Computational Studies on 6-Pyruvoyl Tetrahydropterin Synthase (6-PTPS) of Plasmodium falciparum.

6-Pyruvoyl tetrahydropterin synthase (6-PTPS) is a lyase involved in the synthesis of tetrahydrobiopterin. In Plasmodium species where dihydroneopterin aldolase (DHNA) is absent, it acts in the folate biosynthetic pathway necessary for the growth and survival of the parasite. The 6-pyruvoyl tetrahydropterin synthase of Plasmodium falciparum (PfPTPS) has been identified as a potential antimalarial drug target. This study identified potential inhibitors of PfPTPS using molecular docking techniques. Molecular docking and virtual screening of 62 compounds including the control to the deposited Protein Data Bank (PDB) structure was carried out using AutoDock Vina in PyRx. Five of the compounds, N,N-dimethyl-N'-[4-oxo-6-(2,2,5-trimethyl-1,3-dioxolan-4-yl)-3H-pteridin-2-yl]methanimidamide (140296439), 2-amino-6-[(1R)-3-cyclohexyl-1-hydroxypropyl]-3H-pteridin-4-one (140296495), 2-(2,3-dihydroxypropyl)-8,9-dihydro-6H-pyrimido[2,1-b]pteridine-7,11-dione (144380406), 2-(dimethylamino)-6-[(2,2-dimethyl-1,3-dioxolan-4-yl)-hydroxymethyl]-3H-pteridin-4-one (135573878), and [1-acetyloxy-1-(2-methyl-4-oxo-3H-pteridin-6-yl)propan-2-yl] acetate (136075207), showed better binding affinity than the control ligand, biopterin (135449517), and were selected and screened. Three conformers of 140296439 with the binding energy of -7.2, -7.1, and -7.0 kcal/mol along with 140296495 were better than the control at -5.7 kcal/mol. In silico absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies predicted good pharmacokinetic properties of all the compounds while reporting a high risk of irritant toxicity in 140296439 and 144380406. The study highlights the five compounds, 140296439, 140296495, 144380406, 135573878 and 136075207, as potential inhibitors of PfPTPS and possible compounds for antimalarial drug development.

Structure-based design, and development of amidinyl, amidoximyl and hydroxamic acid based organic molecules as novel antimalarial drug candidates.

Malaria remains a significant global health concern causing numerous fatalities and the emergence of antimalarial drug resistance highlights the urgent need for novel therapeutic options with innovative mechanisms of action and targets. This study aimed to design potential inhibitors of Plasmodium falciparum 6-pyruvoyltetrahydropterin synthase (PfPTPS), synthesize them, and experimentally validate their efficacy as antimalarial agents. A structure-based approach was employed to design a series of novel derivatives, including amidinyl, amidoximyl and hydroxamic acid analogs (1c, 1d, 2b, and 3b), with a focus on their ability to bind to the Zn2+ present in the active site of PfPTPS. The syntheses of these compounds were accomplished through various multi-step synthetic pathways and their structural identities were confirmed using 1H and 13C NMR spectra, mass spectra, and elemental analysis. The compounds were screened for their antiplasmodial activity against the NF54 strain of P. falciparum and in vitro cytotoxicity testing was performed using L-6 cells. The in vivo acute toxicity of the compounds was evaluated in mice. Docking studies of the compounds with the 3D structure of PfPTPS revealed their strong binding affinities, with compound 3b exhibiting notable metal-acceptor interaction with the Zn2+ in the protein binding pocket thereby positioning it as a lead compound for PfPTPS inhibition. The in vitro antiplasmodial studies revealed moderate efficacies against the Pf NF54 strain, particularly compounds 1d and 3b which displayed IC50 < 0.2 µM. No significant cytotoxicity was noted on the L-6 rat cell line. Moreover, in vivo studies suggested that compound 3b exhibited both safety and efficacy in treating rodent malaria. The identified lead compound in this study represents a possible candidate for antimalarial drug development and can be further explored in the search for alternative antifolate drugs to combat the malaria menace.

