Prediction of amyloidogenicity based on the n-gram analysis

Abstract

Amyloids are proteins associated with the number of clinical disorders (e.g., Alzheimer's, CreutzfeldtJakob's and Huntington's diseases). Despite their diversity, all amyloid proteins can undergo aggregation initiated by 6-to 15-residue segments, called hot spots. To find the patterns defining the hotspots, we trained predictors of amyloidogenicity, using n-grams and random forest classifiers, based on data collected in the AmyLoad database. Only the most informative n-grams, selected by our Quick Permutation Test, were considered. Since the amyloidogenicity may not depend on the exact sequence of amino acids but on more general properties of amino acids, we tested 524,284 reduced amino acid alphabets of different lengths (three to six letters) to find the alphabet providing the best performance in cross-validation. The predictor based on this alphabet, called AmyloGram, was benchmarked against the most popular tools for the detection of amyloid peptides using an external data set and obtained the highest values of performance measures (AUC: 0.90, MCC: 0.63). Our results showed sequential patterns in the amyloids, which are strongly correlated with hydrophobicity, a tendency to form β -sheets and rigidity of amino acid residues. Among the most informative n-grams of AmyloGram we identified 15 that were already confirmed experimentally. AmyloGram is available as a web-server: www.smorfland.uni.wroc.pl/amylogram/. The code and results are publicly available at: www.github.com/michbur/prediction amyloidogenicity ngram/.

High dimensional surrogacy: computational aspects of an upscaled analysis

“…It is not limited to the commercially available platforms and can also be used with experimental systems by importing data through the universal REDF format, which follows the IETF RFC 4180 standard. dpcReport provides users with advanced tools for data quality control and it incorporates statistical tests for comparing multiple reactions in an experiment (Burdukiewicz et al 2016), currently absent in many dPCR-related software tools. dpcReport provides users with advanced tools for data quality control.…”

Quantiprot - a Python package for quantitative analysis of protein sequences

“…A more complex approach consists on combining multiple quantitative properties into multi-dimensional sequence descriptors, as implemented in a Python package propy [ 4 ]. Quantitative properties of amino acids can also be used to generate reduced alphabets for generative and discriminative models of proteins [ 5 , 6 ].…”

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