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

Background Amyloid 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. Results First, 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. Conclusions While 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.

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

BackgroundAmyloid signaling motifs are a class of protein motifs which share basic structural and functional features despite lack of apparent 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 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.

The ideas of universal grammar in the area of syntax and their reflection in the Russian educational materials of the early XIX century

The subject of this research is the analysis of universal grammar ideas in the area of syntax and their reflection in the Russian educational materials of the early XIX century. The relevance is defined by the fact that writings of the authors of universal grammars contain ideas currently applied in description of fact of language within the framework of generative grammar. View on grammar of A. S. Nikolsky, F. F. Rozanov, L. H. Jacob, I. F. Timkovsky, I. Ornatovsky repeatedly have become the subject of analysis; however, in light on most recent achievements of the theory of linguistics, previous works require revision. Research methodology leans on the theories, which are founded on the principle of historicism in linguistics that allows establishing own patterns in transformation of the subject of research and clearly understands the internal logics of scientific development. With emergence of works of the world renowned American linguist Noam Chomsky, who claimed that his generative grammar is based on the key postulates of universal grammar, the authors of universal grammars have attracted attention once again. The scientific novelty lies in the more objective assessment of the contribution of A. S. Nikolsky, F. F. Rozanov, L. H. Jacob, I. F. Timkovsky, I. Ornatovsky to the development of grammar science, and syntax in particular.