On the amyloid datasets used for training PAFIG ­ how (not) to extend the experimental dataset of hexapeptides

Abstract

BackgroundAmyloids are proteins capable of forming aberrant intramolecular contact sites, characteristic of beta zipper configuration. Amyloids can underlie serious health conditions, e.g. Alzheimer’s or Parkinson’s diseases. It has been proposed that short segments of amino acids can be responsible for protein amyloidogenicity, but no more than two hundred such hexapeptides have been experimentally found. The authors of the computational tool Pafig published in BMC Bioinformatics a method for extending the amyloid hexapeptide dataset that could be used for training and testing models. They assumed that all hexapeptides belonging to an amyloid protein can be regarded as amylopositive, while those from proteins never reported as amyloid are always amylonegative. Here we show why the above described method of extending datasets is wrong and discuss the reasons why the incorrect data could lead to falsely correct classification.ResultsThe amyloid classification of hexapeptides by Pafig was confronted with the classification results from different state of the art computational methods and the outputs of all methods were studied by clustering analysis. The clustering methods show that Pafig is an outlier with regard to other approaches. Our study of the statistical patterns of its training and testing datasets showed a strong bias towards STVIIE hexapeptide in their positive part. Different statistical patterns of seemingly amylo -positive and -negative hexapeptides allow for a repeatable classification, which is not related to amyloid propensity of the hexapetides.ConclusionsOur study on recognition of amyloid hexapeptides showed that occurrence of incidental patterns in wrongly selected datasets can produce falsely correct results of classification. The assumption that all hexapeptides belonging to amyloid protein can be regarded as amylopositive and those from proteins never reported as amyloid are always amylonegative is not supported by any other computational method. This is in line with experimental observations that amyloid propensity of a full protein can result from only one amyloidogenic fragment in this protein, while the occurrence of amyliodogenic part that is well hidden inside the protein may never lead to fibril formation. This leads to the conclusion that Pafig does not provide correct classification with regard to amyloidogenicity.

Searching for universal model of amyloid signaling motifs using probabilistic context-free grammars

“…However, it also found such regions in 91% of the negative non-prionic NLR N-termini set. Neither of the tested amyloid prediction methods is therefore suitable for searching the NLR-related amyloid signaling motifs, which is not surprising given it is known that short potentially amyloidogenic regions can be found in proteins never observed to form amyloids [ 86 ]. ArchCandy [ 26 ] is a method for detecting beta-arches, which is based on quantitative assessment of several sequence features.…”

Searching for universal model of amyloid signaling motifs using probabilistic context-free grammars

“…Interestingly, it also founds such regions in 91% of the negative non-prionic NLR N-termini set. Indeed, it is known that short potentially amyloidogenic regions can be found in proteins never observed to form amyloids [82]. It is conceivable that combining ArchCandy and PASTA2 may improve the accuracy of BASS search.…”

Amyloidogenic motifs revealed by n-gram analysis

“…Note that amyloid and non-amyloid elements of the set are not necessarily amyloidogenic or non-amyloidogenic. Hence, assuming that only a short part of the sequence in longer amyloids is responsible for amyloidogenicity, our method might result in many false positives in the training data set and in consequence yield inaccurate predictions, as described elsewhere 23 . To diminish this problem and facilitate the extraction of hot spots, we restricted the maximum length of peptides in the training data set to 15 amino acids.…”