Dataset Information

Residue-based propensity of aggregation in the Tau amyloidogenic hexapeptides AcPHF6* and AcPHF6.

ABSTRACT: In Alzheimer's disease and related tauopathies, the aggregation of microtubule-associated protein, Tau, into fibrils occurs via the interaction of two hexapeptide motifs PHF* ²⁷⁵VQIINK²⁸⁰ and PHF ³⁰⁶VQIVYK³¹¹ as β-sheets. To understand the role of the constituent amino acids of PHF and PHF* in the aggregation, a set of 12 alanine mutant peptides was synthesized by replacing each amino acid in PHF and PHF* with alanine and they were characterized by nuclear magnetic resonance (NMR) spectroscopy, circular dichroism (CD), transmission electron microscopy (TEM) and ThS/ANS fluorescence assay. Our studies show that while the aggregation was suppressed in most of the alanine mutant peptides, replacement of glutamine by alanine in both PHF and PHF* enhanced the fibrillization.

SUBMITTER: Dangi A

PROVIDER: S-EPMC9055513 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Residue-based propensity of aggregation in the Tau amyloidogenic hexapeptides AcPHF6* and AcPHF6.

Dangi Abha A Balmik Abhishek Ankur AA Ghorpade Archana Kisan AK Gorantla Nalini Vijay NV Sonawane Shweta Kishor SK Chinnathambi Subashchandrabose S Marelli Udaya Kiran UK

RSC advances 20200721 46

In Alzheimer's disease and related tauopathies, the aggregation of microtubule-associated protein, Tau, into fibrils occurs via the interaction of two hexapeptide motifs PHF* 275VQIINK280 and PHF 306VQIVYK311 as β-sheets. To understand the role of the constituent amino acids of PHF and PHF* in the aggregation, a set of 12 alanine mutant peptides was synthesized by replacing each amino acid in PHF and PHF* with alanine and they were characterized ...[more]

PMID: 35516938

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Project description:BackgroundAmyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods.ResultsWe generated a new dataset of hexapeptides, using more economical 3D profile algorithm, which showed very good classification overlap with ZipperDB (93.5%). The new part of our dataset contains 1779 segments, with 204 classified as amyloidogenic. The dataset of 6-residue sequences with their binary classification, based on the energy of the segment, was applied for training machine learning methods. A separate set of sequences from ZipperDB was used as a test set. The most effective methods were Alternating Decision Tree and Multilayer Perceptron. Both methods obtained area under ROC curve of 0.96, accuracy 91%, true positive rate ca. 78%, and true negative rate 95%. A few other machine learning methods also achieved a good performance. The computational time was reduced from 18-20 CPU-hours (full 3D profile) to 0.5 CPU-hours (simplified 3D profile) to seconds (machine learning).ConclusionsWe showed that the simplified profile generation method does not introduce an error with regard to the original method, while increasing the computational efficiency. Our new dataset proved representative enough to use simple statistical methods for testing the amylogenicity based only on six letter sequences. Statistical machine learning methods such as Alternating Decision Tree and Multilayer Perceptron can replace the energy based classifier, with advantage of very significantly reduced computational time and simplicity to perform the analysis. Additionally, a decision tree provides a set of very easily interpretable rules.

Dataset Information

Residue-based propensity of aggregation in the Tau amyloidogenic hexapeptides AcPHF6* and AcPHF6.

Publications

Residue-based propensity of aggregation in the Tau amyloidogenic hexapeptides AcPHF6* and AcPHF6.

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