From a bimodal to a multi-stage view on trypanosomes' differential RNA editing.

Significant variations in the abundance of mitochondrial RNA processing proteins and their target RNAs across trypanosome life stages present an opportunity to explore the regulatory mechanisms that drive these changes. Utilizing omics approaches can uncover unconventional targets, aiding our understanding of the parasites' adaptation and enabling targeted interventions for differentiation.

Prioritization of Trypanosoma brucei editosome protein interactions interfaces at residue resolution through proteome-scale network analysis.

BACKGROUND: Trypanosoma brucei is the causative agent for trypanosomiasis in humans and livestock, which presents a growing challenge due to drug resistance. While identifying novel drug targets is vital, the process is delayed due to a lack of functional information on many of the pathogen's proteins. Accordingly, this paper presents a computational framework for prioritizing drug targets within the editosome, a vital molecular machinery responsible for mitochondrial RNA processing in T. brucei. Importantly, this framework may eliminate the need for prior gene or protein characterization, potentially accelerating drug discovery efforts. RESULTS: By integrating protein-protein interaction (PPI) network analysis, PPI structural modeling, and residue interaction network (RIN) analysis, we quantitatively ranked and identified top hub editosome proteins, their key interaction interfaces, and hotspot residues. Our findings were cross-validated and further prioritized by incorporating them into gene set analysis and differential expression analysis of existing quantitative proteomics data across various life stages of T. brucei. In doing so, we highlighted PPIs such as KREL2-KREPA1, RESC2-RESC1, RESC12A-RESC13, and RESC10-RESC6 as top candidates for further investigation. This includes examining their interfaces and hotspot residues, which could guide drug candidate selection and functional studies. CONCLUSION: RNA editing offers promise for target-based drug discovery, particularly with proteins and interfaces that play central roles in the pathogen's life cycle. This study introduces an integrative drug target identification workflow combining information from the PPI network, PPI 3D structure, and reside-level information of their interface which can be applicable to diverse pathogens. In the case of T. brucei, via this pipeline, the present study suggested potential drug targets with residue-resolution from RNA editing machinery. However, experimental validation is needed to fully realize its potential in advancing urgently needed antiparasitic drug development.

Structure-Function Analysis of RBP7910: An Editosome Z-Binding Protein in Trypanosomatids.

RNA editing, a unique post-transcriptional modification, is observed in trypanosomatid parasites as a crucial procedure for the maturation of mitochondrial mRNAs. The editosome protein complex, involving multiple protein components, plays a key role in this process. In Trypanosoma brucei, a putative Z-DNA binding protein known as RBP7910 is associated with the editosome. However, the specific Z-DNA/Z-RNA binding activity and the interacting interface of RBP7910 have yet to be determined. In this study, we conducted a comparative analysis of the binding behavior of RBP7910 with different potential ligands using microscale thermophoresis (MST). Additionally, we generated a 3D model of the protein, revealing potential Z-α and Z-ß nucleic acid-binding domains of RBP7910. RBP7910 belongs to the winged-helix-turn-helix (HTH) superfamily of proteins with an α1α2α3ß1ß2 topology. Finally, using docking techniques, potential interacting surface regions of RBP7910 with notable oligonucleotide ligands were identified. Our findings indicate that RBP7910 exhibits a notable affinity for (CG)n Z-DNA, both in single-stranded and double-stranded forms. Moreover, we observed a broader interacting interface across its Z-α domain when bound to Z-DNA/Z-RNA compared to when bound to non-Z-form nucleic acid ligands.

Validated Growth Rate-Dependent Regulation of Lipid Metabolism in Yarrowia lipolytica.

Given the strong potential of Yarrowia lipolytica to produce lipids for use as renewable fuels and oleochemicals, it is important to gain in-depth understanding of the molecular mechanism underlying its lipid accumulation. As cellular growth rate affects biomass lipid content, we performed a comparative proteomic analysis of Y. lipolytica grown in nitrogen-limited chemostat cultures at different dilution rates. After confirming the correlation between growth rate and lipid accumulation, we were able to identify various cellular functions and biological mechanisms involved in oleaginousness. Inspection of significantly up- and downregulated proteins revealed nonintuitive processes associated with lipid accumulation in this yeast. This included proteins related to endoplasmic reticulum (ER) stress, ER-plasma membrane tether proteins, and arginase. Genetic engineering of selected targets validated that some genes indeed affected lipid accumulation. They were able to increase lipid content and were complementary to other genetic engineering strategies to optimize lipid yield.

Systems-level approaches for understanding and engineering of the oleaginous cell factory Yarrowia lipolytica.

