scholarly journals Conservation of gene essentiality in Apicomplexa and its application for prioritization of anti-malarial drug targets

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 23
Author(s):  
Gajinder Pal Singh
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
Large Scale ◽  
Sequence Similarity ◽  
Artemisinin Resistance ◽  
Essential Genes ◽  
Gene Essentiality ◽  
Apicomplexan Parasites ◽  
Functional Classes ◽  
Human Proteins

New anti-malarial drugs are needed to address the challenge of artemisinin resistance and to achieve malaria elimination and eradication. Target-based screening of inhibitors is a major approach for drug discovery, but its application to malaria has been limited by the availability of few validated drug targets in Plasmodium. Here we utilize the recently available large-scale gene essentiality data in Plasmodium berghei and a related apicomplexan pathogen, Toxoplasma gondii, to identify potential anti-malarial drug targets. We find significant conservation of gene essentiality in the two apicomplexan parasites. The conservation of essentiality could be used to prioritize enzymes that are essential across the two parasites and show no or low sequence similarity to human proteins. Novel essential genes in Plasmodium could be predicted based on their essentiality in T. gondii. Essential genes in Plasmodium showed higher expression, evolutionary conservation and association with specific functional classes. We expect that the availability of a large number of novel potential drug targets would significantly accelerate anti-malarial drug discovery.

scholarly journals Re-Envisioning Anti-Apicomplexan Parasite Drug Discovery Approaches

2021 ◽  
Vol 11 ◽  
Author(s):  
Gabriel W. Rangel ◽  
Manuel Llinás
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
Large Scale ◽  
Environmental Changes ◽  
Rapid Evolution ◽  
Antimalarial Drugs ◽  
Life Cycles ◽  
Drug Pressure ◽  
Apicomplexan Parasites ◽  
Resistance Evolution

Parasites of the phylum Apicomplexa impact humans in nearly all parts of the world, causing diseases including to toxoplasmosis, cryptosporidiosis, babesiosis, and malaria. Apicomplexan parasites have complex life cycles comprised of one or more stages characterized by rapid replication and biomass amplification, which enables accelerated evolutionary adaptation to environmental changes, including to drug pressure. The emergence of drug resistant pathogens is a major looming and/or active threat for current frontline chemotherapies, especially for widely used antimalarial drugs. In fact, resistant parasites have been reported against all modern antimalarial drugs within 15 years of clinical introduction, including the current frontline artemisinin-based combination therapies. Chemotherapeutics are a major tool in the public health arsenal for combatting the onset and spread of apicomplexan diseases. All currently approved antimalarial drugs have been discovered either through chemical modification of natural products or through large-scale screening of chemical libraries for parasite death phenotypes, and so far, none have been developed through a gene-to-drug pipeline. However, the limited duration of efficacy of these drugs in the field underscores the need for new and innovative approaches to discover drugs that can counter rapid resistance evolution. This review details both historical and current antimalarial drug discovery approaches. We also highlight new strategies that may be employed to discover resistance-resistant drug targets and chemotherapies in order to circumvent the rapid evolution of resistance in apicomplexan parasites.

scholarly journals A novel CRISPRi platform to study the role of essential genes in antifungal drug-resistant Candida albicans isolates

2021 ◽  
Vol 3 (12) ◽  
Author(s):  
Lauren Wensing ◽  
Rebecca Shapiro ◽  
Deeva Uthayakumar ◽  
Viola Halder ◽  
Jehoshua Sharma ◽  
...  
Keyword(s):  
Candida Albicans ◽  
Transcriptional Repression ◽  
Drug Targets ◽  
Large Scale ◽  
Essential Gene ◽  
Essential Genes ◽  
Antifungal Drugs ◽  
Antifungal Drug ◽  
Drug Resistant ◽  
Fungal Organisms

With the emergence of antifungal resistant Candida albicans strains, the need for new antifungal drugs is critical in combating this fungal pathogen. Investigating essential genes in C. albicans is a vital step in characterizing putative antifungal drug targets. As some of these essential genes are conserved between fungal organisms, developed therapies targeting these genes have the potential to be broad range antifungals. In order to study these essential genes, classical genetic knockout or CRISPR-based approaches cannot be used as disrupting essential genes leads to lethality in the organism. Fortunately, a variation of the CRISPR system (CRISPR interference or CRISPRi) exists that enables precise transcriptional repression of the gene of interest without introducing genetic mutations. CRISPRi utilizes an endonuclease dead Cas9 protein that can be targeted to a precise location but lacks the ability to create a double-stranded break. The binding of the dCas9 protein to DNA prevents the binding of RNA polymerase to the promoter through steric hindrance thereby reducing expression. We recently published the novel use of this technology in C. albicans and are currently working on expanding this technology to large scale repression of essential genes. Through the construction of an essential gene CRISPRi-sgRNA library, we can begin to study the function of essential genes under different conditions and identify genes that are involved in critical processes such as drug tolerance in antifungal resistant background strains. These genes can ultimately be characterized as putative targets for novel antifungal drug development, or targeted as a means to sensitize drug-resistant strains to antifungal treatment.

scholarly journals Reconciling high-throughput gene essentiality data with metabolic network reconstructions

