In silico comparative genomics analysis of Plasmodium falciparum for the identification of putative essential genes and therapeutic candidates

Recent advances in the characterization of essential genes and development of a database of essential genes

Over the past few decades, there has been a significant interest in the study of essential genes, which are crucial for the survival of an organism under specific environmental conditions and thus have practical applications in the fields of synthetic biology and medicine. An increasing amount of experimental data on essential genes has been obtained with the continuous development of technological methods. Meanwhile, various computational prediction methods, related databases and web servers have emerged accordingly. To facilitate the study of essential genes, we have established a database of essential genes (DEG), which has become popular with continuous updates to facilitate essential gene feature analysis and prediction, drug and vaccine development, as well as artificial genome design and construction. In this article, we summarized the studies of essential genes, overviewed the relevant databases, and discussed their practical applications. Furthermore, we provided an overview of the main applications of DEG and conducted comprehensive analyses based on its latest version. However, it should be noted that the essential gene is a dynamic concept instead of a binary one, which presents both opportunities and challenges for their future development.

An update on cerebral malaria for therapeutic intervention

BACKGROUND

Cerebral malaria is often pronounced as a major life-threatening neurological complication of Plasmodium falciparum infection. The complex pathogenic landscape of the parasite and the associated neurological complications are still not elucidated properly. The growing concerns of drugresistant parasite strains along with the failure of anti-malarial drugs to subdue post-recovery neuro-cognitive dysfunctions in cerebral malaria patients have called for a demand to explore novel biomarkers and therapeutic avenues. Due course of the brain infection journey of the parasite, events such as sequestration of infected RBCs, cytoadherence, inflammation, endothelial activation, and blood-brain barrier disruption are considered critical.

METHODS

In this review, we briefly summarize the diverse pathogenesis of the brain-invading parasite associated with loss of the blood-brain barrier integrity. In addition, we also discuss proteomics, transcriptomics, and bioinformatics strategies to identify an array of new biomarkers and drug candidates.

CONCLUSION

A proper understanding of the parasite biology and mechanism of barrier disruption coupled with emerging state-of-art therapeutic approaches could be helpful to tackle cerebral malaria.

Computational identification of Plasmodium falciparum RNA pseudouridylate synthase as a viable drug target, its physicochemical properties, 3D structure prediction and prediction of potential inhibitors

The increased resistance to the currently effective antimalarial drugs against Plasmodium falciparum has necessitated the development of new drugs for malaria treatment. Many proteins have been predicted using various means as potential drug targets for the treatment of the P. falciparum malaria infection. Meanwhile, only a few studies went on to predict the 3-dimensional (3D) structure of potential target. Therefore, this study aimed to predict potential antimalarial drug targets against the deadliest malaria parasite P. falciparum as well as to determine the 3D structure and possible inhibitors of one of the targets. We employed machine learning approach to predict suitable drug targets in P. falciparum. Five of the predicted protein targets were considered as potential drug targets as they were non-homologous to their human counterparts. Out of these, we determined the physicochemical properties, predicted the 3D structure and carried out docking-based virtual screening of P. falciparum RNA pseudouridylate synthase, putative (PfRPuSP). The PfRPuSP was one of the potential five target proteins. Homology modelling and the ab initio methods were used to predict the 3D structure of PfRPuSP. Then, a compound library of 5621 molecules was constructed from PubChem and ChEMBL databases using 5-fluorouridine as the control inhibitor. Docking-based virtual screening was performed using Autodock 4.2 and Autodock Vina to select compounds with high binding affinity. A total of 11 compounds were selected based on their binding energies from 881 compounds which were manually examined after docking. Seven of the 11 compounds that exhibited remarkable interactions with the residues in the active sites of PfRPuSP were analysed. These compounds performed favourably when compared to the control inhibitor and predicted to bind better than 5-fluorouridine. These seven compounds are suggested as new potential lead structures for antimalarial treatment.

