10 research outputs found
PyDPI: Freely Available Python Package for Chemoinformatics, Bioinformatics, and Chemogenomics Studies
The
rapidly increasing amount of publicly available data in biology and
chemistry enables researchers to revisit interaction problems by systematic
integration and analysis of heterogeneous data. Herein, we developed
a comprehensive python package to emphasize the integration of chemoinformatics
and bioinformatics into a molecular informatics platform for drug
discovery. PyDPI (drug–protein interaction with Python) is
a powerful python toolkit for computing commonly used structural and
physicochemical features of proteins and peptides from amino acid
sequences, molecular descriptors of drug molecules from their topology,
and protein–protein interaction and protein–ligand interaction
descriptors. It computes 6 protein feature groups composed of 14 features
that include 52 descriptor types and 9890 descriptors, 9 drug feature
groups composed of 13 descriptor types that include 615 descriptors.
In addition, it provides seven types of molecular fingerprint systems
for drug molecules, including topological fingerprints, electro-topological
state (E-state) fingerprints, MACCS keys, FP4 keys, atom pair fingerprints,
topological torsion fingerprints, and Morgan/circular fingerprints.
By combining different types of descriptors from drugs and proteins
in different ways, interaction descriptors representing protein–protein
or drug–protein interactions could be conveniently generated.
These computed descriptors can be widely used in various fields relevant
to chemoinformatics, bioinformatics, and chemogenomics. PyDPI is freely
available via https://sourceforge.net/projects/pydpicao/
Prediction of Peptide Fragment Ion Mass Spectra by Data Mining Techniques
Accurate
prediction of peptide fragment ion mass spectra is one
of the critical factors to guarantee confident peptide identification
by protein sequence database search in bottom-up proteomics. In an
attempt to accurately and comprehensively predict this type of mass
spectra, a framework named MS<sup>2</sup>PBPI is proposed. MS<sup>2</sup>PBPI first extracts fragment ions from large-scale MS/MS spectra
data sets according to the peptide fragmentation pathways and uses
binary trees to divide the obtained bulky data into tens to more than
1000 regions. For each adequate region, stochastic gradient boosting
tree regression model is constructed. By constructing hundreds of
these models, MS<sup>2</sup>PBPI is able to predict MS/MS spectra
for unmodified and modified peptides with reasonable accuracy. Moreover,
high consistency between predicted and experimental MS/MS spectra
derived from different ion trap instruments with low and high resolving
power is achieved. MS<sup>2</sup>PBPI outperforms existing algorithms
MassAnalyzer and PeptideART
The predictive probability plot of screening all cross-linking drug-target pairs. The size of predictive probability gradually varies from green to red.
<p>The predictive probability plot of screening all cross-linking drug-target pairs. The size of predictive probability gradually varies from green to red.</p
ROCs and precision-recall curves for NaĂŻve Bayes (green) and random forest (red) with full and selected features.
<p>(A) ROCs (B) precision-recall curves.</p
Prediction results of five-fold cross validation using different models.
<p>TP: true positives; FN: false negatives; TN: true negatives; FP: false positives; Sen: sensitivity; Spe: specificity; Acc: accuracy.</p
ROCs and precision-recall curves with different K<sub>i</sub> thresholds using RF.
<p>(A) ROCs (B) precision-recall curves. The auPRCs drop with the decreasing of K<sub>i</sub> thresholds. However, the varying trend of auROCs is consistent with that of auPRCs.</p
Drug-target interaction network using drug-target pairs with prediction probability above 0.99.
<p>Drugs and targets are presented by red circle and blue triangle, respectively. Drug-target interactions are represented by the edges connecting related drugs and targets.</p
The plot of K<sub>i</sub> versus prediction probability on 5-fold cross validation.
<p>non-interaction: red and interaction: green. Linear relationship between K<sub>i</sub> and prediction probability could be observed with correlation coefficient of 0.65.</p
Outline of our methodology.
<p>(A) Interaction features are calculated by combing the fingerprint descriptors from drugs and the CTD and amino acid composition descriptors from protein sequences. These feature vectors are used to find the optimal RF parameters which most accurately separate the positive and negative training sets. The independent validation sets are used for further validation for the RF model. (B) Once the RF model is constructed, we can predict new unknown drug-target associations or screen all cross-linking associations.</p
Prediction statistics on different false discovery rates.
<p>FDR: false discovery rate, Number: Number of drug-target pairs predicted as interactions, Ratio: the ratio between drug target pairs predicted as interactions and all screening pairs on specific FDR.</p