bioRxiv Science⌕ Search

Biology subjects

Ramtirtha, Y.

Publications and source records attributed to Ramtirtha, Y..

2 recordsLinked to original sources

Prediction of SUMOylation targets in Drosophila melanogaster

SUMOylation is a post translational modification that involves covalent attachment of SUMO C-terminus to side chain amino group of lysine residues in target proteins. Disruption of the modification has been linked to neurodegenerative diseases and cancer. Recent improvements in mass spectrometry-coupled proteomics experiments have enabled high-throughput identification of SUMOylated lysines in mammalian cells. One such study was Hendriks et al, 2018, wherein the authors identified SUMOylated lysines in human and mouse cells. Information from this study was used as an input to a sequence homology based method to annotate putative SUMOylatable lysines from the proteome of fruit fly Drosophila melanogaster. 5283 human and 468 mouse SUMOylated proteins led to the identification of 8539 and 1700 fly homologs and putative SUMOylation sites therein respectively. Clustering analysis was carried out on these annotated sites to obtain three typs of information. First type of information revealed amino acid preferences in the local sequence vicinity of the annotated sites. This exercise confirmed that {psi} - K - x - (E/D) where {psi} = I/V/L, is the most frequently occurring sequence motif involving SUMOylated lysines. Second type of information revealed protein families that contain the annotated sites. Results from this exercise reveal that members of thousands of protein families contain annotated SUMOylation sites. Third type of information revealed preferred biological and cellular functions of proteins containing the annotated lysines. This exercise revealed that nucleus and transcription are preferred cellular localization and biological function respectively.

bioinformatics↗

3-D structure based prediction of SUMOylation sites

SUMOylation is a posttranslational modification that involves lysine residues from eukaryotic proteins. Misregulation of the modification has been linked to neuro-degenrative diseases and cancer. All the presently available tools to predict SUMOylation sites are sequence based. Here, we propose a novel structure based prediction tool to discriminate between SUMOylated and non-SUMOylated lysines. We demonstrate the method by carrying out a proof-of-concept study. The method achieved an accuracy of 81% and a Matthews Correlation Coefficient of 0.4.

bioinformatics↗