Machine learning approach to gene essentiality prediction: a review

 

Essential genes are critical for the growth and survival of any organism. The machine learning approach complements the experimental methods to minimize the resources required for essentiality assays. Previous studies revealed the need to discover relevant features that significantly classify essential genes, improve on the generalizability of prediction models across organisms, and construct a robust gold standard as the class label for the train data to enhance prediction. Findings also show that a significant limitation of the machine learning approach is predicting conditionally essential genes. The essentiality status of a gene can change due to a specific condition of the organism. This review examines various methods applied to essential gene prediction task, their strengths, limitations and the factors responsible for effective computational prediction of essential genes. We discussed categories of features and how they contribute to the classification performance of essentiality prediction models. Five categories of features, namely, gene sequence, protein sequence, network topology, homology and gene ontology-based features, were generated for Caenorhabditis elegans to perform a comparative analysis of their essentiality prediction capacity. Gene ontology-based feature category outperformed other categories of features majorly due to its high correlation with the genes' biological functions. However, the topology feature category provided the highest discriminatory power making it more suitable for essentiality prediction. The major limiting factor of machine learning to predict essential genes conditionality is the unavailability of labeled data for interest conditions that can train a classifier. Therefore, cooperative machine learning could further exploit models that can perform well in conditional essentiality predictions.

SHORT ABSTRACT

Identification of essential genes is imperative because it provides an understanding of the core structure and function, accelerating drug targets' discovery, among other functions. Recent studies have applied machine learning to complement the experimental identification of essential genes. However, several factors are limiting the performance of machine learning approaches. This review aims to present the standard procedure and resources available for predicting essential genes in organisms, and also highlight the factors responsible for the current limitation in using machine learning for conditional gene essentiality prediction. The choice of features and ML technique was identified as an important factor to predict essential genes effectively.

Essential gene prediction using limited gene essentiality information-An integrative semi-supervised machine learning strategy

Essential gene prediction helps to find minimal genes indispensable for the survival of any organism. Machine learning (ML) algorithms have been useful for the prediction of gene essentiality. However, currently available ML pipelines perform poorly for organisms with limited experimental data. The objective is the development of a new ML pipeline to help in the annotation of essential genes of less explored disease-causing organisms for which minimal experimental data is available. The proposed strategy combines unsupervised feature selection technique, dimension reduction using the Kamada-Kawai algorithm, and semi-supervised ML algorithm employing Laplacian Support Vector Machine (LapSVM) for prediction of essential and non-essential genes from genome-scale metabolic networks using very limited labeled dataset. A novel scoring technique, Semi-Supervised Model Selection Score, equivalent to area under the ROC curve (auROC), has been proposed for the selection of the best model when supervised performance metrics calculation is difficult due to lack of data. The unsupervised feature selection followed by dimension reduction helped to observe a distinct circular pattern in the clustering of essential and non-essential genes. LapSVM then created a curve that dissected this circle for the classification and prediction of essential genes with high accuracy (auROC > 0.85) even with 1% labeled data for model training. After successful validation of this ML pipeline on both Eukaryotes and Prokaryotes that show high accuracy even when the labeled dataset is very limited, this strategy is used for the prediction of essential genes of organisms with inadequate experimentally known data, such as Leishmania sp. Using a graph-based semi-supervised machine learning scheme, a novel integrative approach has been proposed for essential gene prediction that shows universality in application to both Prokaryotes and Eukaryotes with limited labeled data. The essential genes predicted using the pipeline provide an important lead for the prediction of gene essentiality and identification of novel therapeutic targets for antibiotic and vaccine development against disease-causing parasites.

A computational approach to validate novel drug targets of gentianine from Swertiya chirayita in Plasmodium falciparum

Gentianine is one of the compounds found in the plant Swertiya chirayita that is known for its antimalarial activity. However, its exact molecular mechanism of action is yet to be understood. In our present study, we applied several computational approaches to filter out and determine possible targets of gentianine in Plasmodium falciparum 3D7. Protein-protein networks formed the basis of one of our strategies along with orthologous protein analysis to establish essentiality. Out of 6 essential proteins from unique pathways, haloacid dehalogenase like-hydrolase (PfHAD1), phosphoenolpyruvate carboxykinase (PfPEPCK) and fumarate hydratase (PfFH) were screened as drug targets through this approach. Through our other strategy we established the predicted IC50 (PIC50) value of gentianine with a set of molecular descriptors from 123 Pathogen Box anti-malarial compounds. Afterwards through 2D structural similarity, L-lactate dehydrogenase (PfLDH) was established as another possible target. In our work, we performed in silico docking and analysed the binding of gentianine to the proteins. All of the proteins were reported with favourable binding results and were considered for complex molecular dynamics simulation approach. Our research clears up the molecular mechanism of antimalarial activity of gentianine to some extent paving way for experimental validation of the same in future.