2018 ◽  
Author(s):  
Anna S. Blazier ◽  
Jason A. Papin
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Metabolic Network ◽  
High Throughput ◽  
Large Scale ◽  
Essential Genes ◽  
Growth And Survival ◽  
Gene Essentiality ◽  
Model Driven ◽  
Functional Explanations ◽  
Genome Scale

AbstractThe identification of genes essential for bacterial growth and survival represents a promising strategy for the discovery of antimicrobial targets. Essential genes can be identified on a genome-scale using transposon mutagenesis approaches; however, variability between screens and challenges with interpretation of essentiality data hinder the identification of both condition-independent and condition-dependent essential genes. To illustrate the scope of these challenges, we perform a large-scale comparison of multiple published Pseudomonas aeruginosa gene essentiality datasets, revealing substantial differences between the screens. We then contextualize essentiality using genome-scale metabolic network reconstructions and demonstrate the utility of this approach in providing functional explanations for essentiality and reconciling differences between screens. Genome-scale metabolic network reconstructions also enable a high-throughput, quantitative analysis to assess the impact of media conditions on the identification of condition-independent essential genes. Our computational model-driven analysis provides mechanistic insight into essentiality and contributes novel insights for design of future gene essentiality screens and the identification of core metabolic processes.Author SummaryWith the rise of antibiotic resistance, there is a growing need to discover new therapeutic targets to treat bacterial infections. One attractive strategy is to target genes that are essential for growth and survival. Essential genes can be identified with transposon mutagenesis approaches; however, variability between screens and challenges with interpretation of essentiality data hinder the identification and analysis of essential genes. We performed a large-scale comparison of multiple gene essentiality screens of the microbial pathogen Pseudomonas aeruginosa. We implemented a computational model-driven approach to provide functional explanations for essentiality and reconcile differences between screens. The integration of computational modeling with high-throughput experimental screens may enable the identification of drug targets with high-confidence and provide greater understanding for the development of novel therapeutic strategies.

scholarly journals Improving the consistency of functional genomics screens using molecular features - a multi-omics, pan-cancer study

2018 ◽  
Author(s):  
Wenyu Wang ◽  
Alina Malyutina ◽  
Alberto Pessia ◽  
Caroline A. Heckman ◽  
Jing Tang
Keyword(s):  
Regression Model ◽  
Cell Lines ◽  
Drug Targets ◽  
Tumor Biology ◽  
Cell Types ◽  
Essential Genes ◽  
Minimal Impact ◽  
Gene Essentiality ◽  
Molecular Features ◽  
Target Effects

AbstractProbing the genetic dependencies of cancer cells helps understand the tumor biology and identify potential drug targets. RNAi-based shRNA and CRISPR/Cas9-based sgRNA have been commonly utilized in functional genetic screens to identify essential genes affecting growth rates in cancer cell lines. However, questions remain whether the gene essentiality profiles determined using these two technologies are comparable. In the present study, we collected 42 cell lines representing a variety of 10 tissue types, which had been screened both by shRNA and CRISPR techniques. We observed poor consistency of the essentiality scores between the two screens for the majority of the cell lines. The consistency did not improve after correcting the off-target effects in the shRNA screening, suggesting a minimal impact of off-target effects. We considered a linear regression model where the shRNA essentiality score is the predictor and the CRISPR essentiality score is the response variable. We showed that by including molecular features such as mutation, gene expression and copy number variation as covariates, the predictability of the regression model greatly improved, suggesting that molecular features may provide critical information in explaining the discrepancy between the shRNA and CRISPR-based essentiality scores. We provided a Combined Essentiality Score (CES) based on the model prediction and showed that the CES greatly improved the consensus of common essential genes. Furthermore, the CES also identified novel essential genes that are specific to individual cell types. Taken together, we provided a systematic approach to define a more accurate gene essentiality profile by integrating functional screen data and molecular profiles.

Combating Diseases with Computational Strategies Used for Drug Design and Discovery

2019 ◽  
Vol 18 (32) ◽  
pp. 2743-2773 ◽  
Author(s):  
Farahnaz R. Makhouri ◽  
Jahan B. Ghasemi
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Computational Methods ◽  
Drug Targets ◽  
Structural Information ◽  
Target Identification ◽  
Lead Discovery ◽  
Human Proteins ◽  
Almost All ◽  
Discovery Pipeline

Computer-aided drug discovery (CADD) tools have provided an effective way in the drug discovery pipeline for expediting of this long process and economizing the cost of research and development. Due to the dramatic increase in the availability of human proteins as drug targets and small molecule information due to the advances in bioinformatics, cheminformatics, genomics, proteomics, and structural information, the applicability of in silico drug discovery has been extended. Computational approaches have been used at almost all stages in the drug discovery pipeline including target identification and validation, lead discovery and optimization, and pharmacokinetic and toxicity profiles prediction. As each area covers a variety of computational methods, it is unmanageable to assess comprehensively all areas of CADD applications or every aspect of an area in one review article. However, in this article, we tried to present an overview of computational methods used in almost all the areas concerned with drug design and highlight some of the recent successes.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

2020 ◽  
Author(s):  
Christina Schindler ◽  
Hannah Baumann ◽  
Andreas Blum ◽  
Dietrich Böse ◽  
Hans-Peter Buchstaller ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Large Scale ◽  
Active Drug ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Energy Calculation ◽  
Large Scale Assessment ◽  
New Public ◽  
Binding Free Energy Calculations

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set.<br>

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

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