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

Witold Dyrka,Marlena Gąsior-Głogowska,Natalia Szulc,Monika Szefczyk

doi:10.1186/s12859-021-04139-y

Witold Dyrka, Marlena Gąsior-Głogowska + Show 2 more

Open Access

https://doi.org/10.1186/s12859-021-04139-y

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Abstract

BackgroundAmyloid signaling motifs are a class of protein motifs which share basic structural and functional features despite the lack of clear sequence homology. They are hard to detect in large sequence databases either with the alignment-based profile methods (due to short length and diversity) or with generic amyloid- and prion-finding tools (due to insufficient discriminative power). We propose to address the challenge with a machine learning grammatical model capable of generalizing over diverse collections of unaligned yet related motifs.ResultsFirst, we introduce and test improvements to our probabilistic context-free grammar framework for protein sequences that allow for inferring more sophisticated models achieving high sensitivity at low false positive rates. Then, we infer universal grammars for a collection of recently identified bacterial amyloid signaling motifs and demonstrate that the method is capable of generalizing by successfully searching for related motifs in fungi. The results are compared to available alternative methods. Finally, we conduct spectroscopy and staining analyses of selected peptides to verify their structural and functional relationship.ConclusionsWhile the profile HMMs remain the method of choice for modeling homologous sets of sequences, PCFGs seem more suitable for building meta-family descriptors and extrapolating beyond the seed sample.

Highlights

Amyloid signaling motifs are a class of protein motifs which share basic structural and functional features despite the lack of clear sequence homology
We present a model based on the Probabilistic Context-Free Grammar (PCFG), which extends the profile Hidden Markov Model with capability to capture some dependencies between distant positions in the sequence [27,28,29,30,31,32]
In a previous paper [34], we showed that fungal prion-forming HET-s motifs r1 and r2 can be accurately represented with automatically inferred probabilistic context-free grammars comprising just of three lexical and four structural symbols (l3s4)

Summary

Introduction

Amyloid signaling motifs are a class of protein motifs which share basic structural and functional features despite the lack of clear sequence homology. At least some of these sequence motifs of roughly 20 amino acids form a beta arch fold [14] and often they contain polar amino acids: asparagine and glutamate [15] Despite these common features, the already identified amyloid signaling motifs (ASM) in bacteria and fungi exhibit high sequence diversity beyond noticeable homology [12, 13]. Such a model would allow to identify new amyloid signaling motifs in ever growing data sets of genomic sequences It could facilitate better understanding of mechanisms of conformation transmission and aggregation

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