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\begin{document}

\title{Structure-sequence features based prediction of phosphosites of
Serine/Threonine Protein Kinases of Mycobacterium tuberculosis}



\author[1]{Vipul Nilkanth}%
\author[1]{Shekhar Mande}%
\affil[1]{National Centre for Cell Science}%


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  \date{May 04, 2024}


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\begin{abstract}
Elucidation of signalling events in a pathogen is potentially important
to tackle the infection caused by it. Such events mediated by protein
phosphorylation play important roles in infection and therefore to
predict the phosphosites and substrates of the serine/threonine protein
kinases, we have developed a Machine learning based approach and
predicted the phosphosites for Mycobacterium tuberculosis
serine/threonine protein kinases using kinase-peptide structure-sequence
data. This approach utilizes features derived from kinase 3D-structure
environment and known phosphosite sequences to generate Support Vector
Machine based kinase specific predictions of phosphosites making it
suitable for prediction of phosphosites of STPKs with no or scarce data
of their phosphosites. Support vector machine outperformed the four
machine learning algorithms we tried (random forest, logistic
regression, support vector machine and k-nearest neighbours) with aucROC
value of 0.88 on the independent testing dataset and a ten-fold cross
validation accuracy of \textasciitilde{}81.6\% for the final model. Our
predicted phosphosites of M. tuberculosis STPKs form an useful resource
for experimental biologists enabling elucidation of STPK mediated
post-translational regulation of important cellular processes. The
training features file and model files, together with usage instructions
file, are available at: https://github.com/vipulbiocoder/Mtb-KSPP%
\end{abstract}%



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