Proteomic Atomics Reveals a Distinctive Uracil-5-Methyltransferase

Carbon (C), hydrogen (H), nitrogen (N), oxygen (O), and sulfur (S) atoms intrigue as they are the foundation for amino acid (AA) composition and the folding and functions of proteins and thus define and control the survival of a cell, the smallest unit of life. Here, we calculated the proteomic atom distribution in >1500 randomly selected species across the entire current phylogenetic tree and identified uracil-5-methyltransferase (U5MTase) of the protozoan parasite Plasmodium falciparum (Pf, strain Pf3D7), with a distinct atom and AA distribution pattern. We determined its apicoplast location and in silico 3D protein structure to refocus attention exclusively on U5MTase with tremendous potential for therapeutic intervention in malaria. Around 300 million clinical cases of malaria occur each year in tropical and subtropical regions of the world, resulting in over one million deaths annually, placing malaria among the most serious infectious diseases. Genomic and proteomic research of the clades of parasites containing Pf is progressing slowly and the functions of most of the ∼5300 genes are still unknown. We applied a 'bottom-up' comparative proteomic atomics analysis across the phylogenetic tree to visualize a protein molecule on its actual basis - i. e., its atomic level. We identified a protruding Pf3D7-specific U5MTase, determined its 3D protein structure, and identified potential inhibitory drug molecules through in silico drug screening that might serve as possible remedies for the treatment of malaria. Besides, this atomic-based proteome map provides a unique approach for the identification of parasite-specific proteins that could be considered as novel therapeutic targets.

Comparative genomic analysis of Rickettsia rickettsii for identification of drug and vaccine targets: tolC as a proposed candidate for case study

BACKGROUND

Antibiotic resistance is increasing rapidly in pathogenic organisms, creating more complications for treatment of diseases. Rocky Mountain spotted fever (RMSF) is a neglected tropical disease in humans caused by Rickettsia rickettsii for which no effective therapeutic is available. Subtractive genomics methods facilitate the characterization of non-homologous essential proteins that could be targeted for the discovery of potential therapeutic compounds against R. rickettsii to combat RMSF. Present study followed an in-silico based methodology, involving scanning and filtering the complete proteome of Rickettsia rickettsii by using several prioritization parameters in the search of potential candidates for drug development. Further the putative targets were subjected to series of molecular dockings with ligands obtained from PDB ligand database to identify suitable potential inhibitors. The comparative genomic analysis revealed 606 non-homologous proteins and 233 essential non-homologous proteins of R. rickettsii. The metabolic pathway analysis predicted 120 proteins as putative drug targets, out of which 56 proteins were found to be associated with metabolic pathways unique to the bacteria and further subcellular localization analysis revealed that 9 proteins as potential drug targets which are secretion proteins, involved in peptidoglycan biosynthesis, folate biosynthesis and bacterial secretion system. As secretion proteins are more feasible as vaccine candidates, we have selected a most potential target i.e. tolC, an outer membrane efflux protein that belongs to type I secretion system and has major role in pathogen survival as well as MDR persistence. So for case study, we have modelled the three dimensional structure of tolC (tunnel protein). The model was further subjected to virtual screening and in-silico docking. The study identified three potential inhibitors having PDB Id 19V, 6Q8 and 39H. Further we have suggested that the above study would be most important while considering the selection of candidate targets and drug or vaccine designing against R. rickettsii.