Concerns about climate change and the search for renewable energy sources together with the goal of attaining sustainable product manufacturing have boosted the use of microbial platforms to produce fuels and high-value chemicals. In this regard, Yarrowia lipolytica has been known as a promising yeast with potentials in diverse array of biotechnological applications such as being a host for different oleochemicals, organic acid, and recombinant protein production. Having a rapidly increasing number of molecular and genetic tools available, Y. lipolytica has been well studied amongst oleaginous yeasts and metabolic engineering has been used to explore its potentials. More recently, with the advancement in systems biotechnology and the implementation of mathematical modeling and high throughput omics data-driven approaches, in-depth understanding of cellular mechanisms of cell factories have been made possible resulting in enhanced rational strain design. In case of Y. lipolytica, these systems-level studies and the related cutting-edge technologies have recently been initiated which is expected to result in enabling the biotechnology sector to rationally engineer Y. lipolytica-based cell factories with favorable production metrics. In this regard, here, we highlight the current status of systems metabolic engineering research and assess the potential of this yeast for future cell factory design development.

Genome-scale exploration of transcriptional regulation in the nisin Z producer Lactococcus lactis subsp. lactis IO-1.

Transcription is of the most crucial steps of gene expression in bacteria, whose regulation guarantees the bacteria's ability to adapt to varying environmental conditions. Discovering the molecular basis and genomic principles of the transcriptional regulation is thus one of the most important tasks in cellular and molecular biology. Here, a comprehensive phylogenetic footprinting framework was implemented to predict maximal regulons of Lactococcus lactis subsp. lactis IO-1, a lactic acid bacterium known for its high potentials in nisin Z production as well as efficient xylose consumption which have made it a promising biotechnological strain. A total set of 321 regulons covering more than 90% of all the bacterium's operons have been elucidated and validated according to available data. Multiple novel biologically-relevant members were introduced amongst which arsC, mtlA and mtl operon for BusR, MtlR and XylR regulons can be named, respectively. Moreover, the effect of riboflavin on nisin biosynthesis was assessed in vitro and a negative correlation was observed. It is believed that understandings from such networks not only can be useful for studying transcriptional regulatory potentials of the target organism but also can be implemented in biotechnology to rationally design favorable production conditions.

Genome mining for ribosomally synthesised and post-translationally modified peptides (RiPPs) reveals undiscovered bioactive potentials of actinobacteria.

One of the most diverse groups of bioactive bacterial metabolites is the ribosomally synthesised and post-translationally modified peptides (RiPPs) with different bioactivities. The process of genome mining has made it possible to predict the presence of such clusters among the huge genomic data available today. Despite the great potential of actinobacteria in producing natural products and the myriad of completely sequenced genomes available, a comprehensive genome mining of these bacteria for RiPPs is lacking. Here, a collection of 629 complete actinobacterial genomes were analysed to explore their RiPP biosynthesis potential. Using BAGEL3 genome mining tool, the presence of 477 RiPP biosynthesis gene clusters (BGCs) was shown, including all known classes of bacterial RiPPs. RiPP-encoding potential was shown to be widespread among different members of actinobacteria especially within the plant and soil-inhabiting strains. The notable presence of LAP BGCs in plant-associating actinobacteria was also illustrated. Streptomyces, Amycolatopsis, Kitasatospora and Frankia showed greater potential in RiPP biosynthesis while lanthipeptides and lasso peptides were the most distributed RiPPs. Three cyanobactin BGCs were also detected. Generally evidence of promising ability of actinobacteria to synthesise diverse classes of RiPPs as well as information needed to rationally select appropriate taxa for rational screening of specific RiPPs are presented.

Sequence-based analysis and prediction of lantibiotics: A machine learning approach.

Lantibiotics, an important group of ribosomally synthesized peptides, represent an important arsenal of novel promising antimicrobials showing high potency in fighting against the prevalence of antibiotic resistance among microbial pathogens. However, due to the lack of high throughput strategies for the isolation and identification of these compounds, our information regarding their structure and especially sequence-based properties is far from complete. Therefore, in the present study, a comprehensive sequence-based analysis of these peptides was performed with the help of machine learning approach together with a feature selection technique. Meanwhile, an attempt to develop an accurate computational model for prediction of lantibiotics was made via constructing two datasets of 280 and 190 lantibiotic and non-lantibiotic antimicrobial peptide sequences, respectively. Based on the conducted approach and as a result of our search for a subset of relevant features of lantibiotics, particular types of sequenced-based features were observed to be preferred in lantibiotics, the knowledge-based implementation of which can be used as strategies for lantibiotic bioengineering purposes. Moreover, a SMO-based classifier was developed for the prediction of lantibiotics with the accuracy and specificity values of 88.5% and 94%, respectively which shows the great potential of the developed algorithm for the prediction of lantibiotcs. Conclusively, the accurate predictor algorithm as well as the identified sequence-based distinctiveness properties of lantibiotics can give valuable information in both the fields of lantibiotic discovery and bioengineering.

