Biology subjects

Monzon, V. A.

Publications and source records attributed to Monzon, V. A..

4 recordsLinked to original sources

bioRxiv2022

Reciprocal Best Structure Hits: Using AlphaFold models to discover distant homologues

1The conventional methods to detect homologous protein pairs use the comparison of protein sequences. But the sequences of two homologous proteins may diverge significantly and consequently may be undetectable by standard approaches. The release of the AlphaFold 2.0 software enables the prediction of highly accurate protein structures and opens many opportunities to advance our understanding of protein functions, including the detection of homologous protein structure pairs. In this proof-of-concept work, we search for the closest homologous protein pairs using the structure models of five model organisms from the AlphaFold database. We compare the results with homologous protein pairs detected by their sequence similarity and show that the structural matching approach finds a similar set of results. Additionally, we detect potential novel homologues solely with the structural matching approach, which can help to understand the function of uncharacterised proteins and make previously overlooked connections between well-characterised proteins. We also observe limitations of our implementation of the structure based approach, particularly when handling highly disordered proteins or short protein structures. Our work shows that high accuracy protein structure models can be used to discover homologous protein pairs, and we expose areas for improvement of this structural matching approach.

bioinformatics↗

bioRxiv2021

Large Scale Discovery of Microbial Fibrillar Adhesins and Identification of Novel Adhesive Domain Families

Fibrillar adhesins are bacterial cell surface proteins that mediate interactions with the environment including host cells during colonisation or other bacteria during biofilm formation. These proteins are characterised by a stalk that projects the adhesive domain closer to the binding target. Fibrillar adhesins evolve quickly and thus can be difficult to computationally identify, yet they represent an important component for understanding bacterial host interactions. To detect novel fibrillar adhesins we developed a random forest prediction approach based on common characteristics we identified for this protein class. We applied this approach to Firmicute and Actinobacterial proteomes, yielding over 6,500 confidently predicted fibrillar adhesins. To verify the approach we investigated predicted fibrillar adhesins that lacked a known adhesive domain. Based on these proteins, we identified 24 sequence clusters representing potential novel members of adhesive domain families. We used AlphaFold to verify that 15 clusters showed structural similarity to known adhesive domains such as the TED domain. Overall our study has made a significant contribution to the number of known fibrillar adhesins and has enabled us to identify novel members of adhesive domain families involved in the bacterial pathogenesis. ImportanceFibrillar adhesins are a class of bacterial cell surface proteins that enable bacteria to interact with their environment. We developed a Machine Learning approach to identify fibrillar adhesins and applied this classification approach on the Firmicutes and Actinobacteria Reference Proteomes. This method allowed us to detect a high number of novel fibrillar adhesins, and also novel members of adhesive domain families. To confirm our predictions of these potential adhesin protein domains, we predicted their structure using the AlphaFold tool.

bioinformatics↗

bioRxiv2020

Discovery of Fibrillar Adhesins across Bacterial Species

BackgroundFibrillar adhesins are long multidomain proteins attached at the cell surface and composed of at least one adhesive domain and multiple tandemly repeated domains, which build an elongated stalk that projects the adhesive domain beyond the bacterial cell surface. They are an important yet understudied class of proteins that mediate interactions of bacteria with their environment. This study aims to characterize fibrillar adhesins in a wide range of bacterial phyla and to identify new fibrillar adhesin-like proteins to improve our understanding of host-bacteria interactions. ResultsBy careful search for fibrillar adhesins in the literature and by computational analysis we identified 75 stalk domains and 24 adhesive domains. Based on the presence of these domains in the UniProt Reference Proteomes database, we identified and analysed 3,388 fibrillar adhesin-like proteins across species of the most common bacterial phyla. We found that the bacterial proteomes with the highest fraction of fibrillar adhesins include several known pathogens. We further enumerate the adhesive and stalk domain combinations found in nature and demonstrate that fibrillar adhesins have complex and variable domain architectures, which differ across species. By analysing the domain architecture of fibrillar adhesins we show that in Gram positive bacteria adhesive domains are mostly positioned at the N-terminus of the protein with the cell surface anchor at the C-terminus, while their positions are more variable in Gram negative bacteria. We provide an open repository of fibrillar adhesin-like proteins and domains to facilitate downstream studies of this class of bacterial surface proteins. ConclusionThis study provides a domain-based characterization of fibrillar adhesins and demonstrates that they are widely found across the main bacterial phyla. We have discovered numerous novel fibrillar adhesins and improved the understanding of how pathogens might adhere to and subsequently invade into host cells.

microbiology↗

bioRxiv2017

The primary transcriptome, small RNAs, and regulation of antimicrobial resistance in Acinetobacter baumannii

AO_SCPCAPBSTRACTC_SCPCAPWe present the first high-resolution determination of transcriptome architecture in the priority pathogen Acinetobacter baumannii. Pooled RNA from 16 laboratory conditions was used for differential RNA-seq (dRNA-seq) to identify 3731 transcriptional start sites (TSS) and 110 small RNAs, including the first identification in A. baumannii of sRNAs encoded at the 3 end of coding genes. Most sRNAs were conserved among sequenced A. baumannii genomes, but were only weakly conserved or absent in other Acinetobacter species. Single nucleotide mapping of TSS enabled prediction of -10 and -35 RNA polymerase binding sites and revealed an unprecedented base preference at position +2 that hints at an unrecognized transcriptional regulatory mechanism. To apply functional genomics to the problem of antimicrobial resistance, we dissected the transcriptional regulation of the drug efflux pump responsible for chloramphenicol resistance, craA. The two craA promoters were both down-regulated >1000-fold when cells were shifted to nutrient limited medium. This conditional down-regulation of craA expression renders cells sensitive to chloramphenicol, a highly effective antibiotic for the treatment of multidrug resistant infections. An online interface that facilitates open data access and visualization is provided as \"AcinetoCom\" (http://bioinf.gen.tcd.ie/acinetocom/).

microbiology↗