Distribution of triclosan-resistant genes in major pathogenic microorganisms revealed by metagenome and genome-wide analysis

The substantial use of triclosan (TCS) has been aimed to kill pathogenic bacteria, but TCS resistance seems to be prevalent in microbial species and limited knowledge exists about TCS resistance determinants in a majority of pathogenic bacteria. We aimed to evaluate the distribution of TCS resistance determinants in major pathogenic bacteria (N = 231) and to assess the enrichment of potentially pathogenic genera in TCS contaminated environments. A TCS-resistant gene (TRG) database was constructed and experimentally validated to predict TCS resistance in major pathogenic bacteria. Genome-wide in silico analysis was performed to define the distribution of TCS-resistant determinants in major pathogens. Microbiome analysis of TCS contaminated soil samples was also performed to investigate the abundance of TCS-resistant pathogens. We experimentally confirmed that TCS resistance could be accurately predicted using genome-wide in silico analysis against TRG database. Predicted TCS resistant phenotypes were observed in all of the tested bacterial strains (N = 17), and heterologous expression of selected TCS resistant genes from those strains conferred expected levels of TCS resistance in an alternative host Escherichia coli. Moreover, genome-wide analysis revealed that potential TCS resistance determinants were abundant among the majority of human-associated pathogens (79%) and soil-borne plant pathogenic bacteria (98%). These included a variety of enoyl-acyl carrier protein reductase (ENRs) homologues, AcrB efflux pumps, and ENR substitutions. FabI ENR, which is the only known effective target for TCS, was either co-localized with other TCS resistance determinants or had TCS resistance-associated substitutions. Furthermore, microbiome analysis revealed that pathogenic genera with intrinsic TCS-resistant determinants exist in TCS contaminated environments. We conclude that TCS may not be as effective against the majority of bacterial pathogens as previously presumed. Further, the excessive use of this biocide in natural environments may selectively enrich for not only TCS-resistant bacterial pathogens, but possibly for additional resistance to multiple antibiotics.

Multiple target-based pharmacophore design from active site structures

Health care systems have benefitted from rational drug discovery processes like vHTS, virtual high throughput screening pharmacophores and quantitative structure-activity relationships, and many challenges have been explored using such techniques: decisions on specificity and selectivity are made after screening millions of molecules for multiple targets. Recent challenges in drug research emphasize the design of drugs that bind with more than one target of interest (multi-target) and do not bind with undesirable targets. This work attempts to use a three-dimensional interaction profile of the active site of a class of proteins, identify selective positions for the binding of functional groups, called features, and develop ensembles of multi-targeted pharmacophores that retain specificity and selectivity. The goal of this study is to develop multi-target pharmacophores by computational methods using protein structures alone to guide the discovery of novel inhibitors of plasmepsins, displaying selectivity over their human homologs, cathepsin D and pepsin. The development of such novel tools is attempted using a combination of different approaches such as the molecular interaction field, clique graph and inductive logic programming to identify and compare specific and selective complementary features. The identification of selective combinations of features has resulted in the design of multi-featured specific and selective pharmacophores that are validated using antimalarial compounds in ChEMBL that are known for their anti-plasmepsin II activity. This novel method is computationally less intensive and is applicable to any known class of target structures for finding specific and selective binders simultaneously.

A Comprehensive Overview of Online Resources to Identify and Predict Bacterial Essential Genes

Genes critical for the survival or reproduction of an organism in certain circumstances are classified as essential genes. Essential genes play a significant role in deciphering the survival mechanism of life. They may be greatly applied to pharmaceutics and synthetic biology. The continuous progress of experimental method for essential gene identification has accelerated the accumulation of gene essentiality data which facilitates the study of essential genes in silico. In this article, we present some available online resources related to gene essentiality, including bioinformatic software tools for transposon sequencing (Tn-seq) analysis, essential gene databases and online services to predict bacterial essential genes. We review several computational approaches that have been used to predict essential genes, and summarize the features used for gene essentiality prediction. In addition, we evaluate the available online bacterial essential gene prediction servers based on the experimentally validated essential gene sets of 30 bacteria from DEG. This article is intended to be a quick reference guide for the microbiologists interested in the essential genes.