Homology Modeling and Conformational Epitope Prediction of Envelope Protein of Alkhumra Haemorrhagic Fever Virus.

BACKGROUND: The aim of this study was to generate in silico 3D-structure of the envelope protein of AHFV using homology modeling method to further predict its conformational epitopes and help other studies to investigate its structural features using the model. METHODS: A 3D-structure prediction was developed for the envelope protein of Alkhumra haemorrhagic fever virus (AHFV), an emerging tick-borne flavivirus, based on a homology modeling method using M4T and Modweb servers, as the 3D-structure of the protein is not available yet. Modeled proteins were validated using Modfold 4 server and their accuracies were calculated based on their RSMDs. Having the 3D predicted model with high quality, conformational epitopes were predicted using DiscoTope 2.0. RESULTS: Model generated by M4T was more acceptable than the Modweb-generated model. The global score and P-value calculated by Modfold 4 ensured that a certifiable model was generated by M4T, since its global score was almost near 1 which is the score for a high resolution X-ray crystallography structure. Furthermore, itsthe P-value was much lower than 0.001 which means that the model is completely acceptable. Having 0.46 Å rmsd, this model was shown to be highly accurate. Results from DiscoTope 2.0 showed 26 residues as epitopes, forming conformational epitopes of the modeled protein. CONCLUSION: The predicted model and epitopes for envelope protein of AHFV can be used in several therapeutic and diagnostic approaches including peptide vaccine development, structure based drug design or diagnostic kit development in order to facilitate the time consuming experimental epitope mapping process.

Computational prediction of anti HIV-1 peptides and in vitro evaluation of anti HIV-1 activity of HIV-1 P24-derived peptides.

The world is entering the third decade of the acquired immunodeficiency syndrome (AIDS) pandemic. The primary cause of the disease has known to be human immunodeficiency virus type I (HIV-1). Recently, peptides are shown to have high potency as drugs in the treatment of AIDS. Therefore, in the present study, we have developed a method to predict anti-HIV-1 peptides using support vector machine (SVM) as a powerful machine learning algorithm. Peptide descriptors were represented based on the concept of Chou's pseudo-amino acid composition (PseAAC). HIV-1 P24-derived peptides were examined to predict anti-HIV-1 activity among them. The efficacy of the prediction was then validated in vitro. The mutagenic effect of validated anti-HIV-1 peptides was further investigated by the Ames test. Computational classification using SVM showed the accuracy and sensitivity of 96.76% and 98.1%, respectively. Based on SVM classification algorithm, 3 out of 22 P24-derived peptides were predicted to be anti-HIV-1, while the rest were estimated to be inactive. HIV-1 replication was inhibited by the three predicted anti-HIV-1 peptides as revealed in vitro, while the results of the same test on two of non-anti-HIV-1 peptides showed complete inactivity. The three anti-HIV-1 peptides were shown to be not mutagenic because of the Ames test results. These data suggest that the proposed computational method is highly efficient for predicting the anti-HIV-1 activity of any unknown peptide having only its amino acid sequence. Moreover, further experimental studies can be performed on the mentioned peptides, which may lead to new anti-HIV-1 peptide therapeutics candidates.

A combined cheminformatics and computational approach for the prediction of anti-HIV small molecules.

Acquired immunodeficiency syndrome (AIDS) is one of the most devastating diseases of current century which is caused by the human immunodeficiency virus (HIV). Although great efforts have been done to fight the virus, the need of new therapeutics candidates of any kind still remains. This process needs huge time and experimental endeavor. However, Computer-aided techniques and can speed up the procedure. Currently, cheminformatics tools have proven to be extremely valuable in pharmaceutical research. In the past few decades, a huge number of different molecular descriptors were designed to describe chemical molecules in a quantitative way to make it easy to use them for computational studies. Herein, we present a computational study of anti-HIV small molecules test by the National Cancer Institute (NCI) to introduce the most efficient molecular descriptors for anti-HIV activity. In this regard a dataset of 199 highly active anti-HIV and 174 inactive compounds were defined by 905 molecular descriptors. Data were classified using Random Forest algorithm and the most important molecular descriptors were introduced as the parameters responsible for representing anti-HIV activity. Applying the mentioned computational and cheminformatics methods, it is possible to predict the anti-HIV activity of any given small molecule with high accuracy.