Nov 21, 2015

ASAP: A Machine-Learning Framework for Local Protein Properties

BioRxiv : the Preprint Server for Biology
Dan OferMichal Linial

Abstract

Determining residue level protein properties, such as the sites for post-translational modifications (PTMs) are vital to understanding proteins at all levels of function. Experimental methods are costly and time-consuming, thus high confidence predictions become essential for functional knowledge at a genomic scale. Traditional computational methods based on strict rules (e.g. regular expressions) fail to annotate sites that lack substantial similarity. Thus, Machine Learning (ML) methods become fundamental in annotating proteins with unknown function. We present ASAP (Amino-acid Sequence Annotation Prediction), a universal ML framework for residue-level predictions. ASAP extracts efficiently and fast large set of window-based features from raw sequences. The platform also supports easy integration of external features such as secondary structure or PSSM profiles. The features are then combined to train underlying ML classifiers. We present a detailed case study for ASAP that was used to train CleavePred, a state-of-the-art protein precursor cleavage sites predictor. Protein cleavage is a fundamental PTM shared by a wide variety of protein groups with minimal sequence similarity. Current computational methods have high false po...Continue Reading

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Mentioned in this Paper

Classification
Genome
Post-Translational Protein Processing
Cytokinesis of the Fertilized Ovum
MAP9
PTMS gene
Atypical Small Acinar Proliferation of the Prostate Gland
Mass Spectrometry
Site
Genomics

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