Hidden Markov Model: a shortest unique representative approach to detect the protein toxins, virulence factors and antibiotic resistance genes

Enolase in Meyerozyma guilliermondii strain SO: Sequential and structural insights of MgEno4581 as a putative virulence factor and host-fungal interactions through comprehensive in silico approaches

Meyerozyma guilliermondii is a rare opportunistic fungal pathogen that causes deadly invasive candidiasis in human. M. guilliermondii strain SO is a local yeast isolate that possesses huge industrial interests but also pathogenic towards zebrafish embryos. Enolases that bind to human extracellular matrix (ECM) proteins are among the fungal virulence factors. To understand its pathogenicity mechanism down to molecular level, especially in the rare M. guilliermondii, this study aimed to identify and characterize the potentially virulence-associated enolase in M. guilliermondii strain SO using bioinformatics approaches. Profile Hidden-Markov model was implemented to identify enolase-related sequences in the fungal proteome. Sequence analysis deciphered only one (MgEno4581) out of nine sequences exhibited potent virulence traits observed similarly in the pathogenic Candida albicans. MgEno4581 structure that was predicted via SWISS-MODEL using C. albicans enolase (CaEno1; PDB ID: 7vrd) as the homology modeling template portrayed a highly identical motif with CaEno1 that facilitates ECM proteins binding. Amino acid substitutions (D234K, K235A, Y238H, K239D, G243K, V248C and Y254F) in ECM-binding motif of Saccharomyces cerevisiae enolase (ScEno) compared to MgEno4581 and CaEno1 caused changes in motif's surface charges. Protein-protein docking indicated F253 in ScEno only interacted hydrophobically with human plasminogen (HPG). Hydrogen linkages were observed for both MgEno4581 and CaEno1, suggesting a stronger interaction with HPG in the hydrophilic host microenvironments. Thus, our in silico characterizations on MgEno4581 provided new perspectives on its potential roles in candidiasis (fungal-host interactions) caused by M. guilliermondii, especially M. guilliermondii strain SO on zebrafish embryos that mimic the immunocompromised individuals as previously evident.

Real-Time Assembly Support System with Hidden Markov Model and Hybrid Extensions

This paper presents a context-aware adaptive assembly assistance system meant to support factory workers by embedding predictive capabilities. The research is focused on the predictor which suggests the next assembly step. Hidden Markov models are analyzed for this purpose. Several prediction methods have been previously evaluated and the prediction by partial matching, which was the most efficient, is considered in this work as a component of a hybrid model together with an optimally configured hidden Markov model. The experimental results show that the hidden Markov model is a viable choice to predict the next assembly step, whereas the hybrid predictor is even better, outperforming in some cases all the other models. Nevertheless, an assembly assistance system meant to support factory workers needs to embed multiple models to exhibit valuable predictive capabilities.