prPred

prPred predicts plant resistance (R) proteins using a support vector machine (SVM) classifier and integrates HMMscan and Phobius to identify protein domain families and transmembrane regions, supporting R protein subclass prediction and studies of plant immune responses.

Key Features:

• SVM Algorithm: prPred uses a support vector machine (SVM) classifier to distinguish plant R proteins from non-R proteins.
• Performance Metrics: prPred reports accuracy 0.935, precision 1.000, sensitivity 0.806, specificity 1.000, F1-score 0.893, Matthews correlation coefficient (MCC) 0.857, and area under the curve (AUC) 0.948.
• Integration with Protein Domain Tools: prPred incorporates HMMscan to identify protein domain families and Phobius to predict transmembrane regions, enhancing subclass prediction of cell surface and intracellular R receptors.

Scientific Applications:

• R Protein Identification: prPred identifies R proteins encoded by resistance genes, including cell surface-localized receptors and intracellular receptors, to aid studies of plant immune responses against pathogens.

Methodology:

prPred applies a support vector machine classifier and integrates HMMscan and Phobius; it operates in a Python environment (version 3.0 or above) and requires installation of specific packages for command-line execution.