Enhanced ranking of PknB Inhibitors using data fusion methods

BackgroundMycobacterium tuberculosis encodes 11 putative serine-threonine proteins Kinases (STPK) which regulates transcription, cell development and interaction with the host cells. From the 11 STPKs three kinases namely PknA, PknB and PknG have been related to the mycobacterial growth. From previous studies it has been observed that PknB is essential for mycobacterial growth and expressed during log phase of the growth and phosphorylates substrates involved in peptidoglycan biosynthesis. In recent years many high affinity inhibitors are reported for PknB. Previously implementation of data fusion has shown effective enrichment of active compounds in both structure and ligand based approaches .In this study we have used three types of data fusion ranking algorithms on the PknB dataset namely, sum rank, sum score and reciprocal rank. We have identified reciprocal rank algorithm is capable enough to select compounds earlier in a virtual screening process. We have also screened the Asinex database with reciprocal rank algorithm to identify possible inhibitors for PknB.ResultsIn our work we have used both structure-based and ligand-based approaches for virtual screening, and have combined their results using a variety of data fusion methods. We found that data fusion increases the chance of actives being ranked highly. Specifically, we found that the ranking of Pharmacophore search, ROCS and Glide XP fused with a reciprocal ranking algorithm not only outperforms structure and ligand based approaches but also capable of ranking actives better than the other two data fusion methods using the BEDROC, robust initial enhancement (RIE) and AUC metrics. These fused results were used to identify 45 candidate compounds for further experimental validation.ConclusionWe show that very different structure and ligand based methods for predicting drug-target interactions can be combined effectively using data fusion, outperforming any single method in ranking of actives. Such fused results show promise for a coherent selection of candidates for biological screening.

[1]  N. Gray,et al.  Targeting cancer with small molecule kinase inhibitors , 2009, Nature Reviews Cancer.

[2]  Rabia Nuray-Turan,et al.  Automatic ranking of information retrieval systems using data fusion , 2006, Inf. Process. Manag..

[3]  E. Rubin,et al.  Genes required for mycobacterial growth defined by high density mutagenesis , 2003, Molecular microbiology.

[4]  Jonas Boström,et al.  Assessing the performance of OMEGA with respect to retrieving bioactive conformations. , 2003, Journal of molecular graphics & modelling.

[5]  Charles L. A. Clarke,et al.  Reciprocal rank fusion outperforms condorcet and individual rank learning methods , 2009, SIGIR.

[6]  S. Smerdon,et al.  Effective inhibitors of the essential kinase PknB and their potential as anti-mycobacterial agents , 2011, Tuberculosis.

[7]  Egon L. Willighagen,et al.  The Chemistry Development Kit (CDK): An Open-Source Java Library for Chemo-and Bioinformatics , 2003, J. Chem. Inf. Comput. Sci..

[8]  J. Kuriyan,et al.  The Conformational Plasticity of Protein Kinases , 2002, Cell.

[9]  Matthew P. Repasky,et al.  Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. , 2006, Journal of medicinal chemistry.

[10]  Hsuan-Liang Liu,et al.  The discovery of potential acetylcholinesterase inhibitors: A combination of pharmacophore modeling, virtual screening, and molecular docking studies , 2011, Journal of Biomedical Science.

[11]  Amanda C. Schierz.  Virtual screening of bioassay data , 2009, J. Cheminformatics.

[12]  P. Willett,et al.  PHARMACOPHORE PERCEPTION , DEVELOPMENT , AND USE IN DRUG DESIGN , 2011 .

[13]  F. Lombardo,et al.  Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. , 2001, Advanced drug delivery reviews.

[14]  S. Cole,et al.  The Ser/Thr Protein Kinase PknB Is Essential for Sustaining Mycobacterial Growth , 2006, Journal of bacteriology.

[15]  Peter Willett,et al.  Similarity-based virtual screening using 2D fingerprints. , 2006, Drug discovery today.

[16]  O. Issinger,et al.  Crystal structure of human protein kinase CK2: insights into basic properties of the CK2 holoenzyme , 2001, The EMBO journal.

[17]  Susan S. Taylor,et al.  Crystal structure of a transition state mimic of the catalytic subunit of cAMP-dependent protein kinase , 2002, Nature Structural Biology.

[18]  Miklos Feher,et al.  The Use of Consensus Scoring in Ligand-Based Virtual Screening , 2006, J. Chem. Inf. Model..

[19]  A. Nairn,et al.  Structural Basis for the Autoinhibition of Calcium/Calmodulin-Dependent Protein Kinase I , 1996, Cell.

[20]  David E. Shaw,et al.  PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results , 2006, J. Comput. Aided Mol. Des..

[21]  Robert P. Sheridan,et al.  Protocols for Bridging the Peptide to Nonpeptide Gap in Topological Similarity Searches , 2001, J. Chem. Inf. Comput. Sci..

[22]  Brian K Shoichet,et al.  Prediction of protein-ligand interactions. Docking and scoring: successes and gaps. , 2006, Journal of medicinal chemistry.

[23]  Wei Zhao,et al.  A statistical framework to evaluate virtual screening , 2009, BMC Bioinformatics.

