Fuzzy Filtering in Large-Scale Prediction of Intrinsically Disordered Regions of Proteins on Apache Spark

摘要

Intrinsically disordered proteins (IDPs) participate in many cellular processes. They are also studied for their participation in the course and formation of many diseases. Experimental determination of disordered regions (IDRs) is costly and not always possible. Due to the exponential growth of protein sequences, for which the 3D structure cannot be experimentally determined, computational prediction becomes an important alternative. Spark-IDPP is the large-scale meta-predictor for IDRs and IDPs designed to run on the Apache Spark cluster. The meta-prediction with Spark-IDPP includes fuzzy filtering of produced prediction output. Here, we experimentally validate various fuzzy filters and show that a properly designed characteristic function for fuzzy filtering may improve the prediction quality in all modes of the Spark-IDPP execution.