Enhanced ranking of PknB Inhibitors using data fusion methods

Abstract

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.

Fusing Dual-Event Data Sets for Mycobacterium tuberculosis Machine Learning Models and Their Evaluation

“…concentrated ROC (CROC), Boltzmann-enhanced discrimination of ROC (BEDROC), Guner Henry Score etc.) 71–74 and whether consensus scoring could overcome these.…”

Selective Pharmacologic Inhibition of a PASTA Kinase Increases Listeria monocytogenes Susceptibility to β-Lactam Antibiotics

“…While antibiotic resistance in the model pathogen L. monocytogenes is not a burgeoning issue, we believe that these data act as a proof of principle that specificity in the targeting of bacterial PASTA eSTKs is possible. Prior to our work, pharmacologic inhibitors that target the M. tuberculosis kinase PknB have also been identified, although their development as potential therapeutic agents is still a work in progress (16)(17)(18). Inhibitors of other bacterial PASTA kinases that work in a ␤-lactam-synergistic manner remain to be identified.…”

“…While the substrates and function of the PASTA kinases are incompletely defined, they appear to have various functions in different organisms, ranging from playing a role in biofilm formation (Streptococcus mutans) to being essential in some organisms (M. tuberculosis) (14,15). As such, pharmacologic inhibition of PASTA kinases has been preliminarily pursued as a novel antimicrobial therapeutic strategy against the M. tuberculosis kinase PknB (16)(17)(18). Deletion of Stk1, the PASTA kinase in S. aureus, reverses the methicillin-resistant phenotype in MRSA (19,20).…”

Repurposed kinase inhibitors and β-lactams as a novel therapy for antibiotic resistant bacteria

“…Several PknB structures were previously elucidated, but none with a specific kinase inhibitor bound to the active site (Lombana et al, 2010, Ortiz-Lombardia, Pompeo et al, 2003, Wehenkel, Fernandez et al, 2006, Young, Delagoutte et al, 2003. Additionally, several PknB inhibitors were previously modeled in silico, but structural confirmation of these models was not established (Chapman et al, 2012, Lougheed et al, 2011, Naqvi et al, 2014, Seal et al, 2013. To confirm our model and facilitate future drug development insight, we co-crystallized the catalytic domain of PknB with GSK690693, a compound biochemically equivalent to our best drugs and commercially available in large quantities.…”