A deep population reference panel of tandem repeat variation.

Tandem repeats (TRs) represent one of the largest sources of genetic variation in humans and are implicated in a range of phenotypes. Here we present a deep characterization of TR variation based on high coverage whole genome sequencing from 3550 diverse individuals from the 1000 Genomes Project and H3Africa cohorts. We develop a method, EnsembleTR, to integrate genotypes from four separate methods resulting in high-quality genotypes at more than 1.7 million TR loci. Our catalog reveals novel sequence features influencing TR heterozygosity, identifies population-specific trinucleotide expansions, and finds hundreds of novel eQTL signals. Finally, we generate a phased haplotype panel which can be used to impute most TRs from nearby single nucleotide polymorphisms (SNPs) with high accuracy. Overall, the TR genotypes and reference haplotype panel generated here will serve as valuable resources for future genome-wide and population-wide studies of TRs and their role in human phenotypes.

Synthesis, in silico and in vitro antimicrobial efficacy of substituted arylidene-based quinazolin-4(3H)-one motifs.

Introduction: Quinazolin-4(3H)-one derivatives have attracted considerable attention in the pharmacological profiling of therapeutic drug targets. The present article reveals the development of arylidene-based quinazolin-4(3H)-one motifs as potential antimicrobial drug candidates. Methods: The synthetic pathway was initiated through thermal cyclization of acetic anhydride on anthranilic acid to produce 2-methyl-4H-3,1-benzoxazan-4-one 1, which (upon condensation with hydrazine hydrate) gave 3-amino-2-methylquinazolin-4(3H)-one 2. The reaction of intermediate 2 at its amino side arm with various benzaldehyde derivatives furnished the final products, in the form of substituted benzylidene-based quinazolin-4(3H)-one motifs 3a-l, and with thiophene-2-carbaldehyde to afford 3 m. The purified targeted products 3a-m were effectively characterized for structural authentication using physicochemical parameters, microanalytical data, and spectroscopic methods, including IR, UV, and 1H- and 13C-NMR, as well as mass spectral data. The substituted arylidene-based quinazolin-4(3H)-one motifs 3a-m were screened for both in silico and in vitro antimicrobial properties against selected bacteria and fungi. The in silico studies carried out consisted of predicted ADMET screening, molecular docking, and molecular dynamics (MD) simulation studies. Furthermore, in vitro experimental validation was performed using the agar diffusion method, and the standard antibacterial and antifungal drugs used were gentamicin and ketoconazole, respectively. Results and discussion: Most of the compounds possessed good binding affinities according to the molecular docking studies, while MD simulation revealed their levels of structural stability in the protein-ligand complexes. 2-methyl-3-((thiophen-2-ylmethylene)amino) quinazolin-4(3H)-one 3 m emerged as both the most active antibacterial agent (with an minimum inhibitory concentration (MIC) value of 1.95 µg/mL) against Staphylococcus aureus and the most active antifungal agent (with an MIC value of 3.90 µg/mL) against Candida albicans, Aspergillus niger, and Rhizopus nigricans.

Personalizing medicine in Africa: current state, progress and challenges.

Personalized medicine has been identified as a powerful tool for addressing the myriad of health issues facing different health systems globally. Although recent studies have expanded our understanding of how different factors such as genetics and the environment play significant roles in affecting the health of individuals, there are still several other issues affecting their translation into personalizing health interventions globally. Since African populations have demonstrated huge genetic diversity, there is a significant need to apply the concepts of personalized medicine to overcome various African-specific health challenges. Thus, we review the current state, progress, and challenges facing the adoption of personalized medicine in Africa with a view to providing insights to critical stakeholders on the right approach to deploy.

Heuristic-enabled active machine learning: A case study of predicting essential developmental stage and immune response genes in Drosophila melanogaster.

