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Feb
20

TB Mobile version 2.0

One minute it’s submitted to the AppStore, then its approved and available already. It is amazing how fast these things can happen! Yes you are wondering what is it, well its TB Mobile version 2.0 *.

When the Idea appeared to develop the TB Mobile app in 2012 I had no idea that we would really go to the next step. It’s been a pretty fun time. With the considerable help of Alex Clark and Malabika Sarker we have not just an update to the content, we have an app with brand new functionality as well.

TB mobile was initially built as a repository for the public data set of ~700 Compounds with known TB targets that also resides in CDD Public. We then went about collating additional compounds so that we could evaluate the similarity searching in the app. This enabled us to have enough data for a paper. We have even used the underlying dataset for other comparisons and predictions.

No we have added an additional 60 molecules with targets that have come to our attention. So now we have 96 unique targets and 805 compounds. Its fascinating to see that some targets are well represented like InhA and thiL..and some new comers likely Mmpl3 are increasing.

“Rv0283” (count=1)

    “Rv0678” (count=1)
    “Rv1211” (count=1)
    “Rv1685c” (count=1)
    “Rv1885c” (count=4)
    “Rv3160c” (count=1)
    “Rv3161c” (count=1)
    “TB27.3 (Rv0577)” (count=2)
    “ald (Rv2780)” (count=2)
    “alr (Rv3423c)” (count=8)
    “aroD (Rv2537c)” (count=14)
    “aspS (Rv2572c)” (count=1)
    “atpE (Rv1305)” (count=2)
    “blaC (Rv2068c)” (count=1)
    “clpB (Rv0384c)” (count=1)
    “clpC (Rv3596c)” (count=1)
    “cyp121 (Rv2276)” (count=2)
    “cyp130 (Rv1256c)” (count=2)
    “cyp51 (Rv0764c)” (count=2)
    “cysH (Rv2392)” (count=10)
    “cysS (Rv2130c)” (count=1)
    “dacB2 (Rv2911)” (count=1)
    “dapA (Rv2753c)” (count=12)
    “deaD (Rv1253)” (count=1)
    “def (Rv0429c)” (count=14)
    “dfrA (Rv2763c)” (count=3)
    “dinG (Rv1329c)” (count=1)
    “dlaT (Rv2215)” (count=2)
    “dnaA (Rv0001)” (count=1)
    “dnaB (Rv0058)” (count=1)
    “dnaE2 (Rv3370c)” (count=1)
    “dprE1 (Rv3790)” (count=8)
    “dprE2” (count=1)
    “drpE2 (Rv3791)” (count=2)
    “dxr (Rv2870c)” (count=1)
    “dxs1 (Rv2682C)” (count=29)
    “embA (Rv3794)” (count=2)
    “embB (Rv3795)” (count=1)
    “embC (Rv3793)” (count=1)
    “engA (Rv1713)” (count=1)
    “era (Rv2364c)” (count=1)
    “ethA (Rv3854c)” (count=1)
    “fabG (Rv0242c)” (count=2)
    “fabH (Rv0533)” (count=48)
    “fadD32 (Rv3801c)” (count=5)
    “fbpC (Rv0129C)” (count=21)
    “folP1 (Rv3608C)” (count=1)
    “folP2 (Rv1207)” (count=1)
    “frdA (Rv1552)” (count=1)
    “ftsZ (Rv2150c)” (count=3)
    “fusA1 (Rv0684)” (count=3)
    “fusA2 (Rv0120c)” (count=3)
    “glcB (Rv837c)” (count=1)
    “glf (Rv3809c)” (count=40)
    “glmU (Rv1018c)” (count=1)
    “guab2 (Rv3411)” (count=1)
    “gyrA (Rv0006)” (count=24)
    “gyrB (Rv0005)” (count=9)
    “ilvG (Rv1820)” (count=1)
    “infB (Rv2839c)” (count=1)
    “inhA (Rv1484)” (count=157)
    “kasA (Rv2245)” (count=9)
    “kasB (Rv2246)” (count=5)
    “ldtMt1 (Rv0116c)” (count=4)
    “ldtMt2 (Rv2518c)” (count=1)
    “lpd (Rv0462)” (count=5)
    “lppS (Rv2515c)” (count=1)
    “mbtA (Rv2384)” (count=95)
    “mca (Rv1082)” (count=27)
    “mfd (Rv1020)” (count=1)
    “mmpL3 (Rv0206c)” (count=15)
    “moeW (Rv2338c)” (count=1)
    “mshB (Rv1170)” (count=4)
    “murB (Rv0482)” (count=1)
    “murD (Rv2155c)” (count=2)
    “nadB (Rv1595)” (count=1)
    “ndhA (Rv0392c)” (count=1)
    “nrdR (Rv2718c)” (count=2)
    “pH Homeostasis” (count=5)
    “panC (Rv3602c)” (count=20)
    “pks13 (Rv3800c)” (count=3)
    “proteasome” (count=2)
    “ptpA (Rv2234)” (count=38)
    “ptpB (Rv0153c )” (count=3)
    “purU (Rv2964)” (count=2)
    “qcrB (Rv2196)” (count=5)
    “quinol oxidase” (count=1)
    “recG (Rv2973c)” (count=1)
    “rplC (Rv0701)” (count=2)
    “rplJ (Rv0651)” (count=3)
    “rpoB (Rv0667)” (count=4)
    “sahH (Rv3248c)” (count=2)
    “thiL (Rv2977c)” (count=106)
    “tlyA (Rv1694)” (count=2)
    “tuf (Rv0685)” (count=3)
    “uvrA (Rv1638)” (count=1)

In addition Alex has implemented new fingerprints (which we made open source) to enable the similarity searching. In addition he added Bayesian algorithms to predict targets, a new dynamic clustering visualization, updated the graphics in the app, added more information about CDD TB publications and more functions to enable output of the data to create a workflow.

Well now the work really begins on writing this up for publication. We also have ideas on how we can further extend the app too but more on that later. The updates are only available on the iOS version TB Mobile so Android Users may want to borrow a friends iPad or iPhone to try out the new version 2.0.

*This work was part of an STTR phase II funded by NIAID

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