An in silico strategy for identification of novel drug targets against Plasmodium falciparum

The apicomplexan parasite Plasmodium falciparum is responsible for global malaria burden. With the reported resistance to artemisinin chemotherapy, there is an urgent need to maintain early phase drug discovery and identify novel drug targets for successful eradication of the pathogen from the host. In our previous work on comparative genomics study for identification of putative essential genes and therapeutic candidates in P. falciparum, we predicted 11 proteins as anti-malarial drug targets from PlasmoDB database. In this paper, we made an attempt for identification of novel drug targets in P. falciparum genome using a sequence of computational methods from Malaria Parasite Metabolic Pathway database. The study reported the identification of 71 proteins as potential drug targets for anti-malarial interventions. Furthermore, homology modeling and molecular dynamic simulation study of one of the potential drug targets, aminodeoxychorismate lyase, was carried to predict the 3D structure of the protein. Structure and ligand-based drug designing reported MMV019742 from Pathogen Box and TCAMS-141515 from GSK-TCAMS library as potential hits. The reliability of the binding mode of the inhibitors is confirmed by GROMACS for a simulation time of 20 ns in water environment. This will be helpful for experimental validation of the small-molecule inhibitor.

In silico multiple-targets identification for heme detoxification in the human malaria parasite Plasmodium falciparum

Detoxification of hemoglobin byproducts or free heme is an essential step and considered potential targets for anti-malaria drug development. However, most of anti-malaria drugs are no longer effective due to the emergence and spread of the drug resistant malaria parasites. Therefore, it is an urgent need to identify potential new targets and even for target combinations for effective malaria drug design. In this work, we reconstructed the metabolic networks of Plasmodium falciparum and human red blood cells for the simulation of steady mass and flux flows of the parasite's metabolites under the blood environment by flux balance analysis (FBA). The integrated model, namely iPF-RBC-713, was then adjusted into two stage-specific metabolic models, which first was for the pathological stage metabolic model of the parasite when invaded the red blood cell without any treatment and second was for the treatment stage of the parasite when a drug acted by inhibiting the hemozoin formation and caused high production rate of heme toxicity. The process of identifying target combinations consisted of two main steps. Firstly, the optimal fluxes of reactions in both the pathological and treatment stages were computed and compared to determine the change of fluxes. Corresponding enzymes of the reactions with zero fluxes in the treatment stage but non-zero fluxes in the pathological stage were predicted as a preliminary list of potential targets in inhibiting heme detoxification. Secondly, the combinations of all possible targets listed in the first step were examined to search for the best promising target combinations resulting in more effective inhibition of the detoxification to kill the malaria parasites. Finally, twenty-three enzymes were identified as a preliminary list of candidate targets which mostly were in pyruvate metabolism and citrate cycle. The optimal set of multiple targets for blocking the detoxification was a set of heme ligase, adenosine transporter, myo-inositol 1-phosphate synthase, ferrodoxim reductase-like protein and guanine transporter. In conclusion, the method has shown an effective and efficient way to identify target combinations which are obviously useful in the development of novel antimalarial drug combinations.

Systems analysis of host-parasite interactions

Parasitic diseases caused by protozoan pathogens lead to hundreds of thousands of deaths per year in addition to substantial suffering and socioeconomic decline for millions of people worldwide. The lack of effective vaccines coupled with the widespread emergence of drug‐resistant parasites necessitates that the research community take an active role in understanding host–parasite infection biology in order to develop improved therapeutics. Recent advances in next‐generation sequencing and the rapid development of publicly accessible genomic databases for many human pathogens have facilitated the application of systems biology to the study of host–parasite interactions. Over the past decade, these technologies have led to the discovery of many important biological processes governing parasitic disease. The integration and interpretation of high‐throughput ‐omic data will undoubtedly generate extraordinary insight into host–parasite interaction networks essential to navigate the intricacies of these complex systems. As systems analysis continues to build the foundation for our understanding of host–parasite biology, this will provide the framework necessary to drive drug discovery research forward and accelerate the development of new antiparasitic therapies. WIREs Syst Biol Med 2015, 7:381–400. doi: 10.1002/wsbm.1311

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