[24]  Miklos Feher,et al.  Consensus scoring for protein-ligand interactions. , 2006, Drug discovery today.

[25]  Peter Willett,et al.  Analysis of Data Fusion Methods in Virtual Screening: Similarity and Group Fusion , 2006, J. Chem. Inf. Model..

[26]  D. E. Clark.  What has virtual screening ever done for drug discovery? , 2008, Expert opinion on drug discovery.

[27]  Fredrik Svensson,et al.  Virtual Screening Data Fusion Using Both Structure- and Ligand-Based Methods , 2012, J. Chem. Inf. Model..

[28]  Veerabahu Shanmugasundaram,et al.  Molecular similarity measures. , 2011, Methods in molecular biology.

[29]  G. Kéri,et al.  A novel drug discovery concept for tuberculosis: inhibition of bacterial and host cell signalling. , 2008, Immunology letters.

[30]  M. Murcko,et al.  Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. , 1999, Journal of medicinal chemistry.

[31]  Valerie J. Gillet,et al.  Analysis of Data Fusion Methods in Virtual Screening: Theoretical Model , 2006, J. Chem. Inf. Model..

[32]  Christopher I. Bayly,et al.  Evaluating Virtual Screening Methods: Good and Bad Metrics for the "Early Recognition" Problem , 2007, J. Chem. Inf. Model..

[33]  Hanna Geppert,et al.  Integrating Structure‐ and Ligand‐Based Virtual Screening: Comparison of Individual, Parallel, and Fused Molecular Docking and Similarity Search Calculations on Multiple Targets , 2008, ChemMedChem.

[34]  H. Moser,et al.  Physicochemical properties of antibacterial compounds: implications for drug discovery. , 2008, Journal of medicinal chemistry.

[35]  Hege S. Beard,et al.  Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. , 2004, Journal of medicinal chemistry.

[36]  Marco Bellinzoni,et al.  The structure of PknB in complex with mitoxantrone, an ATP‐competitive inhibitor, suggests a mode of protein kinase regulation in mycobacteria , 2006, FEBS letters.

[37]  Shu Liu,et al.  Application of Consensus Scoring and Principal Component Analysis for Virtual Screening against β-Secretase (BACE-1) , 2012, PloS one.

[38]  Jiwon Choi,et al.  Optimization of High Throughput Virtual Screening by Combining Shape-Matching and Docking Methods , 2008, J. Chem. Inf. Model..

[39]  R. Kiss,et al.  Signalling inhibitors against Mycobacterium tuberculosis--early days of a new therapeutic concept in tuberculosis. , 2008, Current medicinal chemistry.

[40]  E. Casale,et al.  Through the "gatekeeper door": exploiting the active kinase conformation. , 2010, Journal of medicinal chemistry.

[41]  Woody Sherman,et al.  Novel Method for Generating Structure-Based Pharmacophores Using Energetic Analysis , 2009, J. Chem. Inf. Model..

[42]  Abhik Seal,et al.  In-silico Predictive Mutagenicity Model Generation Using Supervised Learning Approaches , 2011 .

[43]  P. Willett,et al.  Combination of molecular similarity measures using data fusion , 2000 .

[44]  Tom Alber,et al.  Structure of Mycobacterium tuberculosis PknB supports a universal activation mechanism for Ser/Thr protein kinases , 2003, Nature Structural Biology.

[45]  P. Alzari,et al.  Crystal Structure of the Catalytic Domain of the PknB Serine/Threonine Kinase from Mycobacterium tuberculosis * , 2003, The Journal of Biological Chemistry.

[46]  Thierry Langer,et al.  Pharmacophores and Pharmacophore Searches: LANGER: PHARMACOPHORES AND PHARMACOPHORE SEARCHES O-BK , 2006 .

[47]  J. Bajorath,et al.  Docking and scoring in virtual screening for drug discovery: methods and applications , 2004, Nature Reviews Drug Discovery.

[48]  Andreas Zell,et al.  Estimation of the applicability domain of kernel-based machine learning models for virtual screening , 2010, J. Cheminformatics.

[49]  Peter Willett,et al.  Promoting Access to White Rose Research Papers Enhancing the Effectiveness of Ligand-based Virtual Screening Using Data Fusion , 2022 .

[50]  Liem Nguyen,et al.  Protein Kinase G from Pathogenic Mycobacteria Promotes Survival Within Macrophages , 2004, Science.

[51]  Pierre Baldi,et al.  Large scale study of multiple-molecule queries , 2009, J. Cheminformatics.

[52]  J. Pin,et al.  Virtual screening workflow development guided by the "receiver operating characteristic" curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. , 2005, Journal of medicinal chemistry.

[53]  S. Smerdon,et al.  Substituted aminopyrimidine protein kinase B (PknB) inhibitors show activity against Mycobacterium tuberculosis , 2012, Bioorganic & medicinal chemistry letters.

[54]  Matthew P. Repasky,et al.  Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. , 2004, Journal of medicinal chemistry.

[55]  Pang-Ning Tan,et al.  Receiver Operating Characteristic , 2009, Encyclopedia of Database Systems.

[56]  M. Saraste,et al.  FEBS Lett , 2000 .