PalmPred: An SVM Based Palmitoylation Prediction Method Using Sequence Profile Information

<div><p>Protein palmitoylation is the covalent attachment of the 16-carbon fatty acid palmitate to a cysteine residue. It is the most common acylation of protein and occurs only in eukaryotes. Palmitoylation plays an important role in the regulation of protein subcellular localization, stability, translocation to lipid rafts and many other protein functions. Hence, the accurate prediction of palmitoylation site(s) can help in understanding the molecular mechanism of palmitoylation and also in designing various related experiments. Here we present a novel <i>in silico</i> predictor called ‘PalmPred’ to identify palmitoylation sites from protein sequence information using a support vector machine model. The best performance of PalmPred was obtained by incorporating sequence conservation features of peptide of window size 11 using a leave-one-out approach. It helped in achieving an accuracy of 91.98%, sensitivity of 79.23%, specificity of 94.30%, and Matthews Correlation Coefficient of 0.71. PalmPred outperformed existing palmitoylation site prediction methods – IFS-Palm and WAP-Palm on an independent dataset. Based on these measures it can be anticipated that PalmPred will be helpful in identifying candidate palmitoylation sites. All the source datasets, standalone and web-server are available at <a href="http://14.139.227.92/mkumar/palmpred/" target="_blank">http://14.139.227.92/mkumar/palmpred/</a>.</p></div

Performance of discrete amino acid and coupled amino acid composition based SVM models during FFCV at 1^st tier.

<p>Performance of discrete amino acid and coupled amino acid composition based SVM models during FFCV at 1^st tier.</p

Prediction performance of PalmPred on dataset D3_ind taken from Nishimura and Linder 2013 (referred as D3_ind).

<p>Prediction performance of PalmPred on dataset D3_ind taken from Nishimura and Linder 2013 (referred as D3_ind).</p

Performance of different machine learning classifiers.

<p>S_n, S_p, A_cc and MCC represent Sensitivity, Specificity, Accuracy and Matthews Correlation Coefficient respectively.</p

Protein distribution in training dataset.

<p>Protein distribution in training dataset.</p

Distribution of HSPs across different families in independent datasets.

<p>HGNC dataset contains human HSPs obtained from HGNC [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155872#pone.0155872.ref014" target="_blank">14</a>] and mixed dataset contains rice HSPs obtained from Wang et al [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155872#pone.0155872.ref015" target="_blank">15</a>] and Sarkar et al [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155872#pone.0155872.ref016" target="_blank">16</a>].</p

Flow chart to show the prediction schema of HSPs and its families.

<p>Flow chart to show the prediction schema of HSPs and its families.</p

Schematic illustration of categorization of prediction into different categories.

<p>Schematic illustration of categorization of prediction into different categories.</p

Enantioselective Alkylation of Aldehydes Using Functionalized Alkylboron Reagents Catalyzed by a Chiral Titanium Complex

A practical method is developed for the synthesis of enantioenriched functionalized secondary alcohols through catalytic enantioselective alkylation of aldehydes. Functionalized alkylboron reagents, [FG–(CH₂)_<i>n</i>]₃B (FG = Br, TIPSO, PhtN, CO₂^<i>i</i>Pr, and CN) prepared from terminal olefin precursors by hydroboration, undergo enantioselective addition to aldehydes in the presence of a catalytic amount (5 mol %) of 3-(3,5-diphenylphenyl)-H₈-BINOL and excess titanium tetraisopropoxide to afford the corresponding functionalized alcohols in high enantioselectivities up to 99% ee

Performance of discrete amino acid and coupled amino acid composition based SVM models during LOOCV at 2^nd tier.

<p>Performance of discrete amino acid and coupled amino acid composition based SVM models during LOOCV at 2^nd tier.</p