Computational prediction of absolute essential genes using machine learning has gained wide attention in recent years. However, essential genes are mostly conditional and not absolute. Experimental techniques provide a reliable approach of identifying conditionally essential genes; however, experimental methods are laborious, time and resource consuming, hence computational techniques have been used to complement the experimental methods. Computational techniques such as supervised machine learning, or flux balance analysis are grossly limited due to the unavailability of required data for training the model or simulating the conditions for gene essentiality. This study developed a heuristic-enabled active machine learning method based on a light gradient boosting model to predict essential immune response and embryonic developmental genes in Drosophila melanogaster. We proposed a new sampling selection technique and introduced a heuristic function which replaces the human component in traditional active learning models. The heuristic function dynamically selects the unlabelled samples to improve the performance of the classifier in the next iteration. Testing the proposed model with four benchmark datasets, the proposed model showed superior performance when compared to traditional active learning models (random sampling and uncertainty sampling). Applying the model to identify conditionally essential genes, four novel essential immune response genes and a list of 48 novel genes that are essential in embryonic developmental condition were identified. We performed functional enrichment analysis of the predicted genes to elucidate their biological processes and the result evidence our predictions. Immune response and embryonic development related processes were significantly enriched in the essential immune response and embryonic developmental genes, respectively. Finally, we propose the predicted essential genes for future experimental studies and use of the developed tool accessible at http://heal.covenantuniversity.edu.ng for conditional essentiality predictions.

Mathematical modeling of malaria transmission dynamics in humans with mobility and control states.

Malaria importation is one of the hypothetical drivers of malaria transmission dynamics across the globe. Several studies on malaria importation focused on the effect of the use of conventional malaria control strategies as approved by the World Health Organization (WHO) on malaria transmission dynamics but did not capture the effect of the use of traditional malaria control strategies by vigilant humans. In order to handle the aforementioned situation, a novel system of Ordinary Differential Equations (ODEs) was developed comprising the human and the malaria vector compartments. Analysis of the system was carried out to assess its quantitative properties. The novel computational algorithm used to solve the developed system of ODEs was implemented and benchmarked with the existing Runge-Kutta numerical solution method. Furthermore, simulations of different vigilant conditions useful to control malaria were carried out. The novel system of malaria models was well-posed and epidemiologically meaningful based on its quantitative properties. The novel algorithm performed relatively better in terms of model simulation accuracy than Runge-Kutta. At the best model-fit condition of 98% vigilance to the use of conventional and traditional malaria control strategies, this study revealed that malaria importation has a persistent impact on malaria transmission dynamics. In lieu of this, this study opined that total vigilance to the use of the WHO-approved and traditional malaria management tools would be the most effective control strategy against malaria importation.

Pharmacophore-Aided Virtual Screening and Molecular Dynamics Simulation Identifies TrkB Agonists for Treatment of CDKL5-Deficiency Disorders.

Therapeutic intervention in cyclin-dependent kinase-like 5 (CDKL5) deficiency disorders (CDDs) has remained a concern over the years. Recent advances into the mechanistic interplay of signalling pathways has revealed the role of deficient tropomyosin receptor kinase B (TrkB)/phospholipase C γ1 signalling cascade in CDD. Novel findings showed that in vivo administration of a TrkB agonist, 7,8-dihydroxyflavone (7,8-DHF), resulted in a remarkable reversal in the molecular pathologic mechanisms underlying CDD. Owing to this discovery, this study aimed to identify more potent TrkB agonists than 7,8-DHF that could serve as alternatives or combinatorial drugs towards effective management of CDD. Using pharmacophore modelling and multiple database screening, we identified 691 compounds with identical pharmacophore features with 7,8-DHF. Virtual screening of these ligands resulted in identification of at least 6 compounds with better binding affinities than 7,8-DHF. The in silico pharmacokinetic and ADMET studies of the compounds also indicated better drug-like qualities than those of 7,8-DHF. Postdocking analyses and molecular dynamics simulations of the best hits, 6-hydroxy-10-(2-oxo-1-azatricyclo[7.3.1.05,13]trideca-3,5(13),6,8-tetraen-3-yl)-8-oxa-13,14,16-triazatetracyclo[7.7.0.02,7.011,15]hexadeca-1,3,6,9,11,15-hexaen-5-one (PubChem: 91637738) and 6-hydroxy-10-(8-methyl-2-oxo-1H-quinolin-3-yl)-8-oxa-13,14,16-triazatetracyclo[7.7.0.02,7.011,15]hexadeca-1,3,6,9,11,15-hexaen-5-one (PubChem ID: 91641310), revealed unique ligand interactions, validating the docking findings. We hereby recommend experimental validation of the best hits in CDKL5 knock out models before consideration as drugs in CDD management.

