Reannotation of Mycoplasma hyopneumoniae hypothetical proteins revealed novel potential virulence factors

Differential domains and endoproteolytic processing in dominant surface proteins of unknown function from Mycoplasma hyopneumoniae and Mycoplasma flocculare

Mycoplasma hyopneumoniae causes porcine enzootic pneumonia (PEP), a chronic respiratory disease that leads to severe economic losses in the pig industry. Swine infection and PEP development depend on the adhesion of the pathogen to the swine respiratory tract and the host immune response, but these and other disease determinants are not fully understood. For instance, M. hyopneumoniae has a large repertoire of proteins of unknown function (PUFs) and some of them are abundant in the cell surface, where they likely mediate so far unknown pathogen-host interactions. Moreover, these surface PUFs may undergo endoproteolytic processing to generate larger repertoires of proteoforms to further complicate this scenario. Here, we investigated the five PUFs more represented on the surface of M. hyopneumoniae pathogenic strain 7448 in comparison with their orthologs from the nonpathogenic M. hyopneumoniae J strain and the closely related commensal species Mycoplasma flocculare. Comparative in silico analyses of deduced amino acid sequences and proteomic data identified differential domains, disordered regions and repeated motifs. We also provide evidence of differential endoproteolytic processing and antigenicity. Phylogenetic analyses were also performed with ortholog sequences, showing higher conservation of three of the assessed PUFs among Mycoplasma species related to respiratory diseases. Overall, our data point out to M. hyopneumoniae surface-dominant PUFs likely associated with pathogenicity.

In-silico functional and structural annotation of hypothetical protein from Klebsiella pneumonia: A potential drug target

Klebsiella pneumonia is known to cause several nosocomial infections in immunocompromised patients. It has developed resistance against a broad range of presently available antibiotics, resulting in high mortality rates in patients and declared an urgent threat. Therefore, exploration of possible novel drug targets against this opportunistic bacteria needs to be undertaken. In the present study, we performed an extensive in-silico analysis for functional and structural annotation and characterized HP CP995_08280 from K. pneumonia as a drug target and aimed to identify potent drug candidates. The functional and structural studies using several bioinformatics tools and databases predicted that HP CP995_08280 is a cytosolic protein that belongs to the β-lactamase family and shares structural similarity with FmtA protein from Staphylococcus aureus (PDB ID: 5ZH8). The structure of HP CP995_08280 was successfully modeled followed by structure-based virtual screening, docking, molecular dynamics, and Molecular mechanic/Poisson-Boltzmann surface area (MMPBSA) were performed to identify the potential compounds. We have found five potent antibacterial molecules, namely BDD 24083171, BDD 24085737, BDE 25098678, BDE 33638819, and BDE 33672484, which exhibited high binding affinity (>-7.5 kcal/mol) and were stabilized by hydrogen bonding and hydrophobic interactions with active site residues (Ser42, Lys45, Tyr126, and Asp128) of protein. Molecular dynamics and MMPBSA revealed that HP CP995_08280 - ligand(s) complexes were less dynamic and more stable than native HP CP995_08280. Hence, the present study may serve as a potential lead for developing inhibitors against drug-resistant Klebsiella pneumonia.

Putative Protein Discovery from Microalgal Genomes as a Synthetic Biology Protein Library for Heavy Metal Bio-Removal

Simple Summary

Nowadays, heavy metal polluted wastewater is one of the global challenges that leads to an insufficient supply of clean water. Taking advantage of what nature has to offer, several organisms, including microalgae, can natively bioremediate these heavy metals. However, the effectiveness of such processes does not meet expectations, especially with the increasing amount of pollution in today’s world. Therefore, with the goal of creating effective strains, synthetic biology via bioengineering is widely used as a strategy to enhance the heavy metal bio-removing capability, either by directly engineering the native ability of organisms or by transferring the ability to a more suitable host. In order to do so, a list of genes or proteins involved in the processes is crucial for stepwise engineering. Yet, a large amount of information remains to be discovered. In this work, a comprehensive library of putative proteins that are involved in heavy metal bio-removal from microalgae was constructed. Moreover, with the development of machine learning, the 3D structures of these proteins are also predicted, using machine learning-based methods, to aid the use of synthetic biology further.

Abstract

Synthetic biology is a principle that aims to create new biological systems with particular functions or to redesign the existing ones through bioengineering. Therefore, this principle is often utilized as a tool to put the knowledge learned to practical use in actual fields. However, there is still a great deal of information remaining to be found, and this limits the possible utilization of synthetic biology, particularly on the topic that is the focus of the present work—heavy metal bio-removal. In this work, we aim to construct a comprehensive library of putative proteins that might support heavy metal bio-removal. Hypothetical proteins were discovered from Chlorella and Scenedesmus genomes and extensively annotated. The protein structures of these putative proteins were also modeled through Alphafold2. Although a portion of this workflow has previously been demonstrated to annotate hypothetical proteins from whole genome sequences, the adaptation of such steps is yet to be done for library construction purposes. We also demonstrated further downstream steps that allow a more accurate function prediction of the hypothetical proteins by subjecting the models generated to structure-based annotation. In conclusion, a total of 72 newly discovered putative proteins were annotated with ready-to-use predicted structures available for further investigation.