for investigations of DNA structure and DNA - ligand interactions , 1990 .

[51]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[52]  Guillaume Bouvier,et al.  Automatic clustering of docking poses in virtual screening process using self-organizing map , 2010, Bioinform..

[53]  Debasish Ghose,et al.  Glowworm Swarm Optimization for Searching Higher Dimensional Spaces , 2009, Innovations in Swarm Intelligence.

[54]  Peter Rossmanith,et al.  Local search algorithms , 2008 .

[55]  W. Delano.  The PyMOL Molecular Graphics System , 2002 .

[56]  C. Spink,et al.  Thermal denaturation as tool to study DNA-ligand interactions. , 2001, Methods in enzymology.

[57]  Garantizar LA Correcta,et al.  Version 2.0 , 2001 .

[58]  J. Correia,et al.  Analysis of drug-DNA binding isotherms: a Monte Carlo approach. , 1994, Methods in enzymology.

[59]  Rob A. Rutenbar,et al.  Simulated annealing algorithms: an overview , 1989, IEEE Circuits and Devices Magazine.