A deep population reference panel of tandem repeat variation.

Tandem repeats (TRs) represent one of the largest sources of genetic variation in humans and are implicated in a range of phenotypes. Here we present a deep characterization of TR variation based on high coverage whole genome sequencing from 3,550 diverse individuals from the 1000 Genomes Project and H3Africa cohorts. We develop a method, EnsembleTR, to integrate genotypes from four separate methods resulting in high-quality genotypes at more than 1.7 million TR loci. Our catalog reveals novel sequence features influencing TR heterozygosity, identifies population-specific trinucleotide expansions, and finds hundreds of novel eQTL signals. Finally, we generate a phased haplotype panel which can be used to impute most TRs from nearby single nucleotide polymorphisms (SNPs) with high accuracy. Overall, the TR genotypes and reference haplotype panel generated here will serve as valuable resources for future genome-wide and population-wide studies of TRs and their role in human phenotypes.

Chemistry and Pharmacological diversity of Benzothiazepine - Excellent pathway to drug discovery.

In this era of sporadic advancement in science and technology, a substantial amount of intervention is being set in motion to reduce health-related diseases. Discoveries from researchers have pinpointed the usefulness of heterocyclic compounds, amongst which benzothiazepine (BTZ) derivatives have been synthesized for their various pharmacological activities. This also contributes to their undeniable application in therapeutic medicine for the development of efficacious drugs. BTZs are compounds with a benzene ring fused with a thiazepine ring. This work contains several methods that have been used to synthesize 1,3-, 1,4-, 1,5-, and 4-1-benzothiazepine derivatives. In addition, up-to-date information about the crucial pharmacological activities of BTZ derivatives has been reviewed in this present study to appreciate their druggable potential in therapeutic medicine for drug development. Drug design and development have further been simplified with the implementation of computer aided approaches to predict biological interactions which can help in the design of several derivatives. Hence, the structural activity relationship (SAR), ADMET and the molecular docking studies of BTZ derivatives were discussed to further establish their interactions and safety in biological systems. This present work aims to expound on the reported chemistry and pharmacological propensity of BTZ moiety in relation to other relevant moieties to validate their potential as excellent pharmacophores in drug design and development.

In silico Structure Prediction, Molecular Docking, and Dynamic Simulation of Plasmodium falciparum AP2-I Transcription Factor.

Plasmodium falciparum Apicomplexan Apetala 2 Invasion (PfAP2-I) transcription factor (TF) is a protein that regulates the expression of a subset of gene families involved in P. falciparum red blood cell (RBC) invasion. Inhibiting PfAP2-I TF with small molecules represents a potential new antimalarial therapeutic target to combat drug resistance, which this study aims to achieve. The 3D model structure of PfAP2-I was predicted ab initio using ROBETTA prediction tool and was validated using Save server 6.0 and MolProbity. Computed Atlas of Surface Topography of proteins (CASTp) 3.0 was used to predict the active sites of the PfAP2-I modeled structure. Pharmacophore modeling of the control ligand and PfAP2-I modeled structure was carried out using the Pharmit server to obtain several compounds used for molecular docking analysis. Molecular docking and postdocking studies were conducted using AutoDock vina and Discovery studio. The designed ligands' toxicity predictions and in silico drug-likeness were performed using the SwissADME predictor and OSIRIS Property Explorer. The modeled protein structure from the ROBETTA showed a validation result of 96.827 for ERRAT, 90.2% of the amino acid residues in the most favored region for the Ramachandran plot, and MolProbity score of 1.30 in the 98th percentile. Five (5) best hit compounds from molecular docking analysis were selected based on their binding affinity (between -8.9 and -11.7 Kcal/mol) to the active site of PfAP2-I and were considered for postdocking studies. For the absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties, compound MCULE-7146940834 had the highest drug score (0.63) and drug-likeness (6.76). MCULE-7146940834 maintained a stable conformation within the flexible protein's active site during simulation. The good, estimated binding energies, drug-likeness, drug score, and molecular dynamics simulation interaction observed for MCULE-7146940834 against PfAP2-I show that MCULE-7146940834 can be considered a lead candidate for PfAP2-I inhibition. Experimental validations should be carried out to ascertain the efficacy of these predicted best hit compounds.

SysBiolPGWAS: simplifying post-GWAS analysis through the use of computational technologies and integration of diverse omics datasets.

MOTIVATION: Post-genome-wide association studies (pGWAS) analysis is designed to decipher the functional consequences of significant single-nucleotide polymorphisms (SNPs) in the era of GWAS. This can be translated into research insights and clinical benefits such as the effectiveness of strategies for disease screening, treatment and prevention. However, the setup of pGWAS (pGWAS) tools can be quite complicated, and it mostly requires big data. The challenge however is, scientists are required to have sufficient experience with several of these technically complex and complicated tools in order to complete the pGWAS analysis. RESULTS: We present SysBiolPGWAS, a pGWAS web application that provides a comprehensive functionality for biologists and non-bioinformaticians to conduct several pGWAS analyses to overcome the above challenges. It provides unique functionalities for analysis involving multi-omics datasets and visualization using various bioinformatics tools. SysBiolPGWAS provides access to individual pGWAS tools and a novel custom pGWAS pipeline that integrates several individual pGWAS tools and data. The SysBiolPGWAS app was developed to be a one-stop shop for pGWAS analysis. It targets researchers in the area of the human genome and performs its analysis mainly in the autosomal chromosomes. AVAILABILITY AND IMPLEMENTATION: SysBiolPGWAS web app was developed using JavaScript/TypeScript web frameworks and is available at: https://spgwas.waslitbre.org/. All codes are available in this GitHub repository https://github.com/covenant-university-bioinformatics.

Anopheles gambiae Trehalase Inhibitors for Malaria Vector Control: A Molecular Docking and Molecular Dynamics Study.

Trehalase inhibitors are considered safe alternatives for insecticides and fungicides. However, there are no studies testing these compounds on Anopheles gambiae, a major vector of human malaria. This study predicted the three-dimensional structure of Anopheles gambiae trehalase (AgTre) and identified potential inhibitors using molecular docking and molecular dynamics methods. Robetta server, C-I-TASSER, and I-TASSER were used to predict the protein structure, while the structural assessment was carried out using SWISS-MODEL, ERRAT, and VERIFY3D. Molecular docking and screening of 3022 compounds was carried out using AutoDock Vina in PyRx, and MD simulation was carried out using NAMD. The Robetta model outperformed all other models and was used for docking and simulation studies. After a post-screening analysis and ADMET studies, uniflorine, 67837201, 10406567, and Compound 2 were considered the best hits with binding energies of -6.9, -8.9, -9, and -8.4 kcal/mol, respectively, better than validamycin A standard (-5.4 kcal/mol). These four compounds were predicted to have no eco-toxicity, Brenk, or PAINS alerts. Similarly, they were predicted to be non-mutagenic, carcinogenic, or hepatoxic. 67837201, 10406567, and Compound 2 showed excellent stability during simulation. The study highlights uniflorine, 67837201, 10406567, and Compound 2 as good inhibitors of AgTre and possible compounds for malaria vector control.

Strengthening Bioinformatics and Genomics Analyses Skills in Africa for Attainment of the Sustainable Development Goals: Report of the 2nd Conference of the Nigerian Bioinformatics and Genomics Network.

The second conference of the Nigerian Bioinformatics and Genomics Network (NBGN21) was held from October 11 to October 13, 2021. The event was organized by the Nigerian Bioinformatics and Genomics Network. A 1-day genomic analysis workshop on genome-wide association study and polygenic risk score analysis was organized as part of the conference. It was organized primarily as a research capacity building initiative to empower Nigerian researchers to take a leading role in this cutting-edge field of genomic data science. The theme of the conference was "Leveraging Bioinformatics and Genomics for the attainments of the Sustainable Development Goals." The conference used a hybrid approach-virtual and in-person. It served as a platform to bring together 235 registered participants mainly from Nigeria and virtually, from all over the world. NBGN21 had four keynote speakers and four leading Nigerian scientists received awards for their contributions to genomics and bioinformatics development in Nigeria. A total of 100 travel fellowships were awarded to delegates within Nigeria. A major topic of discussion was the application of bioinformatics and genomics in the achievement of the Sustainable Development Goals (SDG3-Good Health and Well-Being, SDG4-Quality Education, and SDG 15-Life on Land [Biodiversity]). In closing, most of the NBGN21 conference participants were interviewed and interestingly they agreed that bioinformatics and genomic analysis of African genomes are vital in identifying population-specific genetic variants that confer susceptibility to different diseases that are endemic in Africa. The knowledge of this can empower African healthcare systems and governments for timely intervention, thereby enhancing good health and well-being.

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.

Recent advances in functionalized quinoline scaffolds and hybrids-Exceptional pharmacophore in therapeutic medicine.

Quinoline is one of the most common nitrogen-containing heterocycles owing to its fascinating pharmacological properties and synthetic value in organic and pharmaceutical chemistry. Functionalization of this moiety at different positions has allowed for varying pharmacological activities of its derivative. Several publications over the last few decades have specified various methods of synthesis. This includes classical methods of synthesizing the primary quinoline derivatives and efficient methods that reduce reaction time with increased yield employing procedures that fulfill one of the twelve green chemistry principles, "safer solvent". The metal nanoparticle-catalyzed reaction also serves as a potent and effective technique for the synthesis of quinoline with excellent atom efficiency. The primary focus of this review is to highlight the routes to synthesizing functionalized quinoline derivatives, including hybrids that have moieties with predetermined activities bound to the quinoline moiety which are of interest in synthesizing drug candidates with dual modes of action, overcoming toxicity, and resistance amongst others. This was achieved using updated literature, stating the biological activities and mechanisms through which these compounds administer relief. The ADMET studies and Structure-Activity Relationship (SAR) of novel derivatives were also highlighted to explore the drug-likeness of the quinoline-hybrids and the influence of substituent characteristics and position on the biological activity of the compounds.

Polygenic Risk Score in African populations: progress and challenges.

Polygenic Risk Score (PRS) analysis is a method that predicts the genetic risk of an individual towards targeted traits. Even when there are no significant markers, it gives evidence of a genetic effect beyond the results of Genome-Wide Association Studies (GWAS). Moreover, it selects  single nucleotide polymorphisms (SNPs) that  contribute to the disease with low effect size  making it more precise at individual level risk prediction. PRS  analysis addresses the shortfall of GWAS by taking into account the SNPs/alleles with  low effect size but play an indispensable role to the observed phenotypic/trait variance.  PRS analysis has  applications that investigate the genetic basis of several traits, which includes rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies  show   that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we review the conventional PRS methods and their application to sub-Saharan African communities. We conclude that  lack of sufficient GWAS data and tools is  the limiting factor of applying PRS analysis to sub-Saharan populations.   We recommend developing Africa-specific PRS methods and tools for estimating and analyzing  African population data   for clinical  evaluation of PRSs of interest and predicting  rare diseases.

Structure-based pharmacophore modeling, virtual screening, and molecular dynamics simulation studies for identification of Plasmodium falciparum 5-aminolevulinate synthase inhibitors.

Plasmodium falciparum (Pf) 5-aminolevulinic acid synthase (5-ALAS) is an essential enzyme with high selectivity during liver stage development, signifying its potential as a prophylactic antimalarial drug target. The aim of this study was to identify important potential lead compounds which can serve as inhibitors of Pf 5-ALAS using pharmacophore modeling, virtual screening, qualitative structural assessment, in silico ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) evaluation and molecular dynamics simulation. The best model of the tertiary structure of Pf 5-ALAS was obtained using MolProbity, while the following databases were explored for the pharmacophore-based virtual screening: CHEMBL, ChemDiv, ChemSpace, MCULE, MCULE-ULTIMATE, MolPort, NCI Open Chemical Repository, LabNetwork and ZINC databases. 2,621 compounds were screened against the modeled Pf 5-ALAS using AutoDock vina. The post-screening analysis was carried out using Discovery Studio while molecular dynamics simulation was performed on the best hits using NAMD-VMD and Galaxy Europe platform. Compound CSMS00081585868 was observed as the best hit with a binding affinity of -9.9 kcal/mol and predicted Ki of 52.10 nM, engaging in seven hydrogen bonds with the target's active site amino acid residues. The in silico ADMET prediction showed that all ten best hits possessed relatively good pharmacokinetic properties. The qualitative structural assessment of the best hit, CSMS00081585868, revealed that the presence of two pyridine scaffolds bearing hydroxy and fluorine groups linked by a pyrrolidine scaffold contributed significantly to its ability to have a strong binding affinity with the receptor. The best hit also showed stability in the active site of Pf 5-ALAS as confirmed from the RMSD obtained during the MD simulation.

Identifying essential genes across eukaryotes by machine learning.

Identifying essential genes on a genome scale is resource intensive and has been performed for only a few eukaryotes. For less studied organisms essentiality might be predicted by gene homology. However, this approach cannot be applied to non-conserved genes. Additionally, divergent essentiality information is obtained from studying single cells or whole, multi-cellular organisms, and particularly when derived from human cell line screens and human population studies. We employed machine learning across six model eukaryotes and 60 381 genes, using 41 635 features derived from the sequence, gene function information and network topology. Within a leave-one-organism-out cross-validation, the classifiers showed high generalizability with an average accuracy close to 80% in the left-out species. As a case study, we applied the method to Tribolium castaneum and Bombyx mori and validated predictions experimentally yielding similar performances. Finally, using the classifier based on the studied model organisms enabled linking the essentiality information of human cell line screens and population studies.

Predicting host dependency factors of pathogens in Drosophila melanogaster using machine learning.

Pathogens causing infections, and particularly when invading the host cells, require the host cell machinery for efficient regeneration and proliferation during infection. For their life cycle, host proteins are needed and these Host Dependency Factors (HDF) may serve as therapeutic targets. Several attempts have approached screening for HDF producing large lists of potential HDF with, however, only marginal overlap. To get consistency into the data of these experimental studies, we developed a machine learning pipeline. As a case study, we used publicly available lists of experimentally derived HDF from twelve different screening studies based on gene perturbation in Drosophila melanogaster cells or in vivo upon bacterial or protozoan infection. A total of 50,334 gene features were generated from diverse categories including their functional annotations, topology attributes in protein interaction networks, nucleotide and protein sequence features, homology properties and subcellular localization. Cross-validation revealed an excellent prediction performance. All feature categories contributed to the model. Predicted and experimentally derived HDF showed a good consistency when investigating their common cellular processes and function. Cellular processes and molecular function of these genes were highly enriched in membrane trafficking, particularly in the trans-Golgi network, cell cycle and the Rab GTPase binding family. Using our machine learning approach, we show that HDF in organisms can be predicted with high accuracy evidencing their common investigated characteristics. We elucidated cellular processes which are utilized by invading pathogens during infection. Finally, we provide a list of 208 novel HDF proposed